
"""simple docstring"""



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_snake_case          =						logging.get_logger(__name__)

_snake_case          =						{
    '''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''',
    '''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''': '''ctc_proj''',
    '''mask_emb''': '''masked_spec_embed''',
}
_snake_case          =						[
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					Any     ,  SCREAMING_SNAKE_CASE:					Optional[int]     ,  SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					Dict     ,  SCREAMING_SNAKE_CASE:					int						):






							"""simple docstring"""



							for attribute in key.split('.'						):
														if is_finetuned:
																					if attribute in ["quantizer", "project_q", "project_hid"]:
																												# those layers are only relevant for pretraining and should be dropped
																												return

																					if attribute == "ctc_proj":
																												# we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
																												_lowerCAmelCase  	=			'lm_head'

														_lowerCAmelCase  	=			getattr(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)

							if weight_type is not None:
														_lowerCAmelCase  	=			getattr(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						).shape
							else:
														_lowerCAmelCase  	=			hf_pointer.shape

							assert hf_shape == value.shape, (
							    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":
														_lowerCAmelCase  	=			value
							elif weight_type == "weight_g":
														_lowerCAmelCase  	=			value
							elif weight_type == "weight_v":
														_lowerCAmelCase  	=			value
							elif weight_type == "bias":
														_lowerCAmelCase  	=			value
							else:
														_lowerCAmelCase  	=			value

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






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					int     ,  SCREAMING_SNAKE_CASE:					List[Any]     ,  SCREAMING_SNAKE_CASE:					List[str]						):






							"""simple docstring"""



							_lowerCAmelCase  	=			[]
							_lowerCAmelCase  	=			fairseq_model.state_dict()

							_lowerCAmelCase  	=			hf_model.unispeech.feature_extractor

							for name, value in fairseq_dict.items():
														_lowerCAmelCase  	=			False
														if "conv_layers" in name:
																					load_conv_layer(
																					    SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  hf_model.config.feat_extract_norm == 'group'     ,  )
																					_lowerCAmelCase  	=			True
														else:
																					for key, mapped_key in MAPPING.items():
																												_lowerCAmelCase  	=			'unispeech.' + 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]:
																																			_lowerCAmelCase  	=			True
																																			if "*" in mapped_key:
																																										_lowerCAmelCase  	=			name.split(SCREAMING_SNAKE_CASE						)[0].split('.'						)[-2]
																																										_lowerCAmelCase  	=			mapped_key.replace('*'     ,  SCREAMING_SNAKE_CASE						)
																																			if "weight_g" in name:
																																										_lowerCAmelCase  	=			'weight_g'
																																			elif "weight_v" in name:
																																										_lowerCAmelCase  	=			'weight_v'
																																			elif "bias" in name:
																																										_lowerCAmelCase  	=			'bias'
																																			elif "weight" in name:
																																										# TODO: don't match quantizer.weight_proj
																																										_lowerCAmelCase  	=			'weight'
																																			else:
																																										_lowerCAmelCase  	=			None
																																			set_recursively(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)
																												continue
														if not is_used:
																					unused_weights.append(SCREAMING_SNAKE_CASE						)

							logger.warning(f"""Unused weights: {unused_weights}"""						)






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Dict     ,  SCREAMING_SNAKE_CASE:					Any     ,  SCREAMING_SNAKE_CASE:					List[str]     ,  SCREAMING_SNAKE_CASE:					Union[str, Any]     ,  SCREAMING_SNAKE_CASE:					List[str]						):






							"""simple docstring"""



