


import unittest

from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, 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 (
      SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      SqueezeBertForMaskedLM,
      SqueezeBertForMultipleChoice,
      SqueezeBertForQuestionAnswering,
      SqueezeBertForSequenceClassification,
      SqueezeBertForTokenClassification,
      SqueezeBertModel,
  )







class       lowerCamelCase__   (       lowerCAmelCase):





     def __init__(self		,     UpperCAmelCase		,     UpperCAmelCase=1_3		,     UpperCAmelCase=7		,     UpperCAmelCase=True		,     UpperCAmelCase=True		,     UpperCAmelCase=False		,     UpperCAmelCase=True		,     UpperCAmelCase=9_9		,     UpperCAmelCase=3_2		,     UpperCAmelCase=5		,     UpperCAmelCase=4		,     UpperCAmelCase=6_4		,     UpperCAmelCase="gelu"		,     UpperCAmelCase=0.1		,     UpperCAmelCase=0.1		,     UpperCAmelCase=5_1_2		,     UpperCAmelCase=1_6		,     UpperCAmelCase=2		,     UpperCAmelCase=0.02		,     UpperCAmelCase=3		,     UpperCAmelCase=4		,     UpperCAmelCase=None		,     UpperCAmelCase=2		,     UpperCAmelCase=2		,     UpperCAmelCase=2		,     UpperCAmelCase=2		,     UpperCAmelCase=4		,     UpperCAmelCase=1		,     )     ->   int:
      _lowercase							      =parent
      _lowercase							      =batch_size
      _lowercase							      =seq_length
      _lowercase							      =is_training
      _lowercase							      =use_input_mask
      _lowercase							      =use_token_type_ids
      _lowercase							      =use_labels
      _lowercase							      =vocab_size
      _lowercase							      =hidden_size
      _lowercase							      =num_hidden_layers
      _lowercase							      =num_attention_heads
      _lowercase							      =intermediate_size
      _lowercase							      =hidden_act
      _lowercase							      =hidden_dropout_prob
      _lowercase							      =attention_probs_dropout_prob
      _lowercase							      =max_position_embeddings
      _lowercase							      =type_vocab_size
      _lowercase							      =type_sequence_label_size
      _lowercase							      =initializer_range
      _lowercase							      =num_labels
      _lowercase							      =num_choices
      _lowercase							      =scope
      _lowercase							      =q_groups
      _lowercase							      =k_groups
      _lowercase							      =v_groups
      _lowercase							      =post_attention_groups
      _lowercase							      =intermediate_groups
      _lowercase							      =output_groups





     def      __A					(self       )     ->   Optional[Any]:
      _lowercase							      =ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size       )

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

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

      _lowercase							      =self.get_config()

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





     def      __A					(self       )     ->   Optional[Any]:
      return SqueezeBertConfig(
          embedding_size=self.hidden_size		,     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		,     attention_probs_dropout_prob=self.hidden_dropout_prob		,     attention_dropout=self.attention_probs_dropout_prob		,     max_position_embeddings=self.max_position_embeddings		,     initializer_range=self.initializer_range		,     q_groups=self.q_groups		,     k_groups=self.k_groups		,     v_groups=self.v_groups		,     post_attention_groups=self.post_attention_groups		,     intermediate_groups=self.intermediate_groups		,     output_groups=self.output_groups		,     )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   Any:
      _lowercase							      =SqueezeBertModel(config=UpperCAmelCase       )
      model.to(UpperCAmelCase       )
      model.eval()
      _lowercase							      =model(UpperCAmelCase		,     UpperCAmelCase       )
      _lowercase							      =model(UpperCAmelCase       )
      self.parent.assertEqual(result.last_hidden_state.shape		,     (self.batch_size, self.seq_length, self.hidden_size)       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   Tuple:
      _lowercase							      =SqueezeBertForMaskedLM(config=UpperCAmelCase       )
      model.to(UpperCAmelCase       )
      model.eval()
      _lowercase							      =model(UpperCAmelCase		,     attention_mask=UpperCAmelCase		,     labels=UpperCAmelCase       )
      self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.seq_length, self.vocab_size)       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   Tuple:
      _lowercase							      =SqueezeBertForQuestionAnswering(config=UpperCAmelCase       )
      model.to(UpperCAmelCase       )
      model.eval()
      _lowercase							      =model(
          UpperCAmelCase		,     attention_mask=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      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   Dict:
      _lowercase							      =self.num_labels
      _lowercase							      =SqueezeBertForSequenceClassification(UpperCAmelCase       )
      model.to(UpperCAmelCase       )
      model.eval()
      _lowercase							      =model(UpperCAmelCase		,     attention_mask=UpperCAmelCase		,     labels=UpperCAmelCase       )
      self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.num_labels)       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   Tuple:
      _lowercase							      =self.num_labels
      _lowercase							      =SqueezeBertForTokenClassification(config=UpperCAmelCase       )
      model.to(UpperCAmelCase       )
      model.eval()

