
'''simple docstring'''




from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


lowercase			:				List[str]    		=			logging.get_logger(__name__)

lowercase			:				str    		=			{
    'EleutherAI/gpt-neo-1.3B': 'https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}





class  A				(							__snake_case						):
     __magic_name__						=		'''gpt_neo'''
     __magic_name__						=		['''past_key_values''']
     __magic_name__						=		{'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}





     def __init__(							self						,			SCREAMING_SNAKE_CASE=50257						,			SCREAMING_SNAKE_CASE=2048						,			SCREAMING_SNAKE_CASE=2048						,			SCREAMING_SNAKE_CASE=24						,			SCREAMING_SNAKE_CASE=[[["global", "local"], 12]]						,			SCREAMING_SNAKE_CASE=16						,			SCREAMING_SNAKE_CASE=None						,			SCREAMING_SNAKE_CASE=256						,			SCREAMING_SNAKE_CASE="gelu_new"						,			SCREAMING_SNAKE_CASE=0.0						,			SCREAMING_SNAKE_CASE=0.0						,			SCREAMING_SNAKE_CASE=0.0						,			SCREAMING_SNAKE_CASE=0.1						,			SCREAMING_SNAKE_CASE=1e-5						,			SCREAMING_SNAKE_CASE=0.02						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=50256						,			SCREAMING_SNAKE_CASE=50256						,			**SCREAMING_SNAKE_CASE						,			) ->				int:






         """simple docstring"""






         A       :   Union[str, Any]					=						vocab_size
         A       :   Optional[Any]					=						max_position_embeddings
         A       :   Dict					=						hidden_size
         A       :   Optional[Any]					=						num_layers
         A       :   Tuple					=						num_heads
         A       :   int					=						intermediate_size
         A       :   Optional[Any]					=						window_size
         A       :   List[Any]					=						activation_function
         A       :   Union[str, Any]					=						resid_dropout
         A       :   Any					=						embed_dropout
         A       :   List[Any]					=						attention_dropout
         A       :   str					=						classifier_dropout
         A       :   List[Any]					=						layer_norm_epsilon
         A       :   str					=						initializer_range
         A       :   List[str]					=						use_cache

         A       :   Optional[int]					=						bos_token_id
         A       :   List[Any]					=						eos_token_id

         A       :   int					=						attention_types
         A       :   int					=						self.expand_attention_types_params(SCREAMING_SNAKE_CASE	)

         if len(self.attention_layers	) != self.num_layers:
             raise ValueError(
                 '''Configuration for convolutional module is incorrect. '''
                 '''It is required that `len(config.attention_layers)` == `config.num_layers` '''
                 F'but is `len(config.attention_layers) = {len(self.attention_layers	)}`, '
                 F'`config.num_layers = {self.num_layers}`. '
                 '''`config.attention_layers` is prepared using `config.attention_types`. '''
                 '''Please verify the value of `config.attention_types` argument.'''	)

         super().__init__(bos_token_id=SCREAMING_SNAKE_CASE						,			eos_token_id=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)





     @staticmethod
     def 				__lowerCAmelCase							(							SCREAMING_SNAKE_CASE	) ->				int:






         """simple docstring"""






         A       :   List[str]					=						[]
         for item in attention_types:
             for _ in range(item[1]	):
                 attentions.extend(item[0]	)
         return attentions







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__		,				snake_case__						):




    '''simple docstring'''



    import torch

    A       :   Tuple					=						input.size()
    A       :   Union[str, Any]					=						len(snake_case__						)
    A       :   List[str]					=						shape[dimension]

    A       :   Union[str, Any]					=						torch.arange(0		,				snake_case__		,				snake_case__						)
    A       :   List[str]					=						torch.div(sizedim - size		,				snake_case__		,				rounding_mode='''floor'''						) + 1
    A       :   Optional[int]					=						torch.arange(snake_case__						) + low_indices[:min_length][:, None]

    A       :   str					=						[slice(snake_case__						)] * rank
    A       :   List[Any]					=						indices
    A       :   Union[str, Any]					=						input[s]

    A       :   List[str]					=						list(range(0		,				rank + 1						)						)
    perm.append(perm.pop(dimension + 1						)						)

    return sliced.permute(snake_case__						)







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



    import torch

    A       :   List[str]					=						torch.arange(1		,				snake_case__						)
    A       :   Optional[int]					=						torch.remainder(snake_case__		,				snake_case__						)
    A       :   Optional[int]					=						remainders == 0
    A       :   Optional[Any]					=						candidates[divisor_indices]
    A       :   Optional[int]					=						torch.max(snake_case__						)
    return largest_divisor, torch.div(snake_case__		,				snake_case__		,				rounding_mode='''floor'''						)







class  A				(							__snake_case						):





     @property
     def 				__lowerCAmelCase							(							self	) ->				Mapping[str, Mapping[int, str]]:






         """simple docstring"""






         A       :   Tuple					=						OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}	)
         if self.use_past:
             self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE						,			direction='''inputs'''	)
             A       :   Optional[Any]					=						{0: '''batch''', 1: '''past_sequence + sequence'''}
         else:
             A       :   Dict					=						{0: '''batch''', 1: '''sequence'''}

         return common_inputs





     @property
     def 				__lowerCAmelCase							(							self	) ->				int:






         """simple docstring"""






         return self._config.num_heads





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = -1						,			SCREAMING_SNAKE_CASE = -1						,			SCREAMING_SNAKE_CASE = False						,			SCREAMING_SNAKE_CASE = None						,			) ->				Mapping[str, Any]:






