'''simple docstring'''




import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


_lowercase						: Dict						=						"true"




def 							lowerCamelCase	(      UpperCAmelCase__       :					str      ,      UpperCAmelCase__       :					Dict=82      ,      UpperCAmelCase__       :					Dict=16 )			->      Optional[Any]:
							set_seed(42 )
							lowercase_					:			Optional[int]										=       RegressionModel()
							lowercase_					:			Tuple										=       deepcopy(A__ )
							lowercase_					:			Any										=       RegressionDataset(length=A__ )
							lowercase_					:			Tuple										=       DataLoader(A__      ,      batch_size=A__ )
							model.to(accelerator.device )
							lowercase_						,						lowercase_					:			Tuple										=       accelerator.prepare(A__      ,      A__ )
							return model, ddp_model, dataloader




def 							lowerCamelCase	(      UpperCAmelCase__       :					int      ,      UpperCAmelCase__       :					str=False )			->      Any:
							lowercase_					:			int										=       AutoTokenizer.from_pretrained("""hf-internal-testing/mrpc-bert-base-cased""" )
							lowercase_					:			int										=       load_dataset("""glue"""      ,      """mrpc"""      ,      split="""validation""" )

							def tokenize_function(UpperCAmelCase__       :					Dict ):
														lowercase_					:			Optional[Any]										=       tokenizer(examples["""sentence1"""]      ,      examples["""sentence2"""]      ,      truncation=A__      ,      max_length=A__ )
														return outputs

							with accelerator.main_process_first():
														lowercase_					:			str										=       dataset.map(
														    A__      ,      batched=A__      ,      remove_columns=["""idx""", """sentence1""", """sentence2"""]      ,      )

							lowercase_					:			List[Any]										=       tokenized_datasets.rename_column("""label"""      ,      """labels""" )

							def collate_fn(UpperCAmelCase__       :					Tuple ):
														if use_longest:
																					return tokenizer.pad(A__      ,      padding="""longest"""      ,      return_tensors="""pt""" )
														return tokenizer.pad(A__      ,      padding="""max_length"""      ,      max_length=128      ,      return_tensors="""pt""" )

							return DataLoader(A__      ,      shuffle=A__      ,      collate_fn=A__      ,      batch_size=16 )




def 							lowerCamelCase	(      UpperCAmelCase__       :					Any      ,      UpperCAmelCase__       :					List[Any] )			->      int:
							lowercase_					:			int										=       Accelerator(dispatch_batches=A__      ,      split_batches=A__ )
							lowercase_					:			int										=       get_dataloader(A__      ,      not dispatch_batches )
							lowercase_					:			str										=       AutoModelForSequenceClassification.from_pretrained(
							    """hf-internal-testing/mrpc-bert-base-cased"""      ,      return_dict=A__ )
							lowercase_						,						lowercase_					:			Tuple										=       accelerator.prepare(A__      ,      A__ )
							return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator




def 							lowerCamelCase	(      UpperCAmelCase__       :					Dict      ,      UpperCAmelCase__       :					Dict      ,      UpperCAmelCase__       :					Optional[int] )			->      Any:
							lowercase_					:			Tuple										=       []
							for batch in dataloader:
														lowercase_						,						lowercase_					:			Union[str, Any]										=       batch.values()
														with torch.no_grad():
																					lowercase_					:			Any										=       model(A__ )
																					lowercase_						,						lowercase_					:			List[str]										=       accelerator.gather_for_metrics((logit, target) )
																					logits_and_targets.append((logit, target) )
							lowercase_						,						lowercase_					:			Dict										=       [], []
							for logit, targ in logits_and_targets:
														logits.append(A__ )
														targs.append(A__ )
							lowercase_						,						lowercase_					:			Any										=       torch.cat(A__ ), torch.cat(A__ )
							return logits, targs




def 							lowerCamelCase	(      UpperCAmelCase__       :					Optional[int]      ,      UpperCAmelCase__       :					List[Any]=82      ,      UpperCAmelCase__       :					Any=False      ,      UpperCAmelCase__       :					Optional[Any]=False      ,      UpperCAmelCase__       :					int=16 )			->      Dict:
							lowercase_						,						lowercase_						,						lowercase_					:			Optional[int]										=       get_basic_setup(A__      ,      A__      ,      A__ )
							lowercase_						,						lowercase_					:			Optional[int]										=       generate_predictions(A__      ,      A__      ,      A__ )
							assert (
							    len(A__ ) == num_samples
							), F'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(A__ )}'''




def 							lowerCamelCase	(      UpperCAmelCase__       :					Any = False      ,      UpperCAmelCase__       :					Dict = False )			->      int:
							lowercase_					:			List[str]										=       evaluate.load("""glue"""      ,      """mrpc""" )
							lowercase_						,						lowercase_					:			int										=       get_mrpc_setup(A__      ,      A__ )
							# First do baseline
							lowercase_						,						lowercase_						,						lowercase_					:			Tuple										=       setup["""no"""]
							model.to(A__ )
							model.eval()
							for batch in dataloader:
														batch.to(A__ )
														with torch.inference_mode():
																					lowercase_					:			Union[str, Any]										=       model(**A__ )
														lowercase_					:			str										=       outputs.logits.argmax(dim=-1 )
														metric.add_batch(predictions=A__      ,      references=batch["""labels"""] )
							lowercase_					:			Union[str, Any]										=       metric.compute()

