





import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class        _a(    __A    ):
		lowerCamelCase__     :Dict   				=		['image_processor', 'tokenizer']
		lowerCamelCase__     :Any   				=		'AutoImageProcessor'
		lowerCamelCase__     :Tuple   				=		'AutoTokenizer'







		def __init__(    self ,						__snake_case=None ,						__snake_case=None ,						**__snake_case				)      ->      Union[str, Any]:


							'''simple docstring'''

							_snake_case       :					List[Any]				       =   None
							if "feature_extractor" in kwargs:
												warnings.warn(
												    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
												    " instead." ,						__snake_case ,						)
												_snake_case       :					Optional[int]				       =   kwargs.pop("feature_extractor"				)

							_snake_case       :					Optional[Any]				       =   image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
												raise ValueError("You need to specify an `image_processor`."				)
							if tokenizer is None:
												raise ValueError("You need to specify a `tokenizer`."				)

							super().__init__(__snake_case ,						__snake_case				)
							_snake_case       :					Optional[Any]				       =   self.image_processor
							_snake_case       :					List[Any]				       =   False







		def __call__(    self ,						*__snake_case ,						**__snake_case				)      ->      str:


							'''simple docstring'''

							if self._in_target_context_manager:
												return self.current_processor(*__snake_case ,						**__snake_case				)

							_snake_case       :					Any				       =   kwargs.pop("images" ,						__snake_case				)
							_snake_case       :					List[str]				       =   kwargs.pop("text" ,						__snake_case				)
							if len(__snake_case				) > 0:
												_snake_case       :					Union[str, Any]				       =   args[0]
												_snake_case       :					Optional[int]				       =   args[1:]

							if images is None and text is None:
												raise ValueError("You need to specify either an `images` or `text` input to process."				)

							if images is not None:
												_snake_case       :					int				       =   self.image_processor(__snake_case ,						*__snake_case ,						**__snake_case				)
							if text is not None:
												_snake_case       :					Union[str, Any]				       =   self.tokenizer(__snake_case ,						**__snake_case				)

							if text is None:
												return inputs
							elif images is None:
												return encodings
							else:
												_snake_case       :					Union[str, Any]				       =   encodings["input_ids"]
												return inputs







		def        lowercase       (    self ,						*__snake_case ,						**__snake_case				)      ->      Optional[Any]:


							'''simple docstring'''

							return self.tokenizer.batch_decode(*__snake_case ,						**__snake_case				)







		def        lowercase       (    self ,						*__snake_case ,						**__snake_case				)      ->      List[Any]:


							'''simple docstring'''

							return self.tokenizer.decode(*__snake_case ,						**__snake_case				)







		@contextmanager
		def        lowercase       (    self				)      ->      List[str]:


							'''simple docstring'''

							warnings.warn(
							    "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
							    "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
							    "your images inputs, or in a separate call."				)
							_snake_case       :					List[str]				       =   True
							_snake_case       :					Optional[int]				       =   self.tokenizer
							yield
							_snake_case       :					Optional[int]				       =   self.image_processor
							_snake_case       :					Optional[Any]				       =   False







		def        lowercase       (    self ,						__snake_case ,						__snake_case=False ,						__snake_case=None				)      ->      Tuple:


							'''simple docstring'''

							if added_vocab is None:
												_snake_case       :					Optional[Any]				       =   self.tokenizer.get_added_vocab()

