

import unittest

from transformers import 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, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    RemBertConfig,
							    RemBertForCausalLM,
							    RemBertForMaskedLM,
							    RemBertForMultipleChoice,
							    RemBertForQuestionAnswering,
							    RemBertForSequenceClassification,
							    RemBertForTokenClassification,
							    RemBertModel,
							)
							from transformers.models.rembert.modeling_rembert import REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST







class 						__magic_name__      :

			'''simple docstring'''




			def __init__(    self,   lowercase_,   lowercase_=13,   lowercase_=7,   lowercase_=True,   lowercase_=True,   lowercase_=True,   lowercase_=True,   lowercase_=99,   lowercase_=32,   lowercase_=18,   lowercase_=43,   lowercase_=5,   lowercase_=4,   lowercase_=37,   lowercase_="gelu",   lowercase_=0.1,   lowercase_=0.1,   lowercase_=512,   lowercase_=16,   lowercase_=2,   lowercase_=0.02,   lowercase_=3,   lowercase_=4,   lowercase_=None,   )							->   Union[str, Any]:






					"""simple docstring"""






					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__  =input_embedding_size
					a__  =output_embedding_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	)							->   List[str]:






					"""simple docstring"""






					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__  =RemBertConfig(
					    vocab_size=self.vocab_size,   hidden_size=self.hidden_size,   input_embedding_size=self.input_embedding_size,   output_embedding_size=self.output_embedding_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,   )

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



			def 			_UpperCAmelCase			(    self	)							->   Tuple:






					"""simple docstring"""






					(
					    a__						
					)  =self.prepare_config_and_inputs()

					a__  =True
					a__  =floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
					a__  =ids_tensor([self.batch_size, self.seq_length],   vocab_size=2	)

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



			def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_	)							->   Optional[Any]:






					"""simple docstring"""






					a__  =RemBertModel(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,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   )							->   List[str]:






					"""simple docstring"""






					a__  =True
					a__  =RemBertModel(_A	)
					model.to(_A	)
					model.eval()
					a__  =model(
					    _A,   attention_mask=_A,   token_type_ids=_A,   encoder_hidden_states=_A,   encoder_attention_mask=_A,   )
					a__  =model(
					    _A,   attention_mask=_A,   token_type_ids=_A,   encoder_hidden_states=_A,   )
					a__  =model(_A,   attention_mask=_A,   token_type_ids=_A	)
					self.parent.assertEqual(result.last_hidden_state.shape,   (self.batch_size, self.seq_length, self.hidden_size)	)



			def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   )							->   Tuple:






					"""simple docstring"""






					a__  =RemBertForCausalLM(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,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_	)							->   Optional[Any]:






					"""simple docstring"""






					a__  =RemBertForMaskedLM(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,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   )							->   Any:






					"""simple docstring"""






					a__  =True
					a__  =True
					a__  =RemBertForCausalLM(config=_A	)
					model.to(_A	)
					model.eval()

					# first forward pass
					a__  =model(
					    _A,   attention_mask=_A,   encoder_hidden_states=_A,   encoder_attention_mask=_A,   use_cache=_A,   )
					a__  =outputs.past_key_values

					# create hypothetical multiple next token and extent to next_input_ids
					a__  =ids_tensor((self.batch_size, 3),   config.vocab_size	)
					a__  =ids_tensor((self.batch_size, 3),   vocab_size=2	)

					# append to next input_ids and
					a__  =torch.cat([input_ids, next_tokens],   dim=-1	)
					a__  =torch.cat([input_mask, next_mask],   dim=-1	)

					a__  =model(
					    _A,   attention_mask=_A,   encoder_hidden_states=_A,   encoder_attention_mask=_A,   output_hidden_states=_A,   )['''hidden_states'''][0]
					a__  =model(
					    _A,   attention_mask=_A,   encoder_hidden_states=_A,   encoder_attention_mask=_A,   past_key_values=_A,   output_hidden_states=_A,   )['''hidden_states'''][0]

					# select random slice
					a__  =ids_tensor((1,),   output_from_past.shape[-1]	).item()
					a__  =output_from_no_past[:, -3:, random_slice_idx].detach()
					a__  =output_from_past[:, :, random_slice_idx].detach()

					self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]	)

					# test that outputs are equal for slice
					self.parent.assertTrue(torch.allclose(_A,   _A,   atol=1E-3	)	)



			def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_	)							->   List[str]:






					"""simple docstring"""






					a__  =RemBertForQuestionAnswering(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,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_	)							->   int:






