

from __future__ import annotations

import copy
import inspect
import unittest

import numpy as np

from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers import (
         TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
         TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
         TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
         TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
         TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
         LayoutLMvaConfig,
         TFLayoutLMvaForQuestionAnswering,
         TFLayoutLMvaForSequenceClassification,
         TFLayoutLMvaForTokenClassification,
         TFLayoutLMvaModel,
     )

if is_vision_available():
     from PIL import Image

     from transformers import LayoutLMvaImageProcessor



class 						_A     :





  '''simple docstring'''



  def __init__(  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_=2   ,SCREAMING_SNAKE_CASE_=3   ,SCREAMING_SNAKE_CASE_=4   ,SCREAMING_SNAKE_CASE_=2   ,SCREAMING_SNAKE_CASE_=7   ,SCREAMING_SNAKE_CASE_=True   ,SCREAMING_SNAKE_CASE_=True   ,SCREAMING_SNAKE_CASE_=True   ,SCREAMING_SNAKE_CASE_=True   ,SCREAMING_SNAKE_CASE_=99   ,SCREAMING_SNAKE_CASE_=36   ,SCREAMING_SNAKE_CASE_=2   ,SCREAMING_SNAKE_CASE_=4   ,SCREAMING_SNAKE_CASE_=37   ,SCREAMING_SNAKE_CASE_="gelu"   ,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_=6   ,SCREAMING_SNAKE_CASE_=6   ,SCREAMING_SNAKE_CASE_=3   ,SCREAMING_SNAKE_CASE_=4   ,SCREAMING_SNAKE_CASE_=None   ,SCREAMING_SNAKE_CASE_=1000   ,):

      '''simple docstring'''
      snake_case							:		int        =		parent
      snake_case							:		Optional[Any]        =		batch_size
      snake_case							:		List[Any]        =		num_channels
      snake_case							:		Optional[Any]        =		image_size
      snake_case							:		List[Any]        =		patch_size
      snake_case							:		List[str]        =		is_training
      snake_case							:		Union[str, Any]        =		use_input_mask
      snake_case							:		Tuple        =		use_token_type_ids
      snake_case							:		str        =		use_labels
      snake_case							:		int        =		vocab_size
      snake_case							:		Optional[Any]        =		hidden_size
      snake_case							:		int        =		num_hidden_layers
      snake_case							:		Dict        =		num_attention_heads
      snake_case							:		Any        =		intermediate_size
      snake_case							:		int        =		hidden_act
      snake_case							:		Any        =		hidden_dropout_prob
      snake_case							:		int        =		attention_probs_dropout_prob
      snake_case							:		List[str]        =		max_position_embeddings
      snake_case							:		List[str]        =		type_vocab_size
      snake_case							:		List[Any]        =		type_sequence_label_size
      snake_case							:		Tuple        =		initializer_range
      snake_case							:		Optional[Any]        =		coordinate_size
      snake_case							:		Optional[Any]        =		shape_size
      snake_case							:		Dict        =		num_labels
      snake_case							:		Tuple        =		num_choices
      snake_case							:		str        =		scope
      snake_case							:		Optional[Any]        =		range_bbox

      # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
      snake_case							:		Dict        =		text_seq_length
      snake_case							:		Optional[Any]        =		(image_size // patch_size) ** 2 + 1
      snake_case							:		Union[str, Any]        =		self.text_seq_length + self.image_seq_length





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Dict        =		ids_tensor([self.batch_size, self.text_seq_length]   ,self.vocab_size    )

      snake_case							:		Tuple        =		ids_tensor([self.batch_size, self.text_seq_length, 4]   ,self.range_bbox    )
      snake_case							:		Optional[int]        =		bbox.numpy()
      # Ensure that bbox is legal
      for i in range(bbox.shape[0]    ):
          for j in range(bbox.shape[1]    ):
              if bbox[i, j, 3] < bbox[i, j, 1]:
                  snake_case							:		Dict        =		bbox[i, j, 3]
                  snake_case							:		Dict        =		bbox[i, j, 1]
                  snake_case							:		Optional[int]        =		tmp_coordinate
              if bbox[i, j, 2] < bbox[i, j, 0]:
                  snake_case							:		Dict        =		bbox[i, j, 2]
                  snake_case							:		Optional[Any]        =		bbox[i, j, 0]
                  snake_case							:		Optional[int]        =		tmp_coordinate
      snake_case							:		Optional[Any]        =		tf.constant(SCREAMING_SNAKE_CASE_    )

