





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 					__snake_case	:


    '''simple docstring'''







    def __init__(       self   ,				A_   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			parent
        SCREAMING_SNAKE_CASE__											=			13
        SCREAMING_SNAKE_CASE__											=			7
        SCREAMING_SNAKE_CASE__											=			True
        SCREAMING_SNAKE_CASE__											=			True
        SCREAMING_SNAKE_CASE__											=			True
        SCREAMING_SNAKE_CASE__											=			True
        SCREAMING_SNAKE_CASE__											=			True
        SCREAMING_SNAKE_CASE__											=			False
        SCREAMING_SNAKE_CASE__											=			False
        SCREAMING_SNAKE_CASE__											=			False
        SCREAMING_SNAKE_CASE__											=			2
        SCREAMING_SNAKE_CASE__											=			99
        SCREAMING_SNAKE_CASE__											=			0
        SCREAMING_SNAKE_CASE__											=			32
        SCREAMING_SNAKE_CASE__											=			2
        SCREAMING_SNAKE_CASE__											=			4
        SCREAMING_SNAKE_CASE__											=			0.1
        SCREAMING_SNAKE_CASE__											=			0.1
        SCREAMING_SNAKE_CASE__											=			5_12
        SCREAMING_SNAKE_CASE__											=			16
        SCREAMING_SNAKE_CASE__											=			2
        SCREAMING_SNAKE_CASE__											=			0.02
        SCREAMING_SNAKE_CASE__											=			3
        SCREAMING_SNAKE_CASE__											=			4
        SCREAMING_SNAKE_CASE__											=			'''last'''
        SCREAMING_SNAKE_CASE__											=			True
        SCREAMING_SNAKE_CASE__											=			None
        SCREAMING_SNAKE_CASE__											=			0



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			ids_tensor([self.batch_size, self.seq_length]   ,				self.vocab_size     )
        SCREAMING_SNAKE_CASE__											=			random_attention_mask([self.batch_size, self.seq_length]   ,				dtype=tf.floataa     )

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

        SCREAMING_SNAKE_CASE__											=			None
        if self.use_token_type_ids:
            SCREAMING_SNAKE_CASE__											=			ids_tensor([self.batch_size, self.seq_length]   ,				self.n_langs     )

        SCREAMING_SNAKE_CASE__											=			None
        SCREAMING_SNAKE_CASE__											=			None
        SCREAMING_SNAKE_CASE__											=			None
        if self.use_labels:
            SCREAMING_SNAKE_CASE__											=			ids_tensor([self.batch_size]   ,				self.type_sequence_label_size     )
            SCREAMING_SNAKE_CASE__											=			ids_tensor([self.batch_size, self.seq_length]   ,				self.num_labels     )
            SCREAMING_SNAKE_CASE__											=			ids_tensor([self.batch_size]   ,				2   ,				dtype=tf.floataa     )
            SCREAMING_SNAKE_CASE__											=			ids_tensor([self.batch_size]   ,				self.num_choices     )

        SCREAMING_SNAKE_CASE__											=			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  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			TFFlaubertModel(config=A_     )
        SCREAMING_SNAKE_CASE__											=			{'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
        SCREAMING_SNAKE_CASE__											=			model(A_     )

        SCREAMING_SNAKE_CASE__											=			[input_ids, input_mask]
        SCREAMING_SNAKE_CASE__											=			model(A_     )
        self.parent.assertEqual(result.last_hidden_state.shape   ,				(self.batch_size, self.seq_length, self.hidden_size)     )



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			TFFlaubertWithLMHeadModel(A_     )

        SCREAMING_SNAKE_CASE__											=			{'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
        SCREAMING_SNAKE_CASE__											=			model(A_     )

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



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			TFFlaubertForQuestionAnsweringSimple(A_     )

        SCREAMING_SNAKE_CASE__											=			{'''input_ids''': input_ids, '''lengths''': input_lengths}

        SCREAMING_SNAKE_CASE__											=			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  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			TFFlaubertForSequenceClassification(A_     )

        SCREAMING_SNAKE_CASE__											=			{'''input_ids''': input_ids, '''lengths''': input_lengths}

        SCREAMING_SNAKE_CASE__											=			model(A_     )

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



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.num_labels
        SCREAMING_SNAKE_CASE__											=			TFFlaubertForTokenClassification(config=A_     )
        SCREAMING_SNAKE_CASE__											=			{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
        SCREAMING_SNAKE_CASE__											=			model(A_     )
        self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.seq_length, self.num_labels)     )



