


"""simple docstring"""


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 				_SCREAMING_SNAKE_CASE				:
   """simple docstring"""



   def __init__(						self ,     lowerCamelCase__ ,     lowerCamelCase__=2 ,     lowerCamelCase__=3 ,     lowerCamelCase__=4 ,     lowerCamelCase__=2 ,     lowerCamelCase__=7 ,     lowerCamelCase__=True ,     lowerCamelCase__=True ,     lowerCamelCase__=True ,     lowerCamelCase__=True ,     lowerCamelCase__=99 ,     lowerCamelCase__=36 ,     lowerCamelCase__=2 ,     lowerCamelCase__=4 ,     lowerCamelCase__=37 ,     lowerCamelCase__="gelu" ,     lowerCamelCase__=0.1 ,     lowerCamelCase__=0.1 ,     lowerCamelCase__=512 ,     lowerCamelCase__=16 ,     lowerCamelCase__=2 ,     lowerCamelCase__=0.02 ,     lowerCamelCase__=6 ,     lowerCamelCase__=6 ,     lowerCamelCase__=3 ,     lowerCamelCase__=4 ,     lowerCamelCase__=None ,     lowerCamelCase__=1000 ,     )      ->							Any:
    lowercase__       :       Dict				=						parent
    lowercase__       :       List[str]				=						batch_size
    lowercase__       :       Union[str, Any]				=						num_channels
    lowercase__       :       Any				=						image_size
    lowercase__       :       Optional[int]				=						patch_size
    lowercase__       :       List[str]				=						is_training
    lowercase__       :       int				=						use_input_mask
    lowercase__       :       List[Any]				=						use_token_type_ids
    lowercase__       :       Dict				=						use_labels
    lowercase__       :       Dict				=						vocab_size
    lowercase__       :       str				=						hidden_size
    lowercase__       :       Optional[Any]				=						num_hidden_layers
    lowercase__       :       Any				=						num_attention_heads
    lowercase__       :       Union[str, Any]				=						intermediate_size
    lowercase__       :       Union[str, Any]				=						hidden_act
    lowercase__       :       Union[str, Any]				=						hidden_dropout_prob
    lowercase__       :       List[str]				=						attention_probs_dropout_prob
    lowercase__       :       Any				=						max_position_embeddings
    lowercase__       :       Optional[int]				=						type_vocab_size
    lowercase__       :       Optional[Any]				=						type_sequence_label_size
    lowercase__       :       Dict				=						initializer_range
    lowercase__       :       List[str]				=						coordinate_size
    lowercase__       :       int				=						shape_size
    lowercase__       :       Union[str, Any]				=						num_labels
    lowercase__       :       Any				=						num_choices
    lowercase__       :       Dict				=						scope
    lowercase__       :       Any				=						range_bbox

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


   def 					UpperCAmelCase__(						self						)      ->							int:
    lowercase__       :       int				=						ids_tensor([self.batch_size, self.text_seq_length] ,     self.vocab_size						)

    lowercase__       :       Any				=						ids_tensor([self.batch_size, self.text_seq_length, 4] ,     self.range_bbox						)
    lowercase__       :       Tuple				=						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]:
       lowercase__       :       Union[str, Any]				=						bbox[i, j, 3]
       lowercase__       :       int				=						bbox[i, j, 1]
       lowercase__       :       Optional[Any]				=						tmp_coordinate
      if bbox[i, j, 2] < bbox[i, j, 0]:
       lowercase__       :       Union[str, Any]				=						bbox[i, j, 2]
       lowercase__       :       Dict				=						bbox[i, j, 0]
       lowercase__       :       Tuple				=						tmp_coordinate
    lowercase__       :       Tuple				=						tf.constant(lowerCamelCase__						)

