


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        _lowercase      (			A__       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			str						=		['''image_processor''', '''tokenizer''']
				SCREAMING_SNAKE_CASE__				:			Dict						=		'''LayoutLMv2ImageProcessor'''
				SCREAMING_SNAKE_CASE__				:			Dict						=		('''LayoutXLMTokenizer''', '''LayoutXLMTokenizerFast''')


				def __init__(       self			:Union[str, Any]			,			lowerCAmelCase__			:int=None			,			lowerCAmelCase__			:List[str]=None			,			**lowerCAmelCase__			:Optional[int]	)		->			Union[str, Any]:
										if "feature_extractor" in kwargs:
																warnings.warn(
																    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																    ''' instead.'''			,			lowerCAmelCase__			,			)
																__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		kwargs.pop('''feature_extractor'''	)

										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		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__(lowerCAmelCase__			,			lowerCAmelCase__	)


				def __call__(       self			:Optional[int]			,			lowerCAmelCase__			:List[Any]			,			lowerCAmelCase__			:Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None			,			lowerCAmelCase__			:Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None			,			lowerCAmelCase__			:Union[List[List[int]], List[List[List[int]]]] = None			,			lowerCAmelCase__			:Optional[Union[List[int], List[List[int]]]] = None			,			lowerCAmelCase__			:bool = True			,			lowerCAmelCase__			:Union[bool, str, PaddingStrategy] = False			,			lowerCAmelCase__			:Union[bool, str, TruncationStrategy] = None			,			lowerCAmelCase__			:Optional[int] = None			,			lowerCAmelCase__			:int = 0			,			lowerCAmelCase__			:Optional[int] = None			,			lowerCAmelCase__			:Optional[bool] = None			,			lowerCAmelCase__			:Optional[bool] = None			,			lowerCAmelCase__			:bool = False			,			lowerCAmelCase__			:bool = False			,			lowerCAmelCase__			:bool = False			,			lowerCAmelCase__			:bool = False			,			lowerCAmelCase__			:bool = True			,			lowerCAmelCase__			:Optional[Union[str, TensorType]] = None			,			**lowerCAmelCase__			:List[Any]			,			)		->			BatchEncoding:
										# verify input
										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
										__SCREAMING_SNAKE_CASE						:     str					=		self.image_processor(images=lowerCAmelCase__			,			return_tensors=lowerCAmelCase__	)

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

										__SCREAMING_SNAKE_CASE						:     str					=		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=lowerCAmelCase__			,			add_special_tokens=lowerCAmelCase__			,			padding=lowerCAmelCase__			,			truncation=lowerCAmelCase__			,			max_length=lowerCAmelCase__			,			stride=lowerCAmelCase__			,			pad_to_multiple_of=lowerCAmelCase__			,			return_token_type_ids=lowerCAmelCase__			,			return_attention_mask=lowerCAmelCase__			,			return_overflowing_tokens=lowerCAmelCase__			,			return_special_tokens_mask=lowerCAmelCase__			,			return_offsets_mapping=lowerCAmelCase__			,			return_length=lowerCAmelCase__			,			verbose=lowerCAmelCase__			,			return_tensors=lowerCAmelCase__			,			**lowerCAmelCase__			,			)

										# add pixel values
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		features.pop('''pixel_values'''	)
										if return_overflowing_tokens is True:
																__SCREAMING_SNAKE_CASE						:     List[str]					=		self.get_overflowing_images(lowerCAmelCase__			,			encoded_inputs['''overflow_to_sample_mapping''']	)
										__SCREAMING_SNAKE_CASE						:     List[str]					=		images

										return encoded_inputs


				def 		__magic_name__(       self			:int			,			lowerCAmelCase__			:List[str]			,			lowerCAmelCase__			:Optional[int]	)		->			Optional[int]:
										# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
										__SCREAMING_SNAKE_CASE						:     Dict					=		[]
										for sample_idx in overflow_to_sample_mapping:
																images_with_overflow.append(images[sample_idx]	)

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

										return images_with_overflow


				def 		__magic_name__(       self			:Any			,			*lowerCAmelCase__			:Optional[int]			,			**lowerCAmelCase__			:Optional[Any]	)		->			Union[str, Any]:
										return self.tokenizer.batch_decode(*lowerCAmelCase__			,			**lowerCAmelCase__	)


				def 		__magic_name__(       self			:Union[str, Any]			,			*lowerCAmelCase__			:Tuple			,			**lowerCAmelCase__			:Union[str, Any]	)		->			str:
										return self.tokenizer.decode(*lowerCAmelCase__			,			**lowerCAmelCase__	)


