






'''simple docstring'''







import os
import tempfile
import unittest

from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    MODEL_FOR_PRETRAINING_MAPPING,
							    NezhaForMaskedLM,
							    NezhaForMultipleChoice,
							    NezhaForNextSentencePrediction,
							    NezhaForPreTraining,
							    NezhaForQuestionAnswering,
							    NezhaForSequenceClassification,
							    NezhaForTokenClassification,
							    NezhaModel,
							)
							from transformers.models.nezha.modeling_nezha import NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST

class _lowercase     :




							def __init__(							self:    Dict      ,							UpperCamelCase__:    Optional[Any]      ,							UpperCamelCase__:    Any=13      ,							UpperCamelCase__:    List[Any]=7      ,							UpperCamelCase__:    int=True      ,							UpperCamelCase__:    int=True      ,							UpperCamelCase__:    List[str]=True      ,							UpperCamelCase__:    Optional[int]=True      ,							UpperCamelCase__:    Tuple=99      ,							UpperCamelCase__:    str=32      ,							UpperCamelCase__:    List[str]=5      ,							UpperCamelCase__:    Optional[int]=4      ,							UpperCamelCase__:    int=37      ,							UpperCamelCase__:    Any="gelu"      ,							UpperCamelCase__:    List[Any]=0.1      ,							UpperCamelCase__:    Union[str, Any]=0.1      ,							UpperCamelCase__:    Optional[Any]=128      ,							UpperCamelCase__:    Optional[Any]=32      ,							UpperCamelCase__:    Optional[int]=16      ,							UpperCamelCase__:    List[Any]=2      ,							UpperCamelCase__:    Optional[int]=0.02      ,							UpperCamelCase__:    Optional[int]=3      ,							UpperCamelCase__:    Optional[int]=4      ,							UpperCamelCase__:    List[Any]=None      ,							):
														lowerCamelCase__ :     List[Any]	  =	parent
														lowerCamelCase__ :     Optional[Any]	  =	batch_size
														lowerCamelCase__ :     Union[str, Any]	  =	seq_length
														lowerCamelCase__ :     Any	  =	is_training
														lowerCamelCase__ :     Any	  =	use_input_mask
														lowerCamelCase__ :     List[str]	  =	use_token_type_ids
														lowerCamelCase__ :     Dict	  =	use_labels
														lowerCamelCase__ :     Union[str, Any]	  =	vocab_size
														lowerCamelCase__ :     int	  =	hidden_size
														lowerCamelCase__ :     Any	  =	num_hidden_layers
														lowerCamelCase__ :     Any	  =	num_attention_heads
														lowerCamelCase__ :     str	  =	intermediate_size
														lowerCamelCase__ :     Optional[Any]	  =	hidden_act
														lowerCamelCase__ :     Tuple	  =	hidden_dropout_prob
														lowerCamelCase__ :     Any	  =	attention_probs_dropout_prob
														lowerCamelCase__ :     Optional[int]	  =	max_position_embeddings
														lowerCamelCase__ :     Optional[int]	  =	type_vocab_size
														lowerCamelCase__ :     Tuple	  =	type_sequence_label_size
														lowerCamelCase__ :     Optional[Any]	  =	initializer_range
														lowerCamelCase__ :     Dict	  =	num_labels
														lowerCamelCase__ :     Optional[int]	  =	num_choices
														lowerCamelCase__ :     List[Any]	  =	scope




							def 		lowerCamelCase_    (							self:    Optional[Any]					):
														lowerCamelCase__ :     Dict	  =	ids_tensor([self.batch_size, self.seq_length]      ,							self.vocab_size					)

														lowerCamelCase__ :     List[Any]	  =	None
														if self.use_input_mask:
																					lowerCamelCase__ :     Union[str, Any]	  =	random_attention_mask([self.batch_size, self.seq_length]					)

														lowerCamelCase__ :     Tuple	  =	None
														if self.use_token_type_ids:
																					lowerCamelCase__ :     str	  =	ids_tensor([self.batch_size, self.seq_length]      ,							self.type_vocab_size					)

														lowerCamelCase__ :     Dict	  =	None
														lowerCamelCase__ :     List[Any]	  =	None
														lowerCamelCase__ :     str	  =	None
														if self.use_labels:
																					lowerCamelCase__ :     Optional[Any]	  =	ids_tensor([self.batch_size]      ,							self.type_sequence_label_size					)
																					lowerCamelCase__ :     Any	  =	ids_tensor([self.batch_size, self.seq_length]      ,							self.num_labels					)
																					lowerCamelCase__ :     str	  =	ids_tensor([self.batch_size]      ,							self.num_choices					)