      snake_case							:		Tuple        =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

      snake_case							:		List[str]        =		None
      if self.use_input_mask:
          snake_case							:		str        =		random_attention_mask([self.batch_size, self.text_seq_length]    )

      snake_case							:		Optional[int]        =		None
      if self.use_token_type_ids:
          snake_case							:		Optional[int]        =		ids_tensor([self.batch_size, self.text_seq_length]   ,self.type_vocab_size    )

      snake_case							:		Tuple        =		None
      snake_case							:		Union[str, Any]        =		None
      if self.use_labels:
          snake_case							:		List[Any]        =		ids_tensor([self.batch_size]   ,self.type_sequence_label_size    )
          snake_case							:		Any        =		ids_tensor([self.batch_size, self.text_seq_length]   ,self.num_labels    )

      snake_case							:		Any        =		LayoutLMvaConfig(
          vocab_size=self.vocab_size   ,hidden_size=self.hidden_size   ,num_hidden_layers=self.num_hidden_layers   ,num_attention_heads=self.num_attention_heads   ,intermediate_size=self.intermediate_size   ,hidden_act=self.hidden_act   ,hidden_dropout_prob=self.hidden_dropout_prob   ,attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,max_position_embeddings=self.max_position_embeddings   ,type_vocab_size=self.type_vocab_size   ,initializer_range=self.initializer_range   ,coordinate_size=self.coordinate_size   ,shape_size=self.shape_size   ,input_size=self.image_size   ,patch_size=self.patch_size   ,)

      return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		str        =		TFLayoutLMvaModel(config=SCREAMING_SNAKE_CASE_    )

      # text + image
      snake_case							:		Optional[int]        =		model(SCREAMING_SNAKE_CASE_   ,pixel_values=SCREAMING_SNAKE_CASE_   ,training=SCREAMING_SNAKE_CASE_    )
      snake_case							:		Optional[int]        =		model(
          SCREAMING_SNAKE_CASE_   ,bbox=SCREAMING_SNAKE_CASE_   ,pixel_values=SCREAMING_SNAKE_CASE_   ,attention_mask=SCREAMING_SNAKE_CASE_   ,token_type_ids=SCREAMING_SNAKE_CASE_   ,training=SCREAMING_SNAKE_CASE_   ,)
      snake_case							:		int        =		model(SCREAMING_SNAKE_CASE_   ,bbox=SCREAMING_SNAKE_CASE_   ,pixel_values=SCREAMING_SNAKE_CASE_   ,training=SCREAMING_SNAKE_CASE_    )

      self.parent.assertEqual(result.last_hidden_state.shape   ,(self.batch_size, self.seq_length, self.hidden_size)    )

      # text only
      snake_case							:		str        =		model(SCREAMING_SNAKE_CASE_   ,training=SCREAMING_SNAKE_CASE_    )
      self.parent.assertEqual(
          result.last_hidden_state.shape   ,(self.batch_size, self.text_seq_length, self.hidden_size)    )