				@property
				def 		__magic_name__(       self			:Union[str, Any]	)		->			List[Any]:
										return ["input_ids", "bbox", "attention_mask", "image"]


				@property
				def 		__magic_name__(       self			:str	)		->			Dict:
										warnings.warn(
										    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''			,			lowerCAmelCase__			,			)
										return self.image_processor_class



				@property
				def 		__magic_name__(       self			:Tuple	)		->			List[Any]:
										warnings.warn(
										    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''			,			lowerCAmelCase__			,			)
										return self.image_processor




import copy
import tempfile
import unittest

from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError

from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test






class   _lowerCamelCase (					unittest.TestCase			):




       """simple docstring"""



       @parameterized.expand([(None,), ('''foo.json''',)]		)
       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->Tuple:




         '''simple docstring'''





         A_      :				Dict		     =					GenerationConfig(
             do_sample=_SCREAMING_SNAKE_CASE				,       temperature=0.7				,       length_penalty=1.0				,       bad_words_ids=[[1, 2, 3], [4, 5]]				,       )
         with tempfile.TemporaryDirectory() as tmp_dir:
           config.save_pretrained(_SCREAMING_SNAKE_CASE				,       config_name=_SCREAMING_SNAKE_CASE		)
           A_      :				Tuple		     =					GenerationConfig.from_pretrained(_SCREAMING_SNAKE_CASE				,       config_name=_SCREAMING_SNAKE_CASE		)

         # Checks parameters that were specified
         self.assertEqual(loaded_config.do_sample				,       _SCREAMING_SNAKE_CASE		)
         self.assertEqual(loaded_config.temperature				,       0.7		)
         self.assertEqual(loaded_config.length_penalty				,       1.0		)
         self.assertEqual(loaded_config.bad_words_ids				,       [[1, 2, 3], [4, 5]]		)

         # Checks parameters that were not specified (defaults)
         self.assertEqual(loaded_config.top_k				,       50		)
         self.assertEqual(loaded_config.max_length				,       20		)
         self.assertEqual(loaded_config.max_time				,       _SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self		)->Dict:




         '''simple docstring'''





         A_      :				str		     =					AutoConfig.from_pretrained('''gpt2'''		)
         A_      :				int		     =					GenerationConfig.from_model_config(_SCREAMING_SNAKE_CASE		)
         A_      :				Dict		     =					GenerationConfig()

         # The generation config has loaded a few non-default parameters from the model config
         self.assertNotEqual(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)

         # One of those parameters is eos_token_id -- check if it matches
         self.assertNotEqual(generation_config_from_model.eos_token_id				,       default_generation_config.eos_token_id		)
         self.assertEqual(generation_config_from_model.eos_token_id				,       model_config.eos_token_id		)



       def        _snake_case       (      self		)->Optional[int]:




         '''simple docstring'''





         A_      :				List[Any]		     =					GenerationConfig()
         A_      :				Optional[Any]		     =					{
             '''max_new_tokens''': 1024,
             '''foo''': '''bar''',
         }
         A_      :				Dict		     =					copy.deepcopy(_SCREAMING_SNAKE_CASE		)
         A_      :				Optional[int]		     =					generation_config.update(**_SCREAMING_SNAKE_CASE		)

         # update_kwargs was not modified (no side effects)
         self.assertEqual(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)

         # update_kwargs was used to update the config on valid attributes
         self.assertEqual(generation_config.max_new_tokens				,       1024		)

         # `.update()` returns a dictionary of unused kwargs
         self.assertEqual(_SCREAMING_SNAKE_CASE				,       {'''foo''': '''bar'''}		)



       def        _snake_case       (      self		)->str:




         '''simple docstring'''





         A_      :				List[str]		     =					GenerationConfig()
         A_      :				int		     =					'''bar'''

         with tempfile.TemporaryDirectory('''test-generation-config'''		) as tmp_dir:
           generation_config.save_pretrained(_SCREAMING_SNAKE_CASE		)

           A_      :				Optional[Any]		     =					GenerationConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
         # update_kwargs was used to update the config on valid attributes
         self.assertEqual(new_config.foo				,       '''bar'''		)

         A_      :				Optional[int]		     =					GenerationConfig.from_model_config(_SCREAMING_SNAKE_CASE		)
         assert not hasattr(_SCREAMING_SNAKE_CASE				,       '''foo'''		)  # no new kwargs should be initialized if from config







       def        _snake_case       (      self		)->List[Any]:




         '''simple docstring'''





         A_      :				str		     =					GenerationConfig()
         self.assertEqual(default_config.temperature				,       1.0		)
         self.assertEqual(default_config.do_sample				,       _SCREAMING_SNAKE_CASE		)
         self.assertEqual(default_config.num_beams				,       1		)

