






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      :    Optional[int]   		=   logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							List[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   SCREAMING_SNAKE_CASE       (				snake_case_      :							Optional[Any]  ,						snake_case_      :							str  ,						snake_case_      :							List[Any]     ):
	return max(metric_fn(snake_case_  ,						snake_case_     ) for gt in ground_truths     )







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Tuple  ,						snake_case_      :							Tuple  ,						snake_case_      :							Any     ):
	snake_case__				:   List[str]     =						[line.strip() for line in open(snake_case_  ,						"r"     ).readlines()]
	snake_case__				:   Optional[Any]     =						[]

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

	snake_case__				:   Dict     =						0
	for prediction, ground_truths in zip(snake_case_  ,						snake_case_     ):
		total += 1
		em += metric_max_over_ground_truths(snake_case_  ,						snake_case_  ,						snake_case_     )
		fa += metric_max_over_ground_truths(snake_case_  ,						snake_case_  ,						snake_case_     )

	snake_case__				:   Any     =						1_00.0 * em / total
	snake_case__				:   Optional[Any]     =						1_00.0 * fa / total

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







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							List[str]  ,						snake_case_      :							Dict  ,						snake_case_      :							int     ):
	snake_case__				:   Any     =						args.k
	snake_case__				:   Tuple     =						[line.strip() for line in open(snake_case_  ,						"r"     ).readlines()]
	snake_case__				:   int     =						[line.strip() for line in open(snake_case_  ,						"r"     ).readlines()]

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

	snake_case__				:   Any     =						1_00.0 * em / total
	logger.info(F'''Precision@{k}: {em: .2f}'''     )







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Tuple  ,						snake_case_      :							List[Any]  ,						snake_case_      :							Optional[int]     ):







	def strip_title(snake_case_      :							Union[str, Any]     ):
		if title.startswith("\""     ):
			snake_case__				:   List[Any]     =						title[1:]
		if title.endswith("\""     ):
			snake_case__				:   Union[str, Any]     =						title[:-1]
		return title

	snake_case__				:   List[str]     =						rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
	    snake_case_  ,						return_tensors="pt"  ,						padding=snake_case_  ,						truncation=snake_case_  ,						)["input_ids"].to(args.device     )

	snake_case__				:   int     =						rag_model.rag.question_encoder(snake_case_     )
	snake_case__				:   Optional[Any]     =						question_enc_outputs[0]

	snake_case__				:   Tuple     =						rag_model.retriever(
	    snake_case_  ,						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"  ,						)
	snake_case__				:   Tuple     =						rag_model.retriever.index.get_doc_dicts(result.doc_ids     )
	snake_case__				:   Dict     =						[]
	for docs in all_docs:
		snake_case__				:   Optional[Any]     =						[strip_title(snake_case_     ) for title in docs["title"]]
		provenance_strings.append("\t".join(snake_case_     )     )
	return provenance_strings







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Dict  ,						snake_case_      :							List[Any]  ,						snake_case_      :							Optional[Any]     ):







	with torch.no_grad():
		snake_case__				:   Dict     =						rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
		    snake_case_  ,						return_tensors="pt"  ,						padding=snake_case_  ,						truncation=snake_case_     )

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

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

		return answers







def   SCREAMING_SNAKE_CASE       (				):
	snake_case__				:   Dict     =						argparse.ArgumentParser()
	parser.add_argument(
	    "--model_type"  ,						choices=["rag_sequence", "rag_token", "bart"]  ,						type=snake_case_  ,						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=snake_case_  ,						choices=["exact", "compressed", "legacy"]  ,						type=snake_case_  ,						help="RAG model retriever type"  ,						)
	parser.add_argument(
	    "--index_path"  ,						default=snake_case_  ,						type=snake_case_  ,						help="Path to the retrieval index"  ,						)
	parser.add_argument("--n_docs"  ,						default=5  ,						type=snake_case_  ,						help="Number of retrieved docs"     )
	parser.add_argument(
	    "--model_name_or_path"  ,						default=snake_case_  ,						type=snake_case_  ,						required=snake_case_  ,						help="Path to pretrained checkpoints or model identifier from huggingface.co/models"  ,						)
	parser.add_argument(
	    "--eval_mode"  ,						choices=["e2e", "retrieval"]  ,						default="e2e"  ,						type=snake_case_  ,						help=(
	        "Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"
	        " precision@k."
	    )  ,						)
	parser.add_argument("--k"  ,						default=1  ,						type=snake_case_  ,						help="k for the precision@k calculation"     )
	parser.add_argument(
	    "--evaluation_set"  ,						default=snake_case_  ,						type=snake_case_  ,						required=snake_case_  ,						help="Path to a file containing evaluation samples"  ,						)
	parser.add_argument(
	    "--gold_data_path"  ,						default=snake_case_  ,						type=snake_case_  ,						required=snake_case_  ,						help="Path to a tab-separated file with gold samples"  ,						)
	parser.add_argument(
	    "--gold_data_mode"  ,						default="qa"  ,						type=snake_case_  ,						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=snake_case_  ,						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=snake_case_  ,						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=snake_case_  ,						help="Number of beams to be used when generating answers"  ,						)
	parser.add_argument("--min_length"  ,						default=1  ,						type=snake_case_  ,						help="Min length of the generated answers"     )
	parser.add_argument("--max_length"  ,						default=50  ,						type=snake_case_  ,						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."  ,						)
	snake_case__				:   Union[str, Any]     =						parser.parse_args()
	snake_case__				:   Tuple     =						torch.device("cuda" if torch.cuda.is_available() else "cpu"     )
	return args







