





'''simple docstring'''






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, 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 numpy as np
					import tensorflow as tf

					from transformers import (
					    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
					    FlaubertConfig,
					    TFFlaubertForMultipleChoice,
					    TFFlaubertForQuestionAnsweringSimple,
					    TFFlaubertForSequenceClassification,
					    TFFlaubertForTokenClassification,
					    TFFlaubertModel,
					    TFFlaubertWithLMHeadModel,
					)
class    lowerCamelCase  :



	def __init__(   self		,      a_		,      ):
			lowerCAmelCase     :    str						=   parent
			lowerCAmelCase     :    int						=   13
			lowerCAmelCase     :    Optional[int]						=   7
			lowerCAmelCase     :    str						=   True
			lowerCAmelCase     :    Optional[int]						=   True
			lowerCAmelCase     :    List[str]						=   True
			lowerCAmelCase     :    Optional[Any]						=   True
			lowerCAmelCase     :    Union[str, Any]						=   True
			lowerCAmelCase     :    Any						=   False
			lowerCAmelCase     :    Any						=   False
			lowerCAmelCase     :    Dict						=   False
			lowerCAmelCase     :    int						=   2
			lowerCAmelCase     :    Optional[int]						=   99
			lowerCAmelCase     :    Optional[int]						=   0
			lowerCAmelCase     :    Optional[int]						=   32
			lowerCAmelCase     :    Optional[int]						=   2
			lowerCAmelCase     :    Dict						=   4
			lowerCAmelCase     :    List[str]						=   0.1
			lowerCAmelCase     :    str						=   0.1
			lowerCAmelCase     :    Dict						=   512
			lowerCAmelCase     :    Union[str, Any]						=   16
			lowerCAmelCase     :    Dict						=   2
			lowerCAmelCase     :    Optional[Any]						=   0.02
			lowerCAmelCase     :    str						=   3
			lowerCAmelCase     :    Optional[int]						=   4
			lowerCAmelCase     :    List[str]						=   "last"
			lowerCAmelCase     :    List[str]						=   True
			lowerCAmelCase     :    List[Any]						=   None
			lowerCAmelCase     :    Optional[int]						=   0



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Any						=   ids_tensor([self.batch_size, self.seq_length]		,      self.vocab_size	)
			lowerCAmelCase     :    Any						=   random_attention_mask([self.batch_size, self.seq_length]		,      dtype=tf.floataa	)

			lowerCAmelCase     :    Tuple						=   None
			if self.use_input_lengths:
					lowerCAmelCase     :    Any						=   (
					    ids_tensor([self.batch_size]		,      vocab_size=2	) + self.seq_length - 2
					)  # small variation of seq_length

			lowerCAmelCase     :    str						=   None
			if self.use_token_type_ids:
					lowerCAmelCase     :    List[Any]						=   ids_tensor([self.batch_size, self.seq_length]		,      self.n_langs	)

			lowerCAmelCase     :    str						=   None
			lowerCAmelCase     :    Union[str, Any]						=   None
			lowerCAmelCase     :    List[str]						=   None
			if self.use_labels:
					lowerCAmelCase     :    Dict						=   ids_tensor([self.batch_size]		,      self.type_sequence_label_size	)
					lowerCAmelCase     :    str						=   ids_tensor([self.batch_size, self.seq_length]		,      self.num_labels	)
					lowerCAmelCase     :    List[Any]						=   ids_tensor([self.batch_size]		,      2		,      dtype=tf.floataa	)
					lowerCAmelCase     :    Optional[int]						=   ids_tensor([self.batch_size]		,      self.num_choices	)

			lowerCAmelCase     :    Optional[Any]						=   FlaubertConfig(
			    vocab_size=self.vocab_size		,      n_special=self.n_special		,      emb_dim=self.hidden_size		,      n_layers=self.num_hidden_layers		,      n_heads=self.num_attention_heads		,      dropout=self.hidden_dropout_prob		,      attention_dropout=self.attention_probs_dropout_prob		,      gelu_activation=self.gelu_activation		,      sinusoidal_embeddings=self.sinusoidal_embeddings		,      asm=self.asm		,      causal=self.causal		,      n_langs=self.n_langs		,      max_position_embeddings=self.max_position_embeddings		,      initializer_range=self.initializer_range		,      summary_type=self.summary_type		,      use_proj=self.use_proj		,      bos_token_id=self.bos_token_id		,      )

