



import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
				import os

				# The slow tests are often failing with OOM error on GPU
				# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
				# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
				lowerCAmelCase__					  =  '''platform'''

				import jax
				import jax.numpy as jnp

				from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
				    FlaxBlenderbotSmallForConditionalGeneration,
				    FlaxBlenderbotSmallModel,
				    shift_tokens_right,
				)


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__=None					,			lowerCamelCase__=None					,			lowerCamelCase__=None					,			lowerCamelCase__=None					,			lowerCamelCase__=None					,			lowerCamelCase__=None					,			):

				"""simple docstring"""


				if attention_mask is None:
								lowercase__					:   str													=					np.where(input_ids != config.pad_token_id					,			1					,			0						)
				if decoder_attention_mask is None:
								lowercase__					:   Any													=					np.where(decoder_input_ids != config.pad_token_id					,			1					,			0						)
				if head_mask is None:
								lowercase__					:   Optional[Any]													=					np.ones((config.encoder_layers, config.encoder_attention_heads)						)
				if decoder_head_mask is None:
								lowercase__					:   Tuple													=					np.ones((config.decoder_layers, config.decoder_attention_heads)						)
				if cross_attn_head_mask is None:
								lowercase__					:   Any													=					np.ones((config.decoder_layers, config.decoder_attention_heads)						)
				return {
				    "input_ids": input_ids,
				    "decoder_input_ids": decoder_input_ids,
				    "attention_mask": attention_mask,
				    "decoder_attention_mask": attention_mask,
				}

class 				snake_case__:

					"""simple docstring"""






					def __init__(							self :     int				,       SCREAMING_SNAKE_CASE :     Any				,       SCREAMING_SNAKE_CASE :     List[Any]=13				,       SCREAMING_SNAKE_CASE :     Union[str, Any]=7				,       SCREAMING_SNAKE_CASE :     str=True				,       SCREAMING_SNAKE_CASE :     Union[str, Any]=False				,       SCREAMING_SNAKE_CASE :     List[Any]=99				,       SCREAMING_SNAKE_CASE :     Dict=16				,       SCREAMING_SNAKE_CASE :     Any=2				,       SCREAMING_SNAKE_CASE :     Tuple=4				,       SCREAMING_SNAKE_CASE :     Optional[int]=4				,       SCREAMING_SNAKE_CASE :     List[str]="gelu"				,       SCREAMING_SNAKE_CASE :     Any=0.1				,       SCREAMING_SNAKE_CASE :     int=0.1				,       SCREAMING_SNAKE_CASE :     Optional[int]=32				,       SCREAMING_SNAKE_CASE :     Optional[int]=2				,       SCREAMING_SNAKE_CASE :     Tuple=1				,       SCREAMING_SNAKE_CASE :     List[Any]=0				,       SCREAMING_SNAKE_CASE :     List[str]=0.02				,       ):
									lowercase__					:   Dict													=					parent
									lowercase__					:   Optional[int]													=					batch_size
									lowercase__					:   List[str]													=					seq_length
									lowercase__					:   Optional[Any]													=					is_training
									lowercase__					:   Dict													=					use_labels
									lowercase__					:   Optional[int]													=					vocab_size
									lowercase__					:   str													=					hidden_size
									lowercase__					:   str													=					num_hidden_layers
									lowercase__					:   List[str]													=					num_attention_heads
									lowercase__					:   str													=					intermediate_size
									lowercase__					:   Optional[Any]													=					hidden_act
									lowercase__					:   int													=					hidden_dropout_prob
									lowercase__					:   Any													=					attention_probs_dropout_prob
									lowercase__					:   Union[str, Any]													=					max_position_embeddings
									lowercase__					:   str													=					eos_token_id
									lowercase__					:   str													=					pad_token_id
									lowercase__					:   Optional[Any]													=					bos_token_id
									lowercase__					:   List[Any]													=					initializer_range







					def 			snake_case					(							self :     Optional[Any]					):
									lowercase__					:   Dict													=					np.clip(ids_tensor([self.batch_size, self.seq_length - 1]				,       self.vocab_size					)				,       3				,       self.vocab_size					)
									lowercase__					:   Optional[int]													=					np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)				,       dtype=np.intaa					))				,       -1					)

