






import argparse
import json
import re
import string
import sys
from collections import Counter


def      lowerCamelCase__	(   lowercase ):

  """simple docstring"""






  def remove_articles(lowercase ):
    return re.sub(R"\b(a|an|the)\b"  ,   " "  ,   lowercase )

  def white_space_fix(lowercase ):
    return " ".join(text.split() )

  def remove_punc(lowercase ):
    SCREAMING_SNAKE_CASE				:					Tuple		  =			set(string.punctuation )
    return "".join(ch for ch in text if ch not in exclude )

  def lower(lowercase ):
    return text.lower()

  return white_space_fix(remove_articles(remove_punc(lower(lowercase ) ) ) )


def      lowerCamelCase__	(   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			normalize_answer(lowercase ).split()
  SCREAMING_SNAKE_CASE				:					Dict		  =			normalize_answer(lowercase ).split()
  SCREAMING_SNAKE_CASE				:					Any		  =			Counter(lowercase ) & Counter(lowercase )
  SCREAMING_SNAKE_CASE				:					List[Any]		  =			sum(common.values() )
  if num_same == 0:
    return 0
  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			1.0 * num_same / len(lowercase )
  SCREAMING_SNAKE_CASE				:					Any		  =			1.0 * num_same / len(lowercase )
  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			(2 * precision * recall) / (precision + recall)
  return fa


def      lowerCamelCase__	(   lowercase  ,   lowercase ):

  """simple docstring"""






  return normalize_answer(lowercase ) == normalize_answer(lowercase )


def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					str		  =			[]
  for ground_truth in ground_truths:
    SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			metric_fn(lowercase  ,   lowercase )
    scores_for_ground_truths.append(lowercase )
  return max(lowercase )


def      lowerCamelCase__	(   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Tuple		  =			0
  for article in dataset:
    for paragraph in article["paragraphs"]:
      for qa in paragraph["qas"]:
        total += 1
        if qa["id"] not in predictions:
          SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			"Unanswered question " + qa["id"] + " will receive score 0."
          print(lowercase  ,   file=sys.stderr )
          continue
        SCREAMING_SNAKE_CASE				:					int		  =			[x["text"] for x in qa["answers"]]
        SCREAMING_SNAKE_CASE				:					int		  =			predictions[qa["id"]]
        exact_match += metric_max_over_ground_truths(lowercase  ,   lowercase  ,   lowercase )
        fa += metric_max_over_ground_truths(lowercase  ,   lowercase  ,   lowercase )

  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			100.0 * exact_match / total
  SCREAMING_SNAKE_CASE				:					Dict		  =			100.0 * fa / total

  return {"exact_match": exact_match, "f1": fa}


if __name__ == "__main__":
   snake_case			 =						'1.1'
   snake_case			 =						argparse.ArgumentParser(description="""Evaluation for SQuAD """ + expected_version)
   parser.add_argument("""dataset_file""", help="""Dataset file""")
   parser.add_argument("""prediction_file""", help="""Prediction File""")
   snake_case			 =						parser.parse_args()
   with open(args.dataset_file) as dataset_file:
      snake_case			 =						json.load(dataset_file)
      if dataset_json["version"] != expected_version:
         print(
             """Evaluation expects v-""" + expected_version + """, but got dataset with v-""" + dataset_json["""version"""],
             file=sys.stderr,
         )
      snake_case			 =						dataset_json['data']
   with open(args.prediction_file) as prediction_file:
      snake_case			 =						json.load(prediction_file)
   print(json.dumps(evaluate(dataset, predictions)))








'''simple docstring'''
import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


lowerCAmelCase :  Union[str, Any]      =      logging.get_logger(__name__)

lowerCAmelCase :  List[Any]      =      {
    'facebook/musicgen-small': 'https://huggingface.co/facebook/musicgen-small/resolve/main/config.json',
    # See all Musicgen models at https://huggingface.co/models?filter=musicgen
}


class        SCREAMING_SNAKE_CASE__	(			snake_case_):
				lowerCAmelCase_			       =				"""musicgen_decoder"""
				lowerCAmelCase_			       =				["""past_key_values"""]







				def __init__(    self		,							A_=2048		,							A_=2048		,							A_=24		,							A_=4096		,							A_=16		,							A_=0.0		,							A_=True		,							A_="gelu"		,							A_=1024		,							A_=0.1		,							A_=0.0		,							A_=0.0		,							A_=0.02		,							A_=False		,							A_=4		,							A_=2048		,							A_=2048		,							A_=None		,							A_=False		,							**A_		,							)->						Any:







											'''simple docstring'''






											UpperCamelCase					       =			vocab_size
											UpperCamelCase					       =			max_position_embeddings
											UpperCamelCase					       =			hidden_size
											UpperCamelCase					       =			ffn_dim
											UpperCamelCase					       =			num_hidden_layers
											UpperCamelCase					       =			num_attention_heads
											UpperCamelCase					       =			dropout
											UpperCamelCase					       =			attention_dropout
											UpperCamelCase					       =			activation_dropout
											UpperCamelCase					       =			activation_function
											UpperCamelCase					       =			initializer_factor
											UpperCamelCase					       =			layerdrop
											UpperCamelCase					       =			use_cache
											UpperCamelCase					       =			scale_embedding  # scale factor will be sqrt(d_model) if True
											UpperCamelCase					       =			num_codebooks
											super().__init__(
											    pad_token_id=A_		,							bos_token_id=A_		,							eos_token_id=A_		,							tie_word_embeddings=A_		,							**A_		,							)


class        SCREAMING_SNAKE_CASE__	(			snake_case_):
				lowerCAmelCase_			       =				"""musicgen"""
				lowerCAmelCase_			       =				True







				def __init__(    self		,							**A_			)->						Union[str, Any]:







											'''simple docstring'''






											super().__init__(**A_			)
											if "text_encoder" not in kwargs or "audio_encoder" not in kwargs or "decoder" not in kwargs:
																		raise ValueError('Config has to be initialized with text_encoder, audio_encoder and decoder config'			)

											UpperCamelCase					       =			kwargs.pop('text_encoder'			)
											UpperCamelCase					       =			text_encoder_config.pop('model_type'			)

											UpperCamelCase					       =			kwargs.pop('audio_encoder'			)
											UpperCamelCase					       =			audio_encoder_config.pop('model_type'			)

											UpperCamelCase					       =			kwargs.pop('decoder'			)

											UpperCamelCase					       =			AutoConfig.for_model(A_		,							**A_			)
											UpperCamelCase					       =			AutoConfig.for_model(A_		,							**A_			)
											UpperCamelCase					       =			MusicgenDecoderConfig(**A_			)
											UpperCamelCase					       =			True







				@classmethod
				def 		UpperCAmelCase_  (    cls		,							A_		,							A_		,							A_		,							**A_		,							)->						Tuple:







											'''simple docstring'''






											return cls(
											    text_encoder=text_encoder_config.to_dict()		,							audio_encoder=audio_encoder_config.to_dict()		,							decoder=decoder_config.to_dict()		,							**A_		,							)







				def 		UpperCAmelCase_  (    self			)->						Optional[Any]:







											'''simple docstring'''






											UpperCamelCase					       =			copy.deepcopy(self.__dict__			)
											UpperCamelCase					       =			self.text_encoder.to_dict()
											UpperCamelCase					       =			self.audio_encoder.to_dict()
											UpperCamelCase					       =			self.decoder.to_dict()
											UpperCamelCase					       =			self.__class__.model_type
											return output




