
"""simple docstring"""


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							   =				logging.get_logger(__name__)

_lowercase							   =				{
    '''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							   =				[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''projector''',
    '''classifier''',
]






def       _snake_case				(		snake_case__   :     List[Any]     ):
	A						   =   {}
	with open(snake_case__	,      'r'     ) as file:
		for line_number, line in enumerate(snake_case__     ):
			A						   =   line.strip()
			if line:
				A						   =   line.split()
				A						   =   line_number
				A						   =   words[0]
				A						   =   value
	return result






def       _snake_case				(		snake_case__   :     int	,      snake_case__   :     List[str]	,      snake_case__   :     List[Any]	,      snake_case__   :     List[Any]	,      snake_case__   :     Dict     ):
	for attribute in key.split('.'     ):
		A						   =   getattr(snake_case__	,      snake_case__     )

	A						   =   None
	for param_key in PARAM_MAPPING.keys():
		if full_name.endswith(snake_case__     ):
			A						   =   PARAM_MAPPING[full_name.split('.'     )[-1]]
			A						   =   'param'

	if weight_type is not None and weight_type != "param":
		A						   =   getattr(snake_case__	,      snake_case__     ).shape
	elif weight_type is not None and weight_type == "param":
		A						   =   hf_pointer
		for attribute in hf_param_name.split('.'     ):
			A						   =   getattr(snake_case__	,      snake_case__     )
		A						   =   shape_pointer.shape

		# let's reduce dimension
		A						   =   value[0]
	else:
		A						   =   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						   =   value
	elif weight_type == "weight_g":
		A						   =   value
	elif weight_type == "weight_v":
		A						   =   value
	elif weight_type == "bias":
		A						   =   value
	elif weight_type == "param":
		for attribute in hf_param_name.split('.'     ):
			A						   =   getattr(snake_case__	,      snake_case__     )
		A						   =   value
	else:
		A						   =   value

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






def       _snake_case				(		snake_case__   :     Optional[int]	,      snake_case__   :     Dict	,      snake_case__   :     Optional[Any]	,      snake_case__   :     Optional[Any]	,      snake_case__   :     List[str]     ):
	A						   =   None
	for param_key in PARAM_MAPPING.keys():
		if full_name.endswith(snake_case__     ):
			A						   =   PARAM_MAPPING[full_name.split('.'     )[-1]]
			A						   =   'param'

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

	A						   =   value if 'lm_head' in full_key else value[0]


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






def       _snake_case				(		snake_case__   :     List[str]	,      snake_case__   :     Dict	,      snake_case__   :     int     ):
	A						   =   []
	A						   =   fairseq_model.state_dict()

	A						   =   hf_model.wavaveca.feature_extractor

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

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






def       _snake_case				(		snake_case__   :     Tuple	,      snake_case__   :     Optional[int]	,      snake_case__   :     str	,      snake_case__   :     Any	,      snake_case__   :     List[str]     ):
	A						   =   full_name.split('conv_layers.'     )[-1]
	A						   =   name.split('.'     )
	A						   =   int(items[0]     )
	A						   =   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.'     )
			A						   =   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.'     )
			A						   =   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.'     )
			A						   =   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.'     )
			A						   =   value
			logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'     )
	else:
		unused_weights.append(snake_case__     )






@torch.no_grad()
def       _snake_case				(		snake_case__   :     Dict	,      snake_case__   :     Optional[int]	,      snake_case__   :     List[str]=None	,      snake_case__   :     Tuple=None	,      snake_case__   :     Tuple=True	,      snake_case__   :     Optional[Any]=False     ):
	if config_path is not None:
		A						   =   WavaVecaConfig.from_pretrained(snake_case__     )
	else:
		A						   =   WavaVecaConfig()

	if is_seq_class:
		A						   =   read_txt_into_dict(snake_case__     )
		A						   =   idalabel
		A						   =   WavaVecaForSequenceClassification(snake_case__     )
		A						   =   WavaVecaFeatureExtractor(
		    feature_size=1	,      sampling_rate=1_6000	,      padding_value=0	,      do_normalize=snake_case__	,      return_attention_mask=snake_case__	,      )
		feature_extractor.save_pretrained(snake_case__     )

	elif is_finetuned:
		if dict_path:
			A						   =   Dictionary.load(snake_case__     )

