


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
lowercase						:      str									= logging.get_logger(__name__)

lowercase						:      Union[str, Any]									= {
    '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',
    'adapter_layer': 'encoder.layers.*.adapter_layer',
    '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',
    'mask_emb': 'masked_spec_embed',
    'pooling_layer.linear': 'projector',
    'pooling_layer.projection': 'classifier',
}
lowercase						:      Dict									= [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Optional[int])      ->					Optional[int]:






							'''simple docstring'''





							__UpperCamelCase			:     Any         =      {}
							with open(_lowerCamelCase		,						"r") as file:
														for line_number, line in enumerate(_lowerCamelCase):
																					__UpperCamelCase			:     List[str]         =      line.strip()
																					if line:
																												__UpperCamelCase			:     List[str]         =      line.split()
																												__UpperCamelCase			:     Optional[int]         =      line_number
																												__UpperCamelCase			:     Union[str, Any]         =      words[0]
																												__UpperCamelCase			:     Optional[int]         =      value
							return result




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Any		,						_lowerCamelCase				:    Tuple		,						_lowerCamelCase				:    Any		,						_lowerCamelCase				:    Optional[int]		,						_lowerCamelCase				:    List[Any])      ->					Optional[int]:






							'''simple docstring'''





							for attribute in key.split("."):
														__UpperCamelCase			:     Any         =      getattr(_lowerCamelCase		,						_lowerCamelCase)

							__UpperCamelCase			:     Optional[int]         =      None
							for param_key in PARAM_MAPPING.keys():
														if full_name.endswith(_lowerCamelCase):
																					__UpperCamelCase			:     List[str]         =      PARAM_MAPPING[full_name.split(".")[-1]]
																					__UpperCamelCase			:     int         =      """param"""

							if weight_type is not None and weight_type != "param":
														__UpperCamelCase			:     Union[str, Any]         =      getattr(_lowerCamelCase		,						_lowerCamelCase).shape
							elif weight_type is not None and weight_type == "param":
														__UpperCamelCase			:     Optional[int]         =      hf_pointer
														for attribute in hf_param_name.split("."):
																					__UpperCamelCase			:     Optional[Any]         =      getattr(_lowerCamelCase		,						_lowerCamelCase)
														__UpperCamelCase			:     List[Any]         =      shape_pointer.shape

														# let's reduce dimension
														__UpperCamelCase			:     Union[str, Any]         =      value[0]
							else:
														__UpperCamelCase			:     List[str]         =      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":
														__UpperCamelCase			:     List[str]         =      value
							elif weight_type == "weight_g":
														__UpperCamelCase			:     Any         =      value
							elif weight_type == "weight_v":
														__UpperCamelCase			:     Optional[Any]         =      value
							elif weight_type == "bias":
														__UpperCamelCase			:     Any         =      value
							elif weight_type == "param":
														for attribute in hf_param_name.split("."):
																					__UpperCamelCase			:     int         =      getattr(_lowerCamelCase		,						_lowerCamelCase)
														__UpperCamelCase			:     int         =      value
							else:
														__UpperCamelCase			:     Optional[int]         =      value

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




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Optional[Any]		,						_lowerCamelCase				:    Dict		,						_lowerCamelCase				:    List[Any]		,						_lowerCamelCase				:    Optional[Any]		,						_lowerCamelCase				:    Optional[int])      ->					List[str]:






							'''simple docstring'''





							__UpperCamelCase			:     int         =      None
							for param_key in PARAM_MAPPING.keys():
														if full_name.endswith(_lowerCamelCase):
																					__UpperCamelCase			:     List[str]         =      PARAM_MAPPING[full_name.split(".")[-1]]
																					__UpperCamelCase			:     Tuple         =      """param"""

							if weight_type is not None and weight_type != "param":
														__UpperCamelCase			:     Dict         =      """.""".join([key, weight_type])
							elif weight_type is not None and weight_type == "param":
														__UpperCamelCase			:     Tuple         =      """.""".join([key, hf_param_name])
							else:
														__UpperCamelCase			:     Dict         =      key

							__UpperCamelCase			:     int         =      value if """lm_head""" in full_key else value[0]


lowercase						:      List[Any]									= {
    'W_a': 'linear_1.weight',
    'W_b': 'linear_2.weight',
    'b_a': 'linear_1.bias',
    'b_b': 'linear_2.bias',
    'ln_W': 'norm.weight',
    'ln_b': 'norm.bias',
}




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Optional[int]		,						_lowerCamelCase				:    Tuple		,						_lowerCamelCase				:    Optional[Any]=None		,						_lowerCamelCase				:    Any=None)      ->					List[str]:






