


'''simple docstring'''






import uuid
from typing import Any, Dict, List, Optional, Union

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
 import tensorflow as tf

if is_torch_available():
 import torch


a_   :      Optional[int]				    =      logging.get_logger(__name__)




class 	a       :
 def __init__(	self ,			__magic_name__ = None ,			__magic_name__ = None ,			__magic_name__=None ,			__magic_name__=None  )   ->							List[str]:
      if not conversation_id:
           _a      =							uuid.uuida()
      if past_user_inputs is None:
           _a      =							[]
      if generated_responses is None:
           _a      =							[]

      _a      =							conversation_id
      _a      =							past_user_inputs
      _a      =							generated_responses
      _a      =							text
 def __eq__(	self ,			__magic_name__  )   ->							int:
      if not isinstance(__magic_name__ ,			__magic_name__  ):
           return False
      if self.uuid == other.uuid:
           return True
      return (
          self.new_user_input == other.new_user_input
          and self.past_user_inputs == other.past_user_inputs
          and self.generated_responses == other.generated_responses
      )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ = False  )   ->							Tuple:
      if self.new_user_input:
           if overwrite:
                logger.warning(
                    f'User input added while unprocessed input was existing: "{self.new_user_input}" was overwritten '
                    f'with: "{text}".'  )
                _a      =							text
           else:
                logger.warning(
                    f'User input added while unprocessed input was existing: "{self.new_user_input}" new input '
                    f'ignored: "{text}". Set `overwrite` to True to overwrite unprocessed user input'  )
      else:
           _a      =							text
 def 							__UpperCAmelCase						(	self  )   ->							int:
      if self.new_user_input:
           self.past_user_inputs.append(self.new_user_input  )
      _a      =							None
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							str:
      self.generated_responses.append(__magic_name__  )
 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      for user_input, generated_response in zip(self.past_user_inputs ,			self.generated_responses  ):
           yield True, user_input
           yield False, generated_response
      if self.new_user_input:
           yield True, self.new_user_input




 def __repr__(	self  )   ->							Dict:
      _a      =							f'Conversation id: {self.uuid} \n'
      for is_user, text in self.iter_texts():
           _a      =							'user' if is_user else 'bot'
           output += f'{name} >> {text} \n'
      return output





@add_end_docstrings(
    _SCREAMING_SNAKE_CASE					,			r"""
        min_length_for_response (`int`, *optional*, defaults to 32):
            The minimum length (in number of tokens) for a response.
        minimum_tokens (`int`, *optional*, defaults to 10):
            The minimum length of tokens to leave for a response.
    """					,			)
class 	a       (       _SCREAMING_SNAKE_CASE    ):
 def __init__(	self ,			*__magic_name__ ,			**__magic_name__  )   ->							Any:
      super().__init__(*__magic_name__ ,			**__magic_name__  )
      if self.tokenizer.pad_token_id is None:
           _a      =							self.tokenizer.eos_token
 def 							__UpperCAmelCase						(	self ,			__magic_name__=None ,			__magic_name__=None ,			__magic_name__=None ,			**__magic_name__  )   ->							List[Any]:
      _a      =							{}
      _a      =							{}
      _a      =							{}

      if min_length_for_response is not None:
           _a      =							min_length_for_response
      if minimum_tokens is not None:
           _a      =							minimum_tokens

      if "max_length" in generate_kwargs:
           _a      =							generate_kwargs['max_length']
           # self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
      if clean_up_tokenization_spaces is not None:
           _a      =							clean_up_tokenization_spaces