			return (
			    config,
			    input_ids,
			    token_type_ids,
			    input_lengths,
			    sequence_labels,
			    token_labels,
			    is_impossible_labels,
			    choice_labels,
			    input_mask,
			)



	def   _lowerCamelCase    (   self		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      ):
			lowerCAmelCase     :    List[Any]						=   TFFlaubertModel(config=a_	)
			lowerCAmelCase     :    Optional[Any]						=   {"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
			lowerCAmelCase     :    List[Any]						=   model(a_	)

			lowerCAmelCase     :    int						=   [input_ids, input_mask]
			lowerCAmelCase     :    Optional[Any]						=   model(a_	)
			self.parent.assertEqual(result.last_hidden_state.shape		,      (self.batch_size, self.seq_length, self.hidden_size)	)



	def   _lowerCamelCase    (   self		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      ):
			lowerCAmelCase     :    int						=   TFFlaubertWithLMHeadModel(a_	)

			lowerCAmelCase     :    str						=   {"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
			lowerCAmelCase     :    List[str]						=   model(a_	)

			self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.seq_length, self.vocab_size)	)



	def   _lowerCamelCase    (   self		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      ):
			lowerCAmelCase     :    Optional[Any]						=   TFFlaubertForQuestionAnsweringSimple(a_	)

			lowerCAmelCase     :    List[str]						=   {"input_ids": input_ids, "lengths": input_lengths}

			lowerCAmelCase     :    Union[str, Any]						=   model(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   _lowerCamelCase    (   self		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      ):
			lowerCAmelCase     :    Any						=   TFFlaubertForSequenceClassification(a_	)

			lowerCAmelCase     :    List[str]						=   {"input_ids": input_ids, "lengths": input_lengths}

			lowerCAmelCase     :    str						=   model(a_	)

			self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.type_sequence_label_size)	)



	def   _lowerCamelCase    (   self		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      ):
			lowerCAmelCase     :    Tuple						=   self.num_labels
			lowerCAmelCase     :    Tuple						=   TFFlaubertForTokenClassification(config=a_	)
			lowerCAmelCase     :    str						=   {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
			lowerCAmelCase     :    Optional[int]						=   model(a_	)
			self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.seq_length, self.num_labels)	)



	def   _lowerCamelCase    (   self		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      ):
			lowerCAmelCase     :    Any						=   self.num_choices
			lowerCAmelCase     :    Tuple						=   TFFlaubertForMultipleChoice(config=a_	)
			lowerCAmelCase     :    Optional[int]						=   tf.tile(tf.expand_dims(a_		,      1	)		,      (1, self.num_choices, 1)	)
			lowerCAmelCase     :    Optional[Any]						=   tf.tile(tf.expand_dims(a_		,      1	)		,      (1, self.num_choices, 1)	)
			lowerCAmelCase     :    Optional[int]						=   tf.tile(tf.expand_dims(a_		,      1	)		,      (1, self.num_choices, 1)	)
			lowerCAmelCase     :    Union[str, Any]						=   {
			    "input_ids": multiple_choice_inputs_ids,
			    "attention_mask": multiple_choice_input_mask,
			    "token_type_ids": multiple_choice_token_type_ids,
			}
			lowerCAmelCase     :    Union[str, Any]						=   model(a_	)
			self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.num_choices)	)





	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Any						=   self.prepare_config_and_inputs()
			(
			    lowerCAmelCase		
			)     :    Tuple						=   config_and_inputs
			lowerCAmelCase     :    Dict						=   {
			    "input_ids": input_ids,
			    "token_type_ids": token_type_ids,
			    "langs": token_type_ids,
			    "lengths": input_lengths,
			}
			return config, inputs_dict