							_snake_case       :					Union[str, Any]				       =   {}

							while tokens:
												_snake_case       :					List[Any]				       =   re.search(r"<s_(.*?)>" ,						__snake_case ,						re.IGNORECASE				)
												if start_token is None:
																	break
												_snake_case       :					str				       =   start_token.group(1				)
												_snake_case       :					Union[str, Any]				       =   re.search(rf"""</s_{key}>""" ,						__snake_case ,						re.IGNORECASE				)
												_snake_case       :					Optional[int]				       =   start_token.group()
												if end_token is None:
																	_snake_case       :					Union[str, Any]				       =   tokens.replace(__snake_case ,						""				)
												else:
																	_snake_case       :					Optional[Any]				       =   end_token.group()
																	_snake_case       :					List[str]				       =   re.escape(__snake_case				)
																	_snake_case       :					int				       =   re.escape(__snake_case				)
																	_snake_case       :					Tuple				       =   re.search(f"""{start_token_escaped}(.*?){end_token_escaped}""" ,						__snake_case ,						re.IGNORECASE				)
																	if content is not None:
																						_snake_case       :					Union[str, Any]				       =   content.group(1				).strip()
																						if r"<s_" in content and r"</s_" in content:  # non-leaf node
																											_snake_case       :					Dict				       =   self.tokenajson(__snake_case ,						is_inner_value=__snake_case ,						added_vocab=__snake_case				)
																											if value:
																																if len(__snake_case				) == 1:
																																					_snake_case       :					str				       =   value[0]
																																_snake_case       :					Optional[int]				       =   value
																						else:  # leaf nodes
																											_snake_case       :					Optional[Any]				       =   []
																											for leaf in content.split(r"<sep/>"				):
																																_snake_case       :					Optional[Any]				       =   leaf.strip()
																																if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
																																					_snake_case       :					List[str]				       =   leaf[1:-2]  # for categorical special tokens
																																output[key].append(__snake_case				)
																											if len(output[key]				) == 1:
																																_snake_case       :					Dict				       =   output[key][0]

																	_snake_case       :					str				       =   tokens[tokens.find(__snake_case				) + len(__snake_case				) :].strip()
																	if tokens[:6] == r"<sep/>":  # non-leaf nodes
																						return [output] + self.tokenajson(tokens[6:] ,						is_inner_value=__snake_case ,						added_vocab=__snake_case				)

							if len(__snake_case				):
												return [output] if is_inner_value else output
							else:
												return [] if is_inner_value else {"text_sequence": tokens}







		@property
		def        lowercase       (    self				)      ->      Tuple:


							'''simple docstring'''

							warnings.warn(
							    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." ,						__snake_case ,						)
							return self.image_processor_class







		@property
		def        lowercase       (    self				)      ->      Tuple:


							'''simple docstring'''

							warnings.warn(
							    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." ,						__snake_case ,						)
							return self.image_processor






from __future__ import annotations

import unittest

from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import tensorflow as tf

							from transformers.models.distilbert.modeling_tf_distilbert import (
							    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
							    TFDistilBertForMaskedLM,
							    TFDistilBertForMultipleChoice,
							    TFDistilBertForQuestionAnswering,
							    TFDistilBertForSequenceClassification,
							    TFDistilBertForTokenClassification,
							    TFDistilBertModel,
							)






class        _a:







		def __init__(    self ,						__snake_case ,						)      ->      List[str]:


							'''simple docstring'''

							_snake_case       :					Dict				       =   parent
							_snake_case       :					Any				       =   1_3
							_snake_case       :					Union[str, Any]				       =   7
							_snake_case       :					Any				       =   True
							_snake_case       :					Optional[Any]				       =   True
							_snake_case       :					List[str]				       =   False
							_snake_case       :					str				       =   True
							_snake_case       :					int				       =   9_9
							_snake_case       :					Optional[int]				       =   3_2
							_snake_case       :					Dict				       =   2
							_snake_case       :					Optional[Any]				       =   4
							_snake_case       :					Any				       =   3_7
							_snake_case       :					Optional[int]				       =   "gelu"
							_snake_case       :					Optional[Any]				       =   0.1
							_snake_case       :					List[Any]				       =   0.1
							_snake_case       :					Any				       =   5_1_2
							_snake_case       :					Optional[int]				       =   1_6
							_snake_case       :					Dict				       =   2
							_snake_case       :					Optional[int]				       =   0.02
							_snake_case       :					Any				       =   3
							_snake_case       :					Tuple				       =   4
							_snake_case       :					str				       =   None







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


							'''simple docstring'''

							_snake_case       :					Optional[int]				       =   ids_tensor([self.batch_size, self.seq_length] ,						self.vocab_size				)

							_snake_case       :					List[Any]				       =   None
							if self.use_input_mask:
												_snake_case       :					Tuple				       =   random_attention_mask([self.batch_size, self.seq_length]				)

