
import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


lowerCamelCase_  :       Any					      =     logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()







def 				A__    (  lowerCamelCase  )     ->		str:
				if "token" in model_name_or_path:
								return "rag_token"
				if "sequence" in model_name_or_path:
								return "rag_sequence"
				if "bart" in model_name_or_path:
								return "bart"
				return None







def 				A__    (  lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase  )     ->		Dict:
				return max(metric_fn(lowerCamelCase   ,			lowerCamelCase  ) for gt in ground_truths  )







def 				A__    (  lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase  )     ->		List[str]:
				UpperCamelCase_:							int		=  [line.strip() for line in open(lowerCamelCase   ,			"""r"""  ).readlines()]
				UpperCamelCase_:							Optional[int]		=  []

				if args.gold_data_mode == "qa":
								UpperCamelCase_:							int		=  pd.read_csv(lowerCamelCase   ,			sep="""\t"""   ,			header=lowerCamelCase  )
								for answer_list in data[1]:
												UpperCamelCase_:							Optional[Any]		=  ast.literal_eval(lowerCamelCase  )
												answers.append(lowerCamelCase  )
				else:
								UpperCamelCase_:							Optional[Any]		=  [line.strip() for line in open(lowerCamelCase   ,			"""r"""  ).readlines()]
								UpperCamelCase_:							Optional[Any]		=  [[reference] for reference in references]

				UpperCamelCase_:							Any		=  0
				for prediction, ground_truths in zip(lowerCamelCase   ,			lowerCamelCase  ):
								total += 1
								em += metric_max_over_ground_truths(lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase  )
								fa += metric_max_over_ground_truths(lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase  )

				UpperCamelCase_:							str		=  100.0 * em / total
				UpperCamelCase_:							Optional[Any]		=  100.0 * fa / total

				logger.info(F'''F1: {fa:.2f}'''  )
				logger.info(F'''EM: {em:.2f}'''  )







def 				A__    (  lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase  )     ->		List[Any]:
				UpperCamelCase_:							List[Any]		=  args.k
				UpperCamelCase_:							List[Any]		=  [line.strip() for line in open(lowerCamelCase   ,			"""r"""  ).readlines()]
				UpperCamelCase_:							int		=  [line.strip() for line in open(lowerCamelCase   ,			"""r"""  ).readlines()]

				UpperCamelCase_:							List[str]		=  0
				for hypo, reference in zip(lowerCamelCase   ,			lowerCamelCase  ):
								UpperCamelCase_:							Dict		=  set(hypo.split("""\t"""  )[:k]  )
								UpperCamelCase_:							str		=  set(reference.split("""\t"""  )  )
								total += 1
								em += len(hypo_provenance & ref_provenance  ) / k

				UpperCamelCase_:							Optional[Any]		=  100.0 * em / total
				logger.info(F'''Precision@{k}: {em: .2f}'''  )







def 				A__    (  lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase  )     ->		Optional[int]:
				def strip_title(lowerCamelCase  ):
								if title.startswith("""\""""  ):
												UpperCamelCase_:							Optional[Any]		=  title[1:]
								if title.endswith("""\""""  ):
												UpperCamelCase_:							Tuple		=  title[:-1]
								return title

				UpperCamelCase_:							Union[str, Any]		=  rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
				    lowerCamelCase   ,			return_tensors="""pt"""   ,			padding=lowerCamelCase   ,			truncation=lowerCamelCase   ,			)["""input_ids"""].to(args.device  )

				UpperCamelCase_:							Optional[Any]		=  rag_model.rag.question_encoder(lowerCamelCase  )
				UpperCamelCase_:							Optional[int]		=  question_enc_outputs[0]