      _lowercase							      =model(UpperCAmelCase		,     attention_mask=UpperCAmelCase		,     labels=UpperCAmelCase       )
      self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.seq_length, self.num_labels)       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   Optional[int]:
      _lowercase							      =self.num_choices
      _lowercase							      =SqueezeBertForMultipleChoice(config=UpperCAmelCase       )
      model.to(UpperCAmelCase       )
      model.eval()
      _lowercase							      =input_ids.unsqueeze(1       ).expand(-1		,     self.num_choices		,     -1       ).contiguous()
      _lowercase							      =input_mask.unsqueeze(1       ).expand(-1		,     self.num_choices		,     -1       ).contiguous()
      _lowercase							      =model(
          UpperCAmelCase		,     attention_mask=UpperCAmelCase		,     labels=UpperCAmelCase		,     )
      self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.num_choices)       )





     def      __A					(self       )     ->   str:
      _lowercase							      =self.prepare_config_and_inputs()
      ((_lowercase)      ,    (_lowercase)      ,    (_lowercase)      ,    (_lowercase)      ,    (_lowercase)      ,    (_lowercase))							      =config_and_inputs
      _lowercase							      ={'''input_ids''': input_ids, '''attention_mask''': input_mask}
      return config, inputs_dict







@require_torch
class       lowerCamelCase__   (       lowerCAmelCase   ,							lowerCAmelCase   ,							unittest.TestCase):
     SCREAMING_SNAKE_CASE__      			=			(
         (
             SqueezeBertModel,
             SqueezeBertForMaskedLM,
             SqueezeBertForMultipleChoice,
             SqueezeBertForQuestionAnswering,
             SqueezeBertForSequenceClassification,
             SqueezeBertForTokenClassification,
         )
         if is_torch_available()
         else None
     )
     SCREAMING_SNAKE_CASE__      			=			(
         {
             '''feature-extraction''': SqueezeBertModel,
             '''fill-mask''': SqueezeBertForMaskedLM,
             '''question-answering''': SqueezeBertForQuestionAnswering,
             '''text-classification''': SqueezeBertForSequenceClassification,
             '''token-classification''': SqueezeBertForTokenClassification,
             '''zero-shot''': SqueezeBertForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     SCREAMING_SNAKE_CASE__      			=			False
     SCREAMING_SNAKE_CASE__      			=			True
     SCREAMING_SNAKE_CASE__      			=			False





     def      __A					(self       )     ->   List[Any]:
      _lowercase							      =SqueezeBertModelTester(self       )
      _lowercase							      =ConfigTester(self		,     config_class=UpperCAmelCase		,     dim=3_7       )





     def      __A					(self       )     ->   Optional[int]:
      self.config_tester.run_common_tests()





     def      __A					(self       )     ->   List[str]:
      _lowercase							      =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_squeezebert_model(*UpperCAmelCase       )





     def      __A					(self       )     ->   Union[str, Any]:
      _lowercase							      =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_squeezebert_for_masked_lm(*UpperCAmelCase       )





     def      __A					(self       )     ->   Tuple:
      _lowercase							      =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_squeezebert_for_question_answering(*UpperCAmelCase       )





     def      __A					(self       )     ->   Optional[int]:
      _lowercase							      =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_squeezebert_for_sequence_classification(*UpperCAmelCase       )





     def      __A					(self       )     ->   List[str]:
      _lowercase							      =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_squeezebert_for_token_classification(*UpperCAmelCase       )





     def      __A					(self       )     ->   List[Any]:
      _lowercase							      =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_squeezebert_for_multiple_choice(*UpperCAmelCase       )





     @slow
     def      __A					(self       )     ->   Union[str, Any]:
      for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       _lowercase							      =SqueezeBertModel.from_pretrained(UpperCAmelCase       )
       self.assertIsNotNone(UpperCAmelCase       )







@require_sentencepiece
@require_tokenizers
@require_torch
class       lowerCamelCase__   (       unittest.TestCase):





     @slow
     def      __A					(self       )     ->   Dict:
      _lowercase							      =SqueezeBertForSequenceClassification.from_pretrained('''squeezebert/squeezebert-mnli'''       )

      _lowercase							      =torch.tensor([[1, 2_9_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 1_3, 1_5_8_8, 2]]       )
      _lowercase							      =model(UpperCAmelCase       )[0]
      _lowercase							      =torch.Size((1, 3)       )
      self.assertEqual(output.shape		,     UpperCAmelCase       )
      _lowercase							      =torch.tensor([[0.6401, -0.0349, -0.6041]]       )
      self.assertTrue(torch.allclose(UpperCAmelCase		,     UpperCAmelCase		,     atol=1e-4       )       )





import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
	from .tokenization_ta import TaTokenizer
else:
	lowerCAmelCase           =		None


lowerCAmelCase           =		logging.get_logger(__name__)

lowerCAmelCase           =		{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCAmelCase           =		{
    '''vocab_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/tokenizer.json''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/tokenizer.json''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/tokenizer.json''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/tokenizer.json''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/tokenizer.json''',
    },
}