							_snake_case       :					str				       =   None
							_snake_case       :					Optional[Any]				       =   None
							_snake_case       :					Any				       =   None
							if self.use_labels:
												_snake_case       :					Union[str, Any]				       =   ids_tensor([self.batch_size] ,						self.type_sequence_label_size				)
												_snake_case       :					List[str]				       =   ids_tensor([self.batch_size, self.seq_length] ,						self.num_labels				)
												_snake_case       :					List[Any]				       =   ids_tensor([self.batch_size] ,						self.num_choices				)

							_snake_case       :					Optional[Any]				       =   DistilBertConfig(
							    vocab_size=self.vocab_size ,						dim=self.hidden_size ,						n_layers=self.num_hidden_layers ,						n_heads=self.num_attention_heads ,						hidden_dim=self.intermediate_size ,						hidden_act=self.hidden_act ,						dropout=self.hidden_dropout_prob ,						attention_dropout=self.attention_probs_dropout_prob ,						max_position_embeddings=self.max_position_embeddings ,						initializer_range=self.initializer_range ,						)

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







		def        lowercase       (    self ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case				)      ->      Tuple:


							'''simple docstring'''

							_snake_case       :					str				       =   TFDistilBertModel(config=__snake_case				)
							_snake_case       :					Dict				       =   {"input_ids": input_ids, "attention_mask": input_mask}

							_snake_case       :					Union[str, Any]				       =   model(__snake_case				)

							_snake_case       :					Union[str, Any]				       =   [input_ids, input_mask]

							_snake_case       :					int				       =   model(__snake_case				)

							self.parent.assertEqual(result.last_hidden_state.shape ,						(self.batch_size, self.seq_length, self.hidden_size)				)







		def        lowercase       (    self ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case				)      ->      str:


							'''simple docstring'''

							_snake_case       :					str				       =   TFDistilBertForMaskedLM(config=__snake_case				)
							_snake_case       :					Optional[Any]				       =   {"input_ids": input_ids, "attention_mask": input_mask}
							_snake_case       :					Tuple				       =   model(__snake_case				)
							self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.seq_length, self.vocab_size)				)







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


							'''simple docstring'''

							_snake_case       :					int				       =   TFDistilBertForQuestionAnswering(config=__snake_case				)
							_snake_case       :					Any				       =   {
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							}
							_snake_case       :					Tuple				       =   model(__snake_case				)
							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        lowercase       (    self ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case				)      ->      str:


							'''simple docstring'''

							_snake_case       :					Dict				       =   self.num_labels
							_snake_case       :					Dict				       =   TFDistilBertForSequenceClassification(__snake_case				)
							_snake_case       :					Dict				       =   {"input_ids": input_ids, "attention_mask": input_mask}
							_snake_case       :					Optional[Any]				       =   model(__snake_case				)
							self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.num_labels)				)







		def        lowercase       (    self ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case				)      ->      Dict:


							'''simple docstring'''

							_snake_case       :					str				       =   self.num_choices
							_snake_case       :					List[str]				       =   TFDistilBertForMultipleChoice(__snake_case				)
							_snake_case       :					Optional[int]				       =   tf.tile(tf.expand_dims(__snake_case ,						1				) ,						(1, self.num_choices, 1)				)
							_snake_case       :					int				       =   tf.tile(tf.expand_dims(__snake_case ,						1				) ,						(1, self.num_choices, 1)				)
							_snake_case       :					str				       =   {
							    "input_ids": multiple_choice_inputs_ids,
							    "attention_mask": multiple_choice_input_mask,
							}
							_snake_case       :					List[Any]				       =   model(__snake_case				)
							self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.num_choices)				)







		def        lowercase       (    self ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case				)      ->      Dict:


							'''simple docstring'''

							_snake_case       :					List[str]				       =   self.num_labels
							_snake_case       :					int				       =   TFDistilBertForTokenClassification(__snake_case				)
							_snake_case       :					Any				       =   {"input_ids": input_ids, "attention_mask": input_mask}
							_snake_case       :					Any				       =   model(__snake_case				)
							self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.seq_length, self.num_labels)				)







		def        lowercase       (    self				)      ->      Tuple:


							'''simple docstring'''

							_snake_case       :					List[str]				       =   self.prepare_config_and_inputs()
							((_snake_case)       , (_snake_case)       , (_snake_case)       , (_snake_case)       , (_snake_case)       , (_snake_case))       :					List[str]				       =   config_and_inputs
							_snake_case       :					Optional[int]				       =   {"input_ids": input_ids, "attention_mask": input_mask}
							return config, inputs_dict






@require_tf
class        _a(    __A	,   __A	,   unittest.TestCase    ):
		lowerCamelCase__     :str   				=		(
		    (
		        TFDistilBertModel,
		        TFDistilBertForMaskedLM,
		        TFDistilBertForQuestionAnswering,
		        TFDistilBertForSequenceClassification,
		        TFDistilBertForTokenClassification,
		        TFDistilBertForMultipleChoice,
		    )
		    if is_tf_available()
		    else None
		)
		lowerCamelCase__     :Tuple   				=		(
		    {
		        'feature-extraction': TFDistilBertModel,
		        'fill-mask': TFDistilBertForMaskedLM,
		        'question-answering': TFDistilBertForQuestionAnswering,
		        'text-classification': TFDistilBertForSequenceClassification,
		        'token-classification': TFDistilBertForTokenClassification,
		        'zero-shot': TFDistilBertForSequenceClassification,
		    }
		    if is_tf_available()
		    else {}
		)
		lowerCamelCase__     :Tuple   				=		False
		lowerCamelCase__     :Any   				=		False







		def        lowercase       (    self				)      ->      int:


							'''simple docstring'''

							_snake_case       :					Optional[Any]				       =   TFDistilBertModelTester(self				)
							_snake_case       :					str				       =   ConfigTester(self ,						config_class=__snake_case ,						dim=3_7				)







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


							'''simple docstring'''

							self.config_tester.run_common_tests()







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


							'''simple docstring'''

							_snake_case       :					Dict				       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_distilbert_model(*__snake_case				)







		def        lowercase       (    self				)      ->      Any:


							'''simple docstring'''

							_snake_case       :					Any				       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_distilbert_for_masked_lm(*__snake_case				)







		def        lowercase       (    self				)      ->      List[str]:


							'''simple docstring'''

							_snake_case       :					str				       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_distilbert_for_question_answering(*__snake_case				)







		def        lowercase       (    self				)      ->      Dict:


							'''simple docstring'''

							_snake_case       :					List[Any]				       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_distilbert_for_sequence_classification(*__snake_case				)







		def        lowercase       (    self				)      ->      Dict:


							'''simple docstring'''

							_snake_case       :					List[str]				       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_distilbert_for_multiple_choice(*__snake_case				)







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


							'''simple docstring'''

							_snake_case       :					Dict				       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_distilbert_for_token_classification(*__snake_case				)







		@slow
		def        lowercase       (    self				)      ->      Tuple:


							'''simple docstring'''

							for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]				):
												_snake_case       :					Any				       =   TFDistilBertModel.from_pretrained(__snake_case				)
												self.assertIsNotNone(__snake_case				)






@require_tf
class        _a(    unittest.TestCase    ):







		@slow
		def        lowercase       (    self				)      ->      List[str]:


							'''simple docstring'''

							_snake_case       :					Any				       =   TFDistilBertModel.from_pretrained("distilbert-base-uncased"				)
							_snake_case       :					int				       =   tf.constant([[0, 1, 2, 3, 4, 5]]				)
							_snake_case       :					Dict				       =   model(__snake_case				)[0]

							_snake_case       :					str				       =   [1, 6, 7_6_8]
							self.assertEqual(output.shape ,						__snake_case				)

							_snake_case       :					Optional[Any]				       =   tf.constant(
							    [
							        [
							            [0.19_26_18_85, -0.13_73_29_55, 0.4_11_97_99],
							            [0.22_15_01_56, -0.07_42_26_61, 0.39_03_72_04],
							            [0.22_75_60_18, -0.0_89_64_14, 0.3_70_14_67],
							        ]
							    ]				)
							tf.debugging.assert_near(output[:, :3, :3] ,						__snake_case ,						atol=1E-4				)