							_lowerCAmelCase  	=			full_name.split('conv_layers.'						)[-1]
							_lowerCAmelCase  	=			name.split('.'						)
							_lowerCAmelCase  	=			int(items[0]						)
							_lowerCAmelCase  	=			int(items[1]						)

							if type_id == 0:
														if "bias" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
																					    f"""{full_name} has size {value.shape}, but"""
																					    f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
																					)
																					_lowerCAmelCase  	=			value
																					logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""						)
														elif "weight" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
																					    f"""{full_name} has size {value.shape}, but"""
																					    f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
																					)
																					_lowerCAmelCase  	=			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:
																					assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
																					    f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
																					    " found."
																					)
																					_lowerCAmelCase  	=			value
																					logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""						)
														elif "weight" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
																					    f"""{full_name} has size {value.shape}, but"""
																					    f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
																					)
																					_lowerCAmelCase  	=			value
																					logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""						)
							else:
														unused_weights.append(SCREAMING_SNAKE_CASE						)






@torch.no_grad()
def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Tuple     ,  SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					str=None     ,  SCREAMING_SNAKE_CASE:					Tuple=None     ,  SCREAMING_SNAKE_CASE:					Union[str, Any]=True						):






							"""simple docstring"""



							if config_path is not None:
														_lowerCAmelCase  	=			UniSpeechConfig.from_pretrained(SCREAMING_SNAKE_CASE						)
							else:
														_lowerCAmelCase  	=			UniSpeechConfig()

							if is_finetuned:
														if dict_path:
																					_lowerCAmelCase  	=			Dictionary.load_from_json(SCREAMING_SNAKE_CASE						)

																					# important change bos & pad token id since CTC symbol is <pad> and
																					# not <s> as in fairseq
																					_lowerCAmelCase  	=			target_dict.pad_index
																					_lowerCAmelCase  	=			target_dict.bos_index
																					_lowerCAmelCase  	=			target_dict.eos_index
																					_lowerCAmelCase  	=			len(target_dict.symbols						)
																					_lowerCAmelCase  	=			os.path.join(SCREAMING_SNAKE_CASE     ,  'vocab.json'						)
																					if not os.path.isdir(SCREAMING_SNAKE_CASE						):
																												logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(SCREAMING_SNAKE_CASE						)						)
																												return
																					os.makedirs(SCREAMING_SNAKE_CASE     ,  exist_ok=SCREAMING_SNAKE_CASE						)
																					_lowerCAmelCase  	=			target_dict.indices

																					# fairseq has the <pad> and <s> switched
																					_lowerCAmelCase  	=			42
																					_lowerCAmelCase  	=			43
																					with open(SCREAMING_SNAKE_CASE     ,  'w'     ,  encoding='utf-8'						) as vocab_handle:
																												json.dump(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)
																					_lowerCAmelCase  	=			WavaVecaPhonemeCTCTokenizer(
																					    SCREAMING_SNAKE_CASE     ,  unk_token=target_dict.unk_word     ,  pad_token=target_dict.pad_word     ,  bos_token=target_dict.bos_word     ,  eos_token=target_dict.eos_word     ,  word_delimiter_token='|'     ,  do_lower_case=SCREAMING_SNAKE_CASE     ,  )
																					_lowerCAmelCase  	=			True if config.feat_extract_norm == 'layer' else False
																					_lowerCAmelCase  	=			WavaVecaFeatureExtractor(
																					    feature_size=1     ,  sampling_rate=1_6000     ,  padding_value=0     ,  do_normalize=SCREAMING_SNAKE_CASE     ,  return_attention_mask=SCREAMING_SNAKE_CASE     ,  )
																					_lowerCAmelCase  	=			WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE     ,  tokenizer=SCREAMING_SNAKE_CASE						)
																					processor.save_pretrained(SCREAMING_SNAKE_CASE						)

														_lowerCAmelCase  	=			UniSpeechForCTC(SCREAMING_SNAKE_CASE						)
							else:
														_lowerCAmelCase  	=			UniSpeechForPreTraining(SCREAMING_SNAKE_CASE						)

							if is_finetuned:
														_lowerCAmelCase	,		_lowerCAmelCase	,		_lowerCAmelCase  	=			fairseq.checkpoint_utils.load_model_ensemble_and_task(
														    [checkpoint_path]     ,  arg_overrides={'data': '/'.join(dict_path.split('/'						)[:-1]						), 'w2v_path': checkpoint_path}						)
							else:
														_lowerCAmelCase	,		_lowerCAmelCase	,		_lowerCAmelCase  	=			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]						)

