

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()
_a			=       logging.get_logger(__name__)

_a			=       {
    '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',
}
_a			=       [
    'lm_head',
]







def _a			(	SCREAMING_SNAKE_CASE		:      Any   , SCREAMING_SNAKE_CASE		:      Union[str, Any]   , SCREAMING_SNAKE_CASE		:      List[Any]   , SCREAMING_SNAKE_CASE		:      Dict   , SCREAMING_SNAKE_CASE		:      Union[str, Any]		)   ->   Dict:

	"""simple docstring"""






	for attribute in key.split('.'		):
		__lowerCAmelCase:       Dict            =				getattr(_UpperCAmelCase   , _UpperCAmelCase		)

	if weight_type is not None:
		__lowerCAmelCase:       Dict            =				getattr(_UpperCAmelCase   , _UpperCAmelCase		).shape
	else:
		__lowerCAmelCase:       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":
		__lowerCAmelCase:       Tuple            =				value
	elif weight_type == "weight_g":
		__lowerCAmelCase:       Any            =				value
	elif weight_type == "weight_v":
		__lowerCAmelCase:       Union[str, Any]            =				value
	elif weight_type == "bias":
		__lowerCAmelCase:       str            =				value
	else:
		__lowerCAmelCase:       Optional[Any]            =				value

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







def _a			(	SCREAMING_SNAKE_CASE		:      Union[str, Any]   , SCREAMING_SNAKE_CASE		:      int   , SCREAMING_SNAKE_CASE		:      Optional[Any]		)   ->   int:

	"""simple docstring"""






	__lowerCAmelCase:       Optional[Any]            =				[]
	__lowerCAmelCase:       Dict            =				fairseq_model.state_dict()

	if not is_headless:
		__lowerCAmelCase:       List[str]            =				hf_model.dataavec_audio.feature_extractor
		__lowerCAmelCase:       Optional[int]            =				hf_model.dataavec_audio.encoder.pos_conv_embed

	else:
		__lowerCAmelCase:       str            =				hf_model.feature_extractor
		__lowerCAmelCase:       Dict            =				hf_model.encoder.pos_conv_embed

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

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







def _a			(	SCREAMING_SNAKE_CASE		:      str   , SCREAMING_SNAKE_CASE		:      int		)   ->   Dict:

	"""simple docstring"""






	__lowerCAmelCase:       Optional[int]            =				path.split('.'		)
	return reduce(_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase		)







def _a			(	SCREAMING_SNAKE_CASE		:      Optional[int]   , SCREAMING_SNAKE_CASE		:      Optional[Any]   , SCREAMING_SNAKE_CASE		:      Any   , SCREAMING_SNAKE_CASE		:      List[str]		)   ->   Tuple:

	"""simple docstring"""






	__lowerCAmelCase:       Optional[Any]            =				access_by_string(_UpperCAmelCase   , _UpperCAmelCase		)
	__lowerCAmelCase:       List[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.'''		)
	__lowerCAmelCase:       int            =				fsq_value
	logger.info(f'''{full_name} was correctly initialized from {hf_weight_path}.'''		)







def _a			(	SCREAMING_SNAKE_CASE		:      Dict   , SCREAMING_SNAKE_CASE		:      Optional[int]   , SCREAMING_SNAKE_CASE		:      Dict   , SCREAMING_SNAKE_CASE		:      Union[str, Any]		)   ->   Any:

	"""simple docstring"""






	__lowerCAmelCase:       Optional[Any]            =				full_name.split('conv_layers.'		)[-1]
	__lowerCAmelCase:       Union[str, Any]            =				name.split('.'		)
	__lowerCAmelCase:       int            =				int(items[0]		)
	__lowerCAmelCase:       Optional[Any]            =				int(items[1]		)

