





'''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     (    a_ ):






       """simple docstring"""
       _snake_case	:     int										=    ['image_processor', 'tokenizer']
       _snake_case	:     Any										=    'LayoutLMv2ImageProcessor'
       _snake_case	:     Any										=    ('LayoutXLMTokenizer', 'LayoutXLMTokenizerFast')





       def __init__(							self   :		int       , lowerCAmelCase__   :		int=None       , lowerCAmelCase__   :		Union[str, Any]=None       , **lowerCAmelCase__   :		Optional[int]     )    ->     Any:

            '''simple docstring'''




            if "feature_extractor" in kwargs:
                 warnings.warn(
                     '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                     ''' instead.'''       , _A       , )
                 _UpperCamelCase				    =       kwargs.pop('''feature_extractor'''     )

            _UpperCamelCase				    =       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__(_A       , _A     )





       def __call__(							self   :		Optional[Any]       , lowerCAmelCase__   :		List[str]       , lowerCAmelCase__   :		Optional[int] = None       , lowerCAmelCase__   :		Tuple = None       , lowerCAmelCase__   :		Tuple = None       , lowerCAmelCase__   :		Tuple = None       , lowerCAmelCase__   :		Dict = True       , lowerCAmelCase__   :		List[Any] = False       , lowerCAmelCase__   :		str = None       , lowerCAmelCase__   :		int = None       , lowerCAmelCase__   :		str = 0       , lowerCAmelCase__   :		Union[str, Any] = None       , lowerCAmelCase__   :		List[str] = None       , lowerCAmelCase__   :		str = None       , lowerCAmelCase__   :		Optional[Any] = False       , lowerCAmelCase__   :		Optional[int] = False       , lowerCAmelCase__   :		List[Any] = False       , lowerCAmelCase__   :		str = False       , lowerCAmelCase__   :		List[Any] = True       , lowerCAmelCase__   :		str = None       , **lowerCAmelCase__   :		Any       , )    ->     Optional[Any]:

            '''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
            _UpperCamelCase				    =       self.image_processor(images=_A       , return_tensors=_A     )

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

            _UpperCamelCase				    =       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=_A       , add_special_tokens=_A       , padding=_A       , truncation=_A       , max_length=_A       , stride=_A       , pad_to_multiple_of=_A       , return_token_type_ids=_A       , return_attention_mask=_A       , return_overflowing_tokens=_A       , return_special_tokens_mask=_A       , return_offsets_mapping=_A       , return_length=_A       , verbose=_A       , return_tensors=_A       , **_A       , )

            # add pixel values
            _UpperCamelCase				    =       features.pop('''pixel_values'''     )
            if return_overflowing_tokens is True:
                 _UpperCamelCase				    =       self.get_overflowing_images(_A       , encoded_inputs['''overflow_to_sample_mapping''']     )
            _UpperCamelCase				    =       images

            return encoded_inputs





       def snake_case__						(							self   :		Dict       , lowerCAmelCase__   :		Any       , lowerCAmelCase__   :		Tuple     )    ->     Optional[int]:

            '''simple docstring'''




            _UpperCamelCase				    =       []
            for sample_idx in overflow_to_sample_mapping:
                 images_with_overflow.append(images[sample_idx]     )

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

            return images_with_overflow





       def snake_case__						(							self   :		List[str]       , *lowerCAmelCase__   :		List[Any]       , **lowerCAmelCase__   :		int     )    ->     int:

            '''simple docstring'''




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





       def snake_case__						(							self   :		Any       , *lowerCAmelCase__   :		List[str]       , **lowerCAmelCase__   :		Optional[int]     )    ->     int:

            '''simple docstring'''




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





       @property
       def snake_case__						(							self   :		Union[str, Any]     )    ->     Optional[int]:

            '''simple docstring'''




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





       @property
       def snake_case__						(							self   :		List[Any]     )    ->     Dict:

            '''simple docstring'''