         """simple docstring"""






         A       :   List[str]					=						super(SCREAMING_SNAKE_CASE						,			self	).generate_dummy_inputs(
             SCREAMING_SNAKE_CASE						,			batch_size=SCREAMING_SNAKE_CASE						,			seq_length=SCREAMING_SNAKE_CASE						,			is_pair=SCREAMING_SNAKE_CASE						,			framework=SCREAMING_SNAKE_CASE	)

         # We need to order the input in the way they appears in the forward()
         A       :   Any					=						OrderedDict({'''input_ids''': common_inputs['''input_ids''']}	)

         # Need to add the past_keys
         if self.use_past:
             if not is_torch_available():
                 raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''	)
             else:
                 import torch

                 A,  A       :   Dict					=						common_inputs['''input_ids'''].shape
                 # Not using the same length for past_key_values
                 A       :   str					=						seqlen + 2
                 A       :   List[Any]					=						(
                     batch,
                     self.num_attention_heads,
                     past_key_values_length,
                     self._config.hidden_size // self.num_attention_heads,
                 )
                 A       :   Any					=						[
                     (torch.zeros(SCREAMING_SNAKE_CASE	), torch.zeros(SCREAMING_SNAKE_CASE	)) for _ in range(self.num_layers	)
                 ]

         A       :   str					=						common_inputs['''attention_mask''']
         if self.use_past:
             A       :   Optional[int]					=						ordered_inputs['''attention_mask'''].dtype
             A       :   List[str]					=						torch.cat(
                 [ordered_inputs['''attention_mask'''], torch.ones(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			dtype=SCREAMING_SNAKE_CASE	)]						,			dim=1	)

         return ordered_inputs





     @property
     def 				__lowerCAmelCase							(							self	) ->				int:






         """simple docstring"""






         return 13





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   snake_case_       (		__A       ):
   __A	:		Optional[Any]         =				["image_processor", "tokenizer"]
   __A	:		Tuple         =				"LayoutLMv3ImageProcessor"
   __A	:		List[Any]         =				("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")






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

       lowercase__ :   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__(lowercase_	,						lowercase_  )






   def __call__(      self				:     Dict	,						lowercase_				:     Optional[Any]	,						lowercase_				:     Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None	,						lowercase_				:     Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None	,						lowercase_				:     Union[List[List[int]], List[List[List[int]]]] = None	,						lowercase_				:     Optional[Union[List[int], List[List[int]]]] = None	,						lowercase_				:     bool = True	,						lowercase_				:     Union[bool, str, PaddingStrategy] = False	,						lowercase_				:     Union[bool, str, TruncationStrategy] = None	,						lowercase_				:     Optional[int] = None	,						lowercase_				:     int = 0	,						lowercase_				:     Optional[int] = None	,						lowercase_				:     Optional[bool] = None	,						lowercase_				:     Optional[bool] = None	,						lowercase_				:     bool = False	,						lowercase_				:     bool = False	,						lowercase_				:     bool = False	,						lowercase_				:     bool = False	,						lowercase_				:     bool = True	,						lowercase_				:     Optional[Union[str, TensorType]] = None	,						**lowercase_				:     Dict	,						)					->						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."  )

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

       # second, apply the tokenizer
       if text is not None and self.image_processor.apply_ocr and text_pair is None:
           if isinstance(lowercase_	,						lowercase_  ):
               lowercase__ :   Optional[Any]    					=				[text]  # add batch dimension (as the image processor always adds a batch dimension)
           lowercase__ :   Any    					=				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=lowercase_	,						add_special_tokens=lowercase_	,						padding=lowercase_	,						truncation=lowercase_	,						max_length=lowercase_	,						stride=lowercase_	,						pad_to_multiple_of=lowercase_	,						return_token_type_ids=lowercase_	,						return_attention_mask=lowercase_	,						return_overflowing_tokens=lowercase_	,						return_special_tokens_mask=lowercase_	,						return_offsets_mapping=lowercase_	,						return_length=lowercase_	,						verbose=lowercase_	,						return_tensors=lowercase_	,						**lowercase_	,						)

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

       return encoded_inputs






   def       __UpperCamelCase     (      self				:     List[Any]	,						lowercase_				:     Tuple	,						lowercase_				:     List[Any]  )					->						Dict:
       # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
       lowercase__ :   Tuple    					=				[]
       for sample_idx in overflow_to_sample_mapping:
           images_with_overflow.append(images[sample_idx]  )

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

       return images_with_overflow






   def       __UpperCamelCase     (      self				:     int	,						*lowercase_				:     Union[str, Any]	,						**lowercase_				:     List[str]  )					->						Union[str, Any]:
       return self.tokenizer.batch_decode(*lowercase_	,						**lowercase_  )






   def       __UpperCamelCase     (      self				:     Union[str, Any]	,						*lowercase_				:     str	,						**lowercase_				:     int  )					->						Dict:
       return self.tokenizer.decode(*lowercase_	,						**lowercase_  )






   @property
   def       __UpperCamelCase     (      self				:     Any  )					->						Any:
       return ["input_ids", "bbox", "attention_mask", "pixel_values"]






   @property
   def       __UpperCamelCase     (      self				:     Optional[int]  )					->						Optional[Any]:
       warnings.warn(
           "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."	,						lowercase_	,						)
       return self.image_processor_class







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