														lowerCamelCase__ :     Dict	  =	self.get_config()

														return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




							def 		lowerCamelCase_    (							self:    List[Any]					):
														return NezhaConfig(
														    vocab_size=self.vocab_size      ,							hidden_size=self.hidden_size      ,							num_hidden_layers=self.num_hidden_layers      ,							num_attention_heads=self.num_attention_heads      ,							intermediate_size=self.intermediate_size      ,							hidden_act=self.hidden_act      ,							hidden_dropout_prob=self.hidden_dropout_prob      ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,							max_position_embeddings=self.max_position_embeddings      ,							type_vocab_size=self.type_vocab_size      ,							is_decoder=__A      ,							initializer_range=self.initializer_range      ,							)




							def 		lowerCamelCase_    (							self:    Any					):
														(
														    lowerCamelCase__						
														) :     Any	  =	self.prepare_config_and_inputs()

														lowerCamelCase__ :     Tuple	  =	True
														lowerCamelCase__ :     Dict	  =	floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
														lowerCamelCase__ :     Optional[int]	  =	ids_tensor([self.batch_size, self.seq_length]      ,							vocab_size=2					)

														return (
														    config,
														    input_ids,
														    token_type_ids,
														    input_mask,
														    sequence_labels,
														    token_labels,
														    choice_labels,
														    encoder_hidden_states,
														    encoder_attention_mask,
														)




							def 		lowerCamelCase_    (							self:    List[Any]      ,							UpperCamelCase__:    Any      ,							UpperCamelCase__:    Optional[int]      ,							UpperCamelCase__:    Any      ,							UpperCamelCase__:    Union[str, Any]      ,							UpperCamelCase__:    List[Any]      ,							UpperCamelCase__:    Union[str, Any]      ,							UpperCamelCase__:    Union[str, Any]					):
														lowerCamelCase__ :     Optional[Any]	  =	NezhaModel(config=__A					)
														model.to(__A					)
														model.eval()
														lowerCamelCase__ :     Dict	  =	model(__A      ,							attention_mask=__A      ,							token_type_ids=__A					)
														lowerCamelCase__ :     Optional[Any]	  =	model(__A      ,							token_type_ids=__A					)
														lowerCamelCase__ :     int	  =	model(__A					)
														self.parent.assertEqual(result.last_hidden_state.shape      ,							(self.batch_size, self.seq_length, self.hidden_size)					)
														self.parent.assertEqual(result.pooler_output.shape      ,							(self.batch_size, self.hidden_size)					)




							def 		lowerCamelCase_    (							self:    int      ,							UpperCamelCase__:    List[str]      ,							UpperCamelCase__:    Tuple      ,							UpperCamelCase__:    str      ,							UpperCamelCase__:    Optional[int]      ,							UpperCamelCase__:    Optional[int]      ,							UpperCamelCase__:    Optional[int]      ,							UpperCamelCase__:    Optional[int]      ,							UpperCamelCase__:    List[Any]      ,							UpperCamelCase__:    List[str]      ,							):
														lowerCamelCase__ :     Optional[Any]	  =	True
														lowerCamelCase__ :     List[str]	  =	NezhaModel(__A					)
														model.to(__A					)
														model.eval()
														lowerCamelCase__ :     str	  =	model(
														    __A      ,							attention_mask=__A      ,							token_type_ids=__A      ,							encoder_hidden_states=__A      ,							encoder_attention_mask=__A      ,							)
														lowerCamelCase__ :     int	  =	model(
														    __A      ,							attention_mask=__A      ,							token_type_ids=__A      ,							encoder_hidden_states=__A      ,							)
														lowerCamelCase__ :     Optional[int]	  =	model(__A      ,							attention_mask=__A      ,							token_type_ids=__A					)
														self.parent.assertEqual(result.last_hidden_state.shape      ,							(self.batch_size, self.seq_length, self.hidden_size)					)
														self.parent.assertEqual(result.pooler_output.shape      ,							(self.batch_size, self.hidden_size)					)




							def 		lowerCamelCase_    (							self:    Dict      ,							UpperCamelCase__:    Any      ,							UpperCamelCase__:    Optional[Any]      ,							UpperCamelCase__:    Any      ,							UpperCamelCase__:    Optional[int]      ,							UpperCamelCase__:    Union[str, Any]      ,							UpperCamelCase__:    Union[str, Any]      ,							UpperCamelCase__:    Union[str, Any]					):
														lowerCamelCase__ :     List[str]	  =	NezhaForMaskedLM(config=__A					)
														model.to(__A					)
														model.eval()
														lowerCamelCase__ :     Optional[int]	  =	model(__A      ,							attention_mask=__A      ,							token_type_ids=__A      ,							labels=__A					)
														self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.seq_length, self.vocab_size)					)