	__lowerCAmelCase:       int            =				name.split('.'		)[-1]
	if type_id == 0:
		__lowerCAmelCase:       int            =				'conv'
	elif type_id == 2:
		__lowerCAmelCase:       List[Any]            =				'layer_norm'
	else:
		unused_weights.append(_UpperCAmelCase		)
		return

	set_weights(_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , f'''conv_layers.{layer_id}.{layer_type}.{weight_type}'''		)







def _a			(	SCREAMING_SNAKE_CASE		:      int   , SCREAMING_SNAKE_CASE		:      List[str]   , SCREAMING_SNAKE_CASE		:      List[str]   , SCREAMING_SNAKE_CASE		:      Dict		)   ->   int:

	"""simple docstring"""






	__lowerCAmelCase:       Tuple            =				full_name.split('pos_conv.'		)[-1]
	__lowerCAmelCase:       List[Any]            =				name.split('.'		)
	__lowerCAmelCase:       Union[str, Any]            =				int(items[0]		)
	__lowerCAmelCase:       Optional[int]            =				int(items[1]		)

	__lowerCAmelCase:       Any            =				name.split('.'		)[-1]
	if type_id != 0:
		unused_weights.append(_UpperCAmelCase		)
		return
	else:
		__lowerCAmelCase:       Any            =				'conv'

	set_weights(_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , f'''layers.{layer_id}.{layer_type}.{weight_type}'''		)







@torch.no_grad()
def _a			(	SCREAMING_SNAKE_CASE		:      int   , SCREAMING_SNAKE_CASE		:      Any   , SCREAMING_SNAKE_CASE		:      Dict=None   , SCREAMING_SNAKE_CASE		:      int=None   , SCREAMING_SNAKE_CASE		:      int=True		)   ->   int:

	"""simple docstring"""






	if config_path is not None:
		__lowerCAmelCase:       Tuple            =				DataaVecAudioConfig.from_pretrained(_UpperCAmelCase		)
	else:
		__lowerCAmelCase:       Optional[int]            =				DataaVecAudioConfig()

	if not is_finetuned:
		# Modify final_proj layer name
		__lowerCAmelCase:       int            =				DataaVecAudioModel(_UpperCAmelCase		)
		__lowerCAmelCase:       Any            =				os.path.dirname(_UpperCAmelCase		)

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

	def load_dataavec(SCREAMING_SNAKE_CASE		:      Dict		):
		__lowerCAmelCase:       Any            =				fairseq.checkpoint_utils.load_model_ensemble_and_task([path]		)
		return model[0].eval()

	__lowerCAmelCase:       Any            =				load_dataavec(_UpperCAmelCase		)

	recursively_load_weights(_UpperCAmelCase   , _UpperCAmelCase   , not is_finetuned		)

	__lowerCAmelCase:       Optional[int]            =				WavaVecaProcessor.from_pretrained('facebook/wav2vec2-large-lv60'		)

	__lowerCAmelCase:       Optional[int]            =				load_dataset('patrickvonplaten/librispeech_asr_dummy'   , 'clean'   , split='validation'		)
	__lowerCAmelCase:       Dict            =				[x['array'] for x in ds[:4]['audio']]

	__lowerCAmelCase:       Optional[Any]            =				processor(_UpperCAmelCase   , return_tensors='pt'   , padding=_UpperCAmelCase		)

	__lowerCAmelCase:       Tuple            =				inputs.input_values
	__lowerCAmelCase:       Tuple            =				inputs.attention_mask
	#    input_values = inputs.input_values[:, :-1]
	#    attention_mask = inputs.attention_mask[:, :-1]

	hf_wavavec.eval()
	model.eval()
	if is_finetuned:
		__lowerCAmelCase:       int            =				model(source=_UpperCAmelCase   , padding_mask=(1 - attention_mask)   , mask=_UpperCAmelCase   , features_only=_UpperCAmelCase		)[
		    'encoder_out'
		].transpose(0   , 1		)
		__lowerCAmelCase:       Tuple            =				hf_wavavec(_UpperCAmelCase   , attention_mask=_UpperCAmelCase		)['logits']

