
'''simple docstring'''







import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 					lowerCAmelCase__  (    lowerCAmelCase_	):
					lowerCAmelCase_	  =	["image_processor", "tokenizer"]
					lowerCAmelCase_	  =	"LayoutLMv2ImageProcessor"
					lowerCAmelCase_	  =	("LayoutLMv2Tokenizer", "LayoutLMv2TokenizerFast")






					def __init__(  self							,				__SCREAMING_SNAKE_CASE=None							,				__SCREAMING_SNAKE_CASE=None							,				**__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											lowercase_      :	Optional[int]						=			None
											if "feature_extractor" in kwargs:
																	warnings.warn(
																	    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																	    ''' instead.'''							,				__UpperCamelCase							,				)
																	lowercase_      :	Tuple						=			kwargs.pop('''feature_extractor'''						)

											lowercase_      :	Any						=			image_processor if image_processor is not None else feature_extractor
											if image_processor is None:
																	raise ValueError('''You need to specify an `image_processor`.'''						)
											if tokenizer is None:
																	raise ValueError('''You need to specify a `tokenizer`.'''						)

											super().__init__(__UpperCamelCase							,				__UpperCamelCase						)






					def __call__(  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE = None							,				__SCREAMING_SNAKE_CASE = None							,				__SCREAMING_SNAKE_CASE = None							,				__SCREAMING_SNAKE_CASE = None							,				__SCREAMING_SNAKE_CASE = True							,				__SCREAMING_SNAKE_CASE = False							,				__SCREAMING_SNAKE_CASE = False							,				__SCREAMING_SNAKE_CASE = None							,				__SCREAMING_SNAKE_CASE = 0							,				__SCREAMING_SNAKE_CASE = None							,				__SCREAMING_SNAKE_CASE = None							,				__SCREAMING_SNAKE_CASE = None							,				__SCREAMING_SNAKE_CASE = False							,				__SCREAMING_SNAKE_CASE = False							,				__SCREAMING_SNAKE_CASE = False							,				__SCREAMING_SNAKE_CASE = False							,				__SCREAMING_SNAKE_CASE = True							,				__SCREAMING_SNAKE_CASE = None							,				**__SCREAMING_SNAKE_CASE							,				):


											"""simple docstring"""





											if self.image_processor.apply_ocr and (boxes is not None):
																	raise ValueError(
																	    '''You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.'''						)

											if self.image_processor.apply_ocr and (word_labels is not None):
																	raise ValueError(
																	    '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''						)

											if return_overflowing_tokens is True and return_offsets_mapping is False:
																	raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.'''						)

											# first, apply the image processor
											lowercase_      :	Union[str, Any]						=			self.image_processor(images=__UpperCamelCase							,				return_tensors=__UpperCamelCase						)

											# second, apply the tokenizer
											if text is not None and self.image_processor.apply_ocr and text_pair is None:
																	if isinstance(__UpperCamelCase							,				__UpperCamelCase						):
																							lowercase_      :	List[Any]						=			[text]  # add batch dimension (as the image processor always adds a batch dimension)
																	lowercase_      :	List[str]						=			features['''words''']

											lowercase_      :	Tuple						=			self.tokenizer(
											    text=text if text is not None else features['''words''']							,				text_pair=text_pair if text_pair is not None else None							,				boxes=boxes if boxes is not None else features['''boxes''']							,				word_labels=__UpperCamelCase							,				add_special_tokens=__UpperCamelCase							,				padding=__UpperCamelCase							,				truncation=__UpperCamelCase							,				max_length=__UpperCamelCase							,				stride=__UpperCamelCase							,				pad_to_multiple_of=__UpperCamelCase							,				return_token_type_ids=__UpperCamelCase							,				return_attention_mask=__UpperCamelCase							,				return_overflowing_tokens=__UpperCamelCase							,				return_special_tokens_mask=__UpperCamelCase							,				return_offsets_mapping=__UpperCamelCase							,				return_length=__UpperCamelCase							,				verbose=__UpperCamelCase							,				return_tensors=__UpperCamelCase							,				**__UpperCamelCase							,				)

											# add pixel values
											lowercase_      :	Optional[int]						=			features.pop('''pixel_values'''						)
											if return_overflowing_tokens is True:
																	lowercase_      :	Dict						=			self.get_overflowing_images(__UpperCamelCase							,				encoded_inputs['''overflow_to_sample_mapping''']						)
											lowercase_      :	str						=			images

											return encoded_inputs






					def       _snake_case       (  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											lowercase_      :	Optional[Any]						=			[]
											for sample_idx in overflow_to_sample_mapping:
																	images_with_overflow.append(images[sample_idx]						)

											if len(__UpperCamelCase						) != len(__UpperCamelCase						):
																	raise ValueError(
																	    '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
																	    F''' {len(__UpperCamelCase						)} and {len(__UpperCamelCase						)}'''						)

											return images_with_overflow






					def       _snake_case       (  self							,				*__SCREAMING_SNAKE_CASE							,				**__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											return self.tokenizer.batch_decode(*__UpperCamelCase							,				**__UpperCamelCase						)






					def       _snake_case       (  self							,				*__SCREAMING_SNAKE_CASE							,				**__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											return self.tokenizer.decode(*__UpperCamelCase							,				**__UpperCamelCase						)






					@property
					def       _snake_case       (  self						):