							def 		lowerCamelCase_    (							self:    Union[str, Any]      ,							UpperCamelCase__:    Tuple      ,							UpperCamelCase__:    Any      ,							UpperCamelCase__:    Optional[int]      ,							UpperCamelCase__:    Dict      ,							UpperCamelCase__:    Any      ,							UpperCamelCase__:    List[Any]      ,							UpperCamelCase__:    Any					):
														lowerCamelCase__ :     List[str]	  =	NezhaForNextSentencePrediction(config=__A					)
														model.to(__A					)
														model.eval()
														lowerCamelCase__ :     Dict	  =	model(
														    __A      ,							attention_mask=__A      ,							token_type_ids=__A      ,							labels=__A      ,							)
														self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, 2)					)




							def 		lowerCamelCase_    (							self:    Tuple      ,							UpperCamelCase__:    Optional[Any]      ,							UpperCamelCase__:    Optional[Any]      ,							UpperCamelCase__:    Any      ,							UpperCamelCase__:    Optional[int]      ,							UpperCamelCase__:    List[str]      ,							UpperCamelCase__:    List[Any]      ,							UpperCamelCase__:    Union[str, Any]					):
														lowerCamelCase__ :     str	  =	NezhaForPreTraining(config=__A					)
														model.to(__A					)
														model.eval()
														lowerCamelCase__ :     Any	  =	model(
														    __A      ,							attention_mask=__A      ,							token_type_ids=__A      ,							labels=__A      ,							next_sentence_label=__A      ,							)
														self.parent.assertEqual(result.prediction_logits.shape      ,							(self.batch_size, self.seq_length, self.vocab_size)					)
														self.parent.assertEqual(result.seq_relationship_logits.shape      ,							(self.batch_size, 2)					)




							def 		lowerCamelCase_    (							self:    Any      ,							UpperCamelCase__:    Optional[int]      ,							UpperCamelCase__:    List[Any]      ,							UpperCamelCase__:    str      ,							UpperCamelCase__:    Tuple      ,							UpperCamelCase__:    str      ,							UpperCamelCase__:    Any      ,							UpperCamelCase__:    int					):
														lowerCamelCase__ :     Optional[Any]	  =	NezhaForQuestionAnswering(config=__A					)
														model.to(__A					)
														model.eval()
														lowerCamelCase__ :     List[str]	  =	model(
														    __A      ,							attention_mask=__A      ,							token_type_ids=__A      ,							start_positions=__A      ,							end_positions=__A      ,							)
														self.parent.assertEqual(result.start_logits.shape      ,							(self.batch_size, self.seq_length)					)
														self.parent.assertEqual(result.end_logits.shape      ,							(self.batch_size, self.seq_length)					)




							def 		lowerCamelCase_    (							self:    Optional[int]      ,							UpperCamelCase__:    int      ,							UpperCamelCase__:    Optional[Any]      ,							UpperCamelCase__:    Dict      ,							UpperCamelCase__:    Tuple      ,							UpperCamelCase__:    List[str]      ,							UpperCamelCase__:    int      ,							UpperCamelCase__:    Optional[Any]					):
														lowerCamelCase__ :     Optional[Any]	  =	self.num_labels
														lowerCamelCase__ :     str	  =	NezhaForSequenceClassification(__A					)
														model.to(__A					)
														model.eval()
														lowerCamelCase__ :     Union[str, Any]	  =	model(__A      ,							attention_mask=__A      ,							token_type_ids=__A      ,							labels=__A					)
														self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.num_labels)					)




							def 		lowerCamelCase_    (							self:    Optional[Any]      ,							UpperCamelCase__:    Dict      ,							UpperCamelCase__:    str      ,							UpperCamelCase__:    List[str]      ,							UpperCamelCase__:    Dict      ,							UpperCamelCase__:    Optional[Any]      ,							UpperCamelCase__:    Optional[Any]      ,							UpperCamelCase__:    int					):
														lowerCamelCase__ :     Union[str, Any]	  =	self.num_labels
														lowerCamelCase__ :     List[str]	  =	NezhaForTokenClassification(config=__A					)
														model.to(__A					)
														model.eval()
														lowerCamelCase__ :     Optional[Any]	  =	model(__A      ,							attention_mask=__A      ,							token_type_ids=__A      ,							labels=__A					)
														self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.seq_length, self.num_labels)					)




