


'''simple docstring'''
import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class   lowercase      (				A__   ):

		"""simple docstring"""

		_a       					=							['image_processor', 'tokenizer']
		_a       					=							'FlavaImageProcessor'
		_a       					=							('BertTokenizer', 'BertTokenizerFast')




		def __init__(						self       ,  UpperCamelCase_=None       ,  UpperCamelCase_=None       ,  **UpperCamelCase_				):

							'''simple docstring'''


							UpperCamelCase__  :str				   =					None
							if "feature_extractor" in kwargs:
												warnings.warn(
												    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
												    ''' instead.'''       ,  UpperCamelCase_       ,  )
												UpperCamelCase__  :Dict				   =					kwargs.pop('''feature_extractor'''				)

							UpperCamelCase__  :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__(UpperCamelCase_       ,  UpperCamelCase_				)
							UpperCamelCase__  :str				   =					self.image_processor




		def __call__(						self       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = True       ,  UpperCamelCase_ = False       ,  UpperCamelCase_ = False       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = 0       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = False       ,  UpperCamelCase_ = False       ,  UpperCamelCase_ = False       ,  UpperCamelCase_ = False       ,  UpperCamelCase_ = True       ,  UpperCamelCase_ = None       ,  **UpperCamelCase_       ,  ):

							'''simple docstring'''


							if text is None and images is None:
												raise ValueError('''You have to specify either text or images. Both cannot be none.'''				)

							if text is not None:
												UpperCamelCase__  :int				   =					self.tokenizer(
												    text=UpperCamelCase_       ,  add_special_tokens=UpperCamelCase_       ,  padding=UpperCamelCase_       ,  truncation=UpperCamelCase_       ,  max_length=UpperCamelCase_       ,  stride=UpperCamelCase_       ,  pad_to_multiple_of=UpperCamelCase_       ,  return_token_type_ids=UpperCamelCase_       ,  return_attention_mask=UpperCamelCase_       ,  return_overflowing_tokens=UpperCamelCase_       ,  return_special_tokens_mask=UpperCamelCase_       ,  return_offsets_mapping=UpperCamelCase_       ,  return_length=UpperCamelCase_       ,  verbose=UpperCamelCase_       ,  return_tensors=UpperCamelCase_       ,  **UpperCamelCase_       ,  )
							if images is not None:
												UpperCamelCase__  :Any				   =					self.image_processor(
												    UpperCamelCase_       ,  return_image_mask=UpperCamelCase_       ,  return_codebook_pixels=UpperCamelCase_       ,  return_tensors=UpperCamelCase_       ,  **UpperCamelCase_       ,  )

							if text is not None and images is not None:
												encoding.update(UpperCamelCase_				)
												return encoding
							elif text is not None:
												return encoding
							else:
												return BatchEncoding(data=dict(**UpperCamelCase_				)       ,  tensor_type=UpperCamelCase_				)




		def        lowerCAmelCase__							(						self       ,  *UpperCamelCase_       ,  **UpperCamelCase_				):

							'''simple docstring'''


							return self.tokenizer.batch_decode(*UpperCamelCase_       ,  **UpperCamelCase_				)




		def        lowerCAmelCase__							(						self       ,  *UpperCamelCase_       ,  **UpperCamelCase_				):

							'''simple docstring'''


							return self.tokenizer.decode(*UpperCamelCase_       ,  **UpperCamelCase_				)




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Dict				   =					self.tokenizer.model_input_names
							UpperCamelCase__  :Optional[Any]				   =					self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							warnings.warn(
							    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''       ,  UpperCamelCase_       ,  )
							return self.image_processor_class




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							warnings.warn(
							    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''       ,  UpperCamelCase_       ,  )
							return self.image_processor



'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class   lowercase      (				A__   ):

		"""simple docstring"""

		_a       					=							None
		_a       					=							None
		_a       					=							None
		_a       					=							None



class   lowercase      (				A__   ):

		"""simple docstring"""

		def __init__(						self       ,  UpperCamelCase_=1       ,  UpperCamelCase_=0       ,  UpperCamelCase_=2       ,  UpperCamelCase_=512       ,  UpperCamelCase_="cls"       ,  UpperCamelCase_=False       ,  UpperCamelCase_=True       ,  **UpperCamelCase_       ,  ):

							'''simple docstring'''


							super().__init__(pad_token_id=UpperCamelCase_       ,  bos_token_id=UpperCamelCase_       ,  eos_token_id=UpperCamelCase_       ,  **UpperCamelCase_				)
							UpperCamelCase__  :Optional[int]				   =					project_dim
							UpperCamelCase__  :List[str]				   =					pooler_fn
							UpperCamelCase__  :Dict				   =					learn_encoder
							UpperCamelCase__  :Any				   =					use_attention_mask



class   lowercase      (				A__   ):

		"""simple docstring"""

		_a       					=							[R'pooler', R'logit_scale']
		_a       					=							[R'position_ids', R'predictions.decoder.bias']
		_a       					=							'roberta'
		_a       					=							RobertaSeriesConfig




		def __init__(						self       ,  UpperCamelCase_				):

							'''simple docstring'''


							super().__init__(UpperCamelCase_				)
							UpperCamelCase__  :Optional[Any]				   =					XLMRobertaModel(UpperCamelCase_				)
							UpperCamelCase__  :Tuple				   =					nn.Linear(config.hidden_size       ,  config.project_dim				)
							UpperCamelCase__  :Union[str, Any]				   =					getattr(UpperCamelCase_       ,  '''has_pre_transformation'''       ,  UpperCamelCase_				)
							if self.has_pre_transformation:
												UpperCamelCase__  :Any				   =					nn.Linear(config.hidden_size       ,  config.project_dim				)
												UpperCamelCase__  :str				   =					nn.LayerNorm(config.hidden_size       ,  eps=config.layer_norm_eps				)
							self.post_init()




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None       ,  ):

							'''simple docstring'''


							UpperCamelCase__  :Optional[int]				   =					return_dict if return_dict is not None else self.config.use_return_dict

							UpperCamelCase__  :str				   =					self.base_model(
							    input_ids=UpperCamelCase_       ,  attention_mask=UpperCamelCase_       ,  token_type_ids=UpperCamelCase_       ,  position_ids=UpperCamelCase_       ,  head_mask=UpperCamelCase_       ,  inputs_embeds=UpperCamelCase_       ,  encoder_hidden_states=UpperCamelCase_       ,  encoder_attention_mask=UpperCamelCase_       ,  output_attentions=UpperCamelCase_       ,  output_hidden_states=True if self.has_pre_transformation else output_hidden_states       ,  return_dict=UpperCamelCase_       ,  )

							if self.has_pre_transformation:
												UpperCamelCase__  :Dict				   =					outputs['''hidden_states'''][-2]
												UpperCamelCase__  :Optional[int]				   =					self.pre_LN(UpperCamelCase_				)
												UpperCamelCase__  :Optional[Any]				   =					self.transformation_pre(UpperCamelCase_				)

												return TransformationModelOutput(
												    projection_state=UpperCamelCase_       ,  last_hidden_state=outputs.last_hidden_state       ,  hidden_states=outputs.hidden_states       ,  attentions=outputs.attentions       ,  )
							else:
												UpperCamelCase__  :List[Any]				   =					self.transformation(outputs.last_hidden_state				)
												return TransformationModelOutput(
												    projection_state=UpperCamelCase_       ,  last_hidden_state=outputs.last_hidden_state       ,  hidden_states=outputs.hidden_states       ,  attentions=outputs.attentions       ,  )