


"""simple docstring"""






from __future__ import annotations



def 			A__		(					_UpperCAmelCase    :      list[int]				,    _UpperCAmelCase    :      int	)							->	int:

	'''simple docstring'''


	snake_case__,					snake_case__  :						str		=      0, len(_UpperCAmelCase	) - 1
	while start_ind <= end_ind:
		if array[start_ind] == search_item:
			return start_ind
		elif array[end_ind] == search_item:
			return end_ind
		else:
			start_ind += 1
			end_ind -= 1
    # returns -1 if search_item is not found in array
	return -1


if __name__ == "__main__":
					print(double_linear_search(list(range(100)), 40))









"""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()
lowercase  		=			logging.get_logger(__name__)

lowercase  		=			{
    """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""",
}
lowercase  		=			[
    """lm_head""",
]



def 			A__		(					_UpperCAmelCase    :      str				,    _UpperCAmelCase    :      List[Any]				,    _UpperCAmelCase    :      List[str]				,    _UpperCAmelCase    :      Optional[int]				,    _UpperCAmelCase    :      Tuple	)							->	Union[str, Any]:

	'''simple docstring'''


	for attribute in key.split("."	):
		snake_case__  :						Dict		=      getattr(_UpperCAmelCase				,    _UpperCAmelCase	)

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

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



def 			A__		(					_UpperCAmelCase    :      List[Any]				,    _UpperCAmelCase    :      Dict				,    _UpperCAmelCase    :      Tuple	)							->	List[Any]:

	'''simple docstring'''


	snake_case__  :						Optional[Any]		=      []
	snake_case__  :						Any		=      fairseq_model.state_dict()

	if not is_headless:
		snake_case__  :						int		=      hf_model.dataavec_audio.feature_extractor
		snake_case__  :						Optional[Any]		=      hf_model.dataavec_audio.encoder.pos_conv_embed

	else:
		snake_case__  :						Union[str, Any]		=      hf_model.feature_extractor
		snake_case__  :						str		=      hf_model.encoder.pos_conv_embed

	for name, value in fairseq_dict.items():
		snake_case__  :						Optional[Any]		=      False
		if "conv_layers" in name:
			load_conv_layer(
			    _UpperCAmelCase				,    _UpperCAmelCase				,    _UpperCAmelCase				,    _UpperCAmelCase				,    )
			snake_case__  :						str		=      True
		elif "pos_conv" in name:
			load_pos_conv_layer(
			    _UpperCAmelCase				,    _UpperCAmelCase				,    _UpperCAmelCase				,    _UpperCAmelCase				,    )
			snake_case__  :						Union[str, Any]		=      True
		else:
			for key, mapped_key in MAPPING.items():
				if not is_headless:
					snake_case__  :						Tuple		=      "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]:
					snake_case__  :						Dict		=      True
					if "*" in mapped_key:
						snake_case__  :						List[str]		=      name.split(_UpperCAmelCase	)[0].split("."	)[-2]
						snake_case__  :						Dict		=      mapped_key.replace("*"				,    _UpperCAmelCase	)
					if "weight_g" in name:
						snake_case__  :						str		=      "weight_g"
					elif "weight_v" in name:
						snake_case__  :						Union[str, Any]		=      "weight_v"
					elif "bias" in name:
						snake_case__  :						Tuple		=      "bias"
					elif "weight" in name:
						# TODO: don't match quantizer.weight_proj
						snake_case__  :						List[Any]		=      "weight"
					else:
						snake_case__  :						Any		=      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__		(					_UpperCAmelCase    :      Optional[int]				,    _UpperCAmelCase    :      int	)							->	Tuple:

	'''simple docstring'''


	snake_case__  :						Optional[Any]		=      path.split("."	)
	return reduce(_UpperCAmelCase				,    _UpperCAmelCase				,    _UpperCAmelCase	)



def 			A__		(					_UpperCAmelCase    :      List[str]				,    _UpperCAmelCase    :      str				,    _UpperCAmelCase    :      Dict				,    _UpperCAmelCase    :      str	)							->	Tuple:

	'''simple docstring'''


	snake_case__  :						List[str]		=      access_by_string(_UpperCAmelCase				,    _UpperCAmelCase	)
	snake_case__  :						Optional[Any]		=      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."""	)
	snake_case__  :						Tuple		=      fsq_value
	logger.info(F"""{full_name} was correctly initialized from {hf_weight_path}."""	)



def 			A__		(					_UpperCAmelCase    :      Any				,    _UpperCAmelCase    :      Any				,    _UpperCAmelCase    :      List[str]				,    _UpperCAmelCase    :      Tuple	)							->	Dict:

	'''simple docstring'''


	snake_case__  :						List[str]		=      full_name.split("conv_layers."	)[-1]
	snake_case__  :						Tuple		=      name.split("."	)
	snake_case__  :						Any		=      int(items[0]	)
	snake_case__  :						List[Any]		=      int(items[1]	)

	snake_case__  :						Tuple		=      name.split("."	)[-1]
	if type_id == 0:
		snake_case__  :						Dict		=      "conv"
	elif type_id == 2:
		snake_case__  :						Dict		=      "layer_norm"
	else:
		unused_weights.append(_UpperCAmelCase	)
		return

	set_weights(_UpperCAmelCase				,    _UpperCAmelCase				,    _UpperCAmelCase				,    F"""conv_layers.{layer_id}.{layer_type}.{weight_type}"""	)



def 			A__		(					_UpperCAmelCase    :      Any				,    _UpperCAmelCase    :      Dict				,    _UpperCAmelCase    :      Dict				,    _UpperCAmelCase    :      str	)							->	Tuple:

	'''simple docstring'''


	snake_case__  :						Optional[Any]		=      full_name.split("pos_conv."	)[-1]
	snake_case__  :						Optional[Any]		=      name.split("."	)
	snake_case__  :						Dict		=      int(items[0]	)
	snake_case__  :						Optional[Any]		=      int(items[1]	)

	snake_case__  :						int		=      name.split("."	)[-1]
	if type_id != 0:
		unused_weights.append(_UpperCAmelCase	)
		return
	else:
		snake_case__  :						Optional[int]		=      "conv"

	set_weights(_UpperCAmelCase				,    _UpperCAmelCase				,    _UpperCAmelCase				,    F"""layers.{layer_id}.{layer_type}.{weight_type}"""	)



@torch.no_grad()
def 			A__		(					_UpperCAmelCase    :      Dict				,    _UpperCAmelCase    :      Union[str, Any]				,    _UpperCAmelCase    :      List[str]=None				,    _UpperCAmelCase    :      Optional[Any]=None				,    _UpperCAmelCase    :      int=True	)							->	Dict:

	'''simple docstring'''


	if config_path is not None:
		snake_case__  :						List[str]		=      DataaVecAudioConfig.from_pretrained(_UpperCAmelCase	)
	else:
		snake_case__  :						int		=      DataaVecAudioConfig()

	if not is_finetuned:
		# Modify final_proj layer name
		snake_case__  :						int		=      DataaVecAudioModel(_UpperCAmelCase	)
		snake_case__  :						Union[str, Any]		=      os.path.dirname(_UpperCAmelCase	)

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

	def load_dataavec(_UpperCAmelCase    :      Union[str, Any]	):
		snake_case__,					snake_case__,					snake_case__  :						Dict		=      fairseq.checkpoint_utils.load_model_ensemble_and_task([path]	)
		return model[0].eval()

	snake_case__  :						Optional[int]		=      load_dataavec(_UpperCAmelCase	)

	recursively_load_weights(_UpperCAmelCase				,    _UpperCAmelCase				,    not is_finetuned	)

	snake_case__  :						Any		=      WavaVecaProcessor.from_pretrained("facebook/wav2vec2-large-lv60"	)

	snake_case__  :						Dict		=      load_dataset("patrickvonplaten/librispeech_asr_dummy"				,    "clean"				,    split="validation"	)
	snake_case__  :						Tuple		=      [x["array"] for x in ds[:4]["audio"]]

	snake_case__  :						Union[str, Any]		=      processor(_UpperCAmelCase				,    return_tensors="pt"				,    padding=_UpperCAmelCase	)

	snake_case__  :						Any		=      inputs.input_values
	snake_case__  :						List[str]		=      inputs.attention_mask
	#    input_values = inputs.input_values[:, :-1]
	#    attention_mask = inputs.attention_mask[:, :-1]

	hf_wavavec.eval()
	model.eval()
	if is_finetuned:
		snake_case__  :						Optional[Any]		=      model(source=_UpperCAmelCase				,    padding_mask=(1 - attention_mask)				,    mask=_UpperCAmelCase				,    features_only=_UpperCAmelCase	)[
		    "encoder_out"
		].transpose(0				,    1	)
		snake_case__  :						Union[str, Any]		=      hf_wavavec(_UpperCAmelCase				,    attention_mask=_UpperCAmelCase	)["logits"]

		snake_case__  :						Optional[int]		=      torch.argmax(_UpperCAmelCase				,    dim=-1	)
		snake_case__  :						Union[str, Any]		=      processor.batch_decode(_UpperCAmelCase	)

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

	print(our_output.shape				,    their_output.shape	)
	snake_case__  :						Union[str, Any]		=      torch.max(torch.abs(our_output - their_output	)	).item()
	print(F"""max_absolute_diff = {max_absolute_diff}"""	)  # ~ 1e-7
	snake_case__  :						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__":
					lowercase  		=			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"""
					)
					lowercase  		=			parser.parse_args()
					convert_wavaveca_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
					)