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

    lowercase__       :       Union[str, Any]				=						None
    if self.use_input_mask:
     lowercase__       :       Union[str, Any]				=						random_attention_mask([self.batch_size, self.text_seq_length]						)

    lowercase__       :       List[str]				=						None
    if self.use_token_type_ids:
     lowercase__       :       Dict				=						ids_tensor([self.batch_size, self.text_seq_length] ,     self.type_vocab_size						)

    lowercase__       :       Optional[Any]				=						None
    lowercase__       :       Dict				=						None
    if self.use_labels:
     lowercase__       :       List[str]				=						ids_tensor([self.batch_size] ,     self.type_sequence_label_size						)
     lowercase__       :       Dict				=						ids_tensor([self.batch_size, self.text_seq_length] ,     self.num_labels						)

    lowercase__       :       str				=						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 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__						)      ->							Optional[int]:
    lowercase__       :       Optional[int]				=						TFLayoutLMvaModel(config=lowerCamelCase__						)

    # text + image
    lowercase__       :       Optional[Any]				=						model(lowerCamelCase__ ,     pixel_values=lowerCamelCase__ ,     training=lowerCamelCase__						)
    lowercase__       :       int				=						model(
        lowerCamelCase__ ,     bbox=lowerCamelCase__ ,     pixel_values=lowerCamelCase__ ,     attention_mask=lowerCamelCase__ ,     token_type_ids=lowerCamelCase__ ,     training=lowerCamelCase__ ,     )
    lowercase__       :       str				=						model(lowerCamelCase__ ,     bbox=lowerCamelCase__ ,     pixel_values=lowerCamelCase__ ,     training=lowerCamelCase__						)

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

    # text only
    lowercase__       :       Dict				=						model(lowerCamelCase__ ,     training=lowerCamelCase__						)
    self.parent.assertEqual(
        result.last_hidden_state.shape ,     (self.batch_size, self.text_seq_length, self.hidden_size)						)

    # image only
    lowercase__       :       int				=						model({"""pixel_values""": pixel_values} ,     training=lowerCamelCase__						)
    self.parent.assertEqual(
        result.last_hidden_state.shape ,     (self.batch_size, self.image_seq_length, self.hidden_size)						)


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__						)      ->							List[str]:
    lowercase__       :       List[Any]				=						self.num_labels
    lowercase__       :       Dict				=						TFLayoutLMvaForSequenceClassification(config=lowerCamelCase__						)
    lowercase__       :       Optional[int]				=						model(
        lowerCamelCase__ ,     bbox=lowerCamelCase__ ,     pixel_values=lowerCamelCase__ ,     attention_mask=lowerCamelCase__ ,     token_type_ids=lowerCamelCase__ ,     labels=lowerCamelCase__ ,     training=lowerCamelCase__ ,     )
    self.parent.assertEqual(result.logits.shape ,     (self.batch_size, self.num_labels)						)


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__						)      ->							Dict:
    lowercase__       :       Tuple				=						self.num_labels
    lowercase__       :       int				=						TFLayoutLMvaForTokenClassification(config=lowerCamelCase__						)
    lowercase__       :       Optional[Any]				=						model(
        lowerCamelCase__ ,     bbox=lowerCamelCase__ ,     pixel_values=lowerCamelCase__ ,     attention_mask=lowerCamelCase__ ,     token_type_ids=lowerCamelCase__ ,     labels=lowerCamelCase__ ,     training=lowerCamelCase__ ,     )
    self.parent.assertEqual(result.logits.shape ,     (self.batch_size, self.text_seq_length, self.num_labels)						)


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__						)      ->							int:
    lowercase__       :       Any				=						2
    lowercase__       :       List[Any]				=						TFLayoutLMvaForQuestionAnswering(config=lowerCamelCase__						)
    lowercase__       :       str				=						model(
        lowerCamelCase__ ,     bbox=lowerCamelCase__ ,     pixel_values=lowerCamelCase__ ,     attention_mask=lowerCamelCase__ ,     token_type_ids=lowerCamelCase__ ,     start_positions=lowerCamelCase__ ,     end_positions=lowerCamelCase__ ,     training=lowerCamelCase__ ,     )
    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 					UpperCAmelCase__(						self						)      ->							List[Any]:
    lowercase__       :       str				=						self.prepare_config_and_inputs()
    ((lowercase__)	,       (lowercase__)	,       (lowercase__)	,       (lowercase__)	,       (lowercase__)	,       (lowercase__)	,       (lowercase__)	,       (lowercase__))       :       int				=						config_and_inputs
    lowercase__       :       Tuple				=						{
        """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 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase     ,   __UpperCAmelCase     ,   unittest.TestCase  ):
   """simple docstring"""