									lowercase__					:   List[Any]													=					shift_tokens_right(SCREAMING_SNAKE_CASE				,       1				,       2					)

									lowercase__					:   List[str]													=					BlenderbotSmallConfig(
									    vocab_size=self.vocab_size				,       d_model=self.hidden_size				,       encoder_layers=self.num_hidden_layers				,       decoder_layers=self.num_hidden_layers				,       encoder_attention_heads=self.num_attention_heads				,       decoder_attention_heads=self.num_attention_heads				,       encoder_ffn_dim=self.intermediate_size				,       decoder_ffn_dim=self.intermediate_size				,       dropout=self.hidden_dropout_prob				,       attention_dropout=self.attention_probs_dropout_prob				,       max_position_embeddings=self.max_position_embeddings				,       eos_token_id=self.eos_token_id				,       bos_token_id=self.bos_token_id				,       pad_token_id=self.pad_token_id				,       initializer_range=self.initializer_range				,       use_cache=SCREAMING_SNAKE_CASE				,       )
									lowercase__					:   Optional[int]													=					prepare_blenderbot_inputs_dict(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)
									return config, inputs_dict







					def 			snake_case					(							self :     int					):
									lowercase__							,							lowercase__					:   Dict													=					self.prepare_config_and_inputs()
									return config, inputs_dict







					def 			snake_case					(							self :     Dict				,       SCREAMING_SNAKE_CASE :     int				,       SCREAMING_SNAKE_CASE :     Union[str, Any]				,       SCREAMING_SNAKE_CASE :     List[Any]					):
									lowercase__					:   Tuple													=					20
									lowercase__					:   str													=					model_class_name(SCREAMING_SNAKE_CASE					)

									lowercase__					:   Any													=					model.encode(inputs_dict["input_ids"]					)

									lowercase__							,							lowercase__					:   Union[str, Any]													=					(
									    inputs_dict["decoder_input_ids"],
									    inputs_dict["decoder_attention_mask"],
									)

									lowercase__					:   Optional[Any]													=					model.init_cache(decoder_input_ids.shape[0]				,       SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)
									lowercase__					:   Union[str, Any]													=					jnp.ones((decoder_input_ids.shape[0], max_decoder_length)				,       dtype="i4"					)

									lowercase__					:   int													=					jnp.broadcast_to(
									    jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :]				,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,       )
									lowercase__					:   str													=					model.decode(
									    decoder_input_ids[:, :-1]				,       SCREAMING_SNAKE_CASE				,       decoder_attention_mask=SCREAMING_SNAKE_CASE				,       past_key_values=SCREAMING_SNAKE_CASE				,       decoder_position_ids=SCREAMING_SNAKE_CASE				,       )

									lowercase__					:   Optional[Any]													=					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,       dtype="i4"					)
									lowercase__					:   Optional[Any]													=					model.decode(
									    decoder_input_ids[:, -1:]				,       SCREAMING_SNAKE_CASE				,       decoder_attention_mask=SCREAMING_SNAKE_CASE				,       past_key_values=outputs_cache.past_key_values				,       decoder_position_ids=SCREAMING_SNAKE_CASE				,       )

									lowercase__					:   Any													=					model.decode(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)

									lowercase__					:   Optional[Any]													=					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
									self.parent.assertTrue(diff < 1E-3				,       msg=f"""Max diff is {diff}"""					)







					def 			snake_case					(							self :     Optional[int]				,       SCREAMING_SNAKE_CASE :     Dict				,       SCREAMING_SNAKE_CASE :     Dict				,       SCREAMING_SNAKE_CASE :     Any					):
									lowercase__					:   int													=					20
									lowercase__					:   int													=					model_class_name(SCREAMING_SNAKE_CASE					)

									lowercase__					:   Any													=					model.encode(inputs_dict["input_ids"]					)

									lowercase__							,							lowercase__					:   Optional[Any]													=					(
									    inputs_dict["decoder_input_ids"],
									    inputs_dict["decoder_attention_mask"],
									)