											"""simple docstring"""





											return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]






					@property
					def       _snake_case       (  self						):


											"""simple docstring"""





											warnings.warn(
											    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''							,				__UpperCamelCase							,				)
											return self.image_processor_class






					@property
					def       _snake_case       (  self						):


											"""simple docstring"""





											warnings.warn(
											    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''							,				__UpperCamelCase							,				)
											return self.image_processor








'''simple docstring'''







import unittest

import numpy as np

from transformers import ElectraConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
      from transformers.models.electra.modeling_flax_electra import (
          FlaxElectraForCausalLM,
          FlaxElectraForMaskedLM,
          FlaxElectraForMultipleChoice,
          FlaxElectraForPreTraining,
          FlaxElectraForQuestionAnswering,
          FlaxElectraForSequenceClassification,
          FlaxElectraForTokenClassification,
          FlaxElectraModel,
      )

class    lowerCamelCase_				(						unittest.TestCase	):






   '''simple docstring'''

   def __init__(	self  ,    __UpperCamelCase  ,    __UpperCamelCase=13  ,    __UpperCamelCase=7  ,    __UpperCamelCase=True  ,    __UpperCamelCase=True  ,    __UpperCamelCase=True  ,    __UpperCamelCase=True  ,    __UpperCamelCase=99  ,    __UpperCamelCase=24  ,    __UpperCamelCase=32  ,    __UpperCamelCase=5  ,    __UpperCamelCase=4  ,    __UpperCamelCase=37  ,    __UpperCamelCase="gelu"  ,    __UpperCamelCase=0.1  ,    __UpperCamelCase=0.1  ,    __UpperCamelCase=5_12  ,    __UpperCamelCase=16  ,    __UpperCamelCase=2  ,    __UpperCamelCase=0.02  ,    __UpperCamelCase=4  ,    ):
       UpperCamelCase__	     =      parent
       UpperCamelCase__	     =      batch_size
       UpperCamelCase__	     =      seq_length
       UpperCamelCase__	     =      is_training
       UpperCamelCase__	     =      use_attention_mask
       UpperCamelCase__	     =      use_token_type_ids
       UpperCamelCase__	     =      use_labels
       UpperCamelCase__	     =      vocab_size
       UpperCamelCase__	     =      hidden_size
       UpperCamelCase__	     =      embedding_size
       UpperCamelCase__	     =      num_hidden_layers
       UpperCamelCase__	     =      num_attention_heads
       UpperCamelCase__	     =      intermediate_size
       UpperCamelCase__	     =      hidden_act
       UpperCamelCase__	     =      hidden_dropout_prob
       UpperCamelCase__	     =      attention_probs_dropout_prob
       UpperCamelCase__	     =      max_position_embeddings
       UpperCamelCase__	     =      type_vocab_size
       UpperCamelCase__	     =      type_sequence_label_size
       UpperCamelCase__	     =      initializer_range
       UpperCamelCase__	     =      num_choices


   def 						_UpperCamelCase							(	self			):
       UpperCamelCase__	     =      ids_tensor([self.batch_size, self.seq_length]  ,    self.vocab_size			)

       UpperCamelCase__	     =      None
       if self.use_attention_mask:
           UpperCamelCase__	     =      random_attention_mask([self.batch_size, self.seq_length]			)

       UpperCamelCase__	     =      None
       if self.use_token_type_ids:
           UpperCamelCase__	     =      ids_tensor([self.batch_size, self.seq_length]  ,    self.type_vocab_size			)

       UpperCamelCase__	     =      ElectraConfig(
           vocab_size=self.vocab_size  ,    hidden_size=self.hidden_size  ,    embedding_size=self.embedding_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  ,    )

       return config, input_ids, token_type_ids, attention_mask







   def 						_UpperCamelCase							(	self			):
       UpperCamelCase__	     =      self.prepare_config_and_inputs()
       UpperCamelCase__     ,	UpperCamelCase__     ,	UpperCamelCase__     ,	UpperCamelCase__	     =      config_and_inputs
       UpperCamelCase__	     =      {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
       return config, inputs_dict

@require_flax
class    lowerCamelCase_				(						lowerCAmelCase_       ,     unittest.TestCase	):






   '''simple docstring'''

   A			:  Dict					=						True

   A			:  Optional[Any]					=						(
       (
           FlaxElectraModel,
           FlaxElectraForCausalLM,
           FlaxElectraForMaskedLM,
           FlaxElectraForPreTraining,
           FlaxElectraForTokenClassification,
           FlaxElectraForQuestionAnswering,
           FlaxElectraForMultipleChoice,
           FlaxElectraForSequenceClassification,
       )
       if is_flax_available()
       else ()
   )


   def 						_UpperCamelCase							(	self			):
       UpperCamelCase__	     =      FlaxElectraModelTester(self			)







   @slow
   def 						_UpperCamelCase							(	self			):
       for model_class_name in self.all_model_classes:
           if model_class_name == FlaxElectraForMaskedLM:
               UpperCamelCase__	     =      model_class_name.from_pretrained("google/electra-small-generator"			)
           else:
               UpperCamelCase__	     =      model_class_name.from_pretrained("google/electra-small-discriminator"			)
           UpperCamelCase__	     =      model(np.ones((1, 1)			)			)
           self.assertIsNotNone(__UpperCamelCase			)




