



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 	__snake_case   :
		def __init__(							self    ,a_    ,a_=13    ,a_=7    ,a_=True    ,a_=True    ,a_=True    ,a_=True    ,a_=99    ,a_=32    ,a_=5    ,a_=4    ,a_=37    ,a_="gelu"    ,a_=0.1    ,a_=0.1    ,a_=512    ,a_=16    ,a_=2    ,a_=0.02    ,a_=3    ,a_=4    ,a_=None    ,):







						"""simple docstring"""
						lowerCAmelCase__		  =  parent
						lowerCAmelCase__		  =  batch_size
						lowerCAmelCase__		  =  seq_length
						lowerCAmelCase__		  =  is_training
						lowerCAmelCase__		  =  use_input_mask
						lowerCAmelCase__		  =  use_token_type_ids
						lowerCAmelCase__		  =  use_labels
						lowerCAmelCase__		  =  vocab_size
						lowerCAmelCase__		  =  hidden_size
						lowerCAmelCase__		  =  num_hidden_layers
						lowerCAmelCase__		  =  num_attention_heads
						lowerCAmelCase__		  =  intermediate_size
						lowerCAmelCase__		  =  hidden_act
						lowerCAmelCase__		  =  hidden_dropout_prob
						lowerCAmelCase__		  =  attention_probs_dropout_prob
						lowerCAmelCase__		  =  max_position_embeddings
						lowerCAmelCase__		  =  type_vocab_size
						lowerCAmelCase__		  =  type_sequence_label_size
						lowerCAmelCase__		  =  initializer_range
						lowerCAmelCase__		  =  num_labels
						lowerCAmelCase__		  =  num_choices
						lowerCAmelCase__		  =  scope
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  ids_tensor([self.batch_size, self.seq_length]    ,self.vocab_size						)

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

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

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

						lowerCAmelCase__		  =  self.get_config()

						return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
		def 						SCREAMING_SNAKE_CASE_					(							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=a_    ,initializer_range=self.initializer_range    ,)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_    ,a_    ,a_    ,a_    ,a_    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  NystromformerModel(config=a_						)
						model.to(a_						)
						model.eval()
						lowerCAmelCase__		  =  model(a_    ,attention_mask=a_    ,token_type_ids=a_						)
						lowerCAmelCase__		  =  model(a_    ,token_type_ids=a_						)
						lowerCAmelCase__		  =  model(a_						)
						self.parent.assertEqual(result.last_hidden_state.shape    ,(self.batch_size, self.seq_length, self.hidden_size)						)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_    ,a_    ,a_    ,a_    ,a_    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  NystromformerForMaskedLM(config=a_						)
						model.to(a_						)
						model.eval()
						lowerCAmelCase__		  =  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 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_    ,a_    ,a_    ,a_    ,a_    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  NystromformerForQuestionAnswering(config=a_						)
						model.to(a_						)
						model.eval()
						lowerCAmelCase__		  =  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 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_    ,a_    ,a_    ,a_    ,a_    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.num_labels
						lowerCAmelCase__		  =  NystromformerForSequenceClassification(a_						)
						model.to(a_						)
						model.eval()
						lowerCAmelCase__		  =  model(a_    ,attention_mask=a_    ,token_type_ids=a_    ,labels=a_						)
						self.parent.assertEqual(result.logits.shape    ,(self.batch_size, self.num_labels)						)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_    ,a_    ,a_    ,a_    ,a_    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.num_labels
						lowerCAmelCase__		  =  NystromformerForTokenClassification(config=a_						)
						model.to(a_						)
						model.eval()
						lowerCAmelCase__		  =  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 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_    ,a_    ,a_    ,a_    ,a_    ,a_						):







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



		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.prepare_config_and_inputs()
						(
						    (
						    lowerCAmelCase__
						)  ,   (
						    lowerCAmelCase__
						)  ,   (
						    lowerCAmelCase__
						)  ,   (
						    lowerCAmelCase__
						)  ,   (
						    lowerCAmelCase__
						)  ,   (
						    lowerCAmelCase__
						)  ,   (
						    lowerCAmelCase__
						)  ,   
						)		  =  config_and_inputs
						lowerCAmelCase__		  =  {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
						return config, inputs_dict



@require_torch
class 	__snake_case   ( SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, unittest.TestCase ):
		SCREAMING_SNAKE_CASE__								=	(
		    (
		        NystromformerModel,
		        NystromformerForMaskedLM,
		        NystromformerForMultipleChoice,
		        NystromformerForQuestionAnswering,
		        NystromformerForSequenceClassification,
		        NystromformerForTokenClassification,
		    )
		    if is_torch_available()
		    else ()
		)
		SCREAMING_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 {}
		)
		SCREAMING_SNAKE_CASE__								=	False
		SCREAMING_SNAKE_CASE__								=	False
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  NystromformerModelTester(self						)
						lowerCAmelCase__		  =  ConfigTester(self    ,config_class=a_    ,hidden_size=37						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						self.config_tester.run_common_tests()
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.model_tester.prepare_config_and_inputs()
						for type in ["absolute", "relative_key", "relative_key_query"]:
										lowerCAmelCase__		  =  type
										self.model_tester.create_and_check_model(*a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_multiple_choice(*a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(*a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(*a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(*a_						)



