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 UpperCAmelCase		:


				'''simple docstring'''






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



						"""simple docstring"""





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

						A_						:							str      = conversation_id
						A_						:							List[str]      = past_user_inputs
						A_						:							int      = generated_responses
						A_						:							Union[str, Any]      = text
				def __eq__(		self						,      lowercase   ):



						"""simple docstring"""





						if not isinstance(_UpperCAmelCase						,      _UpperCAmelCase   ):
								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						,      lowercase						,      lowercase = 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}".'''   )
										A_						:							Union[str, Any]      = 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_						:							Optional[Any]      = text
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						if self.new_user_input:
								self.past_user_inputs.append(self.new_user_input   )
						A_						:							Dict      = None
				def     lowerCAmelCase_  (		self						,      lowercase   ):



						"""simple docstring"""





						self.generated_responses.append(_UpperCAmelCase   )
				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"""





						A_						:							Union[str, Any]      = F'''Conversation id: {self.uuid} \n'''
						for is_user, text in self.iter_texts():
								A_						:							Optional[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 UpperCAmelCase		(   UpperCAmelCase__    ):


				'''simple docstring'''






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



						"""simple docstring"""





						super().__init__(*_UpperCAmelCase						,      **_UpperCAmelCase   )
						if self.tokenizer.pad_token_id is None:
								A_						:							Tuple      = self.tokenizer.eos_token
				def     lowerCAmelCase_  (		self						,      lowercase=None						,      lowercase=None						,      lowercase=None						,      **lowercase   ):



						"""simple docstring"""





						A_						:							Dict      = {}
						A_						:							str      = {}
						A_						:							Dict      = {}

						if min_length_for_response is not None:
								A_						:							List[str]      = min_length_for_response
						if minimum_tokens is not None:
								A_						:							Union[str, Any]      = minimum_tokens

						if "max_length" in generate_kwargs:
								A_						:							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:
								A_						:							Optional[Any]      = clean_up_tokenization_spaces

						if generate_kwargs:
								forward_params.update(_UpperCAmelCase   )
						return preprocess_params, forward_params, postprocess_params
				def __call__(		self						,      lowercase						,      lowercase=0						,      **lowercase   ):



						"""simple docstring"""





						A_						:							Tuple      = super().__call__(_UpperCAmelCase						,      num_workers=_UpperCAmelCase						,      **_UpperCAmelCase   )
						if isinstance(_UpperCAmelCase						,      _UpperCAmelCase   ) and len(_UpperCAmelCase   ) == 1:
								return outputs[0]
						return outputs
				def     lowerCAmelCase_  (		self						,      lowercase						,      lowercase=3_2   ):



						"""simple docstring"""





						if not isinstance(_UpperCAmelCase						,      _UpperCAmelCase   ):
								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_						:							Dict      = self.tokenizer._build_conversation_input_ids(_UpperCAmelCase   )
						else:
								# If the tokenizer cannot handle conversations, we default to only the old version
								A_						:							Dict      = self._legacy_parse_and_tokenize(_UpperCAmelCase   )

						if self.framework == "pt":
								A_						:							Union[str, Any]      = torch.LongTensor([input_ids]   )
						elif self.framework == "tf":
								A_						:							str      = tf.constant([input_ids]   )
						return {"input_ids": input_ids, "conversation": conversation}
				def     lowerCAmelCase_  (		self						,      lowercase						,      lowercase=1_0						,      **lowercase   ):



						"""simple docstring"""





						A_						:							Optional[Any]      = generate_kwargs.get('max_length'						,      self.model.config.max_length   )

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



						"""simple docstring"""





						A_						:							Optional[Any]      = model_outputs['output_ids']
						A_						:							Optional[int]      = self.tokenizer.decode(
						    output_ids[0]						,      skip_special_tokens=_UpperCAmelCase						,      clean_up_tokenization_spaces=_UpperCAmelCase						,      )
						A_						:							Dict      = model_outputs['conversation']
						conversation.mark_processed()
						conversation.append_response(_UpperCAmelCase   )
						return conversation







				def     lowerCAmelCase_  (		self						,      lowercase   ):



						"""simple docstring"""





						A_						:							Optional[Any]      = self.tokenizer.eos_token_id
						A_						:							Dict      = []
						for is_user, text in conversation.iter_texts():
								if eos_token_id is not None:
										input_ids.extend(self.tokenizer.encode(_UpperCAmelCase						,      add_special_tokens=_UpperCAmelCase   ) + [eos_token_id]   )
								else:
										input_ids.extend(self.tokenizer.encode(_UpperCAmelCase						,      add_special_tokens=_UpperCAmelCase   )   )

						if len(_UpperCAmelCase   ) > self.tokenizer.model_max_length:
								A_						:							int      = input_ids[-self.tokenizer.model_max_length :]
						return input_ids