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







       @property
       def snake_case__						(							self   :		Dict     )    ->     Dict:

            '''simple docstring'''




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










from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

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


if is_tf_available():
				import tensorflow as tf

				from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel

@require_tf
class 	A_       :
	UpperCAmelCase__					=			MBartConfig
	UpperCAmelCase__					=			{}
	UpperCAmelCase__					=			'''gelu'''
	def __init__(    self							,					_A							,					_A=1_3							,					_A=7							,					_A=True							,					_A=False							,					_A=9_9							,					_A=3_2							,					_A=2							,					_A=4							,					_A=3_7							,					_A=0.1							,					_A=0.1							,					_A=2_0							,					_A=2							,					_A=1							,					_A=0							,					):






				'''simple docstring'''







				UpperCAmelCase   		=				parent
				UpperCAmelCase   		=				batch_size
				UpperCAmelCase   		=				seq_length
				UpperCAmelCase   		=				is_training
				UpperCAmelCase   		=				use_labels
				UpperCAmelCase   		=				vocab_size
				UpperCAmelCase   		=				hidden_size
				UpperCAmelCase   		=				num_hidden_layers
				UpperCAmelCase   		=				num_attention_heads
				UpperCAmelCase   		=				intermediate_size
				UpperCAmelCase   		=				hidden_dropout_prob
				UpperCAmelCase   		=				attention_probs_dropout_prob
				UpperCAmelCase   		=				max_position_embeddings
				UpperCAmelCase   		=				eos_token_id
				UpperCAmelCase   		=				pad_token_id
				UpperCAmelCase   		=				bos_token_id
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				ids_tensor([self.batch_size, self.seq_length - 1]							,					self.vocab_size		)
				UpperCAmelCase   		=				tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size		)							,					1		)
				UpperCAmelCase   		=				tf.concat([input_ids, eos_tensor]							,					axis=1		)

				UpperCAmelCase   		=				ids_tensor([self.batch_size, self.seq_length]							,					self.vocab_size		)

				UpperCAmelCase   		=				self.config_cls(
				    vocab_size=self.vocab_size							,					d_model=self.hidden_size							,					encoder_layers=self.num_hidden_layers							,					decoder_layers=self.num_hidden_layers							,					encoder_attention_heads=self.num_attention_heads							,					decoder_attention_heads=self.num_attention_heads							,					encoder_ffn_dim=self.intermediate_size							,					decoder_ffn_dim=self.intermediate_size							,					dropout=self.hidden_dropout_prob							,					attention_dropout=self.attention_probs_dropout_prob							,					max_position_embeddings=self.max_position_embeddings							,					eos_token_ids=[2]							,					bos_token_id=self.bos_token_id							,					pad_token_id=self.pad_token_id							,					decoder_start_token_id=self.pad_token_id							,					**self.config_updates							,					)
				UpperCAmelCase   		=				prepare_mbart_inputs_dict(_A							,					_A							,					_A		)
				return config, inputs_dict







	def      _lowercase				(    self							,					_A							,					_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				TFMBartModel(config=_A		).get_decoder()
				UpperCAmelCase   		=				inputs_dict['''input_ids''']

				UpperCAmelCase   		=				input_ids[:1, :]
				UpperCAmelCase   		=				inputs_dict['''attention_mask'''][:1, :]
				UpperCAmelCase   		=				inputs_dict['''head_mask''']
				UpperCAmelCase   		=				1

				# first forward pass
				UpperCAmelCase   		=				model(_A							,					attention_mask=_A							,					head_mask=_A							,					use_cache=_A		)

				UpperCAmelCase		,			UpperCAmelCase   		=				outputs.to_tuple()
				UpperCAmelCase   		=				past_key_values[1]





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__=None			,			UpperCamelCase__=None			,			UpperCamelCase__=None			,			UpperCamelCase__=None			,			UpperCamelCase__=None			,			)  ->				List[str]:







			'''simple docstring'''

			if attention_mask is None:
						UpperCAmelCase   		=				tf.cast(tf.math.not_equal(UpperCamelCase__			,			config.pad_token_id )			,			tf.inta )
			if decoder_attention_mask is None:
						UpperCAmelCase   		=				tf.concat(
						    [
						        tf.ones(decoder_input_ids[:, :1].shape			,			dtype=tf.inta ),
						        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]			,			config.pad_token_id )			,			tf.inta ),
						    ]			,			axis=-1			,			)
			if head_mask is None:
						UpperCAmelCase   		=				tf.ones((config.encoder_layers, config.encoder_attention_heads) )
			if decoder_head_mask is None:
						UpperCAmelCase   		=				tf.ones((config.decoder_layers, config.decoder_attention_heads) )
			if cross_attn_head_mask is None:
						UpperCAmelCase   		=				tf.ones((config.decoder_layers, config.decoder_attention_heads) )
			return {
			    "input_ids": input_ids,
			    "decoder_input_ids": decoder_input_ids,
			    "attention_mask": attention_mask,
			    "decoder_attention_mask": decoder_attention_mask,
			    "head_mask": head_mask,
			    "decoder_head_mask": decoder_head_mask,
			    "cross_attn_head_mask": cross_attn_head_mask,
			}

@require_tf
class 	A_       (a_     ,							a_     ,							unittest.TestCase			):
	UpperCAmelCase__					=			(TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
	UpperCAmelCase__					=			(TFMBartForConditionalGeneration,) if is_tf_available() else ()
	UpperCAmelCase__					=			(
	    {
	        '''conversational''': TFMBartForConditionalGeneration,
	        '''feature-extraction''': TFMBartModel,
	        '''summarization''': TFMBartForConditionalGeneration,
	        '''text2text-generation''': TFMBartForConditionalGeneration,
	        '''translation''': TFMBartForConditionalGeneration,
	    }
	    if is_tf_available()
	    else {}
	)
	UpperCAmelCase__					=			True
	UpperCAmelCase__					=			False
	UpperCAmelCase__					=			False
	def      _lowercase				(    self							,					_A							,					_A							,					_A							,					_A							,					_A		):






				'''simple docstring'''







				if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
							# Exception encountered when calling layer '...'
							return True

				return False
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				TFMBartModelTester(self		)
				UpperCAmelCase   		=				ConfigTester(self							,					config_class=_A		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				self.config_tester.run_common_tests()







	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.model_tester.prepare_config_and_inputs_for_common()
				self.model_tester.check_decoder_model_past_large_inputs(*_A		)

@require_sentencepiece
@require_tokenizers
@require_tf
class 	A_       (unittest.TestCase			):
	UpperCAmelCase__					=			[
	    ''' UN Chief Says There Is No Military Solution in Syria''',
	]
	UpperCAmelCase__					=			[
	    '''Şeful ONU declară că nu există o soluţie militară în Siria''',
	]
	UpperCAmelCase__					=			'''facebook/mbart-large-en-ro'''
	@cached_property
	def      _lowercase				(    self		):






				'''simple docstring'''







				return AutoTokenizer.from_pretrained(self.model_name		)
	@cached_property
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name		)
				return model
	def      _lowercase				(    self							,					**_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.translate_src_text(**_A		)
				self.assertListEqual(self.expected_text							,					_A		)
	def      _lowercase				(    self							,					**_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.tokenizer(self.src_text							,					**_A							,					return_tensors='''tf'''		)
				UpperCAmelCase   		=				self.model.generate(
				    model_inputs.input_ids							,					attention_mask=model_inputs.attention_mask							,					num_beams=2		)
				UpperCAmelCase   		=				self.tokenizer.batch_decode(_A							,					skip_special_tokens=_A		)
				return generated_words







	@slow
	def      _lowercase				(    self		):






				'''simple docstring'''







				self._assert_generated_batch_equal_expected()