							'''simple docstring'''





							__UpperCamelCase			:     str         =      False
							for key, mapped_key in MAPPING.items():
														__UpperCamelCase			:     Tuple         =      """wav2vec2.""" + 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]:
																					__UpperCamelCase			:     List[Any]         =      True
																					if "*" in mapped_key:
																												__UpperCamelCase			:     int         =      name.split(_lowerCamelCase)[0].split(".")[-2]
																												__UpperCamelCase			:     Tuple         =      mapped_key.replace("*"		,						_lowerCamelCase)
																					if "weight_g" in name:
																												__UpperCamelCase			:     Optional[int]         =      """weight_g"""
																					elif "weight_v" in name:
																												__UpperCamelCase			:     List[Any]         =      """weight_v"""
																					elif "bias" in name:
																												__UpperCamelCase			:     List[str]         =      """bias"""
																					elif "weight" in name:
																												# TODO: don't match quantizer.weight_proj
																												__UpperCamelCase			:     List[str]         =      """weight"""
																					else:
																												__UpperCamelCase			:     Optional[int]         =      None
																					if hf_dict is not None:
																												rename_dict(_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase)
																					else:
																												set_recursively(_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase)
																					return is_used
							return is_used




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Optional[int]		,						_lowerCamelCase				:    Optional[int]		,						_lowerCamelCase				:    Tuple)      ->					int:






							'''simple docstring'''





							__UpperCamelCase			:     Union[str, Any]         =      []
							__UpperCamelCase			:     Dict         =      fairseq_model.state_dict()

							__UpperCamelCase			:     List[Any]         =      hf_model.wavaveca.feature_extractor

							for name, value in fairseq_dict.items():
														__UpperCamelCase			:     Dict         =      False
														if "conv_layers" in name:
																					load_conv_layer(
																					    _lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase		,						hf_model.config.feat_extract_norm == "group"		,						)
																					__UpperCamelCase			:     Dict         =      True
														else:
																					__UpperCamelCase			:     int         =      load_wavaveca_layer(_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase)
														if not is_used:
																					unused_weights.append(_lowerCamelCase)

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




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Dict		,						_lowerCamelCase				:    int		,						_lowerCamelCase				:    str		,						_lowerCamelCase				:    Tuple		,						_lowerCamelCase				:    Union[str, Any])      ->					str:






							'''simple docstring'''





							__UpperCamelCase			:     Any         =      full_name.split("conv_layers.")[-1]
							__UpperCamelCase			:     List[str]         =      name.split(".")
							__UpperCamelCase			:     Optional[Any]         =      int(items[0])
							__UpperCamelCase			:     Dict         =      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.')
																					__UpperCamelCase			:     Optional[int]         =      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.')
																					__UpperCamelCase			:     List[str]         =      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.conv_layers[layer_id].layer_norm.bias.data.shape} was found.')
																					__UpperCamelCase			:     str         =      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.conv_layers[layer_id].layer_norm.weight.data.shape} was found.')
																					__UpperCamelCase			:     Tuple         =      value
																					logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.')
							else:
														unused_weights.append(_lowerCamelCase)




@torch.no_grad()
def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Optional[Any]		,						_lowerCamelCase				:    str		,						_lowerCamelCase				:    List[Any]=None		,						_lowerCamelCase				:    List[Any]=None		,						_lowerCamelCase				:    Any=True		,						_lowerCamelCase				:    Optional[int]=False)      ->					List[str]:






							'''simple docstring'''





							if config_path is not None:
														__UpperCamelCase			:     Optional[Any]         =      WavaVecaConfig.from_pretrained(_lowerCamelCase)
							else:
														__UpperCamelCase			:     Any         =      WavaVecaConfig()

							if is_seq_class:
														__UpperCamelCase			:     int         =      read_txt_into_dict(_lowerCamelCase)
														__UpperCamelCase			:     List[str]         =      idalabel
														__UpperCamelCase			:     Union[str, Any]         =      WavaVecaForSequenceClassification(_lowerCamelCase)
														__UpperCamelCase			:     Optional[Any]         =      WavaVecaFeatureExtractor(
														    feature_size=1		,						sampling_rate=16_000		,						padding_value=0		,						do_normalize=_lowerCamelCase		,						return_attention_mask=_lowerCamelCase		,						)
														feature_extractor.save_pretrained(_lowerCamelCase)

							elif is_finetuned:
														if dict_path:
																					__UpperCamelCase			:     List[Any]         =      Dictionary.load(_lowerCamelCase)

