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					:
  """simple docstring"""







  def __init__(							self :       Optional[Any]  ,     __magic_name__ :       Optional[int]  ,     )  ->	Any:
        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_          =  512
        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    __A     (							self :       int   )  ->	List[str]:
        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    __A     (							self :       List[str]  ,     __magic_name__ :       Optional[Any]  ,     __magic_name__ :       List[Any]  ,     __magic_name__ :       Union[str, Any]  ,     __magic_name__ :       Optional[int]  ,     __magic_name__ :       Union[str, Any]  ,     __magic_name__ :       Union[str, Any]  ,     __magic_name__ :       str  ,     __magic_name__ :       int  ,     __magic_name__ :       Any  ,     )  ->	Union[str, Any]:
        SCREAMING_SNAKE_CASE_          =  TFFlaubertModel(config=__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  {"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
        SCREAMING_SNAKE_CASE_          =  model(__magic_name__   )

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







  def    __A     (							self :       int  ,     __magic_name__ :       int  ,     __magic_name__ :       str  ,     __magic_name__ :       Any  ,     __magic_name__ :       List[str]  ,     __magic_name__ :       Any  ,     __magic_name__ :       Optional[int]  ,     __magic_name__ :       str  ,     __magic_name__ :       Optional[Any]  ,     __magic_name__ :       Tuple  ,     )  ->	Tuple:
        SCREAMING_SNAKE_CASE_          =  TFFlaubertWithLMHeadModel(__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  {"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
        SCREAMING_SNAKE_CASE_          =  model(__magic_name__   )

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







  def    __A     (							self :       Tuple  ,     __magic_name__ :       List[Any]  ,     __magic_name__ :       str  ,     __magic_name__ :       Tuple  ,     __magic_name__ :       Optional[int]  ,     __magic_name__ :       Optional[Any]  ,     __magic_name__ :       Optional[Any]  ,     __magic_name__ :       Union[str, Any]  ,     __magic_name__ :       Optional[int]  ,     __magic_name__ :       List[str]  ,     )  ->	Optional[Any]:
        SCREAMING_SNAKE_CASE_          =  TFFlaubertForQuestionAnsweringSimple(__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  {"input_ids": input_ids, "lengths": input_lengths}

        SCREAMING_SNAKE_CASE_          =  model(__magic_name__   )

        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 :       Dict  ,     __magic_name__ :       Union[str, Any]  ,     __magic_name__ :       Any  ,     __magic_name__ :       str  ,     __magic_name__ :       str  ,     __magic_name__ :       Union[str, Any]  ,     __magic_name__ :       Tuple  ,     __magic_name__ :       List[Any]  ,     __magic_name__ :       int  ,     __magic_name__ :       Tuple  ,     )  ->	Dict:
        SCREAMING_SNAKE_CASE_          =  TFFlaubertForSequenceClassification(__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  {"input_ids": input_ids, "lengths": input_lengths}

        SCREAMING_SNAKE_CASE_          =  model(__magic_name__   )

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







  def    __A     (							self :       List[str]  ,     __magic_name__ :       Dict  ,     __magic_name__ :       List[Any]  ,     __magic_name__ :       Optional[int]  ,     __magic_name__ :       Optional[int]  ,     __magic_name__ :       Tuple  ,     __magic_name__ :       List[Any]  ,     __magic_name__ :       Optional[Any]  ,     __magic_name__ :       Tuple  ,     __magic_name__ :       Optional[int]  ,     )  ->	Tuple:
        SCREAMING_SNAKE_CASE_          =  self.num_labels
        SCREAMING_SNAKE_CASE_          =  TFFlaubertForTokenClassification(config=__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
        SCREAMING_SNAKE_CASE_          =  model(__magic_name__   )
        self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, self.seq_length, self.num_labels)   )