									lowercase__					:   int													=					jnp.concatenate(
									    [
									        decoder_attention_mask,
									        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])					),
									    ]				,       axis=-1				,       )

									lowercase__					:   Optional[int]													=					model.init_cache(decoder_input_ids.shape[0]				,       SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)
									lowercase__					:   Any													=					jnp.broadcast_to(
									    jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :]				,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,       )

									lowercase__					:   Dict													=					model.decode(
									    decoder_input_ids[:, :-1]				,       SCREAMING_SNAKE_CASE				,       decoder_attention_mask=SCREAMING_SNAKE_CASE				,       past_key_values=SCREAMING_SNAKE_CASE				,       decoder_position_ids=SCREAMING_SNAKE_CASE				,       )
									lowercase__					:   int													=					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,       dtype="i4"					)
									lowercase__					:   Dict													=					model.decode(
									    decoder_input_ids[:, -1:]				,       SCREAMING_SNAKE_CASE				,       past_key_values=outputs_cache.past_key_values				,       decoder_attention_mask=SCREAMING_SNAKE_CASE				,       decoder_position_ids=SCREAMING_SNAKE_CASE				,       )

									lowercase__					:   List[str]													=					model.decode(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE				,       decoder_attention_mask=SCREAMING_SNAKE_CASE					)

									lowercase__					:   str													=					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
									self.parent.assertTrue(diff < 1E-3				,       msg=f"""Max diff is {diff}"""					)






@require_flax
class 				snake_case__(unittest.TestCase      ):

					"""simple docstring"""






					lowercase_								=		9_9







					def 			snake_case					(							self :     Optional[Any]					):
									lowercase__					:   str													=					np.array(
									    [
									        [71, 82, 18, 33, 46, 91, 2],
									        [68, 34, 26, 58, 30, 82, 2],
									        [5, 97, 17, 39, 94, 40, 2],
									        [76, 83, 94, 25, 70, 78, 2],
									        [87, 59, 41, 35, 48, 66, 2],
									        [55, 13, 16, 58, 5, 2, 1],  # note padding
									        [64, 27, 31, 51, 12, 75, 2],
									        [52, 64, 86, 17, 83, 39, 2],
									        [48, 61, 9, 24, 71, 82, 2],
									        [26, 1, 60, 48, 22, 13, 2],
									        [21, 5, 62, 28, 14, 76, 2],
									        [45, 98, 37, 86, 59, 48, 2],
									        [70, 70, 50, 9, 28, 0, 2],
									    ]				,       dtype=np.intaa				,       )

									lowercase__					:   Union[str, Any]													=					input_ids.shape[0]
									lowercase__					:   Tuple													=					BlenderbotSmallConfig(
									    vocab_size=self.vocab_size				,       d_model=24				,       encoder_layers=2				,       decoder_layers=2				,       encoder_attention_heads=2				,       decoder_attention_heads=2				,       encoder_ffn_dim=32				,       decoder_ffn_dim=32				,       max_position_embeddings=48				,       eos_token_id=2				,       pad_token_id=1				,       bos_token_id=0				,       )
									return config, input_ids, batch_size