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

   _a   :       Optional[int]									=    False
   _a   :       str									=    False
   _a   :       Optional[Any]									=    False


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__						)      ->							str:
    return True


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__=False						)      ->							dict:
    lowercase__       :       int				=						copy.deepcopy(lowerCamelCase__						)

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

    if return_labels:
     if model_class in get_values(lowerCamelCase__						):
      lowercase__       :       Union[str, Any]				=						tf.ones(self.model_tester.batch_size ,     dtype=tf.intaa						)
     elif model_class in get_values(lowerCamelCase__						):
      lowercase__       :       Optional[int]				=						tf.zeros(self.model_tester.batch_size ,     dtype=tf.intaa						)
      lowercase__       :       Optional[int]				=						tf.zeros(self.model_tester.batch_size ,     dtype=tf.intaa						)
     elif model_class in get_values(lowerCamelCase__						):
      lowercase__       :       Any				=						tf.zeros(self.model_tester.batch_size ,     dtype=tf.intaa						)
     elif model_class in get_values(lowerCamelCase__						):
      lowercase__       :       str				=						tf.zeros(
          (self.model_tester.batch_size, self.model_tester.text_seq_length) ,     dtype=tf.intaa						)

    return inputs_dict


   def 					UpperCAmelCase__(						self						)      ->							Tuple:
    lowercase__       :       List[Any]				=						TFLayoutLMvaModelTester(self						)
    lowercase__       :       List[str]				=						ConfigTester(self ,     config_class=lowerCamelCase__ ,     hidden_size=37						)


   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    self.config_tester.run_common_tests()


   def 					UpperCAmelCase__(						self						)      ->							List[Any]:
    lowercase__	,       lowercase__       :       Dict				=						self.model_tester.prepare_config_and_inputs_for_common()
    for model_class in self.all_model_classes:
     lowercase__       :       int				=						model_class(lowerCamelCase__						)
     if getattr(lowerCamelCase__ ,     """hf_compute_loss""" ,     lowerCamelCase__						):
      # The number of elements in the loss should be the same as the number of elements in the label
      lowercase__       :       Union[str, Any]				=						self._prepare_for_class(inputs_dict.copy() ,     lowerCamelCase__ ,     return_labels=lowerCamelCase__						)
      lowercase__       :       Dict				=						prepared_for_class[
          sorted(prepared_for_class.keys() - inputs_dict.keys() ,     reverse=lowerCamelCase__						)[0]
      ]
      lowercase__       :       List[str]				=						added_label.shape.as_list()[:1]

      # Test that model correctly compute the loss with kwargs
      lowercase__       :       Optional[Any]				=						self._prepare_for_class(inputs_dict.copy() ,     lowerCamelCase__ ,     return_labels=lowerCamelCase__						)
      lowercase__       :       Dict				=						prepared_for_class.pop("""input_ids"""						)

      lowercase__       :       List[str]				=						model(lowerCamelCase__ ,     **lowerCamelCase__						)[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
      lowercase__       :       int				=						self._prepare_for_class(inputs_dict.copy() ,     lowerCamelCase__ ,     return_labels=lowerCamelCase__						)
      lowercase__       :       List[str]				=						prepared_for_class.pop("""input_ids"""						)
      if "labels" in prepared_for_class:
       lowercase__       :       Any				=						prepared_for_class["""labels"""].numpy()
       if len(labels.shape						) > 1 and labels.shape[1] != 1:
        lowercase__       :       Tuple				=						-100
        lowercase__       :       List[Any]				=						tf.convert_to_tensor(lowerCamelCase__						)
        lowercase__       :       Optional[int]				=						model(lowerCamelCase__ ,     **lowerCamelCase__						)[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
      lowercase__       :       Tuple				=						self._prepare_for_class(inputs_dict.copy() ,     lowerCamelCase__ ,     return_labels=lowerCamelCase__						)
      lowercase__       :       str				=						model(lowerCamelCase__						)[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
      lowercase__       :       str				=						self._prepare_for_class(inputs_dict.copy() ,     lowerCamelCase__ ,     return_labels=lowerCamelCase__						)