# TODO(PVP) - this should be removed in Transformers v5
lowerCAmelCase           =		{
    '''t5-small''': 5_1_2,
    '''t5-base''': 5_1_2,
    '''t5-large''': 5_1_2,
    '''t5-3b''': 5_1_2,
    '''t5-11b''': 5_1_2,
}





class       A    (   A_ ):
							UpperCamelCase_   :							Dict       =VOCAB_FILES_NAMES
							UpperCamelCase_   :							Dict       =PRETRAINED_VOCAB_FILES_MAP
							UpperCamelCase_   :							List[Any]       =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCamelCase_   :							str       =['''input_ids''', '''attention_mask''']
							UpperCamelCase_   :							List[str]       =TaTokenizer

							UpperCamelCase_   :							List[int]       =[]


							def __init__(self      ,				lowerCAmelCase=None      ,				lowerCAmelCase=None      ,				lowerCAmelCase="</s>"      ,				lowerCAmelCase="<unk>"      ,				lowerCAmelCase="<pad>"      ,				lowerCAmelCase=1_0_0      ,				lowerCAmelCase=None      ,				**lowerCAmelCase      ,				):
									# Add extra_ids to the special token list
									if extra_ids > 0 and additional_special_tokens is None:
											__lowercase=			[f'<extra_id_{i}>' for i in range(lowerCAmelCase					)]
									elif extra_ids > 0 and additional_special_tokens is not None:
											# Check that we have the right number of extra special tokens
											__lowercase=			len(set(filter(lambda lowerCAmelCase					: bool('extra_id_' in str(lowerCAmelCase					)					)      ,				lowerCAmelCase					)					)					)
											if extra_tokens != extra_ids:
													raise ValueError(
													    f'Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'
													    ' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'
													    ' tokens'					)

									super().__init__(
									    lowerCAmelCase      ,				tokenizer_file=lowerCAmelCase      ,				eos_token=lowerCAmelCase      ,				unk_token=lowerCAmelCase      ,				pad_token=lowerCAmelCase      ,				extra_ids=lowerCAmelCase      ,				additional_special_tokens=lowerCAmelCase      ,				**lowerCAmelCase      ,				)

									__lowercase=			vocab_file
									__lowercase=			False if not self.vocab_file else True
									__lowercase=			extra_ids


							@staticmethod
							def 			_A       (lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase					):
									if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
											__lowercase=			TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
											if init_max_model_length is not None and init_max_model_length != max_model_length:
													return init_max_model_length
											elif init_max_model_length is None:
													warnings.warn(
													    'This tokenizer was incorrectly instantiated with a model max length of'
													    f' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'
													    ' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'
													    ' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'
													    f' {pretrained_model_name_or_path} automatically truncating your input to'
													    f' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'
													    f' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'
													    ' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'
													    ' instantiate this tokenizer with `model_max_length` set to your preferred value.'      ,				lowerCAmelCase      ,				)

									return max_model_length


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = None					):
									if not self.can_save_slow_tokenizer:
											raise ValueError(
											    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
											    'tokenizer.'					)

									if not os.path.isdir(lowerCAmelCase					):
											logger.error(f'Vocabulary path ({save_directory}) should be a directory'					)
											return
									__lowercase=			os.path.join(
									    lowerCAmelCase      ,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']					)

									if os.path.abspath(self.vocab_file					) != os.path.abspath(lowerCAmelCase					):
											copyfile(self.vocab_file      ,				lowerCAmelCase					)
											logger.info(f'Copy vocab file to {out_vocab_file}'					)

									return (out_vocab_file,)


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = None					):
									__lowercase=			token_ids_a + [self.eos_token_id]
									if token_ids_a is None:
											return self.prefix_tokens + token_ids_a
									else:
											__lowercase=			token_ids_a + [self.eos_token_id]
											return self.prefix_tokens + token_ids_a + token_ids_a


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = None					):
									__lowercase=			[self.eos_token_id]

									if token_ids_a is None:
											return len(token_ids_a + eos					) * [0]
									return len(token_ids_a + eos + token_ids_a + eos					) * [0]


							def 			_A       (self					):
									return list(
									    set(filter(lambda lowerCAmelCase					: bool(re.search(r'<extra_id_\d+>'      ,				lowerCAmelCase					)					) is not None      ,				self.additional_special_tokens					)					)					)







							def 			_A       (self					):
									return [self.convert_tokens_to_ids(lowerCAmelCase					) for token in self.get_sentinel_tokens()]