							def 		lowerCamelCase_    (							self:    List[Any]      ,							UpperCamelCase__:    Optional[int]      ,							UpperCamelCase__:    Optional[int]      ,							UpperCamelCase__:    List[Any]      ,							UpperCamelCase__:    str      ,							UpperCamelCase__:    List[str]      ,							UpperCamelCase__:    Optional[int]      ,							UpperCamelCase__:    List[str]					):
														lowerCamelCase__ :     Any	  =	self.num_choices
														lowerCamelCase__ :     Optional[int]	  =	NezhaForMultipleChoice(config=__A					)
														model.to(__A					)
														model.eval()
														lowerCamelCase__ :     List[str]	  =	input_ids.unsqueeze(1					).expand(-1      ,							self.num_choices      ,							-1					).contiguous()
														lowerCamelCase__ :     str	  =	token_type_ids.unsqueeze(1					).expand(-1      ,							self.num_choices      ,							-1					).contiguous()
														lowerCamelCase__ :     Union[str, Any]	  =	input_mask.unsqueeze(1					).expand(-1      ,							self.num_choices      ,							-1					).contiguous()
														lowerCamelCase__ :     Optional[int]	  =	model(
														    __A      ,							attention_mask=__A      ,							token_type_ids=__A      ,							labels=__A      ,							)
														self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.num_choices)					)




							def 		lowerCamelCase_    (							self:    Dict					):
														lowerCamelCase__ :     List[Any]	  =	self.prepare_config_and_inputs()
														(
														    lowerCamelCase__						
														) :     Tuple	  =	config_and_inputs
														lowerCamelCase__ :     Any	  =	{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
														return config, inputs_dict



@require_torch
class _lowercase     (    lowerCamelCase__    ,				lowerCamelCase__    ,				lowerCamelCase__    ,				unittest.TestCase		):
							a    				=	(
							    (
							        NezhaModel,
							        NezhaForMaskedLM,
							        NezhaForMultipleChoice,
							        NezhaForNextSentencePrediction,
							        NezhaForPreTraining,
							        NezhaForQuestionAnswering,
							        NezhaForSequenceClassification,
							        NezhaForTokenClassification,
							    )
							    if is_torch_available()
							    else ()
							)
							a    				=	(
							    {
							        """feature-extraction""": NezhaModel,
							        """fill-mask""": NezhaForMaskedLM,
							        """question-answering""": NezhaForQuestionAnswering,
							        """text-classification""": NezhaForSequenceClassification,
							        """token-classification""": NezhaForTokenClassification,
							        """zero-shot""": NezhaForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							a    				=	True




							def 		lowerCamelCase_    (							self:    str      ,							UpperCamelCase__:    Union[str, Any]      ,							UpperCamelCase__:    int      ,							UpperCamelCase__:    List[str]=False					):
														lowerCamelCase__ :     Tuple	  =	super()._prepare_for_class(__A      ,							__A      ,							return_labels=__A					)

														if return_labels:
																					if model_class in get_values(__A					):
																												lowerCamelCase__ :     Optional[Any]	  =	torch.zeros(
																												    (self.model_tester.batch_size, self.model_tester.seq_length)      ,							dtype=torch.long      ,							device=__A					)
																												lowerCamelCase__ :     Optional[int]	  =	torch.zeros(
																												    self.model_tester.batch_size      ,							dtype=torch.long      ,							device=__A					)
														return inputs_dict




							def 		lowerCamelCase_    (							self:    int					):
														lowerCamelCase__ :     Dict	  =	NezhaModelTester(self					)
														lowerCamelCase__ :     Any	  =	ConfigTester(self      ,							config_class=__A      ,							hidden_size=37					)




							def 		lowerCamelCase_    (							self:    List[Any]					):
														self.config_tester.run_common_tests()




							def 		lowerCamelCase_    (							self:    str					):
														lowerCamelCase__ :     Tuple	  =	self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*__A					)




							def 		lowerCamelCase_    (							self:    int					):
														lowerCamelCase__ :     List[Any]	  =	self.model_tester.prepare_config_and_inputs_for_decoder()
														self.model_tester.create_and_check_model_as_decoder(*__A					)




							def 		lowerCamelCase_    (							self:    Tuple					):
														# This regression test was failing with PyTorch < 1.3
														(
														    lowerCamelCase__						
														) :     int	  =	self.model_tester.prepare_config_and_inputs_for_decoder()

														lowerCamelCase__ :     str	  =	None

														self.model_tester.create_and_check_model_as_decoder(
														    __A      ,							__A      ,							__A      ,							__A      ,							__A      ,							__A      ,							__A      ,							__A      ,							__A      ,							)




							def 		lowerCamelCase_    (							self:    Dict					):
														lowerCamelCase__ :     int	  =	self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_masked_lm(*__A					)




