
'''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


UpperCAmelCase__ :			Optional[Any]											=		logging.get_logger(__name__)






class  A  :





	def __init__(       self      :							Optional[Any]   ,				__magic_name__      :							str = None   ,				__magic_name__      :							uuid.UUID = None   ,				__magic_name__      :							Optional[Any]=None   ,				__magic_name__      :							Union[str, Any]=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      :							Any   ,				__magic_name__      :							Tuple				):


				"""simple docstring"""

				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 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple   ,				__magic_name__      :							str   ,				__magic_name__      :							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}\"."""				)
										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 				__SCREAMING_SNAKE_CASE	(       self      :							str				):


				"""simple docstring"""

				if self.new_user_input:
							self.past_user_inputs.append(self.new_user_input				)
				lowerCAmelCase__										=				None





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[Any]   ,				__magic_name__      :							str				):


				"""simple docstring"""

				self.generated_responses.append(__magic_name__				)





	def 				__SCREAMING_SNAKE_CASE	(       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"""

				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(
    SCREAMING_SNAKE_CASE__	,   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  A  (						SCREAMING_SNAKE_CASE__     ):





	def __init__(       self      :							Any   ,				*__magic_name__      :							str   ,				**__magic_name__      :							str				):


				"""simple docstring"""

				super().__init__(*__magic_name__   ,				**__magic_name__				)
				if self.tokenizer.pad_token_id is None:
							lowerCAmelCase__										=				self.tokenizer.eos_token





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[Any]   ,				__magic_name__      :							Union[str, Any]=None   ,				__magic_name__      :							Optional[int]=None   ,				__magic_name__      :							Optional[Any]=None   ,				**__magic_name__      :							Dict				):


				"""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(__magic_name__				)
				return preprocess_params, forward_params, postprocess_params





	def __call__(       self      :							List[str]   ,				__magic_name__      :							Union[Conversation, List[Conversation]]   ,				__magic_name__      :							Tuple=0   ,				**__magic_name__      :							Optional[int]				):


				"""simple docstring"""

				lowerCAmelCase__										=				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 				__SCREAMING_SNAKE_CASE	(       self      :							List[str]   ,				__magic_name__      :							Conversation   ,				__magic_name__      :							str=32				):


				"""simple docstring"""

				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"				):
							lowerCAmelCase__										=				self.tokenizer._build_conversation_input_ids(__magic_name__				)
				else:
							# If the tokenizer cannot handle conversations, we default to only the old version
							lowerCAmelCase__										=				self._legacy_parse_and_tokenize(__magic_name__				)

				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 				__SCREAMING_SNAKE_CASE	(       self      :							List[str]   ,				__magic_name__      :							int   ,				__magic_name__      :							Union[str, Any]=10   ,				**__magic_name__      :							int				):


				"""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(**__magic_name__   ,				**__magic_name__				)
				if self.model.config.is_encoder_decoder:
							lowerCAmelCase__										=				1
				else:
							lowerCAmelCase__										=				n
				return {"output_ids": output_ids[:, start_position:], "conversation": conversation}





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[Any]   ,				__magic_name__      :							List[Any]   ,				__magic_name__      :							Dict=True				):


				"""simple docstring"""

				lowerCAmelCase__										=				model_outputs["output_ids"]
				lowerCAmelCase__										=				self.tokenizer.decode(
				    output_ids[0]   ,				skip_special_tokens=__magic_name__   ,				clean_up_tokenization_spaces=__magic_name__   ,				)
				lowerCAmelCase__										=				model_outputs["conversation"]
				conversation.mark_processed()
				conversation.append_response(__magic_name__				)
				return conversation







	def 				__SCREAMING_SNAKE_CASE	(       self      :							str   ,				__magic_name__      :							Conversation				):


				"""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(__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:
							lowerCAmelCase__										=				input_ids[-self.tokenizer.model_max_length :]
				return input_ids












__UpperCamelCase:	str = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}

__UpperCamelCase:	Tuple = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : dict[int, list[int]]   ,    _lowercase     : int   ,    _lowercase     : list[bool]			)   ->							list[int]:




		'''simple docstring'''
		lowercase__					:      str      =		True
		lowercase__					:      int      =		[]

		for neighbour in graph[vert]:
				if not visited[neighbour]:
						order += topology_sort(_lowercase   ,    _lowercase   ,    _lowercase			)

		order.append(_lowercase			)

		return order


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : dict[int, list[int]]   ,    _lowercase     : int   ,    _lowercase     : list[bool]			)   ->							list[int]:




		'''simple docstring'''
		lowercase__					:      Union[str, Any]      =		True
		lowercase__					:      Optional[Any]      =		[vert]

		for neighbour in reversed_graph[vert]:
				if not visited[neighbour]:
						component += find_components(_lowercase   ,    _lowercase   ,    _lowercase			)

		return component





def       SCREAMING_SNAKE_CASE__  (       _lowercase     : dict[int, list[int]]			)   ->							list[list[int]]:




		'''simple docstring'''
		lowercase__					:      Optional[int]      =		len(_lowercase			) * [False]
		lowercase__					:      dict[int, list[int]]      =		{vert: [] for vert in range(len(_lowercase			)			)}

		for vert, neighbours in graph.items():
				for neighbour in neighbours:
						reversed_graph[neighbour].append(_lowercase			)

		lowercase__					:      Dict      =		[]
		for i, was_visited in enumerate(_lowercase			):
				if not was_visited:
						order += topology_sort(_lowercase   ,    _lowercase   ,    _lowercase			)

		lowercase__					:      Dict      =		[]
		lowercase__					:      Optional[Any]      =		len(_lowercase			) * [False]

		for i in range(len(_lowercase			)			):
				lowercase__					:      List[Any]      =		order[len(_lowercase			) - i - 1]
				if not visited[vert]:
						lowercase__					:      Tuple      =		find_components(_lowercase   ,    _lowercase   ,    _lowercase			)
						components_list.append(_lowercase			)

		return components_list