				UpperCamelCase_:							Optional[Any]		=  rag_model.retriever(
				    lowerCamelCase   ,			question_enc_pool_output.cpu().detach().to(torch.floataa  ).numpy()   ,			prefix=rag_model.rag.generator.config.prefix   ,			n_docs=rag_model.config.n_docs   ,			return_tensors="""pt"""   ,			)
				UpperCamelCase_:							str		=  rag_model.retriever.index.get_doc_dicts(result.doc_ids  )
				UpperCamelCase_:							Any		=  []
				for docs in all_docs:
								UpperCamelCase_:							List[Any]		=  [strip_title(lowerCamelCase  ) for title in docs["""title"""]]
								provenance_strings.append("""\t""".join(lowerCamelCase  )  )
				return provenance_strings







def 				A__    (  lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase  )     ->		str:
				with torch.no_grad():
								UpperCamelCase_:							Union[str, Any]		=  rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
								    lowerCamelCase   ,			return_tensors="""pt"""   ,			padding=lowerCamelCase   ,			truncation=lowerCamelCase  )

								UpperCamelCase_:							Optional[Any]		=  inputs_dict.input_ids.to(args.device  )
								UpperCamelCase_:							Tuple		=  inputs_dict.attention_mask.to(args.device  )
								UpperCamelCase_:							List[Any]		=  rag_model.generate(  # rag_model overwrites generate
								    lowerCamelCase   ,			attention_mask=lowerCamelCase   ,			num_beams=args.num_beams   ,			min_length=args.min_length   ,			max_length=args.max_length   ,			early_stopping=lowerCamelCase   ,			num_return_sequences=1   ,			bad_words_ids=[[0, 0]]   ,			)
								UpperCamelCase_:							Optional[Any]		=  rag_model.retriever.generator_tokenizer.batch_decode(lowerCamelCase   ,			skip_special_tokens=lowerCamelCase  )

								if args.print_predictions:
												for q, a in zip(lowerCamelCase   ,			lowerCamelCase  ):
																logger.info("""Q: {} - A: {}""".format(lowerCamelCase   ,			lowerCamelCase  )  )

								return answers







def 				A__    (  )     ->		Dict:
				UpperCamelCase_:							Union[str, Any]		=  argparse.ArgumentParser()
				parser.add_argument(
				    """--model_type"""   ,			choices=["""rag_sequence""", """rag_token""", """bart"""]   ,			type=lowerCamelCase   ,			help=(
				        """RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the"""
				        """ model_name_or_path"""
				    )   ,			)
				parser.add_argument(
				    """--index_name"""   ,			default=lowerCamelCase   ,			choices=["""exact""", """compressed""", """legacy"""]   ,			type=lowerCamelCase   ,			help="""RAG model retriever type"""   ,			)
				parser.add_argument(
				    """--index_path"""   ,			default=lowerCamelCase   ,			type=lowerCamelCase   ,			help="""Path to the retrieval index"""   ,			)
				parser.add_argument("""--n_docs"""   ,			default=5   ,			type=lowerCamelCase   ,			help="""Number of retrieved docs"""  )
				parser.add_argument(
				    """--model_name_or_path"""   ,			default=lowerCamelCase   ,			type=lowerCamelCase   ,			required=lowerCamelCase   ,			help="""Path to pretrained checkpoints or model identifier from huggingface.co/models"""   ,			)
				parser.add_argument(
				    """--eval_mode"""   ,			choices=["""e2e""", """retrieval"""]   ,			default="""e2e"""   ,			type=lowerCamelCase   ,			help=(
				        """Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"""
				        """ precision@k."""
				    )   ,			)
				parser.add_argument("""--k"""   ,			default=1   ,			type=lowerCamelCase   ,			help="""k for the precision@k calculation"""  )
				parser.add_argument(
				    """--evaluation_set"""   ,			default=lowerCamelCase   ,			type=lowerCamelCase   ,			required=lowerCamelCase   ,			help="""Path to a file containing evaluation samples"""   ,			)
				parser.add_argument(
				    """--gold_data_path"""   ,			default=lowerCamelCase   ,			type=lowerCamelCase   ,			required=lowerCamelCase   ,			help="""Path to a tab-separated file with gold samples"""   ,			)
				parser.add_argument(
				    """--gold_data_mode"""   ,			default="""qa"""   ,			type=lowerCamelCase   ,			choices=["""qa""", """ans"""]   ,			help=(
				        """Format of the gold data file"""
				        """qa - a single line in the following format: question [tab] answer_list"""
				        """ans - a single line of the gold file contains the expected answer string"""
				    )   ,			)
				parser.add_argument(
				    """--predictions_path"""   ,			type=lowerCamelCase   ,			default="""predictions.txt"""   ,			help="""Name of the predictions file, to be stored in the checkpoints directory"""   ,			)
				parser.add_argument(
				    """--eval_all_checkpoints"""   ,			action="""store_true"""   ,			help="""Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number"""   ,			)
				parser.add_argument(
				    """--eval_batch_size"""   ,			default=8   ,			type=lowerCamelCase   ,			help="""Batch size per GPU/CPU for evaluation."""   ,			)
				parser.add_argument(
				    """--recalculate"""   ,			help="""Recalculate predictions even if the prediction file exists"""   ,			action="""store_true"""   ,			)
				parser.add_argument(
				    """--num_beams"""   ,			default=4   ,			type=lowerCamelCase   ,			help="""Number of beams to be used when generating answers"""   ,			)
				parser.add_argument("""--min_length"""   ,			default=1   ,			type=lowerCamelCase   ,			help="""Min length of the generated answers"""  )
				parser.add_argument("""--max_length"""   ,			default=50   ,			type=lowerCamelCase   ,			help="""Max length of the generated answers"""  )

