



import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

UpperCamelCase_	       =   {
    "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",
    "mask_emb": "masked_spec_embed",
}







def 				A			(		__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase	)       ->       Optional[Any]:



						'''simple docstring'''






						for attribute in key.split('''.'''	):
												UpperCAmelCase_     							=     getattr(_SCREAMING_SNAKE_CASE							,					_SCREAMING_SNAKE_CASE	)

						if weight_type is not None:
												UpperCAmelCase_     							=     getattr(_SCREAMING_SNAKE_CASE							,					_SCREAMING_SNAKE_CASE	).shape
						else:
												UpperCAmelCase_     							=     hf_pointer.shape

						assert hf_shape == value.shape, (
						    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_     							=     value
						elif weight_type == "weight_g":
												UpperCAmelCase_     							=     value
						elif weight_type == "weight_v":
												UpperCAmelCase_     							=     value
						elif weight_type == "bias":
												UpperCAmelCase_     							=     value
						else:
												UpperCAmelCase_     							=     value

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







def 				A			(		__UpperCAmelCase							,					__UpperCAmelCase	)       ->       Dict:



						'''simple docstring'''






						UpperCAmelCase_     							=     []
						UpperCAmelCase_     							=     fairseq_model.state_dict()

						UpperCAmelCase_     							=     hf_model.feature_extractor

						for name, value in fairseq_dict.items():
												UpperCAmelCase_     							=     False
												if "conv_layers" in name:
																		load_conv_layer(
																		    _SCREAMING_SNAKE_CASE							,					_SCREAMING_SNAKE_CASE							,					_SCREAMING_SNAKE_CASE							,					_SCREAMING_SNAKE_CASE							,					hf_model.config.feat_extract_norm == '''group'''							,					)
																		UpperCAmelCase_     							=     True
												else:
																		for key, mapped_key in MAPPING.items():
																								UpperCAmelCase_     							=     mapped_key

																								if key in name:
																														UpperCAmelCase_     							=     True
																														if "*" in mapped_key:
																																				UpperCAmelCase_     							=     name.split(_SCREAMING_SNAKE_CASE	)[0].split('''.'''	)[-2]
																																				UpperCAmelCase_     							=     mapped_key.replace('''*'''							,					_SCREAMING_SNAKE_CASE	)
																														if "weight_g" in name:
																																				UpperCAmelCase_     							=     "weight_g"
																														elif "weight_v" in name:
																																				UpperCAmelCase_     							=     "weight_v"
																														elif "weight" in name:
																																				UpperCAmelCase_     							=     "weight"
																														elif "bias" in name:
																																				UpperCAmelCase_     							=     "bias"
																														else:
																																				UpperCAmelCase_     							=     None
																														set_recursively(_SCREAMING_SNAKE_CASE							,					_SCREAMING_SNAKE_CASE							,					_SCREAMING_SNAKE_CASE							,					_SCREAMING_SNAKE_CASE							,					_SCREAMING_SNAKE_CASE	)
																								continue
												if not is_used:
																		unused_weights.append(_SCREAMING_SNAKE_CASE	)

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







def 				A			(		__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase	)       ->       int:



						'''simple docstring'''






						UpperCAmelCase_     							=     full_name.split('''conv_layers.'''	)[-1]
						UpperCAmelCase_     							=     name.split('''.'''	)
						UpperCAmelCase_     							=     int(items[0]	)
						UpperCAmelCase_     							=     int(items[1]	)

						if type_id == 0:
												if "bias" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
																		    f"{full_name} has size {value.shape}, but"
																		    f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
																		)
																		UpperCAmelCase_     							=     value
																		logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."	)
												elif "weight" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
																		    f"{full_name} has size {value.shape}, but"
																		    f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
																		)
																		UpperCAmelCase_     							=     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:
																		assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
																		    f"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
																		    " found."
																		)
																		UpperCAmelCase_     							=     value
																		logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."	)
												elif "weight" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
																		    f"{full_name} has size {value.shape}, but"
																		    f" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
																		)
																		UpperCAmelCase_     							=     value
																		logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."	)
						else:
												unused_weights.append(_SCREAMING_SNAKE_CASE	)







def 				A			(		__UpperCAmelCase	)       ->       List[str]:



						'''simple docstring'''






						UpperCAmelCase_     							=     HubertConfig()
						UpperCAmelCase_     							=     model.config