		__lowerCAmelCase:       int            =				torch.argmax(_UpperCAmelCase   , dim=-1		)
		__lowerCAmelCase:       Tuple            =				processor.batch_decode(_UpperCAmelCase		)

		print(f'''Expected Output: {ds[:4]['text']}, Pred: {output_string}'''		)
	else:
		__lowerCAmelCase:       Union[str, Any]            =				model(source=_UpperCAmelCase   , padding_mask=(1 - attention_mask)   , mask=_UpperCAmelCase   , features_only=_UpperCAmelCase		)[
		    'layer_results'
		][-1][0].transpose(0   , 1		)
		__lowerCAmelCase:       Dict            =				hf_wavavec(_UpperCAmelCase   , attention_mask=_UpperCAmelCase		)['last_hidden_state']

	print(our_output.shape   , their_output.shape		)
	__lowerCAmelCase:       List[str]            =				torch.max(torch.abs(our_output - their_output		)		).item()
	print(f'''max_absolute_diff = {max_absolute_diff}'''		)  # ~ 1e-7
	__lowerCAmelCase:       str            =				torch.allclose(_UpperCAmelCase   , _UpperCAmelCase   , 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(_UpperCAmelCase		)

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


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








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


_a			=       getLogger(__name__)


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







def _a			(	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		:      Union[str, Any]=False   , SCREAMING_SNAKE_CASE		:      Tuple="summarization"   , SCREAMING_SNAKE_CASE		:      int=None   , **SCREAMING_SNAKE_CASE		:      Optional[Any]   , )   ->   Dict:

 """simple docstring"""






 __lowerCAmelCase:       Union[str, Any]            =				Path(SCREAMING_SNAKE_CASE		).open('w'   , encoding='utf-8'		)
 __lowerCAmelCase:       Tuple            =				str(SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:       Optional[int]            =				AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE		).to(SCREAMING_SNAKE_CASE		)
 if fpaa:
  __lowerCAmelCase:       List[Any]            =				model.half()

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

 __lowerCAmelCase:       Any            =				time.time()
 # update config with task specific params
 use_task_specific_params(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE		)
 if prefix is None:
  __lowerCAmelCase:       Optional[Any]            =				prefix or getattr(model.config   , 'prefix'   , ''		) or ''
 for examples_chunk in tqdm(list(chunks(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE		)		)		):
  __lowerCAmelCase:       str            =				[prefix + text for text in examples_chunk]
  __lowerCAmelCase:       Optional[Any]            =				tokenizer(SCREAMING_SNAKE_CASE   , return_tensors='pt'   , truncation=SCREAMING_SNAKE_CASE   , padding='longest'		).to(SCREAMING_SNAKE_CASE		)
  __lowerCAmelCase:       List[str]            =				model.generate(
      input_ids=batch.input_ids   , attention_mask=batch.attention_mask   , **SCREAMING_SNAKE_CASE   , )
  __lowerCAmelCase:       Dict            =				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:       str            =				int(time.time() - start_time		)  # seconds
 __lowerCAmelCase:       Tuple            =				len(SCREAMING_SNAKE_CASE		)
 return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs   , 4		)}







def _a			(	)   ->   Dict:

 """simple docstring"""






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







def _a			(	SCREAMING_SNAKE_CASE		:      Tuple=True		)   ->   int:

 """simple docstring"""






 __lowerCAmelCase:       str            =				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:       Union[str, Any]            =				parser.parse_known_args()
 __lowerCAmelCase:       Union[str, Any]            =				parse_numeric_n_bool_cl_kwargs(SCREAMING_SNAKE_CASE		)
 if parsed_args and verbose:
  print(f'''parsed the following generate kwargs: {parsed_args}'''		)
 with open(args.input_path		) as f:
  __lowerCAmelCase:       Optional[int]            =				[' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in f.readlines()]
 if args.n_obs > 0:
  __lowerCAmelCase:       Tuple            =				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.'''		)
 __lowerCAmelCase:       Tuple            =				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:       Dict            =				calculate_bleu if 'translation' in args.task else calculate_rouge
 __lowerCAmelCase:       Optional[Any]            =				[x.rstrip() for x in open(args.save_path		).readlines()]
 __lowerCAmelCase:       Union[str, Any]            =				[x.rstrip() for x in open(args.reference_path		).readlines()][: len(SCREAMING_SNAKE_CASE		)]
 __lowerCAmelCase:       dict            =				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:       Any            =				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)





