




import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin




class    snake_case__							(_UpperCamelCase   ,	_UpperCamelCase   ,	_UpperCamelCase       ):
				"""simple docstring"""



				@register_to_config
				def __init__(				self    :					Any ,				__lowerCamelCase    :					int ,				__lowerCamelCase    :					int ,				__lowerCamelCase    :					int ,				__lowerCamelCase    :					float ,				__lowerCamelCase    :					int ,				__lowerCamelCase    :					int ,				__lowerCamelCase    :					int ,				__lowerCamelCase    :					int ,				__lowerCamelCase    :					str ,				__lowerCamelCase    :					bool = False ,				) ->					Dict:
							super().__init__()

							a   =      nn.Linear(__lowerCamelCase ,				__lowerCamelCase ,				bias=__lowerCamelCase							)

							a   =      nn.Embedding(__lowerCamelCase ,				__lowerCamelCase							)
							a   =      False

							a   =      nn.Dropout(p=__lowerCamelCase							)

							a   =      TaConfig(
							    d_model=__lowerCamelCase ,				num_heads=__lowerCamelCase ,				d_kv=__lowerCamelCase ,				d_ff=__lowerCamelCase ,				feed_forward_proj=__lowerCamelCase ,				dropout_rate=__lowerCamelCase ,				is_decoder=__lowerCamelCase ,				is_encoder_decoder=__lowerCamelCase ,				)
							a   =      nn.ModuleList()
							for lyr_num in range(__lowerCamelCase							):
										a   =      TaBlock(__lowerCamelCase							)
										self.encoders.append(__lowerCamelCase							)

							a   =      TaLayerNorm(__lowerCamelCase							)
							a   =      nn.Dropout(p=__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					Union[str, Any] ,				__lowerCamelCase    :					List[str] ,				__lowerCamelCase    :					List[Any]							) ->					Tuple:
							a   =      self.input_proj(__lowerCamelCase							)

							# terminal relative positional encodings
							a   =      encoder_inputs.shape[1]
							a   =      torch.arange(__lowerCamelCase ,				device=encoder_inputs.device							)

							a   =      encoder_inputs_mask.sum(-1							)
							a   =      torch.roll(input_positions.unsqueeze(0							) ,				tuple(seq_lens.tolist()							) ,				dims=0							)
							x += self.position_encoding(__lowerCamelCase							)

							a   =      self.dropout_pre(__lowerCamelCase							)

							# inverted the attention mask
							a   =      encoder_inputs.size()
							a   =      self.get_extended_attention_mask(__lowerCamelCase ,				__lowerCamelCase							)

							for lyr in self.encoders:
										a   =      lyr(__lowerCamelCase ,				__lowerCamelCase							)[0]
							a   =      self.layer_norm(__lowerCamelCase							)

							return self.dropout_post(__lowerCamelCase							), encoder_inputs_mask












import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class    snake_case__							(_UpperCamelCase       ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	:							List[Any]										= ["""image_processor""", """tokenizer"""]
				SCREAMING_SNAKE_CASE_	:							List[Any]										= """AutoImageProcessor"""
				SCREAMING_SNAKE_CASE_	:							Dict										= """AutoTokenizer"""





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

							a   =      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							)
							a   =      self.image_processor
							a   =      False





				def __call__(				self    :					Any ,				*__lowerCamelCase    :					int ,				**__lowerCamelCase    :					Optional[int]							) ->					int:
							# For backward compatibility
							if self._in_target_context_manager:
										return self.current_processor(*__lowerCamelCase ,				**__lowerCamelCase							)

							a   =      kwargs.pop("images" ,				__lowerCamelCase							)
							a   =      kwargs.pop("text" ,				__lowerCamelCase							)
							if len(__lowerCamelCase							) > 0:
										a   =      args[0]
										a   =      args[1:]

							if images is None and text is None:
										raise ValueError("You need to specify either an `images` or `text` input to process."							)

							if images is not None:
										a   =      self.image_processor(__lowerCamelCase ,				*__lowerCamelCase ,				**__lowerCamelCase							)
							if text is not None:
										a   =      self.tokenizer(__lowerCamelCase ,				**__lowerCamelCase							)

							if text is None:
										return inputs
							elif images is None:
										return encodings
							else:
										a   =      encodings["input_ids"]
										return inputs





				def 							__UpperCAmelCase							(				self    :					Union[str, Any] ,				*__lowerCamelCase    :					str ,				**__lowerCamelCase    :					str							) ->					Optional[Any]:
							return self.tokenizer.batch_decode(*__lowerCamelCase ,				**__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					Optional[Any] ,				*__lowerCamelCase    :					Union[str, Any] ,				**__lowerCamelCase    :					Any							) ->					str:
							return self.tokenizer.decode(*__lowerCamelCase ,				**__lowerCamelCase							)





				@contextmanager
				def 							__UpperCAmelCase							(				self    :					Any							) ->					Optional[int]:
							warnings.warn(
							    "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
							    "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
							    "your images inputs, or in a separate call."							)
							a   =      True
							a   =      self.tokenizer
							yield
							a   =      self.image_processor
							a   =      False





				@property
				def 							__UpperCAmelCase							(				self    :					Dict							) ->					Optional[Any]:
							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 							__UpperCAmelCase							(				self    :					int							) ->					Union[str, Any]:
							warnings.warn(
							    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." ,				__lowerCamelCase ,				)
							return self.image_processor






