
import unittest

from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import (
					    NystromformerForMaskedLM,
					    NystromformerForMultipleChoice,
					    NystromformerForQuestionAnswering,
					    NystromformerForSequenceClassification,
					    NystromformerForTokenClassification,
					    NystromformerModel,
					)
					from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST





class 							UpperCAmelCase				:




	'''simple docstring'''





	def __init__(	self :			Any							,A :			Optional[Any]							,A :			List[str]=13							,A :			List[str]=7							,A :			Optional[int]=True							,A :			Optional[int]=True							,A :			Optional[int]=True							,A :			List[str]=True							,A :			Dict=99							,A :			Tuple=32							,A :			int=5							,A :			Dict=4							,A :			Optional[Any]=37							,A :			Optional[int]="gelu"							,A :			List[str]=0.1							,A :			List[str]=0.1							,A :			List[Any]=5_12							,A :			Dict=16							,A :			List[str]=2							,A :			List[str]=0.02							,A :			int=3							,A :			List[str]=4							,A :			int=None							,):
					__A             =			parent
					__A             =			batch_size
					__A             =			seq_length
					__A             =			is_training
					__A             =			use_input_mask
					__A             =			use_token_type_ids
					__A             =			use_labels
					__A             =			vocab_size
					__A             =			hidden_size
					__A             =			num_hidden_layers
					__A             =			num_attention_heads
					__A             =			intermediate_size
					__A             =			hidden_act
					__A             =			hidden_dropout_prob
					__A             =			attention_probs_dropout_prob
					__A             =			max_position_embeddings
					__A             =			type_vocab_size
					__A             =			type_sequence_label_size
					__A             =			initializer_range
					__A             =			num_labels
					__A             =			num_choices
					__A             =			scope






	def     UpperCamelCase_  (	self :			Any			):
					__A             =			ids_tensor([self.batch_size, self.seq_length]							,self.vocab_size			)

					__A             =			None
					if self.use_input_mask:
									__A             =			random_attention_mask([self.batch_size, self.seq_length]			)

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

					__A             =			None
					__A             =			None
					__A             =			None
					if self.use_labels:
									__A             =			ids_tensor([self.batch_size]							,self.type_sequence_label_size			)
									__A             =			ids_tensor([self.batch_size, self.seq_length]							,self.num_labels			)
									__A             =			ids_tensor([self.batch_size]							,self.num_choices			)

					__A             =			self.get_config()

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






	def     UpperCamelCase_  (	self :			Dict			):
					return NystromformerConfig(
					    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     UpperCamelCase_  (	self :			Optional[Any]							,A :			Optional[Any]							,A :			Union[str, Any]							,A :			Optional[Any]							,A :			Tuple							,A :			Any							,A :			Union[str, Any]							,A :			Union[str, Any]			):
					__A             =			NystromformerModel(config=A			)
					model.to(A			)
					model.eval()
					__A             =			model(A							,attention_mask=A							,token_type_ids=A			)
					__A             =			model(A							,token_type_ids=A			)
					__A             =			model(A			)
					self.parent.assertEqual(result.last_hidden_state.shape							,(self.batch_size, self.seq_length, self.hidden_size)			)






	def     UpperCamelCase_  (	self :			str							,A :			Tuple							,A :			str							,A :			Any							,A :			Optional[Any]							,A :			Optional[Any]							,A :			List[str]							,A :			Union[str, Any]			):
					__A             =			NystromformerForMaskedLM(config=A			)
					model.to(A			)
					model.eval()
					__A             =			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     UpperCamelCase_  (	self :			str							,A :			Union[str, Any]							,A :			Any							,A :			int							,A :			List[str]							,A :			Any							,A :			List[Any]							,A :			Dict			):
					__A             =			NystromformerForQuestionAnswering(config=A			)
					model.to(A			)
					model.eval()
					__A             =			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     UpperCamelCase_  (	self :			Optional[int]							,A :			str							,A :			str							,A :			int							,A :			Dict							,A :			List[str]							,A :			Optional[int]							,A :			List[str]			):
					__A             =			self.num_labels
					__A             =			NystromformerForSequenceClassification(A			)
					model.to(A			)
					model.eval()
					__A             =			model(A							,attention_mask=A							,token_type_ids=A							,labels=A			)
					self.parent.assertEqual(result.logits.shape							,(self.batch_size, self.num_labels)			)