							def 		lowerCamelCase_    (							self:    Union[str, Any]					):
														lowerCamelCase__ :     str	  =	self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_multiple_choice(*__A					)




							def 		lowerCamelCase_    (							self:    str					):
														lowerCamelCase__ :     Tuple	  =	self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_next_sequence_prediction(*__A					)




							def 		lowerCamelCase_    (							self:    Tuple					):
														lowerCamelCase__ :     Any	  =	self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_pretraining(*__A					)




							def 		lowerCamelCase_    (							self:    Dict					):
														lowerCamelCase__ :     str	  =	self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_question_answering(*__A					)




							def 		lowerCamelCase_    (							self:    str					):
														lowerCamelCase__ :     Optional[Any]	  =	self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_sequence_classification(*__A					)




							def 		lowerCamelCase_    (							self:    Optional[Any]					):
														lowerCamelCase__ :     Dict	  =	self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_token_classification(*__A					)




							@slow
							def 		lowerCamelCase_    (							self:    str					):
														for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					lowerCamelCase__ :     Optional[Any]	  =	NezhaModel.from_pretrained(__A					)
																					self.assertIsNotNone(__A					)




							@slow
							@require_torch_gpu
							def 		lowerCamelCase_    (							self:    Any					):
														lowerCamelCase__ :     Dict	  =	self.model_tester.prepare_config_and_inputs_for_common()
														for model_class in self.all_model_classes:
																					# NezhaForMultipleChoice behaves incorrectly in JIT environments.
																					if model_class == NezhaForMultipleChoice:
																												return

																					lowerCamelCase__ :     Any	  =	True
																					lowerCamelCase__ :     Optional[Any]	  =	model_class(config=__A					)

																					lowerCamelCase__ :     Union[str, Any]	  =	self._prepare_for_class(__A      ,							__A					)
																					lowerCamelCase__ :     Dict	  =	torch.jit.trace(
																					    __A      ,							(inputs_dict["""input_ids"""].to("""cpu"""					), inputs_dict["""attention_mask"""].to("""cpu"""					))					)

																					with tempfile.TemporaryDirectory() as tmp:
																												torch.jit.save(__A      ,							os.path.join(__A      ,							"""bert.pt"""					)					)
																												lowerCamelCase__ :     Union[str, Any]	  =	torch.jit.load(os.path.join(__A      ,							"""bert.pt"""					)      ,							map_location=__A					)
																												loaded(inputs_dict["""input_ids"""].to(__A					)      ,							inputs_dict["""attention_mask"""].to(__A					)					)



@require_torch
class _lowercase     (    unittest.TestCase		):




							@slow
							def 		lowerCamelCase_    (							self:    Union[str, Any]					):
														lowerCamelCase__ :     Dict	  =	NezhaModel.from_pretrained("""sijunhe/nezha-cn-base"""					)
														lowerCamelCase__ :     List[str]	  =	torch.tensor([[0, 1, 2, 3, 4, 5]]					)
														lowerCamelCase__ :     Optional[int]	  =	torch.tensor([[0, 1, 1, 1, 1, 1]]					)
														with torch.no_grad():
																					lowerCamelCase__ :     List[str]	  =	model(__A      ,							attention_mask=__A					)[0]
														lowerCamelCase__ :     Tuple	  =	torch.Size((1, 6, 768)					)
														self.assertEqual(output.shape      ,							__A					)
														lowerCamelCase__ :     Optional[Any]	  =	torch.tensor([[[0.0_685, 0.2_441, 0.1_102], [0.0_600, 0.1_906, 0.1_349], [0.0_221, 0.0_819, 0.0_586]]]					)

														self.assertTrue(torch.allclose(output[:, 1:4, 1:4]      ,							__A      ,							atol=1e-4					)					)




							@slow
							def 		lowerCamelCase_    (							self:    Dict					):
														lowerCamelCase__ :     Optional[int]	  =	NezhaForMaskedLM.from_pretrained("""sijunhe/nezha-cn-base"""					)
														lowerCamelCase__ :     int	  =	torch.tensor([[0, 1, 2, 3, 4, 5]]					)
														lowerCamelCase__ :     Tuple	  =	torch.tensor([[1, 1, 1, 1, 1, 1]]					)
														with torch.no_grad():
																					lowerCamelCase__ :     Union[str, Any]	  =	model(__A      ,							attention_mask=__A					)[0]
														lowerCamelCase__ :     Dict	  =	torch.Size((1, 6, 21_128)					)
														self.assertEqual(output.shape      ,							__A					)
														lowerCamelCase__ :     List[Any]	  =	torch.tensor(
														    [[-2.7_939, -1.7_902, -2.2_189], [-2.8_585, -1.8_908, -2.3_723], [-2.6_499, -1.7_750, -2.2_558]]					)

														self.assertTrue(torch.allclose(output[:, 1:4, 1:4]      ,							__A      ,							atol=1e-4					)					)









