





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					,			snake_case					,			)				->  Optional[Any]:
      """simple docstring"""





      a__	:						int      		=  parent
      a__	:						Optional[int]      		=  13
      a__	:						Tuple      		=  7
      a__	:						Union[str, Any]      		=  True
      a__	:						Dict      		=  True
      a__	:						Union[str, Any]      		=  True
      a__	:						int      		=  True
      a__	:						str      		=  True
      a__	:						str      		=  False
      a__	:						Tuple      		=  False
      a__	:						str      		=  False
      a__	:						Any      		=  2
      a__	:						Optional[int]      		=  99
      a__	:						Any      		=  0
      a__	:						Tuple      		=  32
      a__	:						List[str]      		=  2
      a__	:						Optional[int]      		=  4
      a__	:						List[str]      		=  0.1
      a__	:						str      		=  0.1
      a__	:						Tuple      		=  512
      a__	:						Any      		=  16
      a__	:						Optional[Any]      		=  2
      a__	:						Dict      		=  0.02
      a__	:						Dict      		=  3
      a__	:						List[str]      		=  4
      a__	:						Tuple      		=  "last"
      a__	:						Any      		=  True
      a__	:						List[Any]      		=  None
      a__	:						Union[str, Any]      		=  0





   def 	_snake_case ( self      )				->  Dict:
      """simple docstring"""





      a__	:						Optional[int]      		=  ids_tensor([self.batch_size, self.seq_length]					,			self.vocab_size      )
      a__	:						int      		=  random_attention_mask([self.batch_size, self.seq_length]					,			dtype=tf.floataa      )

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

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

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

      a__	:						List[str]      		=  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 	_snake_case ( self					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			)				->  int:
      """simple docstring"""





      a__	:						Optional[Any]      		=  TFFlaubertModel(config=snake_case      )
      a__	:						List[str]      		=  {"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
      a__	:						Dict      		=  model(snake_case      )

      a__	:						List[str]      		=  [input_ids, input_mask]
      a__	:						str      		=  model(snake_case      )
      self.parent.assertEqual(result.last_hidden_state.shape					,			(self.batch_size, self.seq_length, self.hidden_size)      )





   def 	_snake_case ( self					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			)				->  Optional[Any]:
      """simple docstring"""





      a__	:						int      		=  TFFlaubertWithLMHeadModel(snake_case      )

      a__	:						Optional[int]      		=  {"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
      a__	:						Optional[Any]      		=  model(snake_case      )

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





   def 	_snake_case ( self					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			)				->  List[str]:
      """simple docstring"""





      a__	:						str      		=  TFFlaubertForQuestionAnsweringSimple(snake_case      )

      a__	:						str      		=  {"input_ids": input_ids, "lengths": input_lengths}

      a__	:						Any      		=  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 	_snake_case ( self					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			)				->  str:
      """simple docstring"""





      a__	:						Tuple      		=  TFFlaubertForSequenceClassification(snake_case      )

      a__	:						Dict      		=  {"input_ids": input_ids, "lengths": input_lengths}

      a__	:						List[Any]      		=  model(snake_case      )

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





   def 	_snake_case ( self					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			)				->  str:
      """simple docstring"""





      a__	:						List[str]      		=  self.num_labels
      a__	:						List[str]      		=  TFFlaubertForTokenClassification(config=snake_case      )
      a__	:						Tuple      		=  {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
      a__	:						List[Any]      		=  model(snake_case      )
      self.parent.assertEqual(result.logits.shape					,			(self.batch_size, self.seq_length, self.num_labels)      )





   def 	_snake_case ( self					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			)				->  Any:
      """simple docstring"""





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





   def 	_snake_case ( self      )				->  Tuple:
      """simple docstring"""





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





   def 	_snake_case ( self					,			snake_case					,			snake_case					,			snake_case					,			snake_case					,			snake_case      )				->  List[Any]:
      """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 	_snake_case ( self      )				->  List[Any]:
      """simple docstring"""





      a__	:						Union[str, Any]      		=  TFFlaubertModelTester(self      )
      a__	:						Tuple      		=  ConfigTester(self					,			config_class=snake_case					,			emb_dim=37      )





   def 	_snake_case ( self      )				->  Union[str, Any]:
      """simple docstring"""





      self.config_tester.run_common_tests()





   def 	_snake_case ( self      )				->  Any:
      """simple docstring"""





      a__	:						Optional[Any]      		=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_flaubert_model(*snake_case      )





   def 	_snake_case ( self      )				->  List[str]:
      """simple docstring"""





      a__	:						Dict      		=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_flaubert_lm_head(*snake_case      )





   def 	_snake_case ( self      )				->  str:
      """simple docstring"""





      a__	:						List[Any]      		=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_flaubert_qa(*snake_case      )





   def 	_snake_case ( self      )				->  Optional[int]:
      """simple docstring"""





      a__	:						int      		=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_flaubert_sequence_classif(*snake_case      )





   def 	_snake_case ( self      )				->  Dict:
      """simple docstring"""





      a__	:						Any      		=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_flaubert_for_token_classification(*snake_case      )





   def 	_snake_case ( self      )				->  Optional[Any]:
      """simple docstring"""





      a__	:						Any      		=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_flaubert_for_multiple_choice(*snake_case      )





   @slow
   def 	_snake_case ( self      )				->  Any:
      """simple docstring"""





      for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         a__	:						List[Any]      		=  TFFlaubertModel.from_pretrained(snake_case      )
         self.assertIsNotNone(snake_case      )







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





   @slow
   def 	_snake_case ( self      )				->  int:
      """simple docstring"""





      a__	:						Dict      		=  TFFlaubertModel.from_pretrained("jplu/tf-flaubert-small-cased"      )

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

      a__	:						Any      		=  model(snake_case      )[0]
      a__	:						Union[str, Any]      		=  tf.TensorShape((1, 8, 512)      )
      self.assertEqual(output.shape					,			snake_case      )
      # compare the actual values for a slice.
      a__	:						int      		=  tf.convert_to_tensor(
          [
              [
                  [-1.8_768_773, -1.566_555, 0.27_072_418],
                  [-1.6_920_038, -0.5_873_505, 1.9_329_599],
                  [-2.9_563_985, -1.6_993_835, 1.7_972_052],
              ]
          ]					,			dtype=tf.floataa					,			)

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








# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE__       :			int			      =						{
    """configuration_cpmant""": ["""CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CpmAntConfig"""],
    """tokenization_cpmant""": ["""CpmAntTokenizer"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__       :			List[str]			      =						[
      """CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """CpmAntForCausalLM""",
      """CpmAntModel""",
      """CpmAntPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
  from .tokenization_cpmant import CpmAntTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_cpmant import (
        CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
        CpmAntForCausalLM,
        CpmAntModel,
        CpmAntPreTrainedModel,
    )


else:
  import sys

  SCREAMING_SNAKE_CASE__       :			Union[str, Any]			      =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