					def 			snake_case					(							self :     Union[str, Any]					):
									lowercase__							,							lowercase__							,							lowercase__					:   str													=					self._get_config_and_data()
									lowercase__					:   List[Any]													=					FlaxBlenderbotSmallForConditionalGeneration(SCREAMING_SNAKE_CASE					)
									lowercase__					:   Union[str, Any]													=					lm_model(input_ids=SCREAMING_SNAKE_CASE					)
									lowercase__					:   Any													=					(batch_size, input_ids.shape[1], config.vocab_size)
									self.assertEqual(outputs["logits"].shape				,       SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     List[str]					):
									lowercase__					:   Dict													=					BlenderbotSmallConfig(
									    vocab_size=self.vocab_size				,       d_model=14				,       encoder_layers=2				,       decoder_layers=2				,       encoder_attention_heads=2				,       decoder_attention_heads=2				,       encoder_ffn_dim=8				,       decoder_ffn_dim=8				,       max_position_embeddings=48				,       )
									lowercase__					:   List[Any]													=					FlaxBlenderbotSmallForConditionalGeneration(SCREAMING_SNAKE_CASE					)
									lowercase__					:   Optional[int]													=					np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]				,       dtype=np.intaa					)
									lowercase__					:   Tuple													=					np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]				,       dtype=np.intaa					)
									lowercase__					:   Union[str, Any]													=					lm_model(input_ids=SCREAMING_SNAKE_CASE				,       decoder_input_ids=SCREAMING_SNAKE_CASE					)
									lowercase__					:   str													=					(*summary.shape, config.vocab_size)
									self.assertEqual(outputs["logits"].shape				,       SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     Any					):
									lowercase__					:   str													=					np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]				,       dtype=np.intaa					)
									lowercase__					:   Any													=					shift_tokens_right(SCREAMING_SNAKE_CASE				,       1				,       2					)
									lowercase__					:   Any													=					np.equal(SCREAMING_SNAKE_CASE				,       1					).astype(np.floataa					).sum()
									lowercase__					:   Optional[int]													=					np.equal(SCREAMING_SNAKE_CASE				,       1					).astype(np.floataa					).sum()
									self.assertEqual(shifted.shape				,       input_ids.shape					)
									self.assertEqual(SCREAMING_SNAKE_CASE				,       n_pad_before - 1					)
									self.assertTrue(np.equal(shifted[:, 0]				,       2					).all()					)






@require_flax
class 				snake_case__(_UpperCamelCase    ,      unittest.TestCase    ,      _UpperCamelCase      ):

					"""simple docstring"""






					lowercase_								=		True
					lowercase_								=		(
					    (
					        FlaxBlenderbotSmallModel,
					        FlaxBlenderbotSmallForConditionalGeneration,
					    )
					    if is_flax_available()
					    else ()
					)
					lowercase_								=		(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()







					def 			snake_case					(							self :     Optional[Any]					):
									lowercase__					:   Dict													=					FlaxBlenderbotSmallModelTester(self					)







					def 			snake_case					(							self :     int					):
									lowercase__							,							lowercase__					:   Tuple													=					self.model_tester.prepare_config_and_inputs()
									for model_class in self.all_model_classes:
													self.model_tester.check_use_cache_forward(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     List[str]					):
									lowercase__							,							lowercase__					:   int													=					self.model_tester.prepare_config_and_inputs()
									for model_class in self.all_model_classes:
													self.model_tester.check_use_cache_forward_with_attn_mask(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     Dict					):
									lowercase__							,							lowercase__					:   Optional[int]													=					self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__					):
																	lowercase__					:   Any													=					self._prepare_for_class(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)
																	lowercase__					:   Dict													=					model_class(SCREAMING_SNAKE_CASE					)

																	@jax.jit
																	def encode_jitted(SCREAMING_SNAKE_CASE :     Any				,       SCREAMING_SNAKE_CASE :     Optional[int]=None				,       **SCREAMING_SNAKE_CASE :     Any					):
																					return model.encode(input_ids=SCREAMING_SNAKE_CASE				,       attention_mask=SCREAMING_SNAKE_CASE					)

																	with self.subTest("JIT Enabled"					):
																					lowercase__					:   Dict													=					encode_jitted(**SCREAMING_SNAKE_CASE					).to_tuple()

																	with self.subTest("JIT Disabled"					):
																					with jax.disable_jit():
																									lowercase__					:   Optional[Any]													=					encode_jitted(**SCREAMING_SNAKE_CASE					).to_tuple()

																	self.assertEqual(len(SCREAMING_SNAKE_CASE					)				,       len(SCREAMING_SNAKE_CASE					)					)
																	for jitted_output, output in zip(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					):
																					self.assertEqual(jitted_output.shape				,       output.shape					)







					def 			snake_case					(							self :     Union[str, Any]					):
									lowercase__							,							lowercase__					:   Optional[Any]													=					self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__					):
																	lowercase__					:   int													=					model_class(SCREAMING_SNAKE_CASE					)
																	lowercase__					:   Any													=					model.encode(inputs_dict["input_ids"]				,       inputs_dict["attention_mask"]					)

																	lowercase__					:   Any													=					{
																	    "decoder_input_ids": inputs_dict["decoder_input_ids"],
																	    "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
																	    "encoder_outputs": encoder_outputs,
																	}