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Union[str, Any]     ):
	snake_case__				:   List[Any]     =						{}
	if args.model_type is None:
		snake_case__				:   str     =						infer_model_type(args.model_name_or_path     )
		assert args.model_type is not None
	if args.model_type.startswith("rag"     ):
		snake_case__				:   Any     =						RagTokenForGeneration if args.model_type == "rag_token" else RagSequenceForGeneration
		snake_case__				:   Tuple     =						args.n_docs
		if args.index_name is not None:
			snake_case__				:   Tuple     =						args.index_name
		if args.index_path is not None:
			snake_case__				:   Optional[Any]     =						args.index_path
	else:
		snake_case__				:   List[Any]     =						BartForConditionalGeneration

	snake_case__				:   List[Any]     =						(
	    [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"  ,						snake_case_     )

	snake_case__				:   Any     =						get_scores if args.eval_mode == "e2e" else get_precision_at_k
	snake_case__				:   List[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(snake_case_  ,						args.predictions_path  ,						args.gold_data_path     )
			continue

		logger.info("***** Running evaluation for {} *****".format(snake_case_     )     )
		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"     ):
			snake_case__				:   Dict     =						RagRetriever.from_pretrained(snake_case_  ,						**snake_case_     )
			snake_case__				:   int     =						model_class.from_pretrained(snake_case_  ,						retriever=snake_case_  ,						**snake_case_     )
			model.retriever.init_retrieval()
		else:
			snake_case__				:   Optional[int]     =						model_class.from_pretrained(snake_case_  ,						**snake_case_     )
		model.to(args.device     )

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

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


if __name__ == "__main__":
					__lowerCamelCase      :    Dict   		=   get_args()
					main(args)











import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


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

__lowerCamelCase      :    int   		=   {
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """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""",
    """encoder.layer_norm_for_extract""": """layer_norm_for_extract""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """quantizer.weight_proj""": """quantizer.weight_proj""",
    """quantizer.vars""": """quantizer.codevectors""",
    """project_q""": """project_q""",
    """final_proj""": """project_hid""",
    """w2v_encoder.proj""": """lm_head""",
    """label_embs_concat""": """label_embeddings_concat""",
    """mask_emb""": """masked_spec_embed""",
    """spk_proj""": """speaker_proj""",
}
__lowerCamelCase      :    Tuple   		=   [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """label_embeddings_concat""",
    """speaker_proj""",
    """layer_norm_for_extract""",
]







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Tuple  ,						snake_case_      :							Union[str, Any]  ,						snake_case_      :							Union[str, Any]  ,						snake_case_      :							Any  ,						snake_case_      :							Union[str, Any]     ):
	for attribute in key.split("."     ):
		snake_case__				:   int     =						getattr(snake_case_  ,						snake_case_     )

	if weight_type is not None:
		snake_case__				:   Optional[Any]     =						getattr(snake_case_  ,						snake_case_     ).shape
	else:
		snake_case__				:   List[str]     =						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":
		snake_case__				:   str     =						value
	elif weight_type == "weight_g":
		snake_case__				:   Union[str, Any]     =						value
	elif weight_type == "weight_v":
		snake_case__				:   Optional[Any]     =						value
	elif weight_type == "bias":
		snake_case__				:   str     =						value
	else:
		snake_case__				:   Union[str, Any]     =						value

