






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


UpperCamelCase    							=      logging.get_logger(__name__)







class 		_A						:

  def __init__(    self						:	Any   ,				lowerCamelCase__						:	str = None   ,				lowerCamelCase__						:	uuid.UUID = None   ,				lowerCamelCase__						:	Union[str, Any]=None   ,				lowerCamelCase__						:	List[str]=None			):
     """simple docstring"""

     if not conversation_id:
        __UpperCamelCase	:			int					=					uuid.uuida()
     if past_user_inputs is None:
        __UpperCamelCase	:			str					=					[]
     if generated_responses is None:
        __UpperCamelCase	:			str					=					[]

     __UpperCamelCase	:			uuid.UUID					=					conversation_id
     __UpperCamelCase	:			List[str]					=					past_user_inputs
     __UpperCamelCase	:			List[str]					=					generated_responses
     __UpperCamelCase	:			Optional[str]					=					text

  def __eq__(    self						:	int   ,				lowerCamelCase__						:	Union[str, Any]			):
     """simple docstring"""

     if not isinstance(lowerCamelCase__   ,				lowerCamelCase__			):
        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    a					(    self						:	Dict   ,				lowerCamelCase__						:	str   ,				lowerCamelCase__						:	bool = False			):
     """simple docstring"""

     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}".'			)
           __UpperCamelCase	:			Optional[int]					=					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:
        __UpperCamelCase	:			str					=					text

  def    a					(    self						:	str			):
     """simple docstring"""

     if self.new_user_input:
        self.past_user_inputs.append(self.new_user_input			)
     __UpperCamelCase	:			List[str]					=					None

  def    a					(    self						:	List[str]   ,				lowerCamelCase__						:	str			):
     """simple docstring"""

     self.generated_responses.append(lowerCamelCase__			)

  def    a					(    self						:	Dict			):
     """simple docstring"""

     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			):
     """simple docstring"""

     __UpperCamelCase	:			Union[str, Any]					=					f'Conversation id: {self.uuid} \n'
     for is_user, text in self.iter_texts():
        __UpperCamelCase	:			Any					=					"""user""" if is_user else """bot"""
        output += f'{name} >> {text} \n'
     return output







@add_end_docstrings(
    UpperCAmelCase_	,				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						( UpperCAmelCase_ ):

  def __init__(    self						:	Tuple   ,				*lowerCamelCase__						:	Any   ,				**lowerCamelCase__						:	Union[str, Any]			):
     """simple docstring"""

     super().__init__(*lowerCamelCase__   ,				**lowerCamelCase__			)
     if self.tokenizer.pad_token_id is None:
        __UpperCamelCase	:			List[Any]					=					self.tokenizer.eos_token

  def    a					(    self						:	Dict   ,				lowerCamelCase__						:	Optional[Any]=None   ,				lowerCamelCase__						:	Dict=None   ,				lowerCamelCase__						:	Optional[int]=None   ,				**lowerCamelCase__						:	Optional[int]			):
     """simple docstring"""

     __UpperCamelCase	:			List[Any]					=					{}
     __UpperCamelCase	:			Optional[Any]					=					{}
     __UpperCamelCase	:			Tuple					=					{}

     if min_length_for_response is not None:
        __UpperCamelCase	:			Any					=					min_length_for_response
     if minimum_tokens is not None:
        __UpperCamelCase	:			List[Any]					=					minimum_tokens

     if "max_length" in generate_kwargs:
        __UpperCamelCase	:			Optional[Any]					=					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:
        __UpperCamelCase	:			Union[str, Any]					=					clean_up_tokenization_spaces

     if generate_kwargs:
        forward_params.update(lowerCamelCase__			)
     return preprocess_params, forward_params, postprocess_params

  def __call__(    self						:	Optional[Any]   ,				lowerCamelCase__						:	Union[Conversation, List[Conversation]]   ,				lowerCamelCase__						:	List[str]=0   ,				**lowerCamelCase__						:	Optional[int]			):
     """simple docstring"""

