


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					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				str    ,	):
        __lowerCamelCase:				Optional[int]     =	parent
        __lowerCamelCase:				Dict     =	13
        __lowerCamelCase:				List[Any]     =	7
        __lowerCamelCase:				Optional[int]     =	True
        __lowerCamelCase:				str     =	True
        __lowerCamelCase:				Dict     =	False
        __lowerCamelCase:				Union[str, Any]     =	True
        __lowerCamelCase:				Any     =	99
        __lowerCamelCase:				Dict     =	32
        __lowerCamelCase:				str     =	2
        __lowerCamelCase:				Optional[int]     =	4
        __lowerCamelCase:				Optional[Any]     =	37
        __lowerCamelCase:				Union[str, Any]     =	"""gelu"""
        __lowerCamelCase:				List[Any]     =	0.1
        __lowerCamelCase:				Dict     =	0.1
        __lowerCamelCase:				str     =	512
        __lowerCamelCase:				Dict     =	16
        __lowerCamelCase:				List[str]     =	2
        __lowerCamelCase:				Optional[int]     =	0.02
        __lowerCamelCase:				Dict     =	3
        __lowerCamelCase:				Optional[int]     =	4
        __lowerCamelCase:				Tuple     =	None





     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        __lowerCamelCase:				int     =	ids_tensor([self.batch_size, self.seq_length]    ,	self.vocab_size							)

        __lowerCamelCase:				Any     =	None
        if self.use_input_mask:
           __lowerCamelCase:				Optional[int]     =	random_attention_mask([self.batch_size, self.seq_length]							)

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

        __lowerCamelCase:				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  SCREAMING_SNAKE_CASE__       (    self					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				List[str]							):
        __lowerCamelCase:				str     =	TFDistilBertModel(config=SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				List[Any]     =	{"""input_ids""": input_ids, """attention_mask""": input_mask}

        __lowerCamelCase:				Optional[Any]     =	model(SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				int     =	[input_ids, input_mask]

        __lowerCamelCase:				str     =	model(SCREAMING_SNAKE_CASE_							)

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





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				str    ,	SCREAMING_SNAKE_CASE_					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				Dict							):
        __lowerCamelCase:				Any     =	TFDistilBertForMaskedLM(config=SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Dict     =	{"""input_ids""": input_ids, """attention_mask""": input_mask}
        __lowerCamelCase:				str     =	model(SCREAMING_SNAKE_CASE_							)
        self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.seq_length, self.vocab_size)							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]							):
        __lowerCamelCase:				Optional[int]     =	TFDistilBertForQuestionAnswering(config=SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Tuple     =	{
            """input_ids""": input_ids,
            """attention_mask""": input_mask,
        }
        __lowerCamelCase:				Tuple     =	model(SCREAMING_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  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				List[str]    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]							):
        __lowerCamelCase:				Any     =	self.num_labels
        __lowerCamelCase:				Optional[Any]     =	TFDistilBertForSequenceClassification(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Dict     =	{"""input_ids""": input_ids, """attention_mask""": input_mask}
        __lowerCamelCase:				Optional[int]     =	model(SCREAMING_SNAKE_CASE_							)
        self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.num_labels)							)





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





     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				List[str]    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]							):
        __lowerCamelCase:				Union[str, Any]     =	self.num_labels
        __lowerCamelCase:				Optional[int]     =	TFDistilBertForTokenClassification(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Optional[Any]     =	{"""input_ids""": input_ids, """attention_mask""": input_mask}
        __lowerCamelCase:				str     =	model(SCREAMING_SNAKE_CASE_							)
        self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.seq_length, self.num_labels)							)







     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        __lowerCamelCase:				Dict     =	self.prepare_config_and_inputs()
        (__lowerCamelCase):				Tuple     =	config_and_inputs
        __lowerCamelCase:				Dict     =	{"""input_ids""": input_ids, """attention_mask""": input_mask}
        return config, inputs_dict







@require_tf
class        a					(			_UpperCAmelCase     ,_UpperCAmelCase     ,unittest.TestCase   ):
     UpperCAmelCase__ :  Any						      =	(
         (
             TFDistilBertModel,
             TFDistilBertForMaskedLM,
             TFDistilBertForQuestionAnswering,
             TFDistilBertForSequenceClassification,
             TFDistilBertForTokenClassification,
             TFDistilBertForMultipleChoice,
         )
         if is_tf_available()
         else None
     )
     UpperCAmelCase__ :  Dict						      =	(
         {
             "feature-extraction": TFDistilBertModel,
             "fill-mask": TFDistilBertForMaskedLM,
             "question-answering": TFDistilBertForQuestionAnswering,
             "text-classification": TFDistilBertForSequenceClassification,
             "token-classification": TFDistilBertForTokenClassification,
             "zero-shot": TFDistilBertForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     UpperCAmelCase__ :  Optional[Any]						      =	False
     UpperCAmelCase__ :  Tuple						      =	False