						UpperCAmelCase_     							=     fs_config.activation_dropout
						UpperCAmelCase_     							=     False
						UpperCAmelCase_     							=     fs_config.attention_dropout
						UpperCAmelCase_     							=     False
						UpperCAmelCase_     							=     eval(fs_config.extractor_conv_feature_layers	)
						UpperCAmelCase_     							=     [x[0] for x in conv_layers]
						UpperCAmelCase_     							=     [x[1] for x in conv_layers]
						UpperCAmelCase_     							=     [x[2] for x in conv_layers]
						UpperCAmelCase_     							=     "gelu"
						UpperCAmelCase_     							=     "layer" if fs_config.extractor_mode == "layer_norm" else "group"
						UpperCAmelCase_     							=     False
						UpperCAmelCase_     							=     0.0
						UpperCAmelCase_     							=     0.0
						UpperCAmelCase_     							=     fs_config.activation_fn
						UpperCAmelCase_     							=     fs_config.dropout
						UpperCAmelCase_     							=     fs_config.encoder_embed_dim
						UpperCAmelCase_     							=     0.02
						UpperCAmelCase_     							=     fs_config.encoder_ffn_embed_dim
						UpperCAmelCase_     							=     1e-5
						UpperCAmelCase_     							=     0.0
						UpperCAmelCase_     							=     fs_config.encoder_attention_heads
						UpperCAmelCase_     							=     fs_config.conv_pos_groups
						UpperCAmelCase_     							=     fs_config.conv_pos
						UpperCAmelCase_     							=     len(_SCREAMING_SNAKE_CASE	)
						UpperCAmelCase_     							=     fs_config.encoder_layers

						return config







@torch.no_grad()
def 				A			(		__UpperCAmelCase							,					__UpperCAmelCase=None	)       ->       str:



						'''simple docstring'''






						UpperCAmelCase_     							=     distilhubert().model.model

						if config_path is not None:
												UpperCAmelCase_     							=     HubertConfig.from_pretrained(_SCREAMING_SNAKE_CASE	)
						else:
												UpperCAmelCase_     							=     convert_config(_SCREAMING_SNAKE_CASE	)
						UpperCAmelCase_     							=     model.eval()

						UpperCAmelCase_     							=     WavaVecaFeatureExtractor(
						    feature_size=1							,					sampling_rate=1_6000							,					padding_value=0							,					do_normalize=_SCREAMING_SNAKE_CASE							,					return_attention_mask=_SCREAMING_SNAKE_CASE							,					)
						UpperCAmelCase_     							=     HubertModel(_SCREAMING_SNAKE_CASE	)

						recursively_load_weights(_SCREAMING_SNAKE_CASE							,					_SCREAMING_SNAKE_CASE	)

						feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE	)
						hf_model.save_pretrained(_SCREAMING_SNAKE_CASE	)


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("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
							UpperCamelCase_	       =   parser.parse_args()
							convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)








'''simple docstring'''
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from typing import List


def       a__    (  _SCREAMING_SNAKE_CASE					:    List[str]     ,							_SCREAMING_SNAKE_CASE					:    str							)			-> Optional[Any]:


	"""simple docstring"""





	if name != "training_function" and name != "main":
		raise ValueError(F'''Incorrect function name passed: {name}, choose either \'main\' or \'training_function\''''							)
	UpperCAmelCase_ , UpperCAmelCase_			:	int      =   [], False
	for line in lines:
		if not found_start and F'''def {name}''' in line:
			UpperCAmelCase_			:	List[str]      =   True
			good_lines.append(_SCREAMING_SNAKE_CASE							)
			continue
		if found_start:
			if name == "training_function" and "def main" in line:
				return good_lines
			if name == "main" and "if __name__" in line:
				return good_lines
			good_lines.append(_SCREAMING_SNAKE_CASE							)


def       a__    (  _SCREAMING_SNAKE_CASE					:    List[str]							)			-> Optional[Any]:


	"""simple docstring"""





	return [line for line in lines if not line.lstrip().startswith("#"							) and line != "\n"]





def       a__    (  _SCREAMING_SNAKE_CASE					:    str     ,							_SCREAMING_SNAKE_CASE					:    str     ,							_SCREAMING_SNAKE_CASE					:    bool     ,							_SCREAMING_SNAKE_CASE					:    str = None							)			-> int:


	"""simple docstring"""





	with open(_SCREAMING_SNAKE_CASE     ,							"r"							) as f:
		UpperCAmelCase_			:	Union[str, Any]      =   f.readlines()
	with open(os.path.abspath(os.path.join("examples"     ,							"nlp_example.py"							)							)     ,							"r"							) as f:
		UpperCAmelCase_			:	Dict      =   f.readlines()
	with open(_SCREAMING_SNAKE_CASE     ,							"r"							) as f:
		UpperCAmelCase_			:	Any      =   f.readlines()
	if secondary_filename is not None:
		with open(_SCREAMING_SNAKE_CASE     ,							"r"							) as f:
			UpperCAmelCase_			:	Tuple      =   f.readlines()

    # This is our base, we remove all the code from here in our `full_filename` and `feature_filename` to find the new content
	if parser_only:
		UpperCAmelCase_			:	List[str]      =   clean_lines(get_function_contents_by_name(_SCREAMING_SNAKE_CASE     ,							"main"							)							)
		UpperCAmelCase_			:	Tuple      =   clean_lines(get_function_contents_by_name(_SCREAMING_SNAKE_CASE     ,							"main"							)							)
		UpperCAmelCase_			:	Tuple      =   clean_lines(get_function_contents_by_name(_SCREAMING_SNAKE_CASE     ,							"main"							)							)
		if secondary_filename is not None:
			UpperCAmelCase_			:	List[Any]      =   clean_lines(get_function_contents_by_name(_SCREAMING_SNAKE_CASE     ,							"main"							)							)
	else:
		UpperCAmelCase_			:	Union[str, Any]      =   clean_lines(get_function_contents_by_name(_SCREAMING_SNAKE_CASE     ,							"training_function"							)							)
		UpperCAmelCase_			:	str      =   clean_lines(get_function_contents_by_name(_SCREAMING_SNAKE_CASE     ,							"training_function"							)							)
		UpperCAmelCase_			:	List[Any]      =   clean_lines(get_function_contents_by_name(_SCREAMING_SNAKE_CASE     ,							"training_function"							)							)
		if secondary_filename is not None:
			UpperCAmelCase_			:	Tuple      =   clean_lines(
			    get_function_contents_by_name(_SCREAMING_SNAKE_CASE     ,							"training_function"							)							)

	UpperCAmelCase_			:	List[Any]      =   "train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)\n"

	# Specific code in our script that differs from the full version, aka what is new
	UpperCAmelCase_			:	Dict      =   []
	UpperCAmelCase_			:	Any      =   []  # We keep track of the idxs just in case it's a repeated statement
	UpperCAmelCase_			:	Optional[int]      =   iter(_SCREAMING_SNAKE_CASE							)
	for i in range(len(_SCREAMING_SNAKE_CASE							) - 1							):
		if i not in passed_idxs:
			UpperCAmelCase_			:	str      =   next(_SCREAMING_SNAKE_CASE							)
			if (line not in full_file_func) and (line.lstrip() != _dl_line):
				if "TESTING_MOCKED_DATALOADERS" not in line:
					new_feature_code.append(_SCREAMING_SNAKE_CASE							)
					passed_idxs.append(_SCREAMING_SNAKE_CASE							)
				else:
					# Skip over the `config['num_epochs'] = 2` statement
					UpperCAmelCase_			:	List[Any]      =   next(_SCREAMING_SNAKE_CASE							)

    # Extract out just the new parts from the full_file_training_func
	UpperCAmelCase_			:	Tuple      =   []
	UpperCAmelCase_			:	Optional[Any]      =   []  # We keep track of the idxs just in case it's a repeated statement
	for i, line in enumerate(_SCREAMING_SNAKE_CASE							):
		if i not in passed_idxs:
			if (line not in full_file_func) and (line.lstrip() != _dl_line):
				if "TESTING_MOCKED_DATALOADERS" not in line:
					new_full_example_parts.append(_SCREAMING_SNAKE_CASE							)
					passed_idxs.append(_SCREAMING_SNAKE_CASE							)

    # Finally, get the overall diff
	UpperCAmelCase_			:	Union[str, Any]      =   [line for line in new_feature_code if line not in new_full_example_parts]
	if secondary_filename is not None:
		UpperCAmelCase_			:	int      =   [line for line in full_file_contents if line not in secondary_file_func]
		UpperCAmelCase_			:	str      =   [line for line in diff_from_example if line not in diff_from_two]

	return diff_from_example