				parser.add_argument(
				    """--print_predictions"""   ,			action="""store_true"""   ,			help="""If True, prints predictions while evaluating."""   ,			)
				parser.add_argument(
				    """--print_docs"""   ,			action="""store_true"""   ,			help="""If True, prints docs retried while generating."""   ,			)
				UpperCamelCase_:							Any		=  parser.parse_args()
				UpperCamelCase_:							List[Any]		=  torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""  )
				return args







def 				A__    (  lowerCamelCase  )     ->		List[str]:
				UpperCamelCase_:							List[str]		=  {}
				if args.model_type is None:
								UpperCamelCase_:							Optional[Any]		=  infer_model_type(args.model_name_or_path  )
								assert args.model_type is not None
				if args.model_type.startswith("""rag"""  ):
								UpperCamelCase_:							Optional[int]		=  RagTokenForGeneration if args.model_type == """rag_token""" else RagSequenceForGeneration
								UpperCamelCase_:							List[str]		=  args.n_docs
								if args.index_name is not None:
												UpperCamelCase_:							str		=  args.index_name
								if args.index_path is not None:
												UpperCamelCase_:							Dict		=  args.index_path
				else:
								UpperCamelCase_:							Tuple		=  BartForConditionalGeneration

				UpperCamelCase_:							List[str]		=  (
				    [f.path for f in os.scandir(args.model_name_or_path  ) if f.is_dir()]
				    if args.eval_all_checkpoints
				    else [args.model_name_or_path]
				)

				logger.info("""Evaluate the following checkpoints: %s"""   ,			lowerCamelCase  )

				UpperCamelCase_:							str		=  get_scores if args.eval_mode == """e2e""" else get_precision_at_k
				UpperCamelCase_:							Optional[Any]		=  evaluate_batch_eae if args.eval_mode == """e2e""" else evaluate_batch_retrieval

				for checkpoint in checkpoints:
								if os.path.exists(args.predictions_path  ) and (not args.recalculate):
												logger.info("""Calculating metrics based on an existing predictions file: {}""".format(args.predictions_path  )  )
												score_fn(lowerCamelCase   ,			args.predictions_path   ,			args.gold_data_path  )
												continue

								logger.info("""***** Running evaluation for {} *****""".format(lowerCamelCase  )  )
								logger.info("""  Batch size = %d"""   ,			args.eval_batch_size  )
								logger.info("""  Predictions will be stored under {}""".format(args.predictions_path  )  )

								if args.model_type.startswith("""rag"""  ):
												UpperCamelCase_:							List[str]		=  RagRetriever.from_pretrained(lowerCamelCase   ,			**lowerCamelCase  )
												UpperCamelCase_:							Optional[int]		=  model_class.from_pretrained(lowerCamelCase   ,			retriever=lowerCamelCase   ,			**lowerCamelCase  )
												model.retriever.init_retrieval()
								else:
												UpperCamelCase_:							Dict		=  model_class.from_pretrained(lowerCamelCase   ,			**lowerCamelCase  )
								model.to(args.device  )