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

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

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

      for index, value in sorted_tuple_index_mapping:
       lowercase__       :       Dict				=						prepared_for_class[value]

      lowercase__       :       Optional[int]				=						tuple(lowerCamelCase__						)

      # Send to model
      lowercase__       :       Any				=						model(tuple_input[:-1]						)[0]

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


   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    (
        (
        lowercase__
    )	,       (
        lowercase__
    )	,       (
        lowercase__
    )	,       (
        lowercase__
    )	,       (
        lowercase__
    )	,       (
        lowercase__
    )	,       (
        lowercase__
    )	,       (
        lowercase__
    )	,       
    )       :       List[str]				=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							Optional[int]:
    (
        (
        lowercase__
    )	,       (
        lowercase__
    )	,       (
        lowercase__
    )	,       (
        lowercase__
    )	,       (
        lowercase__
    )	,       (
        lowercase__
    )	,       (
        lowercase__
    )	,       (
        lowercase__
    )	,       
    )       :       str				=						self.model_tester.prepare_config_and_inputs()
    for type in ["absolute", "relative_key", "relative_key_query"]:
     lowercase__       :       List[str]				=						type
     self.model_tester.create_and_check_model(lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							Tuple:
    (
        (
        lowercase__
    )	,       (
        lowercase__
    )	,       (
        lowercase__
    )	,       (
        lowercase__
    )	,       (
        lowercase__
    )	,       (
        lowercase__
    )	,       (
        lowercase__
    )	,       (
        lowercase__
    )	,       
    )       :       str				=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_sequence_classification(
        lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							Optional[Any]:
    (
        (
        lowercase__
    )	,       (
        lowercase__
    )	,       (
        lowercase__
    )	,       (
        lowercase__
    )	,       (
        lowercase__
    )	,       (
        lowercase__
    )	,       (
        lowercase__
    )	,       (
        lowercase__
    )	,       
    )       :       Optional[int]				=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_token_classification(
        lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							Optional[Any]:
    (
        (
        lowercase__
    )	,       (
        lowercase__
    )	,       (
        lowercase__
    )	,       (
        lowercase__
    )	,       (
        lowercase__
    )	,       (
        lowercase__
    )	,       (
        lowercase__
    )	,       (
        lowercase__
    )	,       
    )       :       List[str]				=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_question_answering(
        lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__						)







   @slow
   def 					UpperCAmelCase__(						self						)      ->							Optional[int]:
    for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     lowercase__       :       Optional[Any]				=						TFLayoutLMvaModel.from_pretrained(lowerCamelCase__						)
     self.assertIsNotNone(lowerCamelCase__						)


def    _lowerCamelCase   (     ):
 lowercase__       :       List[str]				=						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""  )
 return image






@require_tf
class 				_SCREAMING_SNAKE_CASE				(							unittest.TestCase  ):
   """simple docstring"""



   @cached_property
   def 					UpperCAmelCase__(						self						)      ->							Tuple:
    return LayoutLMvaImageProcessor(apply_ocr=lowerCamelCase__						) if is_vision_available() else None







   @slow
   def 					UpperCAmelCase__(						self						)      ->							Tuple:
    lowercase__       :       Any				=						TFLayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base"""						)

    lowercase__       :       Optional[int]				=						self.default_image_processor
    lowercase__       :       Optional[int]				=						prepare_img()
    lowercase__       :       int				=						image_processor(images=lowerCamelCase__ ,     return_tensors="""tf"""						).pixel_values

    lowercase__       :       List[Any]				=						tf.constant([[1, 2]]						)
    lowercase__       :       Tuple				=						tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]]						) ,     axis=0						)