					"""simple docstring"""






					a__  =self.num_labels
					a__  =RemBertForSequenceClassification(_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,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_	)							->   List[str]:






					"""simple docstring"""






					a__  =self.num_labels
					a__  =RemBertForTokenClassification(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,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_	)							->   Optional[int]:






					"""simple docstring"""






					a__  =self.num_choices
					a__  =RemBertForMultipleChoice(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	)							->   Union[str, Any]:






					"""simple docstring"""






					a__  =self.prepare_config_and_inputs()
					(
					    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 						__magic_name__      (							lowerCAmelCase__		,	lowerCAmelCase__		,	unittest.TestCase							):

			'''simple docstring'''




			lowerCamelCase__			:     List[str]    =       (
			    (
			        RemBertModel,
			        RemBertForMaskedLM,
			        RemBertForCausalLM,
			        RemBertForMultipleChoice,
			        RemBertForQuestionAnswering,
			        RemBertForSequenceClassification,
			        RemBertForTokenClassification,
			    )
			    if is_torch_available()
			    else ()
			)
			lowerCamelCase__			:     Optional[Any]    =       (RemBertForCausalLM,) if is_torch_available() else ()
			lowerCamelCase__			:     str    =       (
			    {
			        'feature-extraction': RemBertModel,
			        'fill-mask': RemBertForMaskedLM,
			        'question-answering': RemBertForQuestionAnswering,
			        'text-classification': RemBertForSequenceClassification,
			        'text-generation': RemBertForCausalLM,
			        'token-classification': RemBertForTokenClassification,
			        'zero-shot': RemBertForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)



			def 			_UpperCAmelCase			(    self	)							->   List[Any]:






					"""simple docstring"""






					a__  =RemBertModelTester(self	)
					a__  =ConfigTester(self,   config_class=_A,   hidden_size=37	)



			def 			_UpperCAmelCase			(    self	)							->   List[Any]:






					"""simple docstring"""






					self.config_tester.run_common_tests()



			def 			_UpperCAmelCase			(    self	)							->   List[Any]:






					"""simple docstring"""






					a__  =self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*_A	)



			def 			_UpperCAmelCase			(    self	)							->   Optional[Any]:






					"""simple docstring"""






					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	)							->   Optional[int]:






					"""simple docstring"""






					a__  =self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_masked_lm(*_A	)



			def 			_UpperCAmelCase			(    self	)							->   Dict:






					"""simple docstring"""






					a__  =self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_multiple_choice(*_A	)



			def 			_UpperCAmelCase			(    self	)							->   Union[str, Any]:






					"""simple docstring"""






					a__  =self.model_tester.prepare_config_and_inputs_for_decoder()
					self.model_tester.create_and_check_decoder_model_past_large_inputs(*_A	)



			def 			_UpperCAmelCase			(    self	)							->   Optional[Any]:






					"""simple docstring"""






					a__  =self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_question_answering(*_A	)



			def 			_UpperCAmelCase			(    self	)							->   Optional[Any]:






					"""simple docstring"""






					a__  =self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_sequence_classification(*_A	)



			def 			_UpperCAmelCase			(    self	)							->   Any:






					"""simple docstring"""






					a__  =self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_token_classification(*_A	)



			def 			_UpperCAmelCase			(    self	)							->   Union[str, Any]:






					"""simple docstring"""






					a__  =self.model_tester.prepare_config_and_inputs_for_decoder()
					self.model_tester.create_and_check_model_as_decoder(*_A	)



			def 			_UpperCAmelCase			(    self	)							->   Dict:






					"""simple docstring"""






					(
					    a__						
					)  =self.model_tester.prepare_config_and_inputs_for_decoder()

					a__  =None

					self.model_tester.create_and_check_model_as_decoder(
					    _A,   _A,   _A,   _A,   _A,   _A,   _A,   _A,   _A,   )



			@slow
			def 			_UpperCAmelCase			(    self	)							->   Optional[Any]:






					"""simple docstring"""






					for model_name in REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							a__  =RemBertModel.from_pretrained(_A	)
							self.assertIsNotNone(_A	)







@require_torch
class 						__magic_name__      (							unittest.TestCase							):

			'''simple docstring'''




			@slow
			def 			_UpperCAmelCase			(    self	)							->   int:






					"""simple docstring"""






					a__  =RemBertModel.from_pretrained('''google/rembert'''	)
					a__  =torch.tensor([[312, 56498, 313, 2125, 313]]	)
					a__  =torch.tensor([[0, 0, 0, 1, 1]]	)
					with torch.no_grad():
							a__  =model(_A,   token_type_ids=_A,   output_hidden_states=_A	)

					a__  =1152

					a__  =torch.Size((1, 5, hidden_size)	)
					self.assertEqual(output['''last_hidden_state'''].shape,   _A	)

					a__  =torch.tensor(
					    [
					        [
					            [0.0754, -0.2022, 0.1904],
					            [-0.3354, -0.3692, -0.4791],
					            [-0.2314, -0.6729, -0.0749],
					            [-0.0396, -0.3105, -0.4234],
					            [-0.1571, -0.0525, 0.5353],
					        ]
					    ]	)

					# Running on the original tf implementation gives slightly different results here.
					# Not clear why this variations is present
					# TODO: Find reason for discrepancy
					# expected_original_implementation = [[
					#     [0.07630594074726105, -0.20146065950393677, 0.19107051193714142],
					#     [-0.3405614495277405, -0.36971670389175415, -0.4808273911476135],
					#     [-0.22587086260318756, -0.6656315922737122, -0.07844287157058716],
					#     [-0.04145475849509239, -0.3077218234539032, -0.42316967248916626],
					#     [-0.15887849032878876, -0.054529931396245956, 0.5356100797653198]
					# ]]

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



from dataclasses import dataclass

lowerCamelCase									= '''0.1'''
lowerCamelCase									= '''Lucia Harcekova'''







@dataclass
class 						__magic_name__      :

   '''simple docstring'''




   lowerCamelCase__			:     int
   lowerCamelCase__			:     int
   lowerCamelCase__			:     str



   def __repr__(    self	)							->   str:






     """simple docstring"""






     return F"""({self.offset}, {self.length}, {self.indicator})"""







class 						__magic_name__      :

   '''simple docstring'''




   def __init__(    self,   lowercase_ = 13,   lowercase_ = 6	)							->   None:






     """simple docstring"""






     a__  =window_size
     a__  =lookahead_buffer_size
     a__  =self.window_size - self.lookahead_buffer_size



   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   list[Token]:






     """simple docstring"""






     a__  =[]
     a__  =''''''

     # while there are still characters in text to compress
     while text:
       # find the next encoding phrase
       # - triplet with offset, length, indicator (the next encoding character)
       a__  =self._find_encoding_token(lowercase_,   lowercase_	)

       # update the search buffer:
       # - add new characters from text into it
       # - check if size exceed the max search buffer size, if so, drop the
       #   oldest elements
       search_buffer += text[: token.length + 1]
       if len(lowercase_	) > self.search_buffer_size:
         a__  =search_buffer[-self.search_buffer_size :]

       # update the text
       a__  =text[token.length + 1 :]

       # append the token to output
       output.append(lowercase_	)

     return output



   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   str:






     """simple docstring"""






     a__  =''''''

     for token in tokens:
       for _ in range(token.length	):
         output += output[-token.offset]
       output += token.indicator

     return output



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_	)							->   Token:






     """simple docstring"""






     if not text:
       raise ValueError('''We need some text to work with.'''	)

     # Initialise result parameters to default values
     a__,				a__  =0, 0

     if not search_buffer:
       return Token(lowercase_,   lowercase_,   text[length]	)

     for i, character in enumerate(lowercase_	):
       a__  =len(lowercase_	) - i
       if character == text[0]:
         a__  =self._match_length_from_index(lowercase_,   lowercase_,   0,   lowercase_	)
         # if the found length is bigger than the current or if it's equal,
         # which means it's offset is smaller: update offset and length
         if found_length >= length:
           a__,				a__  =found_offset, found_length

     return Token(lowercase_,   lowercase_,   text[length]	)



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_	)							->   int:






     """simple docstring"""






     if not text or text[text_index] != window[window_index]:
       return 0
     return 1 + self._match_length_from_index(
         lowercase_,   window + text[text_index],   text_index + 1,   window_index + 1	)


if __name__ == "__main__":
       from doctest import testmod

       testmod()
       # Initialize compressor class
       lowerCamelCase									= LZaaCompressor(window_size=13, lookahead_buffer_size=6)

       # Example
       lowerCamelCase									= '''cabracadabrarrarrad'''
       lowerCamelCase									= lzaa_compressor.compress(TEXT)
       print(lzaa_compressor.compress('''ababcbababaa'''))
       lowerCamelCase									= lzaa_compressor.decompress(compressed_text)
       assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."