  def    __A     (							self :       Optional[int]  ,     __magic_name__ :       Tuple  ,     __magic_name__ :       str  ,     __magic_name__ :       Union[str, Any]  ,     __magic_name__ :       List[Any]  ,     __magic_name__ :       List[str]  ,     __magic_name__ :       List[str]  ,     __magic_name__ :       Any  ,     __magic_name__ :       List[str]  ,     __magic_name__ :       Any  ,     )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  self.num_choices
        SCREAMING_SNAKE_CASE_          =  TFFlaubertForMultipleChoice(config=__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  tf.tile(tf.expand_dims(__magic_name__  ,     1   )  ,     (1, self.num_choices, 1)   )
        SCREAMING_SNAKE_CASE_          =  tf.tile(tf.expand_dims(__magic_name__  ,     1   )  ,     (1, self.num_choices, 1)   )
        SCREAMING_SNAKE_CASE_          =  tf.tile(tf.expand_dims(__magic_name__  ,     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(__magic_name__   )
        self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, self.num_choices)   )







  def    __A     (							self :       str   )  ->	Optional[int]:
        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   lowerCamelCase					(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  ,      unittest.TestCase					):
  """simple docstring"""







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







  def    __A     (							self :       Union[str, Any]  ,     __magic_name__ :       Optional[int]  ,     __magic_name__ :       List[Any]  ,     __magic_name__ :       Optional[Any]  ,     __magic_name__ :       str  ,     __magic_name__ :       int   )  ->	List[str]:
        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    __A     (							self :       List[Any]   )  ->	List[Any]:
        SCREAMING_SNAKE_CASE_          =  TFFlaubertModelTester(self   )
        SCREAMING_SNAKE_CASE_          =  ConfigTester(self  ,     config_class=__magic_name__  ,     emb_dim=37   )







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







  def    __A     (							self :       Union[str, Any]   )  ->	Dict:
        SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_model(*__magic_name__   )







  def    __A     (							self :       str   )  ->	int:
        SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_lm_head(*__magic_name__   )







  def    __A     (							self :       Optional[Any]   )  ->	Union[str, Any]:
        SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_qa(*__magic_name__   )







  def    __A     (							self :       int   )  ->	int:
        SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_sequence_classif(*__magic_name__   )







  def    __A     (							self :       str   )  ->	Optional[Any]:
        SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_for_token_classification(*__magic_name__   )







  def    __A     (							self :       int   )  ->	List[str]:
        SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_for_multiple_choice(*__magic_name__   )







  @slow
  def    __A     (							self :       int   )  ->	Dict:
        for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              SCREAMING_SNAKE_CASE_          =  TFFlaubertModel.from_pretrained(__magic_name__   )
              self.assertIsNotNone(__magic_name__   )



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







  @slow
  def    __A     (							self :       Optional[Any]   )  ->	str:
        SCREAMING_SNAKE_CASE_          =  TFFlaubertModel.from_pretrained("jplu/tf-flaubert-small-cased"   )

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

        SCREAMING_SNAKE_CASE_          =  model(__magic_name__   )[0]
        SCREAMING_SNAKE_CASE_          =  tf.TensorShape((1, 8, 512)   )
        self.assertEqual(output.shape  ,     __magic_name__   )
        # compare the actual values for a slice.
        SCREAMING_SNAKE_CASE_          =  tf.convert_to_tensor(
            [
                [
                    [-1.876_8773, -1.56_6555, 0.2707_2418],
                    [-1.692_0038, -0.587_3505, 1.932_9599],
                    [-2.956_3985, -1.699_3835, 1.797_2052],
                ]
            ]  ,     dtype=tf.floataa  ,     )

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



import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


A     :  str					     =       logging.getLogger(__name__)


@dataclass
class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  lowerCamelCase__     =						field(
      default=0.0  ,      metadata={'''help''': '''The label smoothing epsilon to apply (if not zero).'''}					)
  lowerCamelCase__     =						field(default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''Whether to SortishSamler or not.'''}					)
  lowerCamelCase__     =						field(
      default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}					)
  lowerCamelCase__     =						field(default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''whether to use adafactor'''}					)
  lowerCamelCase__     =						field(
      default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''Encoder layer dropout probability. Goes into model.config.'''}					)
  lowerCamelCase__     =						field(
      default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''Decoder layer dropout probability. Goes into model.config.'''}					)
  lowerCamelCase__     =						field(default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''Dropout probability. Goes into model.config.'''}					)
  lowerCamelCase__     =						field(
      default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''Attention dropout probability. Goes into model.config.'''}					)
  lowerCamelCase__     =						field(
      default='''linear'''  ,      metadata={'''help''': f"Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()					)}"}  ,      )


