


'''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			:
    """simple docstring"""


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


    def        A   (  self      :							Optional[Any]):
           _A					:     List[str]     = ids_tensor([self.batch_size, self.seq_length]				,							self.vocab_size)
           _A					:     Any     = random_attention_mask([self.batch_size, self.seq_length]				,							dtype=tf.floataa)

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

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

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

           _A					:     Optional[int]     = 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      :							Tuple				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							Any				,							SCREAMING_SNAKE_CASE      :							Optional[Any]				,							):
           _A					:     str     = TFFlaubertModel(config=__lowercase)
           _A					:     List[str]     = {'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
           _A					:     Optional[int]     = model(__lowercase)

           _A					:     Optional[int]     = [input_ids, input_mask]
           _A					:     Union[str, Any]     = model(__lowercase)
           self.parent.assertEqual(result.last_hidden_state.shape				,							(self.batch_size, self.seq_length, self.hidden_size))


    def        A   (  self      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							str				,							SCREAMING_SNAKE_CASE      :							List[str]				,							SCREAMING_SNAKE_CASE      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							List[str]				,							SCREAMING_SNAKE_CASE      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							str				,							):
           _A					:     str     = TFFlaubertWithLMHeadModel(__lowercase)

           _A					:     List[str]     = {'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
           _A					:     Optional[int]     = model(__lowercase)

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


    def        A   (  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							List[str]				,							SCREAMING_SNAKE_CASE      :							List[str]				,							SCREAMING_SNAKE_CASE      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							str				,							SCREAMING_SNAKE_CASE      :							Dict				,							SCREAMING_SNAKE_CASE      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							Optional[Any]				,							):
           _A					:     Any     = TFFlaubertForQuestionAnsweringSimple(__lowercase)

           _A					:     str     = {'''input_ids''': input_ids, '''lengths''': input_lengths}

           _A					:     int     = model(__lowercase)

           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				,							SCREAMING_SNAKE_CASE      :							Any				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							List[str]				,							SCREAMING_SNAKE_CASE      :							Any				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							List[str]				,							SCREAMING_SNAKE_CASE      :							Dict				,							):
           _A					:     int     = TFFlaubertForSequenceClassification(__lowercase)

           _A					:     List[Any]     = {'''input_ids''': input_ids, '''lengths''': input_lengths}

           _A					:     Any     = model(__lowercase)

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


    def        A   (  self      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							List[str]				,							SCREAMING_SNAKE_CASE      :							Any				,							SCREAMING_SNAKE_CASE      :							Any				,							SCREAMING_SNAKE_CASE      :							str				,							SCREAMING_SNAKE_CASE      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							Dict				,							):
           _A					:     Optional[int]     = self.num_labels
           _A					:     int     = TFFlaubertForTokenClassification(config=__lowercase)
           _A					:     Union[str, Any]     = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
           _A					:     Optional[Any]     = model(__lowercase)
           self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.seq_length, self.num_labels))


    def        A   (  self      :							List[str]				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							Any				,							SCREAMING_SNAKE_CASE      :							str				,							SCREAMING_SNAKE_CASE      :							Any				,							SCREAMING_SNAKE_CASE      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							Any				,							SCREAMING_SNAKE_CASE      :							Dict				,							):
           _A					:     Any     = self.num_choices
           _A					:     Any     = TFFlaubertForMultipleChoice(config=__lowercase)
           _A					:     List[str]     = tf.tile(tf.expand_dims(__lowercase				,							1)				,							(1, self.num_choices, 1))
           _A					:     Optional[Any]     = tf.tile(tf.expand_dims(__lowercase				,							1)				,							(1, self.num_choices, 1))
           _A					:     Tuple     = tf.tile(tf.expand_dims(__lowercase				,							1)				,							(1, self.num_choices, 1))
           _A					:     Any     = {
               '''input_ids''': multiple_choice_inputs_ids,
               '''attention_mask''': multiple_choice_input_mask,
               '''token_type_ids''': multiple_choice_token_type_ids,
           }
           _A					:     List[Any]     = model(__lowercase)
           self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.num_choices))