	def     UpperCamelCase_  (	self :			Optional[Any]							,A :			Tuple							,A :			str							,A :			Any							,A :			Tuple							,A :			Any							,A :			Dict							,A :			Any			):
					__A             =			self.num_labels
					__A             =			NystromformerForTokenClassification(config=A			)
					model.to(A			)
					model.eval()
					__A             =			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     UpperCamelCase_  (	self :			int							,A :			List[str]							,A :			Union[str, Any]							,A :			Any							,A :			List[str]							,A :			Optional[Any]							,A :			Dict							,A :			List[Any]			):
					__A             =			self.num_choices
					__A             =			NystromformerForMultipleChoice(config=A			)
					model.to(A			)
					model.eval()
					__A             =			input_ids.unsqueeze(1			).expand(-1							,self.num_choices							,-1			).contiguous()
					__A             =			token_type_ids.unsqueeze(1			).expand(-1							,self.num_choices							,-1			).contiguous()
					__A             =			input_mask.unsqueeze(1			).expand(-1							,self.num_choices							,-1			).contiguous()
					__A             =			model(
					    A							,attention_mask=A							,token_type_ids=A							,labels=A							,)
					self.parent.assertEqual(result.logits.shape							,(self.batch_size, self.num_choices)			)







	def     UpperCamelCase_  (	self :			str			):
					__A             =			self.prepare_config_and_inputs()
					(
					    (
					    __A
					)      ,							(
					    __A
					)      ,							(
					    __A
					)      ,							(
					    __A
					)      ,							(
					    __A
					)      ,							(
					    __A
					)      ,							(
					    __A
					)      ,							
					)             =			config_and_inputs
					__A             =			{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
					return config, inputs_dict





@require_torch
class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      unittest.TestCase  ):




	'''simple docstring'''





	snake_case_    =					(
	    (
	        NystromformerModel,
	        NystromformerForMaskedLM,
	        NystromformerForMultipleChoice,
	        NystromformerForQuestionAnswering,
	        NystromformerForSequenceClassification,
	        NystromformerForTokenClassification,
	    )
	    if is_torch_available()
	    else ()
	)
	snake_case_    =					(
	    {
	        "feature-extraction": NystromformerModel,
	        "fill-mask": NystromformerForMaskedLM,
	        "question-answering": NystromformerForQuestionAnswering,
	        "text-classification": NystromformerForSequenceClassification,
	        "token-classification": NystromformerForTokenClassification,
	        "zero-shot": NystromformerForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	snake_case_    =					False
	snake_case_    =					False






	def     UpperCamelCase_  (	self :			int			):
					__A             =			NystromformerModelTester(self			)
					__A             =			ConfigTester(self							,config_class=A							,hidden_size=37			)






	def     UpperCamelCase_  (	self :			List[str]			):
					self.config_tester.run_common_tests()






	def     UpperCamelCase_  (	self :			Tuple			):
					__A             =			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*A			)






	def     UpperCamelCase_  (	self :			str			):
					__A             =			self.model_tester.prepare_config_and_inputs()
					for type in ["absolute", "relative_key", "relative_key_query"]:
									__A             =			type
									self.model_tester.create_and_check_model(*A			)






	def     UpperCamelCase_  (	self :			Any			):
					__A             =			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_masked_lm(*A			)






	def     UpperCamelCase_  (	self :			Dict			):
					__A             =			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_multiple_choice(*A			)






	def     UpperCamelCase_  (	self :			Union[str, Any]			):
					__A             =			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_question_answering(*A			)






	def     UpperCamelCase_  (	self :			Optional[Any]			):
					__A             =			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_sequence_classification(*A			)






	def     UpperCamelCase_  (	self :			int			):
					__A             =			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_token_classification(*A			)







