

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


lowerCamelCase 	=   getLogger(__name__)


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




def      UpperCAmelCase__							(     _A :					Union[str, Any]      , _A :					str      , _A :					Union[str, Any]      , _A :					Union[str, Any] = 8      , _A :					int = DEFAULT_DEVICE      , _A :					List[str]=False      , _A :					Optional[Any]="summarization"      , _A :					Optional[Any]=None      , **_A :					Any      , ):





  '''simple docstring'''







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

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

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




def      UpperCAmelCase__							(     ):





  '''simple docstring'''







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




def      UpperCAmelCase__							(     _A :					List[str]=True				):





  '''simple docstring'''







  a__    =argparse.ArgumentParser()
  parser.add_argument('''model_name'''      , type=__lowercase      , help='''like facebook/bart-large-cnn,t5-base, etc.'''				)
  parser.add_argument('''input_path'''      , type=__lowercase      , help='''like cnn_dm/test.source'''				)
  parser.add_argument('''save_path'''      , type=__lowercase      , help='''where to save summaries'''				)
  parser.add_argument('''--reference_path'''      , type=__lowercase      , required=__lowercase      , help='''like cnn_dm/test.target'''				)
  parser.add_argument('''--score_path'''      , type=__lowercase      , required=__lowercase      , default='''metrics.json'''      , help='''where to save metrics'''				)
  parser.add_argument('''--device'''      , type=__lowercase      , required=__lowercase      , default=__lowercase      , help='''cuda, cuda:1, cpu etc.'''				)
  parser.add_argument(
      '''--prefix'''      , type=__lowercase      , required=__lowercase      , default=__lowercase      , help='''will be added to the begininng of src examples'''				)
  parser.add_argument('''--task'''      , type=__lowercase      , default='''summarization'''      , help='''used for task_specific_params + metrics'''				)
  parser.add_argument('''--bs'''      , type=__lowercase      , default=8      , required=__lowercase      , help='''batch size'''				)
  parser.add_argument(
      '''--n_obs'''      , type=__lowercase      , default=-1      , required=__lowercase      , 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=__lowercase      , 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
  a__    =parser.parse_known_args()
  a__    =parse_numeric_n_bool_cl_kwargs(__lowercase				)
  if parsed_args and verbose:
    print(F"""parsed the following generate kwargs: {parsed_args}"""				)
  with open(args.input_path				) as f:
    a__    =[''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in f.readlines()]
  if args.n_obs > 0:
    a__    =examples[: args.n_obs]
  Path(args.save_path				).parent.mkdir(exist_ok=__lowercase				)
  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."""				)
  a__    =generate_summaries_or_translations(
      __lowercase      , args.save_path      , args.model_name      , batch_size=args.bs      , device=args.device      , fpaa=args.fpaa      , task=args.task      , prefix=args.prefix      , **__lowercase      , )

  if args.reference_path is None:
    return {}

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

  if args.dump_args:
    scores.update(__lowercase				)
  if args.info:
    a__    =args.info

  if verbose:
    print(__lowercase				)

  if args.score_path is not None:
    json.dump(__lowercase      , 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)






'''simple docstring'''


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()
UpperCamelCase			     =					logging.get_logger(__name__)

UpperCamelCase			     =					{
    '''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''',
}
UpperCamelCase			     =					[
    '''lm_head''',
]
def 					SCREAMING_SNAKE_CASE(   __lowercase		,				__lowercase		,				__lowercase		,				__lowercase		,				__lowercase	)			-> Any:
     for attribute in key.split('''.'''	):
          A:		Union[str, Any]          =		getattr(__lowercase		,				__lowercase	)

     if weight_type is not None:
          A:		Tuple          =		getattr(__lowercase		,				__lowercase	).shape
     else:
          A:		Optional[int]          =		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":
          A:		int          =		value
     elif weight_type == "weight_g":
          A:		Any          =		value
     elif weight_type == "weight_v":
          A:		str          =		value
     elif weight_type == "bias":
          A:		List[str]          =		value
     else:
          A:		str          =		value

     logger.info(F"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."""	)
def 					SCREAMING_SNAKE_CASE(   __lowercase		,				__lowercase		,				__lowercase	)			-> Dict:
     A:		Optional[int]          =		[]
     A:		Tuple          =		fairseq_model.state_dict()

     if not is_headless:
          A:		int          =		hf_model.dataavec_audio.feature_extractor
          A:		Dict          =		hf_model.dataavec_audio.encoder.pos_conv_embed

     else:
          A:		str          =		hf_model.feature_extractor
          A:		Tuple          =		hf_model.encoder.pos_conv_embed