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.num_choices
        SCREAMING_SNAKE_CASE__											=			TFFlaubertForMultipleChoice(config=A_     )
        SCREAMING_SNAKE_CASE__											=			tf.tile(tf.expand_dims(A_   ,				1     )   ,				(1, self.num_choices, 1)     )
        SCREAMING_SNAKE_CASE__											=			tf.tile(tf.expand_dims(A_   ,				1     )   ,				(1, self.num_choices, 1)     )
        SCREAMING_SNAKE_CASE__											=			tf.tile(tf.expand_dims(A_   ,				1     )   ,				(1, self.num_choices, 1)     )
        SCREAMING_SNAKE_CASE__											=			{
            '''input_ids''': multiple_choice_inputs_ids,
            '''attention_mask''': multiple_choice_input_mask,
            '''token_type_ids''': multiple_choice_token_type_ids,
        }
        SCREAMING_SNAKE_CASE__											=			model(A_     )
        self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.num_choices)     )




    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.prepare_config_and_inputs()
        (
            (
            SCREAMING_SNAKE_CASE__
        )				,		(
            SCREAMING_SNAKE_CASE__
        )				,		(
            SCREAMING_SNAKE_CASE__
        )				,		(
            SCREAMING_SNAKE_CASE__
        )				,		(
            SCREAMING_SNAKE_CASE__
        )				,		(
            SCREAMING_SNAKE_CASE__
        )				,		(
            SCREAMING_SNAKE_CASE__
        )				,		(
            SCREAMING_SNAKE_CASE__
        )				,		(
            SCREAMING_SNAKE_CASE__
        )				,		
        )											=			config_and_inputs
        SCREAMING_SNAKE_CASE__											=			{
            '''input_ids''': input_ids,
            '''token_type_ids''': token_type_ids,
            '''langs''': token_type_ids,
            '''lengths''': input_lengths,
        }
        return config, inputs_dict





@require_tf
class 					__snake_case	(  __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     unittest.TestCase    ):


    '''simple docstring'''







    lowerCamelCase__    :		Optional[int]				=	(
        (
            TFFlaubertModel,
            TFFlaubertWithLMHeadModel,
            TFFlaubertForSequenceClassification,
            TFFlaubertForQuestionAnsweringSimple,
            TFFlaubertForTokenClassification,
            TFFlaubertForMultipleChoice,
        )
        if is_tf_available()
        else ()
    )
    lowerCamelCase__    :		Union[str, Any]				=	(
        (TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
    )  # TODO (PVP): Check other models whether language generation is also applicable
    lowerCamelCase__    :		Optional[int]				=	(
        {
            """feature-extraction""": TFFlaubertModel,
            """fill-mask""": TFFlaubertWithLMHeadModel,
            """question-answering""": TFFlaubertForQuestionAnsweringSimple,
            """text-classification""": TFFlaubertForSequenceClassification,
            """token-classification""": TFFlaubertForTokenClassification,
            """zero-shot""": TFFlaubertForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )
    lowerCamelCase__    :		Union[str, Any]				=	False
    lowerCamelCase__    :		Optional[Any]				=	False



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_     ):





        '''simple docstring'''

        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  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			TFFlaubertModelTester(self     )
        SCREAMING_SNAKE_CASE__											=			ConfigTester(self   ,				config_class=A_   ,				emb_dim=37     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        self.config_tester.run_common_tests()



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_model(*A_     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_lm_head(*A_     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_qa(*A_     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_sequence_classif(*A_     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_for_token_classification(*A_     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_for_multiple_choice(*A_     )




    @slow
    def  lowercase_					(       self     ):





        '''simple docstring'''

        for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            SCREAMING_SNAKE_CASE__											=			TFFlaubertModel.from_pretrained(A_     )
            self.assertIsNotNone(A_     )





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


    '''simple docstring'''







    @slow
    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			TFFlaubertModel.from_pretrained('''jplu/tf-flaubert-small-cased'''     )

        SCREAMING_SNAKE_CASE__											=			tf.convert_to_tensor(
            [[0, 1_58, 7_35, 25_92, 14_24, 67_27, 82, 1]]   ,				dtype=tf.intaa   ,				)  # "J'aime flaubert !"

        SCREAMING_SNAKE_CASE__											=			model(A_     )[0]
        SCREAMING_SNAKE_CASE__											=			tf.TensorShape((1, 8, 5_12)     )
        self.assertEqual(output.shape   ,				A_     )
        # compare the actual values for a slice.
        SCREAMING_SNAKE_CASE__											=			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 fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer






def 			_lowerCAmelCase     (    __snake_case    :					str					,				__snake_case    :					str					,				**__snake_case    :					List[Any]     )	->	Any:
     __A		:					Optional[Any]						=			AutoConfig.from_pretrained(__snake_case					,				**__snake_case     )
     __A		:					int						=			AutoModelForSeqaSeqLM.from_config(__snake_case     )
     model.save_pretrained(__snake_case     )
     AutoTokenizer.from_pretrained(__snake_case     ).save_pretrained(__snake_case     )
     return model


if __name__ == "__main__":
  fire.Fire(save_randomly_initialized_version)