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

																					# fairseq has the <pad> and <s> switched
																					__UpperCamelCase			:     List[str]         =      0
																					__UpperCamelCase			:     List[str]         =      1
																					with open(_lowerCamelCase		,						"w"		,						encoding="utf-8") as vocab_handle:
																												json.dump(_lowerCamelCase		,						_lowerCamelCase)
																					__UpperCamelCase			:     Optional[int]         =      WavaVecaCTCTokenizer(
																					    _lowerCamelCase		,						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=_lowerCamelCase		,						)
																					__UpperCamelCase			:     Optional[int]         =      True if config.feat_extract_norm == """layer""" else False
																					__UpperCamelCase			:     Optional[int]         =      WavaVecaFeatureExtractor(
																					    feature_size=1		,						sampling_rate=16_000		,						padding_value=0		,						do_normalize=_lowerCamelCase		,						return_attention_mask=_lowerCamelCase		,						)
																					__UpperCamelCase			:     Optional[int]         =      WavaVecaProcessor(feature_extractor=_lowerCamelCase		,						tokenizer=_lowerCamelCase)
																					processor.save_pretrained(_lowerCamelCase)

														__UpperCamelCase			:     List[str]         =      WavaVecaForCTC(_lowerCamelCase)
							else:
														__UpperCamelCase			:     Optional[Any]         =      WavaVecaForPreTraining(_lowerCamelCase)

							if is_finetuned or is_seq_class:
														__UpperCamelCase			:     Optional[Any]         =      fairseq.checkpoint_utils.load_model_ensemble_and_task(
														    [checkpoint_path]		,						arg_overrides={"data": "/".join(dict_path.split("/")[:-1])})
							else:
														__UpperCamelCase			:     int         =      argparse.Namespace(task="audio_pretraining")
														__UpperCamelCase			:     Tuple         =      fairseq.tasks.setup_task(_lowerCamelCase)

														__UpperCamelCase			:     Any         =      fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]		,						task=_lowerCamelCase)

							__UpperCamelCase			:     Any         =      model[0].eval()

							recursively_load_weights(_lowerCamelCase		,						_lowerCamelCase		,						not is_finetuned)

							hf_wavavec.save_pretrained(_lowerCamelCase)


if __name__ == "__main__":
		lowercase						:      List[str]									= 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'
		)
		parser.add_argument(
		    '--is_seq_class',
		    action='store_true',
		    help='Whether the model to convert is a fine-tuned sequence classification model or not',
		)
		lowercase						:      Dict									= parser.parse_args()

		lowercase						:      str									= not args.not_finetuned and not args.is_seq_class
		convert_wavaveca_checkpoint(
		    args.checkpoint_path,
		    args.pytorch_dump_folder_path,
		    args.config_path,
		    args.dict_path,
		    is_finetuned,
		    args.is_seq_class,
		)

import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict




@dataclass
class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  ComputeEnvironment.AMAZON_SAGEMAKER
	_A			=  True
	_A			=  'ml.p3.2xlarge'
	_A			=  'accelerate_sagemaker_execution_role'
	_A			=  'hf-sm'
	_A			=  'us-east-1'
	_A			=  1
	_A			=  'accelerate-sagemaker-1'
	_A			=  '1.6'
	_A			=  '4.4'
	_A			=  'train.py'
	_A			=  [
	    '--model_name_or_path',
	    'bert',
	    '--do_train',
	    'False',
	    '--epochs',
	    '3',
	    '--learning_rate',
	    '5e-5',
	    '--max_steps',
	    '50.5',
	]
	_A			=  [
	    '--model_name_or_path',
	    'bert',
	    '--do_train',
	    '--do_test',
	    'False',
	    '--do_predict',
	    '--epochs',
	    '3',
	    '--learning_rate',
	    '5e-5',
	    '--max_steps',
	    '50.5',
	]




class 				lowerCamelCase__							(       unittest.TestCase):




	'''simple docstring'''




	def      _lowerCamelCase       (      self	:Tuple		)	->				str:
								# If no defaults are changed, `to_kwargs` returns an empty dict.
								__UpperCamelCase			:     str         =      _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args		)
								assert isinstance(converted_args["model_name_or_path"]  ,     a		)
								assert isinstance(converted_args["do_train"]  ,     a		)
								assert isinstance(converted_args["epochs"]  ,     a		)
								assert isinstance(converted_args["learning_rate"]  ,     a		)
								assert isinstance(converted_args["max_steps"]  ,     a		)

								with pytest.raises(a		):
															_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args		)