							_lowerCAmelCase  	=			model[0].eval()

							recursively_load_weights(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)

							hf_unispeech.save_pretrained(SCREAMING_SNAKE_CASE						)


if __name__ == "__main__":
	_snake_case          =						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'''
	)
	_snake_case          =						parser.parse_args()
	convert_unispeech_checkpoint(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
	)








"""simple docstring"""



import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


_snake_case          =						getLogger(__name__)


_snake_case          =						'''cuda''' if torch.cuda.is_available() else '''cpu'''






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					List[str]     ,  SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					int = 8     ,  SCREAMING_SNAKE_CASE:					str = DEFAULT_DEVICE     ,  SCREAMING_SNAKE_CASE:					Optional[int]=False     ,  SCREAMING_SNAKE_CASE:					int="summarization"     ,  SCREAMING_SNAKE_CASE:					List[Any]=None     ,  **SCREAMING_SNAKE_CASE:					Tuple     ,  ):






							"""simple docstring"""



							_lowerCAmelCase  	=			Path(SCREAMING_SNAKE_CASE						).open('w'     ,  encoding='utf-8'						)
							_lowerCAmelCase  	=			str(SCREAMING_SNAKE_CASE						)
							_lowerCAmelCase  	=			AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE						).to(SCREAMING_SNAKE_CASE						)
							if fpaa:
														_lowerCAmelCase  	=			model.half()

							_lowerCAmelCase  	=			AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE						)
							logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}"""						)  # if this is wrong, check config.model_type.

							_lowerCAmelCase  	=			time.time()
							# update config with task specific params
							use_task_specific_params(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)
							if prefix is None:
														_lowerCAmelCase  	=			prefix or getattr(model.config     ,  'prefix'     ,  ''						) or ''
							for examples_chunk in tqdm(list(chunks(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)						)						):
														_lowerCAmelCase  	=			[prefix + text for text in examples_chunk]
														_lowerCAmelCase  	=			tokenizer(SCREAMING_SNAKE_CASE     ,  return_tensors='pt'     ,  truncation=SCREAMING_SNAKE_CASE     ,  padding='longest'						).to(SCREAMING_SNAKE_CASE						)
														_lowerCAmelCase  	=			model.generate(
														    input_ids=batch.input_ids     ,  attention_mask=batch.attention_mask     ,  **SCREAMING_SNAKE_CASE     ,  )
														_lowerCAmelCase  	=			tokenizer.batch_decode(SCREAMING_SNAKE_CASE     ,  skip_special_tokens=SCREAMING_SNAKE_CASE     ,  clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE						)
														for hypothesis in dec:
																					fout.write(hypothesis + '\n'						)
																					fout.flush()
							fout.close()
							_lowerCAmelCase  	=			int(time.time() - start_time						)  # seconds
							_lowerCAmelCase  	=			len(SCREAMING_SNAKE_CASE						)
							return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs     ,  4						)}






def 	__snake_case  (    ):






							"""simple docstring"""



							return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'						)






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Any=True						):






							"""simple docstring"""



