





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


_SCREAMING_SNAKE_CASE 		=						logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE_  :






						def __init__( self : List[str]						,				_A : str = None						,				_A : uuid.UUID = None						,				_A : str=None						,				_A : Optional[Any]=None		)							->      Dict:







							"""simple docstring"""


							if not conversation_id:
								snake_case_						:     List[Any]     =		uuid.uuida()
							if past_user_inputs is None:
								snake_case_						:     Union[str, Any]     =		[]
							if generated_responses is None:
								snake_case_						:     Optional[int]     =		[]

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






						def __eq__( self : List[str]						,				_A : Union[str, Any]		)							->      str:







							"""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 UpperCAmelCase_			( self : List[Any]						,				_A : str						,				_A : bool = False		)							->      List[str]:







							"""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}\"."""		)
									snake_case_						:     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:
								snake_case_						:     Union[str, Any]     =		text






						def UpperCAmelCase_			( self : Optional[int]		)							->      Union[str, Any]:







							"""simple docstring"""


							if self.new_user_input:
								self.past_user_inputs.append(self.new_user_input		)
							snake_case_						:     Dict     =		None






						def UpperCAmelCase_			( self : Union[str, Any]						,				_A : str		)							->      Union[str, Any]:







							"""simple docstring"""


							self.generated_responses.append(_A		)






						def UpperCAmelCase_			( self : Tuple		)							->      Union[str, Any]:







							"""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 : Tuple		)							->      str:







							"""simple docstring"""


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






						def __init__( self : Union[str, Any]						,				*_A : Dict						,				**_A : Any		)							->      Optional[Any]:







							"""simple docstring"""


							super().__init__(*_A						,				**_A		)
							if self.tokenizer.pad_token_id is None:
								snake_case_						:     str     =		self.tokenizer.eos_token






						def UpperCAmelCase_			( self : int						,				_A : Optional[int]=None						,				_A : Optional[int]=None						,				_A : List[Any]=None						,				**_A : Tuple		)							->      List[Any]:







							"""simple docstring"""


							snake_case_						:     str     =		{}
							snake_case_						:     Dict     =		{}
							snake_case_						:     Any     =		{}

							if min_length_for_response is not None:
								snake_case_						:     Any     =		min_length_for_response
							if minimum_tokens is not None:
								snake_case_						:     Any     =		minimum_tokens

							if "max_length" in generate_kwargs:
								snake_case_						:     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:
								snake_case_						:     List[Any]     =		clean_up_tokenization_spaces

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






						def __call__( self : List[Any]						,				_A : Union[Conversation, List[Conversation]]						,				_A : List[Any]=0						,				**_A : Optional[int]		)							->      str:







							"""simple docstring"""


							snake_case_						:     Dict     =		super().__call__(_A						,				num_workers=_A						,				**_A		)
							if isinstance(_A						,				_A		) and len(_A		) == 1:
								return outputs[0]
							return outputs






						def UpperCAmelCase_			( self : Optional[int]						,				_A : Conversation						,				_A : str=32		)							->      Dict[str, Any]:







							"""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'		):
								snake_case_						:     List[str]     =		self.tokenizer._build_conversation_input_ids(_A		)
							else:
								# If the tokenizer cannot handle conversations, we default to only the old version
								snake_case_						:     Dict     =		self._legacy_parse_and_tokenize(_A		)

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






						def UpperCAmelCase_			( self : List[Any]						,				_A : str						,				_A : Union[str, Any]=10						,				**_A : Tuple		)							->      Union[str, Any]:







							"""simple docstring"""


							snake_case_						:     str     =		generate_kwargs.get('max_length'						,				self.model.config.max_length		)

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






						def UpperCAmelCase_			( self : List[Any]						,				_A : List[str]						,				_A : str=True		)							->      Any:







							"""simple docstring"""


							snake_case_						:     int     =		model_outputs['output_ids']
							snake_case_						:     List[str]     =		self.tokenizer.decode(
							    output_ids[0]						,				skip_special_tokens=_A						,				clean_up_tokenization_spaces=_A						,				)
							snake_case_						:     int     =		model_outputs['conversation']
							conversation.mark_processed()
							conversation.append_response(_A		)
							return conversation






						def UpperCAmelCase_			( self : Union[str, Any]						,				_A : Conversation		)							->      Dict:







							"""simple docstring"""


							snake_case_						:     List[Any]     =		self.tokenizer.eos_token_id
							snake_case_						:     Union[str, Any]     =		[]
							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:
								snake_case_						:     Optional[int]     =		input_ids[-self.tokenizer.model_max_length :]
							return input_ids








# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


_SCREAMING_SNAKE_CASE 		=						subprocess.check_output("""git merge-base main HEAD""".split()).decode("""utf-8""")
_SCREAMING_SNAKE_CASE 		=						(
    subprocess.check_output(F'''git diff --diff-filter=d --name-only {fork_point_sha}'''.split()).decode("""utf-8""").split()
)

_SCREAMING_SNAKE_CASE 		=						"""|""".join(sys.argv[1:])
_SCREAMING_SNAKE_CASE 		=						re.compile(RF'''^({joined_dirs}).*?\.py$''')

_SCREAMING_SNAKE_CASE 		=						[x for x in modified_files if regex.match(x)]
print(""" """.join(relevant_modified_files), end="""""")

