




"""simple docstring"""




import re
import string

import numpy as np

import datasets


lowerCAmelCase							:    Optional[Any]  							=							"""
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
"""

lowerCAmelCase							:    Tuple  							=							"""
Args:
    predictions: List of predicted texts.
    references: List of reference texts.
    regexes_to_ignore: List, defaults to None. Regex expressions of characters to
        ignore when calculating the exact matches. Note: these regexes are removed
        from the input data before the changes based on the options below (e.g. ignore_case,
        ignore_punctuation, ignore_numbers) are applied.
    ignore_case: Boolean, defaults to False. If true, turns everything
        to lowercase so that capitalization differences are ignored.
    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before
        comparing predictions and references.
    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before
        comparing predictions and references.
Returns:
    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.
Examples:
    >>> exact_match = datasets.load_metric(\"exact_match\")
    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
    >>> results = exact_match.compute(references=refs, predictions=preds)
    >>> print(round(results[\"exact_match\"], 1))
    25.0

    >>> exact_match = datasets.load_metric(\"exact_match\")
    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\"], ignore_case=True, ignore_punctuation=True)
    >>> print(round(results[\"exact_match\"], 1))
    50.0


    >>> exact_match = datasets.load_metric(\"exact_match\")
    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True)
    >>> print(round(results[\"exact_match\"], 1))
    75.0

    >>> exact_match = datasets.load_metric(\"exact_match\")
    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)
    >>> print(round(results[\"exact_match\"], 1))
    100.0

    >>> exact_match = datasets.load_metric(\"exact_match\")
    >>> refs = [\"The cat sat on the mat.\", \"Theaters are great.\", \"It's like comparing oranges and apples.\"]
    >>> preds = [\"The cat sat on the mat?\", \"Theaters are great.\", \"It's like comparing apples and oranges.\"]
    >>> results = exact_match.compute(references=refs, predictions=preds)
    >>> print(round(results[\"exact_match\"], 1))
    33.3

"""

lowerCAmelCase							:    Dict  							=							"""
"""






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,      _KWARGS_DESCRIPTION       )
class 					__magic_name__							( datasets.Metric       ):






				'''simple docstring'''





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							return datasets.MetricInfo(
							    description=_DESCRIPTION	,				citation=_CITATION	,				inputs_description=_KWARGS_DESCRIPTION	,				features=datasets.Features(
							        {
							            """predictions""": datasets.Value("""string"""	,				id="""sequence"""				),
							            """references""": datasets.Value("""string"""	,				id="""sequence"""				),
							        }				)	,				reference_urls=[]	,				)





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





							"""simple docstring"""
							if regexes_to_ignore is not None:
										for s in regexes_to_ignore:
													lowerCamelCase       =      np.array([re.sub(_a	,				""""""	,				_a				) for x in predictions]				)
													lowerCamelCase       =      np.array([re.sub(_a	,				""""""	,				_a				) for x in references]				)
							else:
										lowerCamelCase       =      np.asarray(_a				)
										lowerCamelCase       =      np.asarray(_a				)

							if ignore_case:
										lowerCamelCase       =      np.char.lower(_a				)
										lowerCamelCase       =      np.char.lower(_a				)

							if ignore_punctuation:
										lowerCamelCase       =      string.punctuation.maketrans(""""""	,				""""""	,				string.punctuation				)
										lowerCamelCase       =      np.char.translate(_a	,				table=_a				)
										lowerCamelCase       =      np.char.translate(_a	,				table=_a				)

							if ignore_numbers:
										lowerCamelCase       =      string.digits.maketrans(""""""	,				""""""	,				string.digits				)
										lowerCamelCase       =      np.char.translate(_a	,				table=_a				)
										lowerCamelCase       =      np.char.translate(_a	,				table=_a				)

							lowerCamelCase       =      predictions == references

							return {"exact_match": np.mean(_a				) * 100}









"""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