							_lowerCAmelCase  	=			argparse.ArgumentParser()
							parser.add_argument('model_name'     ,  type=SCREAMING_SNAKE_CASE     ,  help='like facebook/bart-large-cnn,t5-base, etc.'						)
							parser.add_argument('input_path'     ,  type=SCREAMING_SNAKE_CASE     ,  help='like cnn_dm/test.source'						)
							parser.add_argument('save_path'     ,  type=SCREAMING_SNAKE_CASE     ,  help='where to save summaries'						)
							parser.add_argument('--reference_path'     ,  type=SCREAMING_SNAKE_CASE     ,  required=SCREAMING_SNAKE_CASE     ,  help='like cnn_dm/test.target'						)
							parser.add_argument('--score_path'     ,  type=SCREAMING_SNAKE_CASE     ,  required=SCREAMING_SNAKE_CASE     ,  default='metrics.json'     ,  help='where to save metrics'						)
							parser.add_argument('--device'     ,  type=SCREAMING_SNAKE_CASE     ,  required=SCREAMING_SNAKE_CASE     ,  default=SCREAMING_SNAKE_CASE     ,  help='cuda, cuda:1, cpu etc.'						)
							parser.add_argument(
							    '--prefix'     ,  type=SCREAMING_SNAKE_CASE     ,  required=SCREAMING_SNAKE_CASE     ,  default=SCREAMING_SNAKE_CASE     ,  help='will be added to the begininng of src examples'						)
							parser.add_argument('--task'     ,  type=SCREAMING_SNAKE_CASE     ,  default='summarization'     ,  help='used for task_specific_params + metrics'						)
							parser.add_argument('--bs'     ,  type=SCREAMING_SNAKE_CASE     ,  default=8     ,  required=SCREAMING_SNAKE_CASE     ,  help='batch size'						)
							parser.add_argument(
							    '--n_obs'     ,  type=SCREAMING_SNAKE_CASE     ,  default=-1     ,  required=SCREAMING_SNAKE_CASE     ,  help='How many observations. Defaults to all.'						)
							parser.add_argument('--fp16'     ,  action='store_true'						)
							parser.add_argument('--dump-args'     ,  action='store_true'     ,  help='print the custom hparams with the results'						)
							parser.add_argument(
							    '--info'     ,  nargs='?'     ,  type=SCREAMING_SNAKE_CASE     ,  const=datetime_now()     ,  help=(
							        'use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'
							        ' lang=en-ru. If no value is passed, the current datetime string will be used.'
							    )     ,  )
							# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
							_lowerCAmelCase	,		_lowerCAmelCase  	=			parser.parse_known_args()
							_lowerCAmelCase  	=			parse_numeric_n_bool_cl_kwargs(SCREAMING_SNAKE_CASE						)
							if parsed_args and verbose:
														print(f"""parsed the following generate kwargs: {parsed_args}"""						)
							_lowerCAmelCase  	=			[' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in open(args.input_path						).readlines()]
							if args.n_obs > 0:
														_lowerCAmelCase  	=			examples[: args.n_obs]
							Path(args.save_path						).parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE						)

							if args.reference_path is None and Path(args.score_path						).exists():
														warnings.warn(f"""score_path {args.score_path} will be overwritten unless you type ctrl-c."""						)

							if args.device == "cpu" and args.fpaa:
														# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
														raise ValueError('Can\'t mix --fp16 and --device cpu'						)

							_lowerCAmelCase  	=			generate_summaries_or_translations(
							    SCREAMING_SNAKE_CASE     ,  args.save_path     ,  args.model_name     ,  batch_size=args.bs     ,  device=args.device     ,  fpaa=args.fpaa     ,  task=args.task     ,  prefix=args.prefix     ,  **SCREAMING_SNAKE_CASE     ,  )

							if args.reference_path is None:
														return {}

							# Compute scores
							_lowerCAmelCase  	=			calculate_bleu if 'translation' in args.task else calculate_rouge
							_lowerCAmelCase  	=			[x.rstrip() for x in open(args.save_path						).readlines()]
							_lowerCAmelCase  	=			[x.rstrip() for x in open(args.reference_path						).readlines()][: len(SCREAMING_SNAKE_CASE						)]
							_lowerCAmelCase  	=			score_fn(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)
							scores.update(SCREAMING_SNAKE_CASE						)

							if args.dump_args:
														scores.update(SCREAMING_SNAKE_CASE						)
							if args.info:
														_lowerCAmelCase  	=			args.info

							if verbose:
														print(SCREAMING_SNAKE_CASE						)

							if args.score_path is not None:
														json.dump(SCREAMING_SNAKE_CASE     ,  open(args.score_path     ,  'w'						)						)

							return scores


if __name__ == "__main__":
	# Usage for MT:
	# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
	run_generate(verbose=True)