								with open(args.evaluation_set   ,			"""r"""  ) as eval_file, open(args.predictions_path   ,			"""w"""  ) as preds_file:
												UpperCamelCase_:							Tuple		=  []
												for line in tqdm(lowerCamelCase  ):
																questions.append(line.strip()  )
																if len(lowerCamelCase  ) == args.eval_batch_size:
																				UpperCamelCase_:							str		=  evaluate_batch_fn(lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase  )
																				preds_file.write("""\n""".join(lowerCamelCase  ) + """\n"""  )
																				preds_file.flush()
																				UpperCamelCase_:							Optional[int]		=  []
												if len(lowerCamelCase  ) > 0:
																UpperCamelCase_:							Tuple		=  evaluate_batch_fn(lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase  )
																preds_file.write("""\n""".join(lowerCamelCase  )  )
																preds_file.flush()

												score_fn(lowerCamelCase   ,			args.predictions_path   ,			args.gold_data_path  )


if __name__ == "__main__":
							lowerCamelCase_  :       Any					      =     get_args()
							main(args)








import argparse
import os
from functools import reduce

import fairseq
import torch
from datasets import load_dataset

from transformers import WavaVecaProcessor, logging
from transformers.models.dataavec.configuration_dataavec_audio import DataaVecAudioConfig

# Copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_audio.py
from transformers.models.dataavec.dataavec_audio import DataaVecAudioModel as Dummy  # noqa: F401
from transformers.models.dataavec.modeling_dataavec_audio import DataaVecAudioForCTC, DataaVecAudioModel


logging.set_verbosity_info()
lowerCamelCase_  :       int					      =     logging.get_logger(__name__)

lowerCamelCase_  :       Optional[int]					      =     {
    """post_extract_proj""": """feature_projection.projection""",
    """models.0.layer_norm""": """feature_projection.layer_norm""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}
lowerCamelCase_  :       Any					      =     [
    """lm_head""",
]







def 				A__    (  lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase  )     ->		List[Any]:
				for attribute in key.split("""."""  ):
								UpperCamelCase_:							List[str]		=  getattr(lowerCamelCase   ,			lowerCamelCase  )

				if weight_type is not None:
								UpperCamelCase_:							Tuple		=  getattr(lowerCamelCase   ,			lowerCamelCase  ).shape
				else:
								UpperCamelCase_:							List[Any]		=  hf_pointer.shape

				if hf_shape != value.shape:
								raise ValueError(
								    F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
								    F''' {value.shape} for {full_name}'''  )

				if weight_type == "weight":
								UpperCamelCase_:							str		=  value
				elif weight_type == "weight_g":
								UpperCamelCase_:							List[str]		=  value
				elif weight_type == "weight_v":
								UpperCamelCase_:							Tuple		=  value
				elif weight_type == "bias":
								UpperCamelCase_:							Any		=  value
				else:
								UpperCamelCase_:							Dict		=  value

				logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''  )







def 				A__    (  lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase  )     ->		Union[str, Any]:
				UpperCamelCase_:							Optional[int]		=  []
				UpperCamelCase_:							Tuple		=  fairseq_model.state_dict()

				if not is_headless:
								UpperCamelCase_:							Dict		=  hf_model.dataavec_audio.feature_extractor
								UpperCamelCase_:							Tuple		=  hf_model.dataavec_audio.encoder.pos_conv_embed

				else:
								UpperCamelCase_:							Tuple		=  hf_model.feature_extractor
								UpperCamelCase_:							int		=  hf_model.encoder.pos_conv_embed