import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF

import datasets


A			:						Dict        =  '\\n@inproceedings{popovic-2015-chrf,\n    title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",\n    author = "Popovi{\'c}, Maja",\n    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",\n    month = sep,\n    year = "2015",\n    address = "Lisbon, Portugal",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/W15-3049",\n    doi = "10.18653/v1/W15-3049",\n    pages = "392--395",\n}\n@inproceedings{popovic-2017-chrf,\n    title = "chr{F}++: words helping character n-grams",\n    author = "Popovi{\'c}, Maja",\n    booktitle = "Proceedings of the Second Conference on Machine Translation",\n    month = sep,\n    year = "2017",\n    address = "Copenhagen, Denmark",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/W17-4770",\n    doi = "10.18653/v1/W17-4770",\n    pages = "612--618",\n}\n@inproceedings{post-2018-call,\n    title = "A Call for Clarity in Reporting {BLEU} Scores",\n    author = "Post, Matt",\n    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",\n    month = oct,\n    year = "2018",\n    address = "Belgium, Brussels",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/W18-6319",\n    pages = "186--191",\n}\n'

A			:						List[str]        =  '\\nChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,\nand ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation\nthat is already present in sacrebleu.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.\n'

A			:						Any        =  '\nProduces ChrF(++) scores for hypotheses given reference translations.\n\nArgs:\n    predictions (list of str): The predicted sentences.\n    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.\n    char_order (int): Character n-gram order. Defaults to `6`.\n    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.\n    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.\n    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.\n    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.\n    eps_smoothing (bool): If `True`, applies epsilon smoothing similar\n    to reference chrF++.py, NLTK and Moses implementations. If `False`,\n    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.\n\nReturns:\n    \'score\' (float): The chrF (chrF++) score,\n    \'char_order\' (int): The character n-gram order,\n    \'word_order\' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,\n    \'beta\' (int): Determine the importance of recall w.r.t precision\n\nExamples:\n    Example 1--a simple example of calculating chrF:\n        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]\n        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]\n        >>> chrf = datasets.load_metric("chrf")\n        >>> results = chrf.compute(predictions=prediction, references=reference)\n        >>> print(results)\n        {\'score\': 84.64214891738334, \'char_order\': 6, \'word_order\': 0, \'beta\': 2}\n\n    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:\n        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]\n        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]\n        >>> chrf = datasets.load_metric("chrf")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2)\n        >>> print(results)\n        {\'score\': 82.87263732906315, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}\n\n    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:\n        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]\n        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]\n        >>> chrf = datasets.load_metric("chrf")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2,\n        ...                         lowercase=True)\n        >>> print(results)\n        {\'score\': 92.12853119829202, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}\n'



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,    _KWARGS_DESCRIPTION				)
class 				A       (			datasets.Metric				):
					'''simple docstring'''






					def     lowerCamelCase__	(self       :		Dict						)		->  Union[str, Any]:




							"""simple docstring"""


							if version.parse(scb.__version__						) < version.parse("""1.4.12"""						):
									raise ImportWarning(
									    """To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"""
									    """You can install it with `pip install \"sacrebleu>=1.4.12\"`."""						)
							return datasets.MetricInfo(
							    description=_DESCRIPTION ,   citation=_CITATION ,   homepage="""https://github.com/mjpost/sacreBLEU#chrf--chrf""" ,   inputs_description=_KWARGS_DESCRIPTION ,   features=datasets.Features(
							        {
							            """predictions""": datasets.Value("""string""" ,   id="""sequence"""						),
							            """references""": datasets.Sequence(datasets.Value("""string""" ,   id="""sequence"""						) ,   id="""references"""						),
							        }						) ,   codebase_urls=["""https://github.com/mjpost/sacreBLEU#chrf--chrf"""] ,   reference_urls=[
							        """https://github.com/m-popovic/chrF""",
							    ] ,   )


					def     lowerCamelCase__	(self       :		Optional[int] ,   _UpperCAmelCase       :		Union[str, Any] ,   _UpperCAmelCase       :		Optional[int] ,   _UpperCAmelCase       :		int = CHRF.CHAR_ORDER ,   _UpperCAmelCase       :		int = CHRF.WORD_ORDER ,   _UpperCAmelCase       :		int = CHRF.BETA ,   _UpperCAmelCase       :		bool = False ,   _UpperCAmelCase       :		bool = False ,   _UpperCAmelCase       :		bool = False ,   )		->  int:




							"""simple docstring"""


							lowercase__	     =						len(references[0]						)
							if any(len(_UpperCAmelCase						) != references_per_prediction for refs in references						):
									raise ValueError("""Sacrebleu requires the same number of references for each prediction"""						)
							lowercase__	     =						[[refs[i] for refs in references] for i in range(_UpperCAmelCase						)]

							lowercase__	     =						CHRF(_UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase						)
							lowercase__	     =						sb_chrf.corpus_score(_UpperCAmelCase ,   _UpperCAmelCase						)

							return {
							    "score": output.score,
							    "char_order": output.char_order,
							    "word_order": output.word_order,
							    "beta": output.beta,
							}