    # forward pass
    lowercase__       :       Optional[int]				=						model(input_ids=lowerCamelCase__ ,     bbox=lowerCamelCase__ ,     pixel_values=lowerCamelCase__ ,     training=lowerCamelCase__						)

    # verify the logits
    lowercase__       :       Optional[Any]				=						(1, 199, 768)
    self.assertEqual(outputs.last_hidden_state.shape ,     lowerCamelCase__						)

    lowercase__       :       List[str]				=						tf.constant(
        [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]						)

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



"""simple docstring"""


import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__snake_case    					=					logging.get_logger(__name__)

__snake_case    					=					{'vocab_file': 'sentencepiece.bpe.model'}

__snake_case    					=					{
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    }
}

__snake_case    					=					{
    'camembert-base': 512,
}

__snake_case    					=					'▁'

class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase  ):
   """simple docstring"""



   _a   :       List[Any]									=    VOCAB_FILES_NAMES
   _a   :       List[Any]									=    PRETRAINED_VOCAB_FILES_MAP
   _a   :       List[str]									=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _a   :       Union[str, Any]									=    ['''input_ids''', '''attention_mask''']


   def __init__(						self ,     lowerCamelCase__ ,     lowerCamelCase__="<s>" ,     lowerCamelCase__="</s>" ,     lowerCamelCase__="</s>" ,     lowerCamelCase__="<s>" ,     lowerCamelCase__="<unk>" ,     lowerCamelCase__="<pad>" ,     lowerCamelCase__="<mask>" ,     lowerCamelCase__=["<s>NOTUSED", "</s>NOTUSED"] ,     lowerCamelCase__ = None ,     **lowerCamelCase__ ,     )      ->							None:
    # Mask token behave like a normal word, i.e. include the space before it
    lowercase__       :       Optional[int]				=						AddedToken(lowerCamelCase__ ,     lstrip=lowerCamelCase__ ,     rstrip=lowerCamelCase__						) if isinstance(lowerCamelCase__ ,     lowerCamelCase__						) else mask_token

    lowercase__       :       Dict				=						{} if sp_model_kwargs is None else sp_model_kwargs

    super().__init__(
        bos_token=lowerCamelCase__ ,     eos_token=lowerCamelCase__ ,     unk_token=lowerCamelCase__ ,     sep_token=lowerCamelCase__ ,     cls_token=lowerCamelCase__ ,     pad_token=lowerCamelCase__ ,     mask_token=lowerCamelCase__ ,     additional_special_tokens=lowerCamelCase__ ,     sp_model_kwargs=self.sp_model_kwargs ,     **lowerCamelCase__ ,     )
    lowercase__       :       Optional[int]				=						spm.SentencePieceProcessor(**self.sp_model_kwargs						)
    self.sp_model.Load(str(lowerCamelCase__						)						)
    lowercase__       :       str				=						vocab_file
    # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
    # sentencepiece vocabulary (this is the case for <s> and </s>
    lowercase__       :       Optional[int]				=						{"""<s>NOTUSED""": 0, """<pad>""": 1, """</s>NOTUSED""": 2, """<unk>""": 3}
    lowercase__       :       int				=						len(self.fairseq_tokens_to_ids						)
    lowercase__       :       Dict				=						len(self.sp_model						) + len(self.fairseq_tokens_to_ids						)
    lowercase__       :       List[Any]				=						{v: k for k, v in self.fairseq_tokens_to_ids.items()}


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ = None						)      ->							List[int]:

    if token_ids_a is None:
     return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
    lowercase__       :       Any				=						[self.cls_token_id]
    lowercase__       :       str				=						[self.sep_token_id]
    return cls + token_ids_a + sep + sep + token_ids_a + sep