																	@jax.jit
																	def decode_jitted(SCREAMING_SNAKE_CASE :     List[Any]				,       SCREAMING_SNAKE_CASE :     Tuple				,       SCREAMING_SNAKE_CASE :     Dict					):
																					return model.decode(
																					    decoder_input_ids=SCREAMING_SNAKE_CASE				,       decoder_attention_mask=SCREAMING_SNAKE_CASE				,       encoder_outputs=SCREAMING_SNAKE_CASE				,       )

																	with self.subTest("JIT Enabled"					):
																					lowercase__					:   Optional[Any]													=					decode_jitted(**SCREAMING_SNAKE_CASE					).to_tuple()

																	with self.subTest("JIT Disabled"					):
																					with jax.disable_jit():
																									lowercase__					:   Optional[Any]													=					decode_jitted(**SCREAMING_SNAKE_CASE					).to_tuple()

																	self.assertEqual(len(SCREAMING_SNAKE_CASE					)				,       len(SCREAMING_SNAKE_CASE					)					)
																	for jitted_output, output in zip(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					):
																					self.assertEqual(jitted_output.shape				,       output.shape					)







					@slow
					def 			snake_case					(							self :     Optional[int]					):
									for model_class_name in self.all_model_classes:
													lowercase__					:   Tuple													=					model_class_name.from_pretrained("facebook/blenderbot_small-90M"					)
													# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
													lowercase__					:   Tuple													=					np.ones((1, 1)					) * model.config.eos_token_id
													lowercase__					:   List[str]													=					model(SCREAMING_SNAKE_CASE					)
													self.assertIsNotNone(SCREAMING_SNAKE_CASE					)









import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__=1						):

				"""simple docstring"""


				if n_shave_prefix_segments >= 0:
								return ".".join(path.split("."						)[n_shave_prefix_segments:]						)
				else:
								return ".".join(path.split("."						)[:n_shave_prefix_segments]						)


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__=0						):

				"""simple docstring"""


				lowercase__					:   List[str]													=					[]
				for old_item in old_list:
								lowercase__					:   Optional[Any]													=					old_item.replace("in_layers.0"					,			"norm1"						)
								lowercase__					:   Union[str, Any]													=					new_item.replace("in_layers.2"					,			"conv1"						)

								lowercase__					:   Optional[Any]													=					new_item.replace("out_layers.0"					,			"norm2"						)
								lowercase__					:   Union[str, Any]													=					new_item.replace("out_layers.3"					,			"conv2"						)

								lowercase__					:   Dict													=					new_item.replace("emb_layers.1"					,			"time_emb_proj"						)
								lowercase__					:   int													=					new_item.replace("skip_connection"					,			"conv_shortcut"						)

								lowercase__					:   Tuple													=					shave_segments(lowerCamelCase__					,			n_shave_prefix_segments=lowerCamelCase__						)

								mapping.append({"old": old_item, "new": new_item}						)

				return mapping


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__=0						):

				"""simple docstring"""


				lowercase__					:   str													=					[]
				for old_item in old_list:
								lowercase__					:   Optional[int]													=					old_item

								lowercase__					:   Dict													=					new_item.replace("norm.weight"					,			"group_norm.weight"						)
								lowercase__					:   Optional[int]													=					new_item.replace("norm.bias"					,			"group_norm.bias"						)

								lowercase__					:   Tuple													=					new_item.replace("proj_out.weight"					,			"proj_attn.weight"						)
								lowercase__					:   List[Any]													=					new_item.replace("proj_out.bias"					,			"proj_attn.bias"						)

								lowercase__					:   Optional[Any]													=					shave_segments(lowerCamelCase__					,			n_shave_prefix_segments=lowerCamelCase__						)

								mapping.append({"old": old_item, "new": new_item}						)

				return mapping


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__=None					,			lowerCamelCase__=None					,			lowerCamelCase__=None						):

				"""simple docstring"""


				assert isinstance(lowerCamelCase__					,			lowerCamelCase__						), "Paths should be a list of dicts containing 'old' and 'new' keys."