@require_tf
class    lowerCamelCase  (      _A      ,   _A      ,   unittest.TestCase       ):
	snake_case_      =   (
	    (
	        TFFlaubertModel,
	        TFFlaubertWithLMHeadModel,
	        TFFlaubertForSequenceClassification,
	        TFFlaubertForQuestionAnsweringSimple,
	        TFFlaubertForTokenClassification,
	        TFFlaubertForMultipleChoice,
	    )
	    if is_tf_available()
	    else ()
	)
	snake_case_      =   (
	    (TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
	)  # TODO (PVP): Check other models whether language generation is also applicable
	snake_case_      =   (
	    {
	        "feature-extraction": TFFlaubertModel,
	        "fill-mask": TFFlaubertWithLMHeadModel,
	        "question-answering": TFFlaubertForQuestionAnsweringSimple,
	        "text-classification": TFFlaubertForSequenceClassification,
	        "token-classification": TFFlaubertForTokenClassification,
	        "zero-shot": TFFlaubertForSequenceClassification,
	    }
	    if is_tf_available()
	    else {}
	)
	snake_case_      =   False
	snake_case_      =   False



	def   _lowerCamelCase    (   self		,      a_		,      a_		,      a_		,      a_		,      a_	):
			if (
			    pipeline_test_casse_name == "QAPipelineTests"
			    and tokenizer_name is not None
			    and not tokenizer_name.endswith("Fast"	)
			):
					# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
					# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
					# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
					return True

			return False



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Dict						=   TFFlaubertModelTester(self	)
			lowerCAmelCase     :    int						=   ConfigTester(self		,      config_class=a_		,      emb_dim=37	)



	def   _lowerCamelCase    (   self	):
			self.config_tester.run_common_tests()



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Tuple						=   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_flaubert_model(*a_	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    List[str]						=   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_flaubert_lm_head(*a_	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Optional[Any]						=   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_flaubert_qa(*a_	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Dict						=   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_flaubert_sequence_classif(*a_	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    List[Any]						=   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_flaubert_for_token_classification(*a_	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    str						=   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_flaubert_for_multiple_choice(*a_	)





	@slow
	def   _lowerCamelCase    (   self	):
			for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					lowerCAmelCase     :    str						=   TFFlaubertModel.from_pretrained(a_	)
					self.assertIsNotNone(a_	)






@require_tf
@require_sentencepiece
@require_tokenizers
class    lowerCamelCase  (      unittest.TestCase       ):



	@slow
	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Tuple						=   TFFlaubertModel.from_pretrained("jplu/tf-flaubert-small-cased"	)

			lowerCAmelCase     :    Optional[Any]						=   tf.convert_to_tensor(
			    [[0, 158, 735, 2_592, 1_424, 6_727, 82, 1]]		,      dtype=tf.intaa		,      )  # "J'aime flaubert !"

			lowerCAmelCase     :    Tuple						=   model(a_	)[0]
			lowerCAmelCase     :    Union[str, Any]						=   tf.TensorShape((1, 8, 512)	)
			self.assertEqual(output.shape		,      a_	)
			# compare the actual values for a slice.
			lowerCAmelCase     :    Optional[Any]						=   tf.convert_to_tensor(
			    [
			        [
			            [-1.8768773, -1.566555, 0.27072418],
			            [-1.6920038, -0.5873505, 1.9329599],
			            [-2.9563985, -1.6993835, 1.7972052],
			        ]
			    ]		,      dtype=tf.floataa		,      )

			self.assertTrue(np.allclose(output[:, :3, :3].numpy()		,      expected_slice.numpy()		,      atol=1e-4	)	)










'''simple docstring'''






import warnings

from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor


lowerCAmelCase       		=      logging.get_logger(__name__)
class 		lowerCamelCase  (      _A       ):



	def __init__(   self		,      *a_		,      **a_	):
			warnings.warn(
			    "The class ChineseCLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
			    " Please use ChineseCLIPImageProcessor instead."		,      a_		,      )
			super().__init__(*a_		,      **a_	)