'''simple docstring'''







import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


_A     :      str   					={
    '''sample_size''': 32,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': 1_000,
    '''block_out_channels''': [32, 64],
    '''attention_head_dim''': 8,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''scale_shift''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

_A     :      Union[str, Any]   					={
    '''sample_size''': 64,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 3,
    '''num_class_embeds''': 1_000,
    '''block_out_channels''': [192, 192 * 2, 192 * 3, 192 * 4],
    '''attention_head_dim''': 64,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''scale_shift''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

_A     :      Dict   					={
    '''sample_size''': 256,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': None,
    '''block_out_channels''': [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
    '''attention_head_dim''': 64,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''ResnetDownsampleBlock2D''',
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
        '''ResnetUpsampleBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''default''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

_A     :      Dict   					={
    '''num_train_timesteps''': 40,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

_A     :      str   					={
    '''num_train_timesteps''': 201,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

_A     :      int   					={
    '''num_train_timesteps''': 151,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	Dict:
							if isinstance(UpperCamelCase    ,      UpperCamelCase				):
														return v
							if v.lower() in ("yes", "true", "t", "y", "1"):
														return True
							elif v.lower() in ("no", "false", "f", "n", "0"):
														return False
							else:
														raise argparse.ArgumentTypeError("""boolean value expected"""				)





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase=False				)				->	Any:
							lowerCamelCase__ :     Any	  =	checkpoint[f'''{old_prefix}.in_layers.0.weight''']
							lowerCamelCase__ :     int	  =	checkpoint[f'''{old_prefix}.in_layers.0.bias''']
							lowerCamelCase__ :     Any	  =	checkpoint[f'''{old_prefix}.in_layers.2.weight''']
							lowerCamelCase__ :     Any	  =	checkpoint[f'''{old_prefix}.in_layers.2.bias''']
							lowerCamelCase__ :     Optional[Any]	  =	checkpoint[f'''{old_prefix}.emb_layers.1.weight''']
							lowerCamelCase__ :     Optional[int]	  =	checkpoint[f'''{old_prefix}.emb_layers.1.bias''']
							lowerCamelCase__ :     Dict	  =	checkpoint[f'''{old_prefix}.out_layers.0.weight''']
							lowerCamelCase__ :     Tuple	  =	checkpoint[f'''{old_prefix}.out_layers.0.bias''']
							lowerCamelCase__ :     str	  =	checkpoint[f'''{old_prefix}.out_layers.3.weight''']
							lowerCamelCase__ :     int	  =	checkpoint[f'''{old_prefix}.out_layers.3.bias''']

							if has_skip:
														lowerCamelCase__ :     Tuple	  =	checkpoint[f'''{old_prefix}.skip_connection.weight''']
														lowerCamelCase__ :     List[Any]	  =	checkpoint[f'''{old_prefix}.skip_connection.bias''']

							return new_checkpoint





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase=None				)				->	str:
							lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__ :     Dict	  =	checkpoint[f'''{old_prefix}.qkv.weight'''].chunk(3    ,      dim=0				)
							lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__ :     str	  =	checkpoint[f'''{old_prefix}.qkv.bias'''].chunk(3    ,      dim=0				)

							lowerCamelCase__ :     Any	  =	checkpoint[f'''{old_prefix}.norm.weight''']
							lowerCamelCase__ :     Optional[int]	  =	checkpoint[f'''{old_prefix}.norm.bias''']

							lowerCamelCase__ :     List[Any]	  =	weight_q.squeeze(-1				).squeeze(-1				)
							lowerCamelCase__ :     List[Any]	  =	bias_q.squeeze(-1				).squeeze(-1				)
							lowerCamelCase__ :     Any	  =	weight_k.squeeze(-1				).squeeze(-1				)
							lowerCamelCase__ :     Optional[Any]	  =	bias_k.squeeze(-1				).squeeze(-1				)
							lowerCamelCase__ :     Dict	  =	weight_v.squeeze(-1				).squeeze(-1				)
							lowerCamelCase__ :     Union[str, Any]	  =	bias_v.squeeze(-1				).squeeze(-1				)

							lowerCamelCase__ :     Optional[Any]	  =	(
							    checkpoint[f'''{old_prefix}.proj_out.weight'''].squeeze(-1				).squeeze(-1				)
							)
							lowerCamelCase__ :     Dict	  =	checkpoint[f'''{old_prefix}.proj_out.bias'''].squeeze(-1				).squeeze(-1				)