				# Splits the attention layers into three variables.
				if attention_paths_to_split is not None:
								for path, path_map in attention_paths_to_split.items():
												lowercase__					:   List[str]													=					old_checkpoint[path]
												lowercase__					:   str													=					old_tensor.shape[0] // 3

												lowercase__					:   List[str]													=					(-1, channels) if len(old_tensor.shape						) == 3 else (-1)

												lowercase__					:   Union[str, Any]													=					old_tensor.shape[0] // config["num_head_channels"] // 3

												lowercase__					:   Union[str, Any]													=					old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]						)
												lowercase__							,							lowercase__							,							lowercase__					:   Optional[Any]													=					old_tensor.split(channels // num_heads					,			dim=1						)

												lowercase__					:   Dict													=					query.reshape(lowerCamelCase__						)
												lowercase__					:   Dict													=					key.reshape(lowerCamelCase__						)
												lowercase__					:   int													=					value.reshape(lowerCamelCase__						)

				for path in paths:
								lowercase__					:   Union[str, Any]													=					path["new"]

								# These have already been assigned
								if attention_paths_to_split is not None and new_path in attention_paths_to_split:
												continue

								# Global renaming happens here
								lowercase__					:   List[Any]													=					new_path.replace("middle_block.0"					,			"mid_block.resnets.0"						)
								lowercase__					:   Optional[Any]													=					new_path.replace("middle_block.1"					,			"mid_block.attentions.0"						)
								lowercase__					:   List[str]													=					new_path.replace("middle_block.2"					,			"mid_block.resnets.1"						)

								if additional_replacements is not None:
												for replacement in additional_replacements:
																lowercase__					:   Tuple													=					new_path.replace(replacement["old"]					,			replacement["new"]						)

        # proj_attn.weight has to be converted from conv 1D to linear
								if "proj_attn.weight" in new_path:
												lowercase__					:   List[Any]													=					old_checkpoint[path["old"]][:, :, 0]
								else:
												lowercase__					:   List[Any]													=					old_checkpoint[path["old"]]


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__						):

				"""simple docstring"""


				lowercase__					:   Union[str, Any]													=					{}

				lowercase__					:   Optional[Any]													=					checkpoint["time_embed.0.weight"]
				lowercase__					:   Tuple													=					checkpoint["time_embed.0.bias"]
				lowercase__					:   Dict													=					checkpoint["time_embed.2.weight"]
				lowercase__					:   Optional[Any]													=					checkpoint["time_embed.2.bias"]

				lowercase__					:   Optional[int]													=					checkpoint["input_blocks.0.0.weight"]
				lowercase__					:   List[Any]													=					checkpoint["input_blocks.0.0.bias"]

				lowercase__					:   Tuple													=					checkpoint["out.0.weight"]
				lowercase__					:   List[Any]													=					checkpoint["out.0.bias"]
				lowercase__					:   Tuple													=					checkpoint["out.2.weight"]
				lowercase__					:   Optional[Any]													=					checkpoint["out.2.bias"]

				# Retrieves the keys for the input blocks only
				lowercase__					:   Dict													=					len({".".join(layer.split("."						)[:2]						) for layer in checkpoint if "input_blocks" in layer}						)
				lowercase__					:   str													=					{
				    layer_id: [key for key in checkpoint if F"""input_blocks.{layer_id}""" in key]
				    for layer_id in range(lowerCamelCase__						)
				}

				# Retrieves the keys for the middle blocks only
				lowercase__					:   Tuple													=					len({".".join(layer.split("."						)[:2]						) for layer in checkpoint if "middle_block" in layer}						)
				lowercase__					:   Union[str, Any]													=					{
				    layer_id: [key for key in checkpoint if F"""middle_block.{layer_id}""" in key]
				    for layer_id in range(lowerCamelCase__						)
				}