							# Then do distributed
							lowercase_						,						lowercase_						,						lowercase_					:			Optional[int]										=       setup["""ddp"""]
							model.eval()
							for batch in dataloader:
														with torch.inference_mode():
																					lowercase_					:			Union[str, Any]										=       model(**A__ )
														lowercase_					:			Any										=       outputs.logits.argmax(dim=-1 )
														lowercase_					:			Optional[int]										=       batch["""labels"""]
														lowercase_						,						lowercase_					:			List[str]										=       accelerator.gather_for_metrics((preds, references) )
														metric.add_batch(predictions=A__      ,      references=A__ )
							lowercase_					:			Dict										=       metric.compute()

							for key in "accuracy f1".split():
														assert math.isclose(
														    baseline[key]      ,      distributed[key] ), F'''Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'''




def 							lowerCamelCase	(      )			->      int:
							lowercase_					:			List[Any]										=       Accelerator(split_batches=A__      ,      dispatch_batches=A__ )
							if accelerator.is_local_main_process:
														datasets.utils.logging.set_verbosity_warning()
														transformers.utils.logging.set_verbosity_warning()
							else:
														datasets.utils.logging.set_verbosity_error()
														transformers.utils.logging.set_verbosity_error()
							# These are a bit slower so they should only be ran on the GPU or TPU
							if torch.cuda.is_available() or is_tpu_available():
														if accelerator.is_local_main_process:
																					print("""**Testing gather_for_metrics**""" )
														for split_batches in [True, False]:
																					for dispatch_batches in [True, False]:
																												if accelerator.is_local_main_process:
																																			print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`''' )
																												test_mrpc(A__      ,      A__ )
																												accelerator.state._reset_state()
							if accelerator.is_local_main_process:
														print("""**Test torch metrics**""" )
							for split_batches in [True, False]:
														for dispatch_batches in [True, False]:
																					lowercase_					:			Tuple										=       Accelerator(split_batches=A__      ,      dispatch_batches=A__ )
																					if accelerator.is_local_main_process:
																												print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99''' )
																					test_torch_metrics(A__      ,      99 )
																					accelerator.state._reset_state()
							if accelerator.is_local_main_process:
														print("""**Test last batch is not dropped when perfectly divisible**""" )
							lowercase_					:			Union[str, Any]										=       Accelerator()
							test_torch_metrics(A__      ,      512 )
							accelerator.state._reset_state()




def 							lowerCamelCase	(      UpperCAmelCase__       :					Any )			->      List[str]:
							main()


if __name__ == "__main__":
						main()








'''simple docstring'''






import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


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

lowerCAmelCase__											=	{
    '''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__											=	[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''label_embeddings_concat''',
    '''speaker_proj''',
    '''layer_norm_for_extract''',
]


def 		_A     (    A__     ,				A__     ,				A__     ,				A__     ,				A__      ):






 """simple docstring"""



 for attribute in key.split('''.'''      ):
  __lowercase							  = getattr(A__     ,				A__      )

 if weight_type is not None:
  __lowercase							  = getattr(A__     ,				A__      ).shape
 else:
  __lowercase							  = 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":
  __lowercase							  = value
 elif weight_type == "weight_g":
  __lowercase							  = value
 elif weight_type == "weight_v":
  __lowercase							  = value
 elif weight_type == "bias":
  __lowercase							  = value
 else:
  __lowercase							  = value

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


def 		_A     (    A__     ,				A__      ):






 """simple docstring"""



 __lowercase							  = []
 __lowercase							  = fairseq_model.state_dict()

 __lowercase							  = hf_model.unispeech_sat.feature_extractor

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

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


def 		_A     (    A__     ,				A__     ,				A__     ,				A__     ,				A__      ):






 """simple docstring"""



 __lowercase							  = full_name.split('''conv_layers.'''      )[-1]
 __lowercase							  = name.split('''.'''      )
 __lowercase							  = int(items[0]      )
 __lowercase							  = 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."      )
   __lowercase							  = 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."      )
   __lowercase							  = 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."      )
   __lowercase							  = 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."      )
   __lowercase							  = value
   logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."      )
 else:
  unused_weights.append(A__      )


@torch.no_grad()
def 		_A     (    A__     ,				A__     ,				A__=None     ,				A__=None     ,				A__=True      ):






 """simple docstring"""



 if config_path is not None:
  __lowercase							  = UniSpeechSatConfig.from_pretrained(A__      )
 else:
  __lowercase							  = UniSpeechSatConfig()

 __lowercase							  = ''''''

 if is_finetuned:
  __lowercase							  = UniSpeechSatForCTC(A__      )
 else:
  __lowercase							  = UniSpeechSatForPreTraining(A__      )

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

 recursively_load_weights(A__     ,				A__      )

 hf_wavavec.save_pretrained(A__      )


if __name__ == "__main__":
 lowerCAmelCase__											=	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__											=	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
 )