      # image only
      snake_case							:		Optional[int]        =		model({"""pixel_values""": pixel_values}   ,training=SCREAMING_SNAKE_CASE_    )
      self.parent.assertEqual(
          result.last_hidden_state.shape   ,(self.batch_size, self.image_seq_length, self.hidden_size)    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		Dict        =		self.num_labels
      snake_case							:		Optional[int]        =		TFLayoutLMvaForSequenceClassification(config=SCREAMING_SNAKE_CASE_    )
      snake_case							:		int        =		model(
          SCREAMING_SNAKE_CASE_   ,bbox=SCREAMING_SNAKE_CASE_   ,pixel_values=SCREAMING_SNAKE_CASE_   ,attention_mask=SCREAMING_SNAKE_CASE_   ,token_type_ids=SCREAMING_SNAKE_CASE_   ,labels=SCREAMING_SNAKE_CASE_   ,training=SCREAMING_SNAKE_CASE_   ,)
      self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.num_labels)    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		List[str]        =		self.num_labels
      snake_case							:		Optional[int]        =		TFLayoutLMvaForTokenClassification(config=SCREAMING_SNAKE_CASE_    )
      snake_case							:		Optional[Any]        =		model(
          SCREAMING_SNAKE_CASE_   ,bbox=SCREAMING_SNAKE_CASE_   ,pixel_values=SCREAMING_SNAKE_CASE_   ,attention_mask=SCREAMING_SNAKE_CASE_   ,token_type_ids=SCREAMING_SNAKE_CASE_   ,labels=SCREAMING_SNAKE_CASE_   ,training=SCREAMING_SNAKE_CASE_   ,)
      self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.text_seq_length, self.num_labels)    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		Optional[int]        =		2
      snake_case							:		Dict        =		TFLayoutLMvaForQuestionAnswering(config=SCREAMING_SNAKE_CASE_    )
      snake_case							:		Tuple        =		model(
          SCREAMING_SNAKE_CASE_   ,bbox=SCREAMING_SNAKE_CASE_   ,pixel_values=SCREAMING_SNAKE_CASE_   ,attention_mask=SCREAMING_SNAKE_CASE_   ,token_type_ids=SCREAMING_SNAKE_CASE_   ,start_positions=SCREAMING_SNAKE_CASE_   ,end_positions=SCREAMING_SNAKE_CASE_   ,training=SCREAMING_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    ):

      '''simple docstring'''
      snake_case							:		Optional[Any]        =		self.prepare_config_and_inputs()
      ((snake_case)      ,		(snake_case)      ,		(snake_case)      ,		(snake_case)      ,		(snake_case)      ,		(snake_case)      ,		(snake_case)      ,		(snake_case))							:		Dict        =		config_and_inputs
      snake_case							:		Union[str, Any]        =		{
          """input_ids""": input_ids,
          """bbox""": bbox,
          """pixel_values""": pixel_values,
          """token_type_ids""": token_type_ids,
          """attention_mask""": input_mask,
      }
      return config, inputs_dict







@require_tf
class 						_A     (				snake_case						,    snake_case						,    unittest.TestCase				):





  '''simple docstring'''



  __lowerCamelCase			:      Union[str, Any]					=   (
      (
          TFLayoutLMvaModel,
          TFLayoutLMvaForQuestionAnswering,
          TFLayoutLMvaForSequenceClassification,
          TFLayoutLMvaForTokenClassification,
      )
      if is_tf_available()
      else ()
  )
  __lowerCamelCase			:      List[str]					=   (
      {'''document-question-answering''': TFLayoutLMvaForQuestionAnswering, '''feature-extraction''': TFLayoutLMvaModel}
      if is_tf_available()
      else {}
  )

  __lowerCamelCase			:      Optional[Any]					=   False
  __lowerCamelCase			:      List[Any]					=   False
  __lowerCamelCase			:      Any					=   False





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      return True





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_=False    ):

      '''simple docstring'''
      snake_case							:		str        =		copy.deepcopy(SCREAMING_SNAKE_CASE_    )

      if model_class in get_values(SCREAMING_SNAKE_CASE_    ):
          snake_case							:		Any        =		{
              k: tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_   ,1    )   ,(1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)    )
              if isinstance(SCREAMING_SNAKE_CASE_   ,tf.Tensor    ) and v.ndim > 0
              else v
              for k, v in inputs_dict.items()
          }

      if return_labels:
          if model_class in get_values(SCREAMING_SNAKE_CASE_    ):
              snake_case							:		Optional[int]        =		tf.ones(self.model_tester.batch_size   ,dtype=tf.intaa    )
          elif model_class in get_values(SCREAMING_SNAKE_CASE_    ):
              snake_case							:		Dict        =		tf.zeros(self.model_tester.batch_size   ,dtype=tf.intaa    )
              snake_case							:		Tuple        =		tf.zeros(self.model_tester.batch_size   ,dtype=tf.intaa    )
          elif model_class in get_values(SCREAMING_SNAKE_CASE_    ):
              snake_case							:		List[Any]        =		tf.zeros(self.model_tester.batch_size   ,dtype=tf.intaa    )
          elif model_class in get_values(SCREAMING_SNAKE_CASE_    ):
              snake_case							:		Dict        =		tf.zeros(
                  (self.model_tester.batch_size, self.model_tester.text_seq_length)   ,dtype=tf.intaa    )

      return inputs_dict





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		str        =		TFLayoutLMvaModelTester(self    )
      snake_case							:		str        =		ConfigTester(self   ,config_class=SCREAMING_SNAKE_CASE_   ,hidden_size=37    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      self.config_tester.run_common_tests()