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

			# fairseq has the <pad> and <s> switched
			A						   =   0
			A						   =   1
			with open(snake_case__	,      'w'	,      encoding='utf-8'     ) as vocab_handle:
				json.dump(snake_case__	,      snake_case__     )
			A						   =   WavaVecaCTCTokenizer(
			    snake_case__	,      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=snake_case__	,      )
			A						   =   True if config.feat_extract_norm == 'layer' else False
			A						   =   WavaVecaFeatureExtractor(
			    feature_size=1	,      sampling_rate=1_6000	,      padding_value=0	,      do_normalize=snake_case__	,      return_attention_mask=snake_case__	,      )
			A						   =   WavaVecaProcessor(feature_extractor=snake_case__	,      tokenizer=snake_case__     )
			processor.save_pretrained(snake_case__     )

		A						   =   WavaVecaForCTC(snake_case__     )
	else:
		A						   =   WavaVecaForPreTraining(snake_case__     )

	if is_finetuned or is_seq_class:
		A   ,      A   ,      A						   =   fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]	,      arg_overrides={'data': '/'.join(dict_path.split('/'     )[:-1]     )}     )
	else:
		A						   =   argparse.Namespace(task='audio_pretraining'     )
		A						   =   fairseq.tasks.setup_task(snake_case__     )

		A   ,      A   ,      A						   =   fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]	,      task=snake_case__     )

	A						   =   model[0].eval()

	recursively_load_weights(snake_case__	,      snake_case__	,      not is_finetuned     )

	hf_wavavec.save_pretrained(snake_case__     )


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'''
		)
		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							   =				parser.parse_args()

		_lowercase							   =				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,
		)

"""simple docstring"""


import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List

from ..utils import logging
from . import BaseTransformersCLICommand


try:
		from cookiecutter.main import cookiecutter

		_lowercase							   =				True
except ImportError:
		_lowercase							   =				False

_lowercase							   =				logging.get_logger(__name__)  # pylint: disable=invalid-name






def       _snake_case				(		snake_case__   :     Namespace     ):
	return AddNewModelCommand(args.testing	,      args.testing_file	,      path=args.path     )







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						@staticmethod
						def 						_SCREAMING_SNAKE_CASE      ( A_     :      ArgumentParser      )     ->   Any:
							A						   =   parser.add_parser('add-new-model'      )
							add_new_model_parser.add_argument('--testing'  ,action='store_true'  ,help='If in testing mode.'      )
							add_new_model_parser.add_argument('--testing_file'  ,type=A_  ,help='Configuration file on which to run.'      )
							add_new_model_parser.add_argument(
							    '--path'  ,type=A_  ,help='Path to cookiecutter. Should only be used for testing purposes.'      )
							add_new_model_parser.set_defaults(func=A_      )


						def __init__( self     :      Tuple  ,A_     :      bool  ,A_     :      str  ,A_     :      Tuple=None  ,*A_     :      List[str]      )     ->   Union[str, Any]:
							A						   =   testing
							A						   =   testing_file
							A						   =   path




						def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   int:
							warnings.warn(
							    'The command `transformers-cli add-new-model` is deprecated and will be removed in v5 of Transformers. '
							    'It is not actively maintained anymore, so might give a result that won\'t pass all tests and quality '
							    'checks, you should use `transformers-cli add-new-model-like` instead.'      )
							if not _has_cookiecutter:
								raise ImportError(
								    'Model creation dependencies are required to use the `add_new_model` command. Install them by running '
								    'the following at the root of your `transformers` clone:\n\n\t$ pip install -e .[modelcreation]\n'      )
							# Ensure that there is no other `cookiecutter-template-xxx` directory in the current working directory
							A						   =   [directory for directory in os.listdir() if 'cookiecutter-template-' == directory[:22]]
							if len(A_      ) > 0:
								raise ValueError(
								    'Several directories starting with `cookiecutter-template-` in current working directory. '
								    'Please clean your directory by removing all folders starting with `cookiecutter-template-` or '
								    'change your working directory.'      )

