

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 SCREAMING_SNAKE_CASE	:






			"""simple docstring"""




			def __init__(     self			,					__UpperCamelCase			,					__UpperCamelCase=13			,					__UpperCamelCase=7			,					__UpperCamelCase=True			,					__UpperCamelCase=True			,					__UpperCamelCase=True			,					__UpperCamelCase=True			,					__UpperCamelCase=99			,					__UpperCamelCase=32			,					__UpperCamelCase=5			,					__UpperCamelCase=4			,					__UpperCamelCase=37			,					__UpperCamelCase="gelu"			,					__UpperCamelCase=0.1			,					__UpperCamelCase=0.1			,					__UpperCamelCase=5_12			,					__UpperCamelCase=16			,					__UpperCamelCase=2			,					__UpperCamelCase=0.02			,					__UpperCamelCase=3			,					__UpperCamelCase=4			,					__UpperCamelCase=None			,					):

				"""simple docstring"""


				snake_case_   		=			parent
				snake_case_   		=			batch_size
				snake_case_   		=			seq_length
				snake_case_   		=			is_training
				snake_case_   		=			use_input_mask
				snake_case_   		=			use_token_type_ids
				snake_case_   		=			use_labels
				snake_case_   		=			vocab_size
				snake_case_   		=			hidden_size
				snake_case_   		=			num_hidden_layers
				snake_case_   		=			num_attention_heads
				snake_case_   		=			intermediate_size
				snake_case_   		=			hidden_act
				snake_case_   		=			hidden_dropout_prob
				snake_case_   		=			attention_probs_dropout_prob
				snake_case_   		=			max_position_embeddings
				snake_case_   		=			type_vocab_size
				snake_case_   		=			type_sequence_label_size
				snake_case_   		=			initializer_range
				snake_case_   		=			num_labels
				snake_case_   		=			num_choices
				snake_case_   		=			scope


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			ids_tensor([self.batch_size, self.seq_length]			,					self.vocab_size				)

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

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

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

				snake_case_   		=			self.get_config()

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


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				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=__UpperCamelCase			,					initializer_range=self.initializer_range			,					)


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			NystromformerModel(config=__UpperCamelCase				)
				model.to(__UpperCamelCase				)
				model.eval()
				snake_case_   		=			model(__UpperCamelCase			,					attention_mask=__UpperCamelCase			,					token_type_ids=__UpperCamelCase				)
				snake_case_   		=			model(__UpperCamelCase			,					token_type_ids=__UpperCamelCase				)
				snake_case_   		=			model(__UpperCamelCase				)
				self.parent.assertEqual(result.last_hidden_state.shape			,					(self.batch_size, self.seq_length, self.hidden_size)				)


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			NystromformerForMaskedLM(config=__UpperCamelCase				)
				model.to(__UpperCamelCase				)
				model.eval()
				snake_case_   		=			model(__UpperCamelCase			,					attention_mask=__UpperCamelCase			,					token_type_ids=__UpperCamelCase			,					labels=__UpperCamelCase				)
				self.parent.assertEqual(result.logits.shape			,					(self.batch_size, self.seq_length, self.vocab_size)				)


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			NystromformerForQuestionAnswering(config=__UpperCamelCase				)
				model.to(__UpperCamelCase				)
				model.eval()
				snake_case_   		=			model(
				    __UpperCamelCase			,					attention_mask=__UpperCamelCase			,					token_type_ids=__UpperCamelCase			,					start_positions=__UpperCamelCase			,					end_positions=__UpperCamelCase			,					)
				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			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			self.num_labels
				snake_case_   		=			NystromformerForSequenceClassification(__UpperCamelCase				)
				model.to(__UpperCamelCase				)
				model.eval()
				snake_case_   		=			model(__UpperCamelCase			,					attention_mask=__UpperCamelCase			,					token_type_ids=__UpperCamelCase			,					labels=__UpperCamelCase				)
				self.parent.assertEqual(result.logits.shape			,					(self.batch_size, self.num_labels)				)


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			self.num_labels
				snake_case_   		=			NystromformerForTokenClassification(config=__UpperCamelCase				)
				model.to(__UpperCamelCase				)
				model.eval()
				snake_case_   		=			model(__UpperCamelCase			,					attention_mask=__UpperCamelCase			,					token_type_ids=__UpperCamelCase			,					labels=__UpperCamelCase				)
				self.parent.assertEqual(result.logits.shape			,					(self.batch_size, self.seq_length, self.num_labels)				)