		@slow
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowerCAmelCase__		  =  NystromformerModel.from_pretrained(a_						)
										self.assertIsNotNone(a_						)



@require_torch
class 	__snake_case   ( unittest.TestCase ):
		@slow
		def 						SCREAMING_SNAKE_CASE_					(							self						):







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

						with torch.no_grad():
										lowerCAmelCase__		  =  model(a_						)[0]

						lowerCAmelCase__		  =  torch.Size((1, 6, 768)						)
						self.assertEqual(output.shape    ,a_						)

						lowerCAmelCase__		  =  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]    ,a_    ,atol=1e-4						)						)



		@slow
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  'the [MASK] of Belgium is Brussels'

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

						lowerCAmelCase__		  =  tokenizer(a_    ,return_tensors='pt'						)

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

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

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





import os
from collections import deque

import torch
from torch.utils.data import Dataset


class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		def __init__(							self    ,a_=""    ,a_="train"						):







						"""simple docstring"""
						assert os.path.isdir(a_						)

						lowerCAmelCase__		  =  []
						lowerCAmelCase__		  =  os.listdir(a_						)
						for story_filename in story_filenames_list:
										if "summary" in story_filename:
														continue
										lowerCAmelCase__		  =  os.path.join(a_    ,a_						)
										if not os.path.isfile(a_						):
														continue
										self.documents.append(a_						)
		def __len__(							self						):







						"""simple docstring"""
						return len(self.documents						)



		def __getitem__(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.documents[idx]
						lowerCAmelCase__		  =  document_path.split('/'						)[-1]
						with open(a_    ,encoding='utf-8'						) as source:
										lowerCAmelCase__		  =  source.read()
										lowerCAmelCase__  ,   lowerCAmelCase__		  =  process_story(a_						)
						return document_name, story_lines, summary_lines







def     UpperCAmelCase_		(					snake_case__					)	->     str:



				"""simple docstring"""





				lowerCAmelCase__		  =  list(filter(lambda snake_case__					: len(snake_case__					) != 0    ,			[line.strip() for line in raw_story.split('\n'					)]					)					)

				# for some unknown reason some lines miss a period, add it
				lowerCAmelCase__		  =  [_add_missing_period(snake_case__					) for line in nonempty_lines]

				# gather article lines
				lowerCAmelCase__		  =  []
				lowerCAmelCase__		  =  deque(snake_case__					)
				while True:
								try:
												lowerCAmelCase__		  =  lines.popleft()
												if element.startswith('@highlight'					):
																break
												story_lines.append(snake_case__					)
								except IndexError:
												# if "@highlight" is absent from the file we pop
												# all elements until there is None, raising an exception.
												return story_lines, []

    # gather summary lines
				lowerCAmelCase__		  =  list(filter(lambda snake_case__					: not t.startswith('@highlight'					)    ,			snake_case__					)					)

				return story_lines, summary_lines







def     UpperCAmelCase_		(					snake_case__					)	->     Optional[Any]:



				"""simple docstring"""





				lowerCAmelCase__		  =  ['.', '!', '?', '...', '\'', '`', '"', '\u2019', '\u2019', ')']
				if line.startswith('@highlight'					):
								return line
				if line[-1] in END_TOKENS:
								return line
				return line + "."







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__					)	->     str:



				"""simple docstring"""





				if len(snake_case__					) > block_size:
								return sequence[:block_size]
				else:
								sequence.extend([pad_token_id] * (block_size - len(snake_case__					))					)
								return sequence







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     Optional[int]:



				"""simple docstring"""





				lowerCAmelCase__		  =  torch.ones_like(snake_case__					)
				lowerCAmelCase__		  =  sequence == pad_token_id
				lowerCAmelCase__		  =  0
				return mask







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__					)	->     Dict:



				"""simple docstring"""





				lowerCAmelCase__		  =  [tokenizer.encode(snake_case__					) for line in story_lines]
				lowerCAmelCase__		  =  [token for sentence in story_lines_token_ids for token in sentence]
				lowerCAmelCase__		  =  [tokenizer.encode(snake_case__					) for line in summary_lines]
				lowerCAmelCase__		  =  [token for sentence in summary_lines_token_ids for token in sentence]

				return story_token_ids, summary_token_ids







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     Union[str, Any]:



				"""simple docstring"""





				lowerCAmelCase__		  =  []
				for sequence in batch:
								lowerCAmelCase__		  =  -1
								lowerCAmelCase__		  =  []
								for s in sequence:
												if s == separator_token_id:
																sentence_num += 1
												embeddings.append(sentence_num % 2					)
								batch_embeddings.append(snake_case__					)
				return torch.tensor(snake_case__					)