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







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Any  ,						snake_case_      :							Union[str, Any]     ):
	snake_case__				:   str     =						[]
	snake_case__				:   Optional[int]     =						fairseq_model.state_dict()

	snake_case__				:   int     =						hf_model.unispeech_sat.feature_extractor

	for name, value in fairseq_dict.items():
		snake_case__				:   Dict     =						False
		if "conv_layers" in name:
			load_conv_layer(
			    snake_case_  ,						snake_case_  ,						snake_case_  ,						snake_case_  ,						hf_model.config.feat_extract_norm == "group"  ,						)
			snake_case__				:   str     =						True
		else:
			for key, mapped_key in MAPPING.items():
				snake_case__				:   Optional[int]     =						"unispeech_sat." + 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]:
					if "layer_norm_for_extract" in name and (".".join(name.split("."     )[:-1]     ) != key):
						# special case since naming is very similar
						continue
					snake_case__				:   int     =						True
					if "*" in mapped_key:
						snake_case__				:   Any     =						name.split(snake_case_     )[0].split("."     )[-2]
						snake_case__				:   Any     =						mapped_key.replace("*"  ,						snake_case_     )
					if "weight_g" in name:
						snake_case__				:   List[Any]     =						"weight_g"
					elif "weight_v" in name:
						snake_case__				:   Optional[Any]     =						"weight_v"
					elif "bias" in name:
						snake_case__				:   Optional[Any]     =						"bias"
					elif "weight" in name:
						# TODO: don't match quantizer.weight_proj
						snake_case__				:   Optional[Any]     =						"weight"
					else:
						snake_case__				:   Optional[Any]     =						None
					set_recursively(snake_case_  ,						snake_case_  ,						snake_case_  ,						snake_case_  ,						snake_case_     )
				continue
		if not is_used:
			unused_weights.append(snake_case_     )

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







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Any  ,						snake_case_      :							List[str]  ,						snake_case_      :							List[Any]  ,						snake_case_      :							Optional[Any]  ,						snake_case_      :							str     ):
	snake_case__				:   Tuple     =						full_name.split("conv_layers."     )[-1]
	snake_case__				:   Union[str, Any]     =						name.split("."     )
	snake_case__				:   str     =						int(items[0]     )
	snake_case__				:   str     =						int(items[1]     )

	if type_id == 0:
		if "bias" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
				raise ValueError(
				    F'''{full_name} has size {value.shape}, but'''
				    F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''     )
			snake_case__				:   Any     =						value
			logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''     )
		elif "weight" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
				raise ValueError(
				    F'''{full_name} has size {value.shape}, but'''
				    F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''     )
			snake_case__				:   Any     =						value
			logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''     )
	elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
		if "bias" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
				raise ValueError(
				    F'''{full_name} has size {value.shape}, but'''
				    F''' {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.'''     )
			snake_case__				:   Optional[Any]     =						value
			logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''     )
		elif "weight" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
				raise ValueError(
				    F'''{full_name} has size {value.shape}, but'''
				    F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''     )
			snake_case__				:   int     =						value
			logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''     )
	else:
		unused_weights.append(snake_case_     )







@torch.no_grad()
def   SCREAMING_SNAKE_CASE       (				snake_case_      :							int  ,						snake_case_      :							Any  ,						snake_case_      :							Optional[int]=None  ,						snake_case_      :							Optional[int]=None  ,						snake_case_      :							Any=True     ):
	if config_path is not None:
		snake_case__				:   Tuple     =						UniSpeechSatConfig.from_pretrained(snake_case_     )
	else:
		snake_case__				:   Tuple     =						UniSpeechSatConfig()

	snake_case__				:   str     =						""

	if is_finetuned:
		snake_case__				:   Tuple     =						UniSpeechSatForCTC(snake_case_     )
	else:
		snake_case__				:   Any     =						UniSpeechSatForPreTraining(snake_case_     )

	snake_case__,		snake_case__,		snake_case__				:   Any     =						fairseq.checkpoint_utils.load_model_ensemble_and_task(
	    [checkpoint_path]  ,						arg_overrides={"data": "/".join(dict_path.split("/"     )[:-1]     )}     )
	snake_case__				:   Tuple     =						model[0].eval()

	recursively_load_weights(snake_case_  ,						snake_case_     )

	hf_wavavec.save_pretrained(snake_case_     )


if __name__ == "__main__":
	__lowerCamelCase      :    int   		=   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      :    List[Any]   		=   parser.parse_args()
	convert_unispeech_sat_checkpoint(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
	)