				for name, value in fairseq_dict.items():
								UpperCamelCase_:							Union[str, Any]		=  False
								if "conv_layers" in name:
												load_conv_layer(
												    lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			)
												UpperCamelCase_:							Optional[Any]		=  True
								elif "pos_conv" in name:
												load_pos_conv_layer(
												    lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			)
												UpperCamelCase_:							Optional[Any]		=  True
								else:
												for key, mapped_key in MAPPING.items():
																if not is_headless:
																				UpperCamelCase_:							List[str]		=  """data2vec_audio.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
																if key in name or key.split("""w2v_model."""  )[-1] == name.split("""."""  )[0]:
																				UpperCamelCase_:							str		=  True
																				if "*" in mapped_key:
																								UpperCamelCase_:							Any		=  name.split(lowerCamelCase  )[0].split("""."""  )[-2]
																								UpperCamelCase_:							int		=  mapped_key.replace("""*"""   ,			lowerCamelCase  )
																				if "weight_g" in name:
																								UpperCamelCase_:							Tuple		=  """weight_g"""
																				elif "weight_v" in name:
																								UpperCamelCase_:							List[Any]		=  """weight_v"""
																				elif "bias" in name:
																								UpperCamelCase_:							Union[str, Any]		=  """bias"""
																				elif "weight" in name:
																								# TODO: don't match quantizer.weight_proj
																								UpperCamelCase_:							str		=  """weight"""
																				else:
																								UpperCamelCase_:							Tuple		=  None
																				set_recursively(lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase  )
																continue
								if not is_used:
												unused_weights.append(lowerCamelCase  )

				logger.warning(F'''Unused weights: {unused_weights}'''  )







def 				A__    (  lowerCamelCase   ,			lowerCamelCase  )     ->		Dict:
				UpperCamelCase_:							Union[str, Any]		=  path.split("""."""  )
				return reduce(lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase  )







def 				A__    (  lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase  )     ->		str:
				UpperCamelCase_:							Union[str, Any]		=  access_by_string(lowerCamelCase   ,			lowerCamelCase  )
				UpperCamelCase_:							str		=  hf_weight.data

				if fsq_value.shape != hf_value.shape:
								raise ValueError(F'''{full_name} has size {fsq_value.shape}, but {hf_value.shape} was found.'''  )
				UpperCamelCase_:							Optional[int]		=  fsq_value
				logger.info(F'''{full_name} was correctly initialized from {hf_weight_path}.'''  )







def 				A__    (  lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase  )     ->		Optional[int]:
				UpperCamelCase_:							str		=  full_name.split("""conv_layers."""  )[-1]
				UpperCamelCase_:							Union[str, Any]		=  name.split("""."""  )
				UpperCamelCase_:							Union[str, Any]		=  int(items[0]  )
				UpperCamelCase_:							Union[str, Any]		=  int(items[1]  )

				UpperCamelCase_:							Tuple		=  name.split("""."""  )[-1]
				if type_id == 0:
								UpperCamelCase_:							List[Any]		=  """conv"""
				elif type_id == 2:
								UpperCamelCase_:							Tuple		=  """layer_norm"""
				else:
								unused_weights.append(lowerCamelCase  )
								return

				set_weights(lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			F'''conv_layers.{layer_id}.{layer_type}.{weight_type}'''  )







def 				A__    (  lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase  )     ->		Optional[Any]:
				UpperCamelCase_:							Optional[int]		=  full_name.split("""pos_conv."""  )[-1]
				UpperCamelCase_:							Any		=  name.split("""."""  )
				UpperCamelCase_:							List[str]		=  int(items[0]  )
				UpperCamelCase_:							str		=  int(items[1]  )

				UpperCamelCase_:							List[str]		=  name.split("""."""  )[-1]
				if type_id != 0:
								unused_weights.append(lowerCamelCase  )
								return
				else:
								UpperCamelCase_:							Tuple		=  """conv"""

				set_weights(lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			F'''layers.{layer_id}.{layer_type}.{weight_type}'''  )







@torch.no_grad()
def 				A__    (  lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase=None   ,			lowerCamelCase=None   ,			lowerCamelCase=True  )     ->		Dict:
				if config_path is not None:
								UpperCamelCase_:							str		=  DataaVecAudioConfig.from_pretrained(lowerCamelCase  )
				else:
								UpperCamelCase_:							Tuple		=  DataaVecAudioConfig()

				if not is_finetuned:
								# Modify final_proj layer name
								UpperCamelCase_:							Union[str, Any]		=  DataaVecAudioModel(lowerCamelCase  )
								UpperCamelCase_:							Optional[int]		=  os.path.dirname(lowerCamelCase  )