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]							):
        __lowerCamelCase:				List[str]     =	TFDistilBertModelTester(self							)
        __lowerCamelCase:				Union[str, Any]     =	ConfigTester(self    ,	config_class=SCREAMING_SNAKE_CASE_    ,	dim=37							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]							):
        self.config_tester.run_common_tests()





     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]							):
        __lowerCamelCase:				Dict     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_distilbert_model(*SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        __lowerCamelCase:				List[Any]     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_distilbert_for_masked_lm(*SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]							):
        __lowerCamelCase:				Tuple     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_distilbert_for_question_answering(*SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]							):
        __lowerCamelCase:				List[Any]     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_distilbert_for_sequence_classification(*SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]							):
        __lowerCamelCase:				List[Any]     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_distilbert_for_multiple_choice(*SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        __lowerCamelCase:				int     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_distilbert_for_token_classification(*SCREAMING_SNAKE_CASE_							)







     @slow
     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]							):
           __lowerCamelCase:				List[str]     =	TFDistilBertModel.from_pretrained(SCREAMING_SNAKE_CASE_							)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE_							)







@require_tf
class        a					(			unittest.TestCase   ):





     @slow
     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]							):
        __lowerCamelCase:				int     =	TFDistilBertModel.from_pretrained("""distilbert-base-uncased"""							)
        __lowerCamelCase:				Optional[Any]     =	tf.constant([[0, 1, 2, 3, 4, 5]]							)
        __lowerCamelCase:				Any     =	model(SCREAMING_SNAKE_CASE_							)[0]

        __lowerCamelCase:				Union[str, Any]     =	[1, 6, 768]
        self.assertEqual(output.shape    ,	SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				List[Any]     =	tf.constant(
            [
                [
                    [0.1926_1885, -0.1373_2955, 0.411_9799],
                    [0.2215_0156, -0.0742_2661, 0.3903_7204],
                    [0.2275_6018, -0.089_6414, 0.370_1467],
                ]
            ]							)
        tf.debugging.assert_near(output[:, :3, :3]    ,	SCREAMING_SNAKE_CASE_    ,	atol=1E-4							)






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin



class        a					(			_UpperCAmelCase   ):
     UpperCAmelCase__ :  Dict						      =	"Speech2TextFeatureExtractor"
     UpperCAmelCase__ :  str						      =	"Speech2TextTokenizer"





     def __init__(    self					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				List[str]    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]							):
        super().__init__(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Any     =	self.feature_extractor
        __lowerCamelCase:				List[Any]     =	False





     def __call__(    self					:				List[str]    ,	*SCREAMING_SNAKE_CASE_					:				Union[str, Any]    ,	**SCREAMING_SNAKE_CASE_					:				List[Any]							):
        # For backward compatibility
        if self._in_target_context_manager:
           return self.current_processor(*SCREAMING_SNAKE_CASE_    ,	**SCREAMING_SNAKE_CASE_							)

        if "raw_speech" in kwargs:
           warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."""							)
           __lowerCamelCase:				Dict     =	kwargs.pop("""raw_speech"""							)
        else:
           __lowerCamelCase:				int     =	kwargs.pop("""audio"""    ,	SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Dict     =	kwargs.pop("""sampling_rate"""    ,	SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				int     =	kwargs.pop("""text"""    ,	SCREAMING_SNAKE_CASE_							)
        if len(SCREAMING_SNAKE_CASE_							) > 0:
           __lowerCamelCase:				Dict     =	args[0]
           __lowerCamelCase:				Optional[Any]     =	args[1:]

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

        if audio is not None:
           __lowerCamelCase:				List[str]     =	self.feature_extractor(SCREAMING_SNAKE_CASE_    ,	*SCREAMING_SNAKE_CASE_    ,	sampling_rate=SCREAMING_SNAKE_CASE_    ,	**SCREAMING_SNAKE_CASE_							)
        if text is not None:
           __lowerCamelCase:				Any     =	self.tokenizer(SCREAMING_SNAKE_CASE_    ,	**SCREAMING_SNAKE_CASE_							)

        if text is None:
           return inputs
        elif audio is None:
           return encodings
        else:
           __lowerCamelCase:				List[Any]     =	encodings["""input_ids"""]
           return inputs





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]    ,	*SCREAMING_SNAKE_CASE_					:				Optional[Any]    ,	**SCREAMING_SNAKE_CASE_					:				str							):
        return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_    ,	**SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple    ,	*SCREAMING_SNAKE_CASE_					:				Any    ,	**SCREAMING_SNAKE_CASE_					:				List[Any]							):
        return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_    ,	**SCREAMING_SNAKE_CASE_							)







     @contextmanager
     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        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 audio inputs, or in a separate call."""							)
        __lowerCamelCase:				Any     =	True
        __lowerCamelCase:				Any     =	self.tokenizer
        yield
        __lowerCamelCase:				Any     =	self.feature_extractor
        __lowerCamelCase:				Optional[Any]     =	False