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case      ,		snake_case							:		Tuple        =		self.model_tester.prepare_config_and_inputs_for_common()
      for model_class in self.all_model_classes:
          snake_case							:		List[str]        =		model_class(SCREAMING_SNAKE_CASE_    )
          if getattr(SCREAMING_SNAKE_CASE_   ,"""hf_compute_loss"""   ,SCREAMING_SNAKE_CASE_    ):
              # The number of elements in the loss should be the same as the number of elements in the label
              snake_case							:		Optional[Any]        =		self._prepare_for_class(inputs_dict.copy()   ,SCREAMING_SNAKE_CASE_   ,return_labels=SCREAMING_SNAKE_CASE_    )
              snake_case							:		List[str]        =		prepared_for_class[
                  sorted(prepared_for_class.keys() - inputs_dict.keys()   ,reverse=SCREAMING_SNAKE_CASE_    )[0]
              ]
              snake_case							:		List[Any]        =		added_label.shape.as_list()[:1]

              # Test that model correctly compute the loss with kwargs
              snake_case							:		Tuple        =		self._prepare_for_class(inputs_dict.copy()   ,SCREAMING_SNAKE_CASE_   ,return_labels=SCREAMING_SNAKE_CASE_    )
              snake_case							:		Optional[int]        =		prepared_for_class.pop("""input_ids"""    )

              snake_case							:		Dict        =		model(SCREAMING_SNAKE_CASE_   ,**SCREAMING_SNAKE_CASE_    )[0]
              self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]    )

              # Test that model correctly compute the loss when we mask some positions
              snake_case							:		Union[str, Any]        =		self._prepare_for_class(inputs_dict.copy()   ,SCREAMING_SNAKE_CASE_   ,return_labels=SCREAMING_SNAKE_CASE_    )
              snake_case							:		Any        =		prepared_for_class.pop("""input_ids"""    )
              if "labels" in prepared_for_class:
                  snake_case							:		Union[str, Any]        =		prepared_for_class["""labels"""].numpy()
                  if len(labels.shape    ) > 1 and labels.shape[1] != 1:
                      snake_case							:		Dict        =		-100
                      snake_case							:		Optional[Any]        =		tf.convert_to_tensor(SCREAMING_SNAKE_CASE_    )
                      snake_case							:		int        =		model(SCREAMING_SNAKE_CASE_   ,**SCREAMING_SNAKE_CASE_    )[0]
                      self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]    )
                      self.assertTrue(not np.any(np.isnan(loss.numpy()    )    )    )

                # Test that model correctly compute the loss with a dict
              snake_case							:		str        =		self._prepare_for_class(inputs_dict.copy()   ,SCREAMING_SNAKE_CASE_   ,return_labels=SCREAMING_SNAKE_CASE_    )
              snake_case							:		Dict        =		model(SCREAMING_SNAKE_CASE_    )[0]
              self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]    )

              # Test that model correctly compute the loss with a tuple
              snake_case							:		Any        =		self._prepare_for_class(inputs_dict.copy()   ,SCREAMING_SNAKE_CASE_   ,return_labels=SCREAMING_SNAKE_CASE_    )

              # Get keys that were added with the _prepare_for_class function
              snake_case							:		Optional[Any]        =		prepared_for_class.keys() - inputs_dict.keys()
              snake_case							:		Optional[int]        =		inspect.signature(model.call    ).parameters
              snake_case							:		Tuple        =		list(signature.keys()    )

              # Create a dictionary holding the location of the tensors in the tuple
              snake_case							:		Optional[Any]        =		{0: """input_ids"""}
              for label_key in label_keys:
                  snake_case							:		Dict        =		signature_names.index(SCREAMING_SNAKE_CASE_    )
                  snake_case							:		Union[str, Any]        =		label_key
              snake_case							:		List[str]        =		sorted(tuple_index_mapping.items()    )
              # Initialize a list with their default values, update the values and convert to a tuple
              snake_case							:		Dict        =		[]

              for name in signature_names:
                  if name != "kwargs":
                      list_input.append(signature[name].default    )

              for index, value in sorted_tuple_index_mapping:
                  snake_case							:		Optional[Any]        =		prepared_for_class[value]

              snake_case							:		Optional[int]        =		tuple(SCREAMING_SNAKE_CASE_    )