							return new_checkpoint





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	List[Any]:
							lowerCamelCase__ :     str	  =	torch.load(UpperCamelCase    ,      map_location="""cpu"""				)
							lowerCamelCase__ :     Optional[int]	  =	{}

							lowerCamelCase__ :     Optional[int]	  =	checkpoint["""time_embed.0.weight"""]
							lowerCamelCase__ :     List[Any]	  =	checkpoint["""time_embed.0.bias"""]
							lowerCamelCase__ :     int	  =	checkpoint["""time_embed.2.weight"""]
							lowerCamelCase__ :     Optional[Any]	  =	checkpoint["""time_embed.2.bias"""]

							if unet_config["num_class_embeds"] is not None:
														lowerCamelCase__ :     Optional[Any]	  =	checkpoint["""label_emb.weight"""]

							lowerCamelCase__ :     Tuple	  =	checkpoint["""input_blocks.0.0.weight"""]
							lowerCamelCase__ :     List[str]	  =	checkpoint["""input_blocks.0.0.bias"""]

							lowerCamelCase__ :     Optional[Any]	  =	unet_config["""down_block_types"""]
							lowerCamelCase__ :     Any	  =	unet_config["""layers_per_block"""]
							lowerCamelCase__ :     Any	  =	unet_config["""attention_head_dim"""]
							lowerCamelCase__ :     List[Any]	  =	unet_config["""block_out_channels"""]
							lowerCamelCase__ :     str	  =	1
							lowerCamelCase__ :     str	  =	channels_list[0]

							for i, layer_type in enumerate(UpperCamelCase				):
														lowerCamelCase__ :     List[Any]	  =	channels_list[i]
														lowerCamelCase__ :     List[Any]	  =	current_channels != prev_channels
														if layer_type == "ResnetDownsampleBlock2D":
																					for j in range(UpperCamelCase				):
																												lowerCamelCase__ :     int	  =	f'''down_blocks.{i}.resnets.{j}'''
																												lowerCamelCase__ :     Dict	  =	f'''input_blocks.{current_layer}.0'''
																												lowerCamelCase__ :     Tuple	  =	True if j == 0 and downsample_block_has_skip else False
																												lowerCamelCase__ :     List[Any]	  =	convert_resnet(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      has_skip=UpperCamelCase				)
																												current_layer += 1

														elif layer_type == "AttnDownBlock2D":
																					for j in range(UpperCamelCase				):
																												lowerCamelCase__ :     Tuple	  =	f'''down_blocks.{i}.resnets.{j}'''
																												lowerCamelCase__ :     Optional[Any]	  =	f'''input_blocks.{current_layer}.0'''
																												lowerCamelCase__ :     str	  =	True if j == 0 and downsample_block_has_skip else False
																												lowerCamelCase__ :     Union[str, Any]	  =	convert_resnet(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      has_skip=UpperCamelCase				)
																												lowerCamelCase__ :     Any	  =	f'''down_blocks.{i}.attentions.{j}'''
																												lowerCamelCase__ :     Dict	  =	f'''input_blocks.{current_layer}.1'''
																												lowerCamelCase__ :     Tuple	  =	convert_attention(
																												    UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)
																												current_layer += 1

														if i != len(UpperCamelCase				) - 1:
																					lowerCamelCase__ :     Tuple	  =	f'''down_blocks.{i}.downsamplers.0'''
																					lowerCamelCase__ :     str	  =	f'''input_blocks.{current_layer}.0'''
																					lowerCamelCase__ :     Union[str, Any]	  =	convert_resnet(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)
																					current_layer += 1

														lowerCamelCase__ :     Union[str, Any]	  =	current_channels

							# hardcoded the mid-block for now
							lowerCamelCase__ :     Any	  =	"""mid_block.resnets.0"""
							lowerCamelCase__ :     Optional[Any]	  =	"""middle_block.0"""
							lowerCamelCase__ :     int	  =	convert_resnet(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)
							lowerCamelCase__ :     List[Any]	  =	"""mid_block.attentions.0"""
							lowerCamelCase__ :     Dict	  =	"""middle_block.1"""
							lowerCamelCase__ :     int	  =	convert_attention(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)
							lowerCamelCase__ :     Any	  =	"""mid_block.resnets.1"""
							lowerCamelCase__ :     Tuple	  =	"""middle_block.2"""
							lowerCamelCase__ :     int	  =	convert_resnet(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)