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

     logger.warning(F"""Unused weights: {unused_weights}"""	)
def 					SCREAMING_SNAKE_CASE(   __lowercase		,				__lowercase	)			-> str:
     A:		int          =		path.split('''.'''	)
     return reduce(__lowercase		,				__lowercase		,				__lowercase	)
def 					SCREAMING_SNAKE_CASE(   __lowercase		,				__lowercase		,				__lowercase		,				__lowercase	)			-> Optional[Any]:
     A:		Tuple          =		access_by_string(__lowercase		,				__lowercase	)
     A:		Tuple          =		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."""	)
     A:		List[Any]          =		fsq_value
     logger.info(F"""{full_name} was correctly initialized from {hf_weight_path}."""	)
def 					SCREAMING_SNAKE_CASE(   __lowercase		,				__lowercase		,				__lowercase		,				__lowercase	)			-> List[Any]:
     A:		List[Any]          =		full_name.split('''conv_layers.'''	)[-1]
     A:		List[str]          =		name.split('''.'''	)
     A:		Tuple          =		int(items[0]	)
     A:		Optional[int]          =		int(items[1]	)

     A:		List[str]          =		name.split('''.'''	)[-1]
     if type_id == 0:
          A:		Optional[Any]          =		'''conv'''
     elif type_id == 2:
          A:		List[str]          =		'''layer_norm'''
     else:
          unused_weights.append(__lowercase	)
          return

     set_weights(__lowercase		,				__lowercase		,				__lowercase		,				F"""conv_layers.{layer_id}.{layer_type}.{weight_type}"""	)
def 					SCREAMING_SNAKE_CASE(   __lowercase		,				__lowercase		,				__lowercase		,				__lowercase	)			-> Optional[Any]:
     A:		Any          =		full_name.split('''pos_conv.'''	)[-1]
     A:		Any          =		name.split('''.'''	)
     A:		int          =		int(items[0]	)
     A:		Optional[int]          =		int(items[1]	)

     A:		Optional[Any]          =		name.split('''.'''	)[-1]
     if type_id != 0:
          unused_weights.append(__lowercase	)
          return
     else:
          A:		List[str]          =		'''conv'''

     set_weights(__lowercase		,				__lowercase		,				__lowercase		,				F"""layers.{layer_id}.{layer_type}.{weight_type}"""	)
@torch.no_grad()
def 					SCREAMING_SNAKE_CASE(   __lowercase		,				__lowercase		,				__lowercase=None		,				__lowercase=None		,				__lowercase=True	)			-> Optional[Any]:
     if config_path is not None:
          A:		Tuple          =		DataaVecAudioConfig.from_pretrained(__lowercase	)
     else:
          A:		Optional[Any]          =		DataaVecAudioConfig()

     if not is_finetuned:
          # Modify final_proj layer name
          A:		Union[str, Any]          =		DataaVecAudioModel(__lowercase	)
          A:		List[Any]          =		os.path.dirname(__lowercase	)

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

     def load_dataavec(__lowercase	):
          A     ,    A     ,    A:		List[Any]          =		fairseq.checkpoint_utils.load_model_ensemble_and_task([path]	)
          return model[0].eval()

     A:		Any          =		load_dataavec(__lowercase	)

     recursively_load_weights(__lowercase		,				__lowercase		,				not is_finetuned	)

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

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

     A:		Dict          =		processor(__lowercase		,				return_tensors='''pt'''		,				padding=__lowercase	)

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

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

          A:		str          =		torch.argmax(__lowercase		,				dim=-1	)
          A:		Any          =		processor.batch_decode(__lowercase	)

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

     print(our_output.shape		,				their_output.shape	)
     A:		List[Any]          =		torch.max(torch.abs(our_output - their_output	)	).item()
     print(F"""max_absolute_diff = {max_absolute_diff}"""	)  # ~ 1e-7
     A:		Optional[int]          =		torch.allclose(__lowercase		,				__lowercase		,				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(__lowercase	)

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


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






