




"""simple docstring"""




from typing import TYPE_CHECKING

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


lowerCAmelCase							:    str  							=							{"""configuration_encoder_decoder""": ["""EncoderDecoderConfig"""]}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase							:    Union[str, Any]  							=							["""EncoderDecoderModel"""]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase							:    Tuple  							=							["""TFEncoderDecoderModel"""]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase							:    Optional[Any]  							=							["""FlaxEncoderDecoderModel"""]

if TYPE_CHECKING:
					from .configuration_encoder_decoder import EncoderDecoderConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_encoder_decoder import EncoderDecoderModel

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_encoder_decoder import TFEncoderDecoderModel

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel

else:
					import sys

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









"""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


lowerCAmelCase							:    List[str]  							=							logging.get_logger(__name__)






class 					__magic_name__							:






				'''simple docstring'''





				def __init__( self	,				_a = None	,				_a = None	,				_a=None	,				_a=None				):





							"""simple docstring"""
							if not conversation_id:
										lowerCamelCase       =      uuid.uuida()
							if past_user_inputs is None:
										lowerCamelCase       =      []
							if generated_responses is None:
										lowerCamelCase       =      []

							lowerCamelCase       =      conversation_id
							lowerCamelCase       =      past_user_inputs
							lowerCamelCase       =      generated_responses
							lowerCamelCase       =      text





				def __eq__( self	,				_a				):





							"""simple docstring"""
							if not isinstance(_a	,				_a				):
										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      _lowerCAmelCase       ( self	,				_a	,				_a = 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}".'				)
													lowerCamelCase       =      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:
										lowerCamelCase       =      text





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							if self.new_user_input:
										self.past_user_inputs.append(self.new_user_input				)
							lowerCamelCase       =      None





				def      _lowerCAmelCase       ( self	,				_a				):





							"""simple docstring"""
							self.generated_responses.append(_a				)





				def      _lowerCAmelCase       ( self				):





							"""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				):





							"""simple docstring"""
							lowerCamelCase       =      f'Conversation id: {self.uuid} \n'
							for is_user, text in self.iter_texts():
										lowerCamelCase       =      """user""" if is_user else """bot"""
										output += f'{name} >> {text} \n'
							return output






@add_end_docstrings(
    UpperCAmelCase__		,      R"\n        min_length_for_response (`int`, *optional*, defaults to 32):\n            The minimum length (in number of tokens) for a response.\n        minimum_tokens (`int`, *optional*, defaults to 10):\n            The minimum length of tokens to leave for a response.\n    "		,      )
class 					__magic_name__							( UpperCAmelCase__       ):






				'''simple docstring'''





				def __init__( self	,				*_a	,				**_a				):





							"""simple docstring"""
							super().__init__(*_a	,				**_a				)
							if self.tokenizer.pad_token_id is None:
										lowerCamelCase       =      self.tokenizer.eos_token





				def      _lowerCAmelCase       ( self	,				_a=None	,				_a=None	,				_a=None	,				**_a				):





							"""simple docstring"""
							lowerCamelCase       =      {}
							lowerCamelCase       =      {}
							lowerCamelCase       =      {}

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

							if "max_length" in generate_kwargs:
										lowerCamelCase       =      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:
										lowerCamelCase       =      clean_up_tokenization_spaces

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





				def __call__( self	,				_a	,				_a=0	,				**_a				):





							"""simple docstring"""
							lowerCamelCase       =      super().__call__(_a	,				num_workers=_a	,				**_a				)
							if isinstance(_a	,				_a				) and len(_a				) == 1:
										return outputs[0]
							return outputs





				def      _lowerCAmelCase       ( self	,				_a	,				_a=32				):





							"""simple docstring"""
							if not isinstance(_a	,				_a				):
										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"""				):
										lowerCamelCase       =      self.tokenizer._build_conversation_input_ids(_a				)
							else:
										# If the tokenizer cannot handle conversations, we default to only the old version
										lowerCamelCase       =      self._legacy_parse_and_tokenize(_a				)

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





				def      _lowerCAmelCase       ( self	,				_a	,				_a=10	,				**_a				):





							"""simple docstring"""
							lowerCamelCase       =      generate_kwargs.get("""max_length"""	,				self.model.config.max_length				)

							lowerCamelCase       =      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})'				)
										lowerCamelCase       =      max_length - minimum_tokens
										lowerCamelCase       =      model_inputs["""input_ids"""][:, -trim:]
										if "attention_mask" in model_inputs:
													lowerCamelCase       =      model_inputs["""attention_mask"""][:, -trim:]
							lowerCamelCase       =      model_inputs.pop("""conversation"""				)
							lowerCamelCase       =      max_length
							lowerCamelCase       =      self.model.generate(**_a	,				**_a				)
							if self.model.config.is_encoder_decoder:
										lowerCamelCase       =      1
							else:
										lowerCamelCase       =      n
							return {"output_ids": output_ids[:, start_position:], "conversation": conversation}





				def      _lowerCAmelCase       ( self	,				_a	,				_a=True				):





							"""simple docstring"""
							lowerCamelCase       =      model_outputs["""output_ids"""]
							lowerCamelCase       =      self.tokenizer.decode(
							    output_ids[0]	,				skip_special_tokens=_a	,				clean_up_tokenization_spaces=_a	,				)
							lowerCamelCase       =      model_outputs["""conversation"""]
							conversation.mark_processed()
							conversation.append_response(_a				)
							return conversation





				def      _lowerCAmelCase       ( self	,				_a				):





							"""simple docstring"""
							lowerCamelCase       =      self.tokenizer.eos_token_id
							lowerCamelCase       =      []
							for is_user, text in conversation.iter_texts():
										if eos_token_id is not None:
													input_ids.extend(self.tokenizer.encode(_a	,				add_special_tokens=_a				) + [eos_token_id]				)
										else:
													input_ids.extend(self.tokenizer.encode(_a	,				add_special_tokens=_a				)				)

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