              # Send to model
              snake_case							:		Union[str, Any]        =		model(tuple_input[:-1]    )[0]

              self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      (
          (
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		
      )							:		Tuple        =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      (
          (
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		
      )							:		int        =		self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
          snake_case							:		int        =		type
          self.model_tester.create_and_check_model(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      (
          (
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		
      )							:		Dict        =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(
          SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      (
          (
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		
      )							:		Dict        =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(
          SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      (
          (
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		
      )							:		Optional[int]        =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(
          SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )





  @slow
  def    snake_case_       (  self    ):

      '''simple docstring'''
      for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          snake_case							:		str        =		TFLayoutLMvaModel.from_pretrained(SCREAMING_SNAKE_CASE_    )
          self.assertIsNotNone(SCREAMING_SNAKE_CASE_    )







def       lowercase					(	)				->			Dict:





    '''simple docstring'''




    snake_case							:		List[Any]        =		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
    return image



@require_tf
class 						_A     (				unittest.TestCase				):





  '''simple docstring'''



  @cached_property
  def    snake_case_       (  self    ):

      '''simple docstring'''
      return LayoutLMvaImageProcessor(apply_ocr=SCREAMING_SNAKE_CASE_    ) if is_vision_available() else None





  @slow
  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Any        =		TFLayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base"""    )

      snake_case							:		int        =		self.default_image_processor
      snake_case							:		Optional[Any]        =		prepare_img()
      snake_case							:		str        =		image_processor(images=SCREAMING_SNAKE_CASE_   ,return_tensors="""tf"""    ).pixel_values

      snake_case							:		Optional[int]        =		tf.constant([[1, 2]]    )
      snake_case							:		List[str]        =		tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]]    )   ,axis=0    )

      # forward pass
      snake_case							:		List[str]        =		model(input_ids=SCREAMING_SNAKE_CASE_   ,bbox=SCREAMING_SNAKE_CASE_   ,pixel_values=SCREAMING_SNAKE_CASE_   ,training=SCREAMING_SNAKE_CASE_    )

      # verify the logits
      snake_case							:		List[Any]        =		(1, 199, 768)
      self.assertEqual(outputs.last_hidden_state.shape   ,SCREAMING_SNAKE_CASE_    )

      snake_case							:		str        =		tf.constant(
          [[-0.05_29, 0.36_18, 0.16_32], [-0.15_87, -0.16_67, -0.04_00], [-0.15_57, -0.16_71, -0.05_05]]    )

      self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]   ,SCREAMING_SNAKE_CASE_   ,atol=1E-4    )    )











'''simple docstring'''







import inspect
import os
import re

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__lowerCAmelCase									=   'src/transformers'


# This is to make sure the transformers module imported is the one in the repo.
__lowerCAmelCase									=   direct_transformers_import(PATH_TO_TRANSFORMERS)

