
"""simple docstring"""

import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
class        _UpperCAmelCase							(						SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE				:							int							 =		["image_processor", "tokenizer"]
      __SCREAMING_SNAKE_CASE				:							Tuple							 =		"AutoImageProcessor"
      __SCREAMING_SNAKE_CASE				:							List[Any]							 =		"AutoTokenizer"




      def __init__(							self				,    lowercase_=None				,    lowercase_=None				,    **lowercase_							)  ->   int:
          UpperCAmelCase       =   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_				,    )
              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__(lowercase_				,    lowercase_							)
          UpperCAmelCase       =   self.image_processor
          UpperCAmelCase       =   False




      def __call__(							self				,    *lowercase_				,    **lowercase_							)  ->   Dict:
          # For backward compatibility
          if self._in_target_context_manager:
              return self.current_processor(*lowercase_				,    **lowercase_							)

          UpperCAmelCase       =   kwargs.pop('images'				,    lowercase_							)
          UpperCAmelCase       =   kwargs.pop('text'				,    lowercase_							)
          if len(lowercase_							) > 0:
              UpperCAmelCase       =   args[0]
              UpperCAmelCase       =   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:
              UpperCAmelCase       =   self.image_processor(lowercase_				,    *lowercase_				,    **lowercase_							)
          if text is not None:
              UpperCAmelCase       =   self.tokenizer(lowercase_				,    **lowercase_							)

          if text is None:
              return inputs
          elif images is None:
              return encodings
          else:
              UpperCAmelCase       =   encodings['input_ids']
              return inputs




      def       a_ (							self				,    *lowercase_				,    **lowercase_							)  ->   Optional[Any]:
          return self.tokenizer.batch_decode(*lowercase_				,    **lowercase_							)




      def       a_ (							self				,    *lowercase_				,    **lowercase_							)  ->   int:
          return self.tokenizer.decode(*lowercase_				,    **lowercase_							)




      @contextmanager
      def       a_ (							self							)  ->   List[Any]:
          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.'							)
          UpperCAmelCase       =   True
          UpperCAmelCase       =   self.tokenizer
          yield
          UpperCAmelCase       =   self.image_processor
          UpperCAmelCase       =   False




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





"""simple docstring"""

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        _UpperCAmelCase							(						SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_							):




      @register_to_config
      def __init__(							self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_ = False				,    )  ->   List[Any]:
          super().__init__()

          UpperCAmelCase       =   nn.Linear(lowercase_				,    lowercase_				,    bias=lowercase_							)

          UpperCAmelCase       =   nn.Embedding(lowercase_				,    lowercase_							)
          UpperCAmelCase       =   False

          UpperCAmelCase       =   nn.Dropout(p=lowercase_							)

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

          UpperCAmelCase       =   TaLayerNorm(lowercase_							)
          UpperCAmelCase       =   nn.Dropout(p=lowercase_							)




      def       a_ (							self				,    lowercase_				,    lowercase_							)  ->   Union[str, Any]:
          UpperCAmelCase       =   self.input_proj(lowercase_							)

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

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

          UpperCAmelCase       =   self.dropout_pre(lowercase_							)

          # inverted the attention mask
          UpperCAmelCase       =   encoder_inputs.size()
          UpperCAmelCase       =   self.get_extended_attention_mask(lowercase_				,    lowercase_							)

          for lyr in self.encoders:
              UpperCAmelCase       =   lyr(lowercase_				,    lowercase_							)[0]
          UpperCAmelCase       =   self.layer_norm(lowercase_							)

          return self.dropout_post(lowercase_							), encoder_inputs_mask