							lowerCamelCase__ :     Union[str, Any]	  =	0
							lowerCamelCase__ :     Any	  =	unet_config["""up_block_types"""]

							for i, layer_type in enumerate(UpperCamelCase				):
														if layer_type == "ResnetUpsampleBlock2D":
																					for j in range(layers_per_block + 1				):
																												lowerCamelCase__ :     int	  =	f'''up_blocks.{i}.resnets.{j}'''
																												lowerCamelCase__ :     Optional[Any]	  =	f'''output_blocks.{current_layer}.0'''
																												lowerCamelCase__ :     Any	  =	convert_resnet(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      has_skip=UpperCamelCase				)
																												current_layer += 1

																					if i != len(UpperCamelCase				) - 1:
																												lowerCamelCase__ :     Dict	  =	f'''up_blocks.{i}.upsamplers.0'''
																												lowerCamelCase__ :     List[str]	  =	f'''output_blocks.{current_layer-1}.1'''
																												lowerCamelCase__ :     Optional[Any]	  =	convert_resnet(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)
														elif layer_type == "AttnUpBlock2D":
																					for j in range(layers_per_block + 1				):
																												lowerCamelCase__ :     str	  =	f'''up_blocks.{i}.resnets.{j}'''
																												lowerCamelCase__ :     List[Any]	  =	f'''output_blocks.{current_layer}.0'''
																												lowerCamelCase__ :     Optional[Any]	  =	convert_resnet(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      has_skip=UpperCamelCase				)
																												lowerCamelCase__ :     Optional[Any]	  =	f'''up_blocks.{i}.attentions.{j}'''
																												lowerCamelCase__ :     Any	  =	f'''output_blocks.{current_layer}.1'''
																												lowerCamelCase__ :     Optional[int]	  =	convert_attention(
																												    UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)
																												current_layer += 1

																					if i != len(UpperCamelCase				) - 1:
																												lowerCamelCase__ :     Tuple	  =	f'''up_blocks.{i}.upsamplers.0'''
																												lowerCamelCase__ :     Tuple	  =	f'''output_blocks.{current_layer-1}.2'''
																												lowerCamelCase__ :     List[str]	  =	convert_resnet(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)

							lowerCamelCase__ :     Dict	  =	checkpoint["""out.0.weight"""]
							lowerCamelCase__ :     Dict	  =	checkpoint["""out.0.bias"""]
							lowerCamelCase__ :     Dict	  =	checkpoint["""out.2.weight"""]
							lowerCamelCase__ :     Tuple	  =	checkpoint["""out.2.bias"""]

							return new_checkpoint


if __name__ == "__main__":
			_A     :      Tuple   					=argparse.ArgumentParser()

			parser.add_argument('''--unet_path''', default=None, type=str, required=True, help='''Path to the unet.pt to convert.''')
			parser.add_argument(
			    '''--dump_path''', default=None, type=str, required=True, help='''Path to output the converted UNet model.'''
			)
			parser.add_argument('''--class_cond''', default=True, type=str, help='''Whether the model is class-conditional.''')

			_A     :      Tuple   					=parser.parse_args()
			_A     :      Optional[int]   					=strabool(args.class_cond)

			_A     :      List[str]   					=os.path.basename(args.unet_path)
			print(F'Checkpoint: {ckpt_name}')

			# Get U-Net config
			if "imagenet64" in ckpt_name:
						_A     :      int   					=IMAGENET_64_UNET_CONFIG
			elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
						_A     :      Tuple   					=LSUN_256_UNET_CONFIG
			elif "test" in ckpt_name:
						_A     :      Any   					=TEST_UNET_CONFIG
			else:
						raise ValueError(F'Checkpoint type {ckpt_name} is not currently supported.')

			if not args.class_cond:
						_A     :      str   					=None

			_A     :      Optional[int]   					=con_pt_to_diffuser(args.unet_path, unet_config)

			_A     :      Optional[int]   					=UNetaDModel(**unet_config)
			image_unet.load_state_dict(converted_unet_ckpt)

			# Get scheduler config
			if "cd" in ckpt_name or "test" in ckpt_name:
						_A     :      Tuple   					=CD_SCHEDULER_CONFIG
			elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
						_A     :      int   					=CT_IMAGENET_64_SCHEDULER_CONFIG
			elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
						_A     :      Union[str, Any]   					=CT_LSUN_256_SCHEDULER_CONFIG
			else:
						raise ValueError(F'Checkpoint type {ckpt_name} is not currently supported.')

			_A     :      str   					=CMStochasticIterativeScheduler(**scheduler_config)

			_A     :      Optional[Any]   					=ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
			consistency_model.save_pretrained(args.dump_path)