     __UpperCamelCase	:			int					=					super().__call__(lowerCamelCase__   ,				num_workers=lowerCamelCase__   ,				**lowerCamelCase__			)
     if isinstance(lowerCamelCase__   ,				lowerCamelCase__			) and len(lowerCamelCase__			) == 1:
        return outputs[0]
     return outputs

  def    a					(    self						:	List[Any]   ,				lowerCamelCase__						:	Conversation   ,				lowerCamelCase__						:	Tuple=32			):
     """simple docstring"""

     if not isinstance(lowerCamelCase__   ,				lowerCamelCase__			):
        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"""			):
        __UpperCamelCase	:			List[str]					=					self.tokenizer._build_conversation_input_ids(lowerCamelCase__			)
     else:
        # If the tokenizer cannot handle conversations, we default to only the old version
        __UpperCamelCase	:			Dict					=					self._legacy_parse_and_tokenize(lowerCamelCase__			)

     if self.framework == "pt":
        __UpperCamelCase	:			Optional[Any]					=					torch.LongTensor([input_ids]			)
     elif self.framework == "tf":
        __UpperCamelCase	:			List[Any]					=					tf.constant([input_ids]			)
     return {"input_ids": input_ids, "conversation": conversation}

  def    a					(    self						:	Tuple   ,				lowerCamelCase__						:	List[str]   ,				lowerCamelCase__						:	Dict=10   ,				**lowerCamelCase__						:	Any			):
     """simple docstring"""

     __UpperCamelCase	:			Union[str, Any]					=					generate_kwargs.get("""max_length"""   ,				self.model.config.max_length			)

     __UpperCamelCase	:			Optional[int]					=					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})'			)
        __UpperCamelCase	:			Tuple					=					max_length - minimum_tokens
        __UpperCamelCase	:			str					=					model_inputs["""input_ids"""][:, -trim:]
        if "attention_mask" in model_inputs:
           __UpperCamelCase	:			List[Any]					=					model_inputs["""attention_mask"""][:, -trim:]
     __UpperCamelCase	:			List[Any]					=					model_inputs.pop("""conversation"""			)
     __UpperCamelCase	:			str					=					max_length
     __UpperCamelCase	:			int					=					self.model.generate(**lowerCamelCase__   ,				**lowerCamelCase__			)
     if self.model.config.is_encoder_decoder:
        __UpperCamelCase	:			Dict					=					1
     else:
        __UpperCamelCase	:			List[str]					=					n
     return {"output_ids": output_ids[:, start_position:], "conversation": conversation}

  def    a					(    self						:	Tuple   ,				lowerCamelCase__						:	List[Any]   ,				lowerCamelCase__						:	Dict=True			):
     """simple docstring"""

     __UpperCamelCase	:			Any					=					model_outputs["""output_ids"""]
     __UpperCamelCase	:			Optional[Any]					=					self.tokenizer.decode(
         output_ids[0]   ,				skip_special_tokens=lowerCamelCase__   ,				clean_up_tokenization_spaces=lowerCamelCase__   ,				)
     __UpperCamelCase	:			List[str]					=					model_outputs["""conversation"""]
     conversation.mark_processed()
     conversation.append_response(lowerCamelCase__			)
     return conversation

  def    a					(    self						:	str   ,				lowerCamelCase__						:	Conversation			):
     """simple docstring"""

     __UpperCamelCase	:			str					=					self.tokenizer.eos_token_id
     __UpperCamelCase	:			int					=					[]
     for is_user, text in conversation.iter_texts():
        if eos_token_id is not None:
           input_ids.extend(self.tokenizer.encode(lowerCamelCase__   ,				add_special_tokens=lowerCamelCase__			) + [eos_token_id]			)
        else:
           input_ids.extend(self.tokenizer.encode(lowerCamelCase__   ,				add_special_tokens=lowerCamelCase__			)			)

     if len(lowerCamelCase__			) > self.tokenizer.model_max_length:
        __UpperCamelCase	:			List[str]					=					input_ids[-self.tokenizer.model_max_length :]
     return input_ids











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 		_A						( UpperCAmelCase_ ):
  lowercase_						:			Tuple						=					['''image_processor''', '''tokenizer''']
  lowercase_						:			str						=					'''BlipImageProcessor'''
  lowercase_						:			str						=					('''BertTokenizer''', '''BertTokenizerFast''')

  def __init__(    self						:	List[str]   ,				lowerCamelCase__						:	str   ,				lowerCamelCase__						:	Union[str, Any]			):
     """simple docstring"""