__lowerCAmelCase									=   transformers.models.auto.configuration_auto.CONFIG_MAPPING

__lowerCAmelCase									=   {
    # used to compute the property `self.chunk_length`
    'EncodecConfig': ['overlap'],
    # used as `self.bert_model = BertModel(config, ...)`
    'DPRConfig': True,
    # not used in modeling files, but it's an important information
    'FSMTConfig': ['langs'],
    # used internally in the configuration class file
    'GPTNeoConfig': ['attention_types'],
    # used internally in the configuration class file
    'EsmConfig': ['is_folding_model'],
    # used during training (despite we don't have training script for these models yet)
    'Mask2FormerConfig': ['ignore_value'],
    # `ignore_value` used during training (despite we don't have training script for these models yet)
    # `norm` used in conversion script (despite not using in the modeling file)
    'OneFormerConfig': ['ignore_value', 'norm'],
    # used during preprocessing and collation, see `collating_graphormer.py`
    'GraphormerConfig': ['spatial_pos_max'],
    # used internally in the configuration class file
    'T5Config': ['feed_forward_proj'],
    # used internally in the configuration class file
    # `tokenizer_class` get default value `T5Tokenizer` intentionally
    'MT5Config': ['feed_forward_proj', 'tokenizer_class'],
    'UMT5Config': ['feed_forward_proj', 'tokenizer_class'],
    # used internally in the configuration class file
    'LongT5Config': ['feed_forward_proj'],
    # used internally in the configuration class file
    'SwitchTransformersConfig': ['feed_forward_proj'],
    # having default values other than `1e-5` - we can't fix them without breaking
    'BioGptConfig': ['layer_norm_eps'],
    # having default values other than `1e-5` - we can't fix them without breaking
    'GLPNConfig': ['layer_norm_eps'],
    # having default values other than `1e-5` - we can't fix them without breaking
    'SegformerConfig': ['layer_norm_eps'],
    # having default values other than `1e-5` - we can't fix them without breaking
    'CvtConfig': ['layer_norm_eps'],
    # having default values other than `1e-5` - we can't fix them without breaking
    'PerceiverConfig': ['layer_norm_eps'],
    # used internally to calculate the feature size
    'InformerConfig': ['num_static_real_features', 'num_time_features'],
    # used internally to calculate the feature size
    'TimeSeriesTransformerConfig': ['num_static_real_features', 'num_time_features'],
    # used internally to calculate the feature size
    'AutoformerConfig': ['num_static_real_features', 'num_time_features'],
    # used internally to calculate `mlp_dim`
    'SamVisionConfig': ['mlp_ratio'],
    # For (head) training, but so far not implemented
    'ClapAudioConfig': ['num_classes'],
    # Not used, but providing useful information to users
    'SpeechT5HifiGanConfig': ['sampling_rate'],
}


# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
    {
        'CLIPSegConfig': True,
        'DeformableDetrConfig': True,
        'DetaConfig': True,
        'DinatConfig': True,
        'DonutSwinConfig': True,
        'EfficientFormerConfig': True,
        'FSMTConfig': True,
        'JukeboxConfig': True,
        'LayoutLMv2Config': True,
        'MaskFormerSwinConfig': True,
        'MT5Config': True,
        'NatConfig': True,
        'OneFormerConfig': True,
        'PerceiverConfig': True,
        'RagConfig': True,
        'SpeechT5Config': True,
        'SwinConfig': True,
        'Swin2SRConfig': True,
        'Swinv2Config': True,
        'SwitchTransformersConfig': True,
        'TableTransformerConfig': True,
        'TapasConfig': True,
        'TransfoXLConfig': True,
        'UniSpeechConfig': True,
        'UniSpeechSatConfig': True,
        'WavLMConfig': True,
        'WhisperConfig': True,
        # TODO: @Arthur (for `alignment_head` and `alignment_layer`)
        'JukeboxPriorConfig': True,
        # TODO: @Younes (for `is_decoder`)
        'Pix2StructTextConfig': True,
    }
)





def 		_UpperCAmelCase (				__A				: Optional[Any]    ,		__A				: List[str]    ,		__A				: List[str]    ,		__A				: Union[str, Any]   ):
				a_		:	List[str]									=   False
				for attribute in attributes:
								for modeling_source in source_strings:
												# check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
												if (
												    f'config.{attribute}' in modeling_source
												    or f'getattr(config, "{attribute}"' in modeling_source
												    or f'getattr(self.config, "{attribute}"' in modeling_source
												):
																a_		:	Tuple									=   True
												# Deal with multi-line cases
												elif (
												    re.search(
												        Rf'getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*"{attribute}"'    ,		__A    ,		)
												    is not None
												):
																a_		:	Union[str, Any]									=   True
												# `SequenceSummary` is called with `SequenceSummary(config)`
												elif attribute in [
												    "summary_type",
												    "summary_use_proj",
												    "summary_activation",
												    "summary_last_dropout",
												    "summary_proj_to_labels",
												    "summary_first_dropout",
												]:
																if "SequenceSummary" in modeling_source:
																				a_		:	Any									=   True
												if attribute_used:
																break
								if attribute_used:
												break