								UpperCamelCase_:							Optional[Any]		=  torch.load(lowerCamelCase  )
								UpperCamelCase_:							List[str]		=  state_dict["""model"""].pop("""final_proj.0.weight"""  )
								UpperCamelCase_:							int		=  state_dict["""model"""].pop("""final_proj.0.bias"""  )
								UpperCamelCase_:							Optional[int]		=  os.path.join(lowerCamelCase   ,			"""converted.pt"""  )
								torch.save(lowerCamelCase   ,			lowerCamelCase  )
				else:
								UpperCamelCase_:							int		=  DataaVecAudioForCTC(lowerCamelCase  )
								UpperCamelCase_:							Optional[int]		=  checkpoint_path

				def load_dataavec(lowerCamelCase  ):
								UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_:							Optional[int]		=  fairseq.checkpoint_utils.load_model_ensemble_and_task([path]  )
								return model[0].eval()

				UpperCamelCase_:							List[Any]		=  load_dataavec(lowerCamelCase  )

				recursively_load_weights(lowerCamelCase   ,			lowerCamelCase   ,			not is_finetuned  )

				UpperCamelCase_:							Tuple		=  WavaVecaProcessor.from_pretrained("""facebook/wav2vec2-large-lv60"""  )

				UpperCamelCase_:							Dict		=  load_dataset("""patrickvonplaten/librispeech_asr_dummy"""   ,			"""clean"""   ,			split="""validation"""  )
				UpperCamelCase_:							Optional[Any]		=  [x["""array"""] for x in ds[:4]["""audio"""]]

				UpperCamelCase_:							List[Any]		=  processor(lowerCamelCase   ,			return_tensors="""pt"""   ,			padding=lowerCamelCase  )

				UpperCamelCase_:							str		=  inputs.input_values
				UpperCamelCase_:							Dict		=  inputs.attention_mask
				#    input_values = inputs.input_values[:, :-1]
				#    attention_mask = inputs.attention_mask[:, :-1]

				hf_wavavec.eval()
				model.eval()
				if is_finetuned:
								UpperCamelCase_:							Optional[Any]		=  model(source=lowerCamelCase   ,			padding_mask=(1 - attention_mask)   ,			mask=lowerCamelCase   ,			features_only=lowerCamelCase  )[
								    """encoder_out"""
								].transpose(0   ,			1  )
								UpperCamelCase_:							Tuple		=  hf_wavavec(lowerCamelCase   ,			attention_mask=lowerCamelCase  )["""logits"""]

								UpperCamelCase_:							Optional[Any]		=  torch.argmax(lowerCamelCase   ,			dim=-1  )
								UpperCamelCase_:							Any		=  processor.batch_decode(lowerCamelCase  )

								print(F'''Expected Output: {ds[:4]["text"]}, Pred: {output_string}'''  )
				else:
								UpperCamelCase_:							Optional[Any]		=  model(source=lowerCamelCase   ,			padding_mask=(1 - attention_mask)   ,			mask=lowerCamelCase   ,			features_only=lowerCamelCase  )[
								    """layer_results"""
								][-1][0].transpose(0   ,			1  )
								UpperCamelCase_:							List[str]		=  hf_wavavec(lowerCamelCase   ,			attention_mask=lowerCamelCase  )["""last_hidden_state"""]

				print(our_output.shape   ,			their_output.shape  )
				UpperCamelCase_:							Optional[Any]		=  torch.max(torch.abs(our_output - their_output  )  ).item()
				print(F'''max_absolute_diff = {max_absolute_diff}'''  )  # ~ 1e-7
				UpperCamelCase_:							List[str]		=  torch.allclose(lowerCamelCase   ,			lowerCamelCase   ,			atol=1E-3  )
				print("""Do both models output the same tensors?"""   ,			"""🔥""" if success else """💩"""  )
				if not success:
								raise Exception("""Something went wRoNg"""  )

				hf_wavavec.save_pretrained(lowerCamelCase  )

				if is_finetuned:
								processor.save_pretrained(lowerCamelCase  )
				else:
								processor.feature_extractor.save_pretrained(lowerCamelCase  )


if __name__ == "__main__":
							lowerCamelCase_  :       str					      =     argparse.ArgumentParser()
							parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
							parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
							parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
							parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
							parser.add_argument(
							    """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
							)
							lowerCamelCase_  :       Dict					      =     parser.parse_args()
							convert_wavaveca_checkpoint(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
							)