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ = None ,     lowerCamelCase__ = False						)      ->							List[int]:
    if already_has_special_tokens:
     return super().get_special_tokens_mask(
         token_ids_a=lowerCamelCase__ ,     token_ids_a=lowerCamelCase__ ,     already_has_special_tokens=lowerCamelCase__						)

    if token_ids_a is None:
     return [1] + ([0] * len(lowerCamelCase__						)) + [1]
    return [1] + ([0] * len(lowerCamelCase__						)) + [1, 1] + ([0] * len(lowerCamelCase__						)) + [1]


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ = None						)      ->							List[int]:
    lowercase__       :       int				=						[self.sep_token_id]
    lowercase__       :       Tuple				=						[self.cls_token_id]

    if token_ids_a is None:
     return len(cls + token_ids_a + sep						) * [0]
    return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]


   @property
   def 					UpperCAmelCase__(						self						)      ->							Optional[Any]:
    return len(self.fairseq_tokens_to_ids						) + len(self.sp_model						)


   def 					UpperCAmelCase__(						self						)      ->							Dict:
    lowercase__       :       Dict				=						{self.convert_ids_to_tokens(lowerCamelCase__						): i for i in range(self.vocab_size						)}
    vocab.update(self.added_tokens_encoder						)
    return vocab


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							List[str]:
    return self.sp_model.encode(lowerCamelCase__ ,     out_type=lowerCamelCase__						)


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							str:
    if token in self.fairseq_tokens_to_ids:
     return self.fairseq_tokens_to_ids[token]
    elif self.sp_model.PieceToId(lowerCamelCase__						) == 0:
     # Convert sentence piece unk token to fairseq unk token index
     return self.unk_token_id
    return self.fairseq_offset + self.sp_model.PieceToId(lowerCamelCase__						)


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							int:
    if index in self.fairseq_ids_to_tokens:
     return self.fairseq_ids_to_tokens[index]
    return self.sp_model.IdToPiece(index - self.fairseq_offset						)


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							str:
    lowercase__       :       Dict				=						[]
    lowercase__       :       Dict				=						""""""
    lowercase__       :       Tuple				=						False
    for token in tokens:
     # make sure that special tokens are not decoded using sentencepiece model
     if token in self.all_special_tokens:
      if not prev_is_special:
       out_string += " "
      out_string += self.sp_model.decode(lowerCamelCase__						) + token
      lowercase__       :       List[Any]				=						True
      lowercase__       :       str				=						[]
     else:
      current_sub_tokens.append(lowerCamelCase__						)
      lowercase__       :       Optional[Any]				=						False
    out_string += self.sp_model.decode(lowerCamelCase__						)
    return out_string.strip()


   def __getstate__(						self						)      ->							int:
    lowercase__       :       Dict				=						self.__dict__.copy()
    lowercase__       :       Union[str, Any]				=						None
    return state


   def __setstate__(						self ,     lowerCamelCase__						)      ->							int:
    lowercase__       :       Any				=						d

    # for backward compatibility
    if not hasattr(self ,     """sp_model_kwargs"""						):
     lowercase__       :       Tuple				=						{}

    lowercase__       :       Optional[Any]				=						spm.SentencePieceProcessor(**self.sp_model_kwargs						)
    self.sp_model.Load(self.vocab_file						)







   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ = None						)      ->							Tuple[str]:
    if not os.path.isdir(lowerCamelCase__						):
     logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''						)
     return
    lowercase__       :       Any				=						os.path.join(
        lowerCamelCase__ ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

    if os.path.abspath(self.vocab_file						) != os.path.abspath(lowerCamelCase__						) and os.path.isfile(self.vocab_file						):
     copyfile(self.vocab_file ,     lowerCamelCase__						)
    elif not os.path.isfile(self.vocab_file						):
     with open(lowerCamelCase__ ,     """wb"""						) as fi:
      lowercase__       :       int				=						self.sp_model.serialized_model_proto()
      fi.write(lowerCamelCase__						)

    return (out_vocab_file,)