				# Retrieves the keys for the output blocks only
				lowercase__					:   Tuple													=					len({".".join(layer.split("."						)[:2]						) for layer in checkpoint if "output_blocks" in layer}						)
				lowercase__					:   Tuple													=					{
				    layer_id: [key for key in checkpoint if F"""output_blocks.{layer_id}""" in key]
				    for layer_id in range(lowerCamelCase__						)
				}

				for i in range(1					,			lowerCamelCase__						):
								lowercase__					:   Tuple													=					(i - 1) // (config["num_res_blocks"] + 1)
								lowercase__					:   Optional[int]													=					(i - 1) % (config["num_res_blocks"] + 1)

								lowercase__					:   List[Any]													=					[key for key in input_blocks[i] if F"""input_blocks.{i}.0""" in key]
								lowercase__					:   Dict													=					[key for key in input_blocks[i] if F"""input_blocks.{i}.1""" in key]

								if F"""input_blocks.{i}.0.op.weight""" in checkpoint:
												lowercase__					:   int													=					checkpoint[
												    F"""input_blocks.{i}.0.op.weight"""
												]
												lowercase__					:   List[str]													=					checkpoint[
												    F"""input_blocks.{i}.0.op.bias"""
												]
												continue

								lowercase__					:   Union[str, Any]													=					renew_resnet_paths(lowerCamelCase__						)
								lowercase__					:   Optional[int]													=					{"old": F"""input_blocks.{i}.0""", "new": F"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
								lowercase__					:   Optional[int]													=					{"old": "resnets.2.op", "new": "downsamplers.0.op"}
								assign_to_checkpoint(
								    lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			additional_replacements=[meta_path, resnet_op]					,			config=lowerCamelCase__						)

								if len(lowerCamelCase__						):
												lowercase__					:   Tuple													=					renew_attention_paths(lowerCamelCase__						)
												lowercase__					:   str													=					{
												    "old": F"""input_blocks.{i}.1""",
												    "new": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
												}
												lowercase__					:   List[str]													=					{
												    F"""input_blocks.{i}.1.qkv.bias""": {
												        "key": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
												        "query": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
												        "value": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
												    },
												    F"""input_blocks.{i}.1.qkv.weight""": {
												        "key": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
												        "query": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
												        "value": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
												    },
												}
												assign_to_checkpoint(
												    lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			additional_replacements=[meta_path]					,			attention_paths_to_split=lowerCamelCase__					,			config=lowerCamelCase__					,			)

				lowercase__					:   int													=					middle_blocks[0]
				lowercase__					:   Dict													=					middle_blocks[1]
				lowercase__					:   Dict													=					middle_blocks[2]

				lowercase__					:   Any													=					renew_resnet_paths(lowerCamelCase__						)
				assign_to_checkpoint(lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			config=lowerCamelCase__						)

				lowercase__					:   List[Any]													=					renew_resnet_paths(lowerCamelCase__						)
				assign_to_checkpoint(lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			config=lowerCamelCase__						)

				lowercase__					:   Optional[int]													=					renew_attention_paths(lowerCamelCase__						)
				lowercase__					:   Optional[int]													=					{
				    "middle_block.1.qkv.bias": {
				        "key": "mid_block.attentions.0.key.bias",
				        "query": "mid_block.attentions.0.query.bias",
				        "value": "mid_block.attentions.0.value.bias",
				    },
				    "middle_block.1.qkv.weight": {
				        "key": "mid_block.attentions.0.key.weight",
				        "query": "mid_block.attentions.0.query.weight",
				        "value": "mid_block.attentions.0.value.weight",
				    },
				}
				assign_to_checkpoint(
				    lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			attention_paths_to_split=lowerCamelCase__					,			config=lowerCamelCase__						)

				for i in range(lowerCamelCase__						):
								lowercase__					:   List[Any]													=					i // (config["num_res_blocks"] + 1)
								lowercase__					:   Optional[int]													=					i % (config["num_res_blocks"] + 1)
								lowercase__					:   List[Any]													=					[shave_segments(lowerCamelCase__					,			2						) for name in output_blocks[i]]
								lowercase__					:   Optional[Any]													=					{}