         A_      :				str		     =					GenerationConfig(
             do_sample=_SCREAMING_SNAKE_CASE				,       temperature=0.7				,       length_penalty=1.0				,       bad_words_ids=[[1, 2, 3], [4, 5]]				,       )
         self.assertEqual(config.temperature				,       0.7		)
         self.assertEqual(config.do_sample				,       _SCREAMING_SNAKE_CASE		)
         self.assertEqual(config.num_beams				,       1		)

         with tempfile.TemporaryDirectory() as tmp_dir:
           config.save_pretrained(_SCREAMING_SNAKE_CASE		)
           A_      :				Dict		     =					GenerationConfig.from_pretrained(_SCREAMING_SNAKE_CASE				,       temperature=1.0		)

         self.assertEqual(loaded_config.temperature				,       1.0		)
         self.assertEqual(loaded_config.do_sample				,       _SCREAMING_SNAKE_CASE		)
         self.assertEqual(loaded_config.num_beams				,       1		)  # default value






@is_staging_test
class   _lowerCamelCase (					unittest.TestCase			):




       """simple docstring"""



       @classmethod
       def        _snake_case       (      cls		)->Optional[int]:




         '''simple docstring'''





         A_      :				str		     =					TOKEN
         HfFolder.save_token(_SCREAMING_SNAKE_CASE		)



       @classmethod
       def        _snake_case       (      cls		)->List[Any]:




         '''simple docstring'''





         try:
           delete_repo(token=cls._token				,       repo_id='''test-generation-config'''		)
         except HTTPError:
           pass

         try:
           delete_repo(token=cls._token				,       repo_id='''valid_org/test-generation-config-org'''		)
         except HTTPError:
           pass



       def        _snake_case       (      self		)->List[Any]:




         '''simple docstring'''





         A_      :				List[Any]		     =					GenerationConfig(
             do_sample=_SCREAMING_SNAKE_CASE				,       temperature=0.7				,       length_penalty=1.0				,       )
         config.push_to_hub('''test-generation-config'''				,       use_auth_token=self._token		)

         A_      :				Optional[int]		     =					GenerationConfig.from_pretrained(F'''{USER}/test-generation-config'''		)
         for k, v in config.to_dict().items():
           if k != "transformers_version":
             self.assertEqual(_SCREAMING_SNAKE_CASE				,       getattr(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)		)

        # Reset repo
         delete_repo(token=self._token				,       repo_id='''test-generation-config'''		)

         # Push to hub via save_pretrained
         with tempfile.TemporaryDirectory() as tmp_dir:
           config.save_pretrained(
               _SCREAMING_SNAKE_CASE				,       repo_id='''test-generation-config'''				,       push_to_hub=_SCREAMING_SNAKE_CASE				,       use_auth_token=self._token		)

         A_      :				Optional[Any]		     =					GenerationConfig.from_pretrained(F'''{USER}/test-generation-config'''		)
         for k, v in config.to_dict().items():
           if k != "transformers_version":
             self.assertEqual(_SCREAMING_SNAKE_CASE				,       getattr(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)		)







       def        _snake_case       (      self		)->List[str]:




         '''simple docstring'''





         A_      :				Tuple		     =					GenerationConfig(
             do_sample=_SCREAMING_SNAKE_CASE				,       temperature=0.7				,       length_penalty=1.0				,       )
         config.push_to_hub('''valid_org/test-generation-config-org'''				,       use_auth_token=self._token		)

         A_      :				Union[str, Any]		     =					GenerationConfig.from_pretrained('''valid_org/test-generation-config-org'''		)
         for k, v in config.to_dict().items():
           if k != "transformers_version":
             self.assertEqual(_SCREAMING_SNAKE_CASE				,       getattr(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)		)

        # Reset repo
         delete_repo(token=self._token				,       repo_id='''valid_org/test-generation-config-org'''		)

         # Push to hub via save_pretrained
         with tempfile.TemporaryDirectory() as tmp_dir:
           config.save_pretrained(
               _SCREAMING_SNAKE_CASE				,       repo_id='''valid_org/test-generation-config-org'''				,       push_to_hub=_SCREAMING_SNAKE_CASE				,       use_auth_token=self._token		)

         A_      :				Dict		     =					GenerationConfig.from_pretrained('''valid_org/test-generation-config-org'''		)
         for k, v in config.to_dict().items():
           if k != "transformers_version":
             self.assertEqual(_SCREAMING_SNAKE_CASE				,       getattr(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)		)