    def        A   (  self      :							int):
           _A					:     Optional[int]     = self.prepare_config_and_inputs()
           (
               _A	
           )					:     Any     = 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	):
    """simple docstring"""
    a													=   (
        (
            TFFlaubertModel,
            TFFlaubertWithLMHeadModel,
            TFFlaubertForSequenceClassification,
            TFFlaubertForQuestionAnsweringSimple,
            TFFlaubertForTokenClassification,
            TFFlaubertForMultipleChoice,
        )
        if is_tf_available()
        else ()
    )
    a													=   (
        (TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
    )  # TODO (PVP): Check other models whether language generation is also applicable
    a													=   (
        {
            """feature-extraction""": TFFlaubertModel,
            """fill-mask""": TFFlaubertWithLMHeadModel,
            """question-answering""": TFFlaubertForQuestionAnsweringSimple,
            """text-classification""": TFFlaubertForSequenceClassification,
            """token-classification""": TFFlaubertForTokenClassification,
            """zero-shot""": TFFlaubertForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )
    a													=   False
    a													=   False


    def        A   (  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							List[str]				,							SCREAMING_SNAKE_CASE      :							Tuple				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							Tuple):
           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      :							int):
           _A					:     int     = TFFlaubertModelTester(self)
           _A					:     Dict     = ConfigTester(self				,							config_class=__lowercase				,							emb_dim=37)


    def        A   (  self      :							Tuple):
           self.config_tester.run_common_tests()


    def        A   (  self      :							List[Any]):
           _A					:     int     = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_model(*__lowercase)


    def        A   (  self      :							Union[str, Any]):
           _A					:     Optional[int]     = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_lm_head(*__lowercase)


    def        A   (  self      :							Dict):
           _A					:     Optional[Any]     = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_qa(*__lowercase)


    def        A   (  self      :							List[Any]):
           _A					:     Optional[Any]     = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_sequence_classif(*__lowercase)


    def        A   (  self      :							int):
           _A					:     Dict     = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_for_token_classification(*__lowercase)


    def        A   (  self      :							Optional[int]):
           _A					:     Union[str, Any]     = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_for_multiple_choice(*__lowercase)


    @slow
    def        A   (  self      :							List[str]):
           for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  _A					:     Any     = TFFlaubertModel.from_pretrained(__lowercase)
                  self.assertIsNotNone(__lowercase)




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


    @slow
    def        A   (  self      :							List[str]):
           _A					:     List[Any]     = TFFlaubertModel.from_pretrained('jplu/tf-flaubert-small-cased')

           _A					:     Tuple     = tf.convert_to_tensor(
               [[0, 158, 735, 2592, 1424, 6727, 82, 1]]				,							dtype=tf.intaa				,							)  # "J'aime flaubert !"

           _A					:     Union[str, Any]     = model(__lowercase)[0]
           _A					:     str     = tf.TensorShape((1, 8, 512))
           self.assertEqual(output.shape				,							__lowercase)
           # compare the actual values for a slice.
           _A					:     Optional[int]     = 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))










'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A				:					Optional[int]    						=			{
    '''configuration_mask2former''': [
        '''MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Mask2FormerConfig''',
    ],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					Tuple    						=			['''Mask2FormerImageProcessor''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					Dict    						=			[
       '''MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''Mask2FormerForUniversalSegmentation''',
       '''Mask2FormerModel''',
       '''Mask2FormerPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_maskaformer import MaskaFormerImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_maskaformer import (
          MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          MaskaFormerForUniversalSegmentation,
          MaskaFormerModel,
          MaskaFormerPreTrainedModel,
      )


else:
   import sys

   A				:					Optional[Any]    						=			_LazyModule(__name__, globals()['''__file__'''], _import_structure)