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			self.num_choices
				snake_case_   		=			NystromformerForMultipleChoice(config=__UpperCamelCase				)
				model.to(__UpperCamelCase				)
				model.eval()
				snake_case_   		=			input_ids.unsqueeze(1				).expand(-1			,					self.num_choices			,					-1				).contiguous()
				snake_case_   		=			token_type_ids.unsqueeze(1				).expand(-1			,					self.num_choices			,					-1				).contiguous()
				snake_case_   		=			input_mask.unsqueeze(1				).expand(-1			,					self.num_choices			,					-1				).contiguous()
				snake_case_   		=			model(
				    __UpperCamelCase			,					attention_mask=__UpperCamelCase			,					token_type_ids=__UpperCamelCase			,					labels=__UpperCamelCase			,					)
				self.parent.assertEqual(result.logits.shape			,					(self.batch_size, self.num_choices)				)







			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.prepare_config_and_inputs()
				(
				    (
				    snake_case_
				)		,							(
				    snake_case_
				)		,							(
				    snake_case_
				)		,							(
				    snake_case_
				)		,							(
				    snake_case_
				)		,							(
				    snake_case_
				)		,							(
				    snake_case_
				)		,							
				)   		=			config_and_inputs
				snake_case_   		=			{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
				return config, inputs_dict







@require_torch
class SCREAMING_SNAKE_CASE	(		__snake_case				,    __snake_case				,    unittest.TestCase				):






			"""simple docstring"""




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


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			NystromformerModelTester(self				)
				snake_case_   		=			ConfigTester(self			,					config_class=__UpperCamelCase			,					hidden_size=37				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				self.config_tester.run_common_tests()


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*__UpperCamelCase				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.model_tester.prepare_config_and_inputs()
				for type in ["absolute", "relative_key", "relative_key_query"]:
					snake_case_   		=			type
					self.model_tester.create_and_check_model(*__UpperCamelCase				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_lm(*__UpperCamelCase				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_multiple_choice(*__UpperCamelCase				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_question_answering(*__UpperCamelCase				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_sequence_classification(*__UpperCamelCase				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_token_classification(*__UpperCamelCase				)







			@slow
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					snake_case_   		=			NystromformerModel.from_pretrained(__UpperCamelCase				)
					self.assertIsNotNone(__UpperCamelCase				)







@require_torch
class SCREAMING_SNAKE_CASE	(		unittest.TestCase				):






			"""simple docstring"""




			@slow
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			NystromformerModel.from_pretrained('uw-madison/nystromformer-512'				)
				snake_case_   		=			torch.tensor([[0, 1, 2, 3, 4, 5]]				)

				with torch.no_grad():
					snake_case_   		=			model(__UpperCamelCase				)[0]

				snake_case_   		=			torch.Size((1, 6, 7_68)				)
				self.assertEqual(output.shape			,					__UpperCamelCase				)

				snake_case_   		=			torch.tensor(
				    [[[-0.4532, -0.0936, 0.5137], [-0.2676, 0.0628, 0.6186], [-0.3629, -0.1726, 0.4716]]]				)

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







			@slow
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			'the [MASK] of Belgium is Brussels'

				snake_case_   		=			AutoTokenizer.from_pretrained('uw-madison/nystromformer-512'				)
				snake_case_   		=			NystromformerForMaskedLM.from_pretrained('uw-madison/nystromformer-512'				)

				snake_case_   		=			tokenizer(__UpperCamelCase			,					return_tensors='pt'				)

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

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

				self.assertEqual(tokenizer.decode(__UpperCamelCase				)			,					'capital'				)



from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


A									=  logging.get_logger(__name__)

A									=  {
    'EleutherAI/gpt-neo-1.3B': 'https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}


class SCREAMING_SNAKE_CASE	(		__snake_case				):






			"""simple docstring"""




			__A    	=	"""gpt_neo"""
			__A    	=	["""past_key_values"""]
			__A    	=	{"""num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}


			def __init__(     self			,					__UpperCamelCase=5_02_57			,					__UpperCamelCase=20_48			,					__UpperCamelCase=20_48			,					__UpperCamelCase=24			,					__UpperCamelCase=[[["global", "local"], 12]]			,					__UpperCamelCase=16			,					__UpperCamelCase=None			,					__UpperCamelCase=2_56			,					__UpperCamelCase="gelu_new"			,					__UpperCamelCase=0.0			,					__UpperCamelCase=0.0			,					__UpperCamelCase=0.0			,					__UpperCamelCase=0.1			,					__UpperCamelCase=1E-5			,					__UpperCamelCase=0.02			,					__UpperCamelCase=True			,					__UpperCamelCase=5_02_56			,					__UpperCamelCase=5_02_56			,					**__UpperCamelCase			,					):