      if generate_kwargs:
           forward_params.update(__magic_name__  )
      return preprocess_params, forward_params, postprocess_params
 def __call__(	self ,			__magic_name__ ,			__magic_name__=0 ,			**__magic_name__  )   ->							int:
      _a      =							super().__call__(__magic_name__ ,			num_workers=__magic_name__ ,			**__magic_name__  )
      if isinstance(__magic_name__ ,			__magic_name__  ) and len(__magic_name__  ) == 1:
           return outputs[0]
      return outputs
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__=32  )   ->							Dict[str, Any]:
      if not isinstance(__magic_name__ ,			__magic_name__  ):
           raise ValueError('ConversationalPipeline, expects Conversation as inputs'  )
      if conversation.new_user_input is None:
           raise ValueError(
               f'Conversation with UUID {type(conversation.uuid  )} does not contain new user input to process. '
               'Add user inputs with the conversation\'s `add_user_input` method'  )
      if hasattr(self.tokenizer ,			'_build_conversation_input_ids'  ):
           _a      =							self.tokenizer._build_conversation_input_ids(__magic_name__  )
      else:
           # If the tokenizer cannot handle conversations, we default to only the old version
           _a      =							self._legacy_parse_and_tokenize(__magic_name__  )

      if self.framework == "pt":
           _a      =							torch.LongTensor([input_ids]  )
      elif self.framework == "tf":
           _a      =							tf.constant([input_ids]  )
      return {"input_ids": input_ids, "conversation": conversation}
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__=10 ,			**__magic_name__  )   ->							List[str]:
      _a      =							generate_kwargs.get('max_length' ,			self.model.config.max_length  )

      _a      =							model_inputs['input_ids'].shape[1]
      if max_length - minimum_tokens < n:
           logger.warning(f'Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})'  )
           _a      =							max_length - minimum_tokens
           _a      =							model_inputs['input_ids'][:, -trim:]
           if "attention_mask" in model_inputs:
                _a      =							model_inputs['attention_mask'][:, -trim:]
      _a      =							model_inputs.pop('conversation'  )
      _a      =							max_length
      _a      =							self.model.generate(**__magic_name__ ,			**__magic_name__  )
      if self.model.config.is_encoder_decoder:
           _a      =							1
      else:
           _a      =							n
      return {"output_ids": output_ids[:, start_position:], "conversation": conversation}
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__=True  )   ->							Optional[int]:
      _a      =							model_outputs['output_ids']
      _a      =							self.tokenizer.decode(
          output_ids[0] ,			skip_special_tokens=__magic_name__ ,			clean_up_tokenization_spaces=__magic_name__ ,			)
      _a      =							model_outputs['conversation']
      conversation.mark_processed()
      conversation.append_response(__magic_name__  )
      return conversation




 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							Dict:
      _a      =							self.tokenizer.eos_token_id
      _a      =							[]
      for is_user, text in conversation.iter_texts():
           if eos_token_id is not None:
                input_ids.extend(self.tokenizer.encode(__magic_name__ ,			add_special_tokens=__magic_name__  ) + [eos_token_id]  )
           else:
                input_ids.extend(self.tokenizer.encode(__magic_name__ ,			add_special_tokens=__magic_name__  )  )

      if len(__magic_name__  ) > self.tokenizer.model_max_length:
           _a      =							input_ids[-self.tokenizer.model_max_length :]
      return input_ids





'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


a_   :      Optional[int]				    =      {"configuration_deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig", "DeiTOnnxConfig"]}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      Dict				    =      ["DeiTFeatureExtractor"]
 a_   :      List[Any]				    =      ["DeiTImageProcessor"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      Tuple				    =      [
     "DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "DeiTForImageClassification",
     "DeiTForImageClassificationWithTeacher",
     "DeiTForMaskedImageModeling",
     "DeiTModel",
     "DeiTPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      Optional[Any]				    =      [
     "TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFDeiTForImageClassification",
     "TFDeiTForImageClassificationWithTeacher",
     "TFDeiTForMaskedImageModeling",
     "TFDeiTModel",
     "TFDeiTPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_deit import DeiTFeatureExtractor
  from .image_processing_deit import DeiTImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_deit import (
      DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      DeiTForImageClassification,
      DeiTForImageClassificationWithTeacher,
      DeiTForMaskedImageModeling,
      DeiTModel,
      DeiTPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_deit import (
      TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDeiTForImageClassification,
      TFDeiTForImageClassificationWithTeacher,
      TFDeiTForMaskedImageModeling,
      TFDeiTModel,
      TFDeiTPreTrainedModel,
  )


else:
 import sys

 a_   :      List[Any]				    =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