    # common and important attributes, even if they do not always appear in the modeling files
				a_		:	Tuple									=   [
				    '''bos_index''',
				    '''eos_index''',
				    '''pad_index''',
				    '''unk_index''',
				    '''mask_index''',
				    '''image_size''',
				    '''use_cache''',
				    '''out_features''',
				    '''out_indices''',
				]
				a_		:	Tuple									=   ['''encoder_no_repeat_ngram_size''']

				# Special cases to be allowed
				a_		:	Optional[int]									=   True
				if not attribute_used:
								a_		:	Optional[Any]									=   False
								for attribute in attributes:
												# Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
												if attribute in ["is_encoder_decoder"] and default_value is True:
																a_		:	Dict									=   True
												elif attribute in ["tie_word_embeddings"] and default_value is False:
																a_		:	List[str]									=   True

												# Allow cases without checking the default value in the configuration class
												elif attribute in attributes_to_allow + attributes_used_in_generation:
																a_		:	Optional[int]									=   True
												elif attribute.endswith('''_token_id'''   ):
																a_		:	Tuple									=   True

												# configuration class specific cases
												if not case_allowed:
																a_		:	Dict									=   SPECIAL_CASES_TO_ALLOW.get(config_class.__name__    ,		[]   )
																a_		:	int									=   allowed_cases is True or attribute in allowed_cases

				return attribute_used or case_allowed





def 		_UpperCAmelCase (				__A				: Union[str, Any]   ):
				a_		:	str									=   dict(inspect.signature(config_class.__init__   ).parameters   )
				a_		:	List[str]									=   [x for x in list(signature.keys()   ) if x not in ['''self''', '''kwargs''']]
				a_		:	Optional[Any]									=   [signature[param].default for param in parameter_names]

				# If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
				# as one variant is used, the test should pass
				a_		:	int									=   {}
				if len(config_class.attribute_map   ) > 0:
								a_		:	List[str]									=   {v: k for k, v in config_class.attribute_map.items()}

				# Get the path to modeling source files
				a_		:	Tuple									=   inspect.getsourcefile(__A   )
				a_		:	str									=   os.path.dirname(__A   )
				# Let's check against all frameworks: as long as one framework uses an attribute, we are good.
				a_		:	Tuple									=   [os.path.join(__A    ,		__A   ) for fn in os.listdir(__A   ) if fn.startswith('''modeling_'''   )]

				# Get the source code strings
				a_		:	Dict									=   []
				for path in modeling_paths:
								if os.path.isfile(__A   ):
												with open(__A   ) as fp:
																modeling_sources.append(fp.read()   )

				a_		:	List[Any]									=   []
				for config_param, default_value in zip(__A    ,		__A   ):
								# `attributes` here is all the variant names for `config_param`
								a_		:	int									=   [config_param]
								# some configuration classes have non-empty `attribute_map`, and both names could be used in the
								# corresponding modeling files. As long as one of them appears, it is fine.
								if config_param in reversed_attribute_map:
												attributes.append(reversed_attribute_map[config_param]   )

								if not check_attribute_being_used(__A    ,		__A    ,		__A    ,		__A   ):
												unused_attributes.append(attributes[0]   )

				return sorted(__A   )





def 		_UpperCAmelCase (				):
				a_		:	Optional[int]									=   {}
				for _config_class in list(CONFIG_MAPPING.values()   ):
								# Skip deprecated models
								if "models.deprecated" in _config_class.__module__:
												continue
								# Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
								a_		:	int									=   [
								    cls
								    for name, cls in inspect.getmembers(
								        inspect.getmodule(_config_class   )    ,		lambda __A   : inspect.isclass(__A   )
								        and issubclass(__A    ,		__A   )
								        and inspect.getmodule(__A   ) == inspect.getmodule(_config_class   )    ,		)
								]
								for config_class in config_classes_in_module:
												a_		:	int									=   check_config_attributes_being_used(__A   )
												if len(__A   ) > 0:
																a_		:	Dict									=   unused_attributes

				if len(__A   ) > 0:
								a_		:	Dict									=   '''The following configuration classes contain unused attributes in the corresponding modeling files:\n'''
								for name, attributes in configs_with_unused_attributes.items():
												error += f'{name}: {attributes}\n'

								raise ValueError(__A   )


if __name__ == "__main__":
		check_config_attributes()