     __UpperCamelCase	:			Optional[int]					=					False
     super().__init__(lowerCamelCase__   ,				lowerCamelCase__			)
     __UpperCamelCase	:			Tuple					=					self.image_processor

  def __call__(    self						:	Optional[Any]   ,				lowerCamelCase__						:	ImageInput = None   ,				lowerCamelCase__						:	Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None   ,				lowerCamelCase__						:	bool = True   ,				lowerCamelCase__						:	Union[bool, str, PaddingStrategy] = False   ,				lowerCamelCase__						:	Union[bool, str, TruncationStrategy] = None   ,				lowerCamelCase__						:	Optional[int] = None   ,				lowerCamelCase__						:	int = 0   ,				lowerCamelCase__						:	Optional[int] = None   ,				lowerCamelCase__						:	Optional[bool] = None   ,				lowerCamelCase__						:	bool = False   ,				lowerCamelCase__						:	bool = False   ,				lowerCamelCase__						:	bool = False   ,				lowerCamelCase__						:	bool = False   ,				lowerCamelCase__						:	bool = False   ,				lowerCamelCase__						:	bool = True   ,				lowerCamelCase__						:	Optional[Union[str, TensorType]] = None   ,				**lowerCamelCase__						:	int   ,				):
     """simple docstring"""

     if images is None and text is None:
        raise ValueError("""You have to specify either images or text."""			)

     # Get only text
     if images is None:
        __UpperCamelCase	:			Optional[int]					=					self.tokenizer
        __UpperCamelCase	:			Dict					=					self.tokenizer(
            text=lowerCamelCase__   ,				add_special_tokens=lowerCamelCase__   ,				padding=lowerCamelCase__   ,				truncation=lowerCamelCase__   ,				max_length=lowerCamelCase__   ,				stride=lowerCamelCase__   ,				pad_to_multiple_of=lowerCamelCase__   ,				return_attention_mask=lowerCamelCase__   ,				return_overflowing_tokens=lowerCamelCase__   ,				return_special_tokens_mask=lowerCamelCase__   ,				return_offsets_mapping=lowerCamelCase__   ,				return_token_type_ids=lowerCamelCase__   ,				return_length=lowerCamelCase__   ,				verbose=lowerCamelCase__   ,				return_tensors=lowerCamelCase__   ,				**lowerCamelCase__   ,				)
        return text_encoding

     # add pixel_values
     __UpperCamelCase	:			str					=					self.image_processor(lowerCamelCase__   ,				return_tensors=lowerCamelCase__			)

     if text is not None:
        __UpperCamelCase	:			Optional[int]					=					self.tokenizer(
            text=lowerCamelCase__   ,				add_special_tokens=lowerCamelCase__   ,				padding=lowerCamelCase__   ,				truncation=lowerCamelCase__   ,				max_length=lowerCamelCase__   ,				stride=lowerCamelCase__   ,				pad_to_multiple_of=lowerCamelCase__   ,				return_attention_mask=lowerCamelCase__   ,				return_overflowing_tokens=lowerCamelCase__   ,				return_special_tokens_mask=lowerCamelCase__   ,				return_offsets_mapping=lowerCamelCase__   ,				return_token_type_ids=lowerCamelCase__   ,				return_length=lowerCamelCase__   ,				verbose=lowerCamelCase__   ,				return_tensors=lowerCamelCase__   ,				**lowerCamelCase__   ,				)
     else:
        __UpperCamelCase	:			Optional[Any]					=					None

     if text_encoding is not None:
        encoding_image_processor.update(lowerCamelCase__			)

     return encoding_image_processor

  def    a					(    self						:	List[str]   ,				*lowerCamelCase__						:	Any   ,				**lowerCamelCase__						:	List[Any]			):
     """simple docstring"""

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

  def    a					(    self						:	List[str]   ,				*lowerCamelCase__						:	int   ,				**lowerCamelCase__						:	Tuple			):
     """simple docstring"""

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

  @property
  def    a					(    self						:	Any			):
     """simple docstring"""

     __UpperCamelCase	:			str					=					self.tokenizer.model_input_names
     __UpperCamelCase	:			str					=					self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)