								for layer in output_block_layers:
												lowercase__							,							lowercase__					:   str													=					layer.split("."						)[0], shave_segments(lowerCamelCase__					,			1						)
												if layer_id in output_block_list:
																output_block_list[layer_id].append(lowerCamelCase__						)
												else:
																lowercase__					:   Tuple													=					[layer_name]

								if len(lowerCamelCase__						) > 1:
												lowercase__					:   Dict													=					[key for key in output_blocks[i] if F"""output_blocks.{i}.0""" in key]
												lowercase__					:   Dict													=					[key for key in output_blocks[i] if F"""output_blocks.{i}.1""" in key]

												lowercase__					:   Optional[Any]													=					renew_resnet_paths(lowerCamelCase__						)
												lowercase__					:   Optional[Any]													=					renew_resnet_paths(lowerCamelCase__						)

												lowercase__					:   Tuple													=					{"old": F"""output_blocks.{i}.0""", "new": F"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
												assign_to_checkpoint(lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			additional_replacements=[meta_path]					,			config=lowerCamelCase__						)

												if ["conv.weight", "conv.bias"] in output_block_list.values():
																lowercase__					:   List[str]													=					list(output_block_list.values()						).index(["conv.weight", "conv.bias"]						)
																lowercase__					:   Tuple													=					checkpoint[
																    F"""output_blocks.{i}.{index}.conv.weight"""
																]
																lowercase__					:   Optional[Any]													=					checkpoint[
																    F"""output_blocks.{i}.{index}.conv.bias"""
																]

																# Clear attentions as they have been attributed above.
																if len(lowerCamelCase__						) == 2:
																				lowercase__					:   int													=					[]

												if len(lowerCamelCase__						):
																lowercase__					:   Tuple													=					renew_attention_paths(lowerCamelCase__						)
																lowercase__					:   str													=					{
																    "old": F"""output_blocks.{i}.1""",
																    "new": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
																}
																lowercase__					:   Union[str, Any]													=					{
																    F"""output_blocks.{i}.1.qkv.bias""": {
																        "key": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
																        "query": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
																        "value": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
																    },
																    F"""output_blocks.{i}.1.qkv.weight""": {
																        "key": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
																        "query": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
																        "value": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
																    },
																}
																assign_to_checkpoint(
																    lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			additional_replacements=[meta_path]					,			attention_paths_to_split=to_split if any("qkv" in key for key in attentions						) else None					,			config=lowerCamelCase__					,			)
								else:
												lowercase__					:   int													=					renew_resnet_paths(lowerCamelCase__					,			n_shave_prefix_segments=1						)
												for path in resnet_0_paths:
																lowercase__					:   List[Any]													=					".".join(["output_blocks", str(lowerCamelCase__						), path["old"]]						)
																lowercase__					:   Any													=					".".join(["up_blocks", str(lowerCamelCase__						), "resnets", str(lowerCamelCase__						), path["new"]]						)

																lowercase__					:   List[Any]													=					checkpoint[old_path]

				return new_checkpoint


if __name__ == "__main__":
				lowerCAmelCase__					  =  argparse.ArgumentParser()

				parser.add_argument(
				    '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
				)

				parser.add_argument(
				    '''--config_file''',
				    default=None,
				    type=str,
				    required=True,
				    help='''The config json file corresponding to the architecture.''',
				)

				parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

				lowerCAmelCase__					  =  parser.parse_args()

				lowerCAmelCase__					  =  torch.load(args.checkpoint_path)

				with open(args.config_file) as f:
								lowerCAmelCase__					  =  json.loads(f.read())

				lowerCAmelCase__					  =  convert_ldm_checkpoint(checkpoint, config)

				if "ldm" in config:
								del config["ldm"]

				lowerCAmelCase__					  =  UNetaDModel(**config)
				model.load_state_dict(converted_checkpoint)

				try:
								lowerCAmelCase__					  =  DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
								lowerCAmelCase__					  =  VQModel.from_pretrained('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

								lowerCAmelCase__					  =  LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
								pipe.save_pretrained(args.dump_path)
				except:  # noqa: E722
								model.save_pretrained(args.dump_path)