	@slow
	def     UpperCamelCase_  (	self :			int			):
					for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__A             =			NystromformerModel.from_pretrained(A			)
									self.assertIsNotNone(A			)





@require_torch
class 							UpperCAmelCase				(  unittest.TestCase  ):




	'''simple docstring'''





	@slow
	def     UpperCamelCase_  (	self :			Optional[Any]			):
					__A             =			NystromformerModel.from_pretrained("uw-madison/nystromformer-512"			)
					__A             =			torch.tensor([[0, 1, 2, 3, 4, 5]]			)

					with torch.no_grad():
									__A             =			model(A			)[0]

					__A             =			torch.Size((1, 6, 7_68)			)
					self.assertEqual(output.shape							,A			)

					__A             =			torch.tensor(
					    [[[-0.45_32, -0.09_36, 0.51_37], [-0.26_76, 0.06_28, 0.61_86], [-0.36_29, -0.17_26, 0.47_16]]]			)

					self.assertTrue(torch.allclose(output[:, :3, :3]							,A							,atol=1E-4			)			)







	@slow
	def     UpperCamelCase_  (	self :			Optional[int]			):
					__A             =			"the [MASK] of Belgium is Brussels"

					__A             =			AutoTokenizer.from_pretrained("uw-madison/nystromformer-512"			)
					__A             =			NystromformerForMaskedLM.from_pretrained("uw-madison/nystromformer-512"			)

					__A             =			tokenizer(A							,return_tensors="pt"			)

					with torch.no_grad():
									__A             =			model(encoding.input_ids			).logits

					__A             =			token_logits[:, 2, :].argmax(-1			)[0]

					self.assertEqual(tokenizer.decode(A			)							,"capital"			)


from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	def __init__(	self :			List[str]							,A :			Optional[Any]							,A :			List[Any]			):
					super().__init__()

					# make sure scheduler can always be converted to DDIM
					__A             =			DDIMScheduler.from_config(scheduler.config			)

					self.register_modules(unet=A							,scheduler=A			)







	@torch.no_grad()
	def __call__(	self :			Tuple							,A :			int = 1							,A :			Optional[Union[torch.Generator, List[torch.Generator]]] = None							,A :			float = 0.0							,A :			int = 50							,A :			Optional[bool] = None							,A :			Optional[str] = "pil"							,A :			bool = True							,):

					# Sample gaussian noise to begin loop
					if isinstance(self.unet.config.sample_size							,A			):
									__A             =			(
									    batch_size,
									    self.unet.config.in_channels,
									    self.unet.config.sample_size,
									    self.unet.config.sample_size,
									)
					else:
									__A             =			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

					if isinstance(A							,A			) and len(A			) != batch_size:
									raise ValueError(
									    f'''You have passed a list of generators of length {len(A			)}, but requested an effective batch'''
									    f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''			)

					__A             =			randn_tensor(A							,generator=A							,device=self.device							,dtype=self.unet.dtype			)

					# set step values
					self.scheduler.set_timesteps(A			)

					for t in self.progress_bar(self.scheduler.timesteps			):
									# 1. predict noise model_output
									__A             =			self.unet(A							,A			).sample

									# 2. predict previous mean of image x_t-1 and add variance depending on eta
									# eta corresponds to η in paper and should be between [0, 1]
									# do x_t -> x_t-1
									__A             =			self.scheduler.step(
									    A							,A							,A							,eta=A							,use_clipped_model_output=A							,generator=A			).prev_sample

					__A             =			(image / 2 + 0.5).clamp(0							,1			)
					__A             =			image.cpu().permute(0							,2							,3							,1			).numpy()
					if output_type == "pil":
									__A             =			self.numpy_to_pil(A			)

					if not return_dict:
									return (image,)

					return ImagePipelineOutput(images=A			)