				"""simple docstring"""


				snake_case_   		=			vocab_size
				snake_case_   		=			max_position_embeddings
				snake_case_   		=			hidden_size
				snake_case_   		=			num_layers
				snake_case_   		=			num_heads
				snake_case_   		=			intermediate_size
				snake_case_   		=			window_size
				snake_case_   		=			activation_function
				snake_case_   		=			resid_dropout
				snake_case_   		=			embed_dropout
				snake_case_   		=			attention_dropout
				snake_case_   		=			classifier_dropout
				snake_case_   		=			layer_norm_epsilon
				snake_case_   		=			initializer_range
				snake_case_   		=			use_cache

				snake_case_   		=			bos_token_id
				snake_case_   		=			eos_token_id

				snake_case_   		=			attention_types
				snake_case_   		=			self.expand_attention_types_params(__UpperCamelCase				)

				if len(self.attention_layers				) != self.num_layers:
					raise ValueError(
					    'Configuration for convolutional module is incorrect. '
					    'It is required that `len(config.attention_layers)` == `config.num_layers` '
					    f"""but is `len(config.attention_layers) = {len(self.attention_layers				)}`, """
					    f"""`config.num_layers = {self.num_layers}`. """
					    '`config.attention_layers` is prepared using `config.attention_types`. '
					    'Please verify the value of `config.attention_types` argument.'				)

				super().__init__(bos_token_id=__UpperCamelCase			,					eos_token_id=__UpperCamelCase			,					**__UpperCamelCase				)







			@staticmethod
			def       __lowerCAmelCase			(     __UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			[]
				for item in attention_types:
					for _ in range(item[1]				):
						attentions.extend(item[0]				)
				return attentions







def a(lowercase__      ,			lowercase__      ,			lowercase__      ,			lowercase__						):





	'''simple docstring'''
	import torch

	snake_case_   		=			input.size()
	snake_case_   		=			len(lowercase__						)
	snake_case_   		=			shape[dimension]

	snake_case_   		=			torch.arange(0      ,			lowercase__      ,			lowercase__						)
	snake_case_   		=			torch.div(sizedim - size      ,			lowercase__      ,			rounding_mode='floor'						) + 1
	snake_case_   		=			torch.arange(lowercase__						) + low_indices[:min_length][:, None]

	snake_case_   		=			[slice(lowercase__						)] * rank
	snake_case_   		=			indices
	snake_case_   		=			input[s]

	snake_case_   		=			list(range(0      ,			rank + 1						)						)
	perm.append(perm.pop(dimension + 1						)						)

	return sliced.permute(lowercase__						)





def a(lowercase__      ,			lowercase__						):





	'''simple docstring'''
	import torch

	snake_case_   		=			torch.arange(1      ,			lowercase__						)
	snake_case_   		=			torch.remainder(lowercase__      ,			lowercase__						)
	snake_case_   		=			remainders == 0
	snake_case_   		=			candidates[divisor_indices]
	snake_case_   		=			torch.max(lowercase__						)
	return largest_divisor, torch.div(lowercase__      ,			lowercase__      ,			rounding_mode='floor'						)






class SCREAMING_SNAKE_CASE	(		__snake_case				):






			"""simple docstring"""




			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}				)
				if self.use_past:
					self.fill_with_past_key_values_(__UpperCamelCase			,					direction='inputs'				)
					snake_case_   		=			{0: 'batch', 1: 'past_sequence + sequence'}
				else:
					snake_case_   		=			{0: 'batch', 1: 'sequence'}

				return common_inputs


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return self._config.num_heads


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase = -1			,					__UpperCamelCase = -1			,					__UpperCamelCase = False			,					__UpperCamelCase = None			,					):

				"""simple docstring"""


				snake_case_   		=			super(__UpperCamelCase			,					self				).generate_dummy_inputs(
				    __UpperCamelCase			,					batch_size=__UpperCamelCase			,					seq_length=__UpperCamelCase			,					is_pair=__UpperCamelCase			,					framework=__UpperCamelCase				)

				# We need to order the input in the way they appears in the forward()
				snake_case_   		=			OrderedDict({'input_ids': common_inputs['input_ids']}				)

				# Need to add the past_keys
				if self.use_past:
					if not is_torch_available():
						raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'				)
					else:
						import torch

						snake_case_		,							snake_case_   		=			common_inputs['input_ids'].shape
						# Not using the same length for past_key_values
						snake_case_   		=			seqlen + 2
						snake_case_   		=			(
						    batch,
						    self.num_attention_heads,
						    past_key_values_length,
						    self._config.hidden_size // self.num_attention_heads,
						)
						snake_case_   		=			[
						    (torch.zeros(__UpperCamelCase				), torch.zeros(__UpperCamelCase				)) for _ in range(self.num_layers				)
						]

				snake_case_   		=			common_inputs['attention_mask']
				if self.use_past:
					snake_case_   		=			ordered_inputs['attention_mask'].dtype
					snake_case_   		=			torch.cat(
					    [ordered_inputs['attention_mask'], torch.ones(__UpperCamelCase			,					__UpperCamelCase			,					dtype=__UpperCamelCase				)]			,					dim=1				)

				return ordered_inputs







			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return 13
