
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


a__				:      Tuple    		=     logging.get_logger(__name__)






class     a_       (			UpperCAmelCase_							):



       """simple docstring"""
       __SCREAMING_SNAKE_CASE       :					Optional[int]						=  'mt5'
       __SCREAMING_SNAKE_CASE       :					Any						=  ['past_key_values']






       def __init__(		self				,       _lowerCamelCase=25_0112				,       _lowerCamelCase=512				,       _lowerCamelCase=64				,       _lowerCamelCase=1024				,       _lowerCamelCase=8				,       _lowerCamelCase=None				,       _lowerCamelCase=6				,       _lowerCamelCase=32				,       _lowerCamelCase=128				,       _lowerCamelCase=0.1				,       _lowerCamelCase=1e-6				,       _lowerCamelCase=1.0				,       _lowerCamelCase="gated-gelu"				,       _lowerCamelCase=True				,       _lowerCamelCase=True				,       _lowerCamelCase="T5Tokenizer"				,       _lowerCamelCase=False				,       _lowerCamelCase=0				,       _lowerCamelCase=1				,       _lowerCamelCase=0				,       **_lowerCamelCase				,       )					->List[Any]:
           super().__init__(
               is_encoder_decoder=a__				,       tokenizer_class=a__				,       tie_word_embeddings=a__				,       pad_token_id=a__				,       eos_token_id=a__				,       decoder_start_token_id=a__				,       **a__				,       )
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = vocab_size
           SCREAMING_SNAKE_CASE				:				Tuple				      = d_model
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = d_kv
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = d_ff
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = num_layers
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = (
               num_decoder_layers if num_decoder_layers is not None else self.num_layers
           )  # default = symmetry
           SCREAMING_SNAKE_CASE				:				Any				      = num_heads
           SCREAMING_SNAKE_CASE				:				Any				      = relative_attention_num_buckets
           SCREAMING_SNAKE_CASE				:				Any				      = relative_attention_max_distance
           SCREAMING_SNAKE_CASE				:				Any				      = dropout_rate
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = layer_norm_epsilon
           SCREAMING_SNAKE_CASE				:				List[str]				      = initializer_factor
           SCREAMING_SNAKE_CASE				:				List[Any]				      = feed_forward_proj
           SCREAMING_SNAKE_CASE				:				Any				      = use_cache

           SCREAMING_SNAKE_CASE				:				int				      = self.feed_forward_proj.split('''-'''   )
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = act_info[-1]
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = act_info[0] == '''gated'''

           if len(a__   ) > 1 and act_info[0] != "gated" or len(a__   ) > 2:
               raise ValueError(
                   F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
                   '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
                   '''\'gated-gelu\' or \'relu\''''   )

           # for backwards compatibility
           if feed_forward_proj == "gated-gelu":
               SCREAMING_SNAKE_CASE				:				int				      = '''gelu_new'''






       @property
       def 	__lowerCAmelCase     (		self   )					->Dict:
           return self.d_model






       @property
       def 	__lowerCAmelCase     (		self   )					->Tuple:
           return self.num_heads






       @property
       def 	__lowerCAmelCase     (		self   )					->List[str]:
           return self.num_layers






class     a_       (			UpperCAmelCase_							):



       """simple docstring"""






       @property
       # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
       def 	__lowerCAmelCase     (		self   )					->Mapping[str, Mapping[int, str]]:
           SCREAMING_SNAKE_CASE				:				List[Any]				      = {
               '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
               '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
           }
           if self.use_past:
               SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = '''past_encoder_sequence + sequence'''
               SCREAMING_SNAKE_CASE				:				str				      = {0: '''batch'''}
               SCREAMING_SNAKE_CASE				:				List[Any]				      = {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
           else:
               SCREAMING_SNAKE_CASE				:				int				      = {0: '''batch''', 1: '''decoder_sequence'''}
               SCREAMING_SNAKE_CASE				:				Tuple				      = {0: '''batch''', 1: '''decoder_sequence'''}

           if self.use_past:
               self.fill_with_past_key_values_(a__				,       direction='''inputs'''   )

           return common_inputs






       @property
       # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
       def 	__lowerCAmelCase     (		self   )					->int:
           return 13






       @property
       def 	__lowerCAmelCase     (		self   )					->float:
           return 5e-4






'''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     lowercase  ( UpperCAmelCase_):
      __lowercase					=	['''image_processor''', '''tokenizer''']
      __lowercase					=	'''LayoutLMv2ImageProcessor'''
      __lowercase					=	('''LayoutLMv2Tokenizer''', '''LayoutLMv2TokenizerFast''')






      def __init__(self			,	a__=None			,	a__=None			,	**a__       )							-> Any:
         SCREAMING_SNAKE_CASE__      :					Optional[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.'''			,	a__			,	)
            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			kwargs.pop('''feature_extractor'''       )

         SCREAMING_SNAKE_CASE__      :					List[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__(a__			,	a__       )






      def __call__(self			,	a__			,	a__ = None			,	a__ = None			,	a__ = None			,	a__ = None			,	a__ = True			,	a__ = False			,	a__ = False			,	a__ = None			,	a__ = 0			,	a__ = None			,	a__ = None			,	a__ = None			,	a__ = False			,	a__ = False			,	a__ = False			,	a__ = False			,	a__ = True			,	a__ = None			,	**a__			,	)							-> 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=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__       ):
               SCREAMING_SNAKE_CASE__      :					Tuple							    =			[text]  # add batch dimension (as the image processor always adds a batch dimension)
            SCREAMING_SNAKE_CASE__      :					Any							    =			features['''words''']

         SCREAMING_SNAKE_CASE__      :					int							    =			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
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			features.pop('''pixel_values'''       )
         if return_overflowing_tokens is True:
            SCREAMING_SNAKE_CASE__      :					Tuple							    =			self.get_overflowing_images(a__			,	encoded_inputs['''overflow_to_sample_mapping''']       )
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			images

         return encoded_inputs






      def  A__(self			,	a__			,	a__       )							-> str:
         # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
         SCREAMING_SNAKE_CASE__      :					int							    =			[]
         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  A__(self			,	*a__			,	**a__       )							-> List[Any]:
         return self.tokenizer.batch_decode(*a__			,	**a__       )






      def  A__(self			,	*a__			,	**a__       )							-> Optional[Any]:
         return self.tokenizer.decode(*a__			,	**a__       )






      @property
      def  A__(self       )							-> Tuple:
         return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]






      @property
      def  A__(self       )							-> str:
         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  A__(self       )							-> Dict:
         warnings.warn(
             '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''			,	a__			,	)
         return self.image_processor