							A						   =   (
							    Path(A_      ).parent.parent.parent.parent if self._path is None else Path(self._path      ).parent.parent
							)
							A						   =   path_to_transformer_root / 'templates' / 'adding_a_new_model'

							# Execute cookiecutter
							if not self._testing:
								cookiecutter(str(A_      )      )
							else:
								with open(self._testing_file  ,'r'      ) as configuration_file:
									A						   =   json.load(A_      )

								cookiecutter(
								    str(path_to_cookiecutter if self._path is None else self._path      )  ,no_input=A_  ,extra_context=A_  ,)

							A						   =   [directory for directory in os.listdir() if 'cookiecutter-template-' in directory[:22]][0]

							# Retrieve configuration
							with open(directory + '/configuration.json'  ,'r'      ) as configuration_file:
								A						   =   json.load(A_      )

							A						   =   configuration['lowercase_modelname']
							A						   =   configuration['generate_tensorflow_pytorch_and_flax']
							os.remove(F'{directory}/configuration.json'      )

							A						   =   'PyTorch' in generate_tensorflow_pytorch_and_flax
							A						   =   'TensorFlow' in generate_tensorflow_pytorch_and_flax
							A						   =   'Flax' in generate_tensorflow_pytorch_and_flax

							A						   =   F'{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}'
							os.makedirs(A_  ,exist_ok=A_      )
							os.makedirs(F'{path_to_transformer_root}/tests/models/{lowercase_model_name}'  ,exist_ok=A_      )

							# Tests require submodules as they have parent imports
							with open(F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/__init__.py'  ,'w'      ):
								pass

							shutil.move(
							    F'{directory}/__init__.py'  ,F'{model_dir}/__init__.py'  ,)
							shutil.move(
							    F'{directory}/configuration_{lowercase_model_name}.py'  ,F'{model_dir}/configuration_{lowercase_model_name}.py'  ,)

							def remove_copy_lines(A_     :      int      ):
								with open(A_  ,'r'      ) as f:
									A						   =   f.readlines()
								with open(A_  ,'w'      ) as f:
									for line in lines:
										if "# Copied from transformers." not in line:
											f.write(A_      )

							if output_pytorch:
								if not self._testing:
									remove_copy_lines(F'{directory}/modeling_{lowercase_model_name}.py'      )

								shutil.move(
								    F'{directory}/modeling_{lowercase_model_name}.py'  ,F'{model_dir}/modeling_{lowercase_model_name}.py'  ,)

								shutil.move(
								    F'{directory}/test_modeling_{lowercase_model_name}.py'  ,F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_{lowercase_model_name}.py'  ,)
							else:
								os.remove(F'{directory}/modeling_{lowercase_model_name}.py'      )
								os.remove(F'{directory}/test_modeling_{lowercase_model_name}.py'      )

							if output_tensorflow:
								if not self._testing:
									remove_copy_lines(F'{directory}/modeling_tf_{lowercase_model_name}.py'      )

								shutil.move(
								    F'{directory}/modeling_tf_{lowercase_model_name}.py'  ,F'{model_dir}/modeling_tf_{lowercase_model_name}.py'  ,)

								shutil.move(
								    F'{directory}/test_modeling_tf_{lowercase_model_name}.py'  ,F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_tf_{lowercase_model_name}.py'  ,)
							else:
								os.remove(F'{directory}/modeling_tf_{lowercase_model_name}.py'      )
								os.remove(F'{directory}/test_modeling_tf_{lowercase_model_name}.py'      )

							if output_flax:
								if not self._testing:
									remove_copy_lines(F'{directory}/modeling_flax_{lowercase_model_name}.py'      )

								shutil.move(
								    F'{directory}/modeling_flax_{lowercase_model_name}.py'  ,F'{model_dir}/modeling_flax_{lowercase_model_name}.py'  ,)

								shutil.move(
								    F'{directory}/test_modeling_flax_{lowercase_model_name}.py'  ,F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_flax_{lowercase_model_name}.py'  ,)
							else:
								os.remove(F'{directory}/modeling_flax_{lowercase_model_name}.py'      )
								os.remove(F'{directory}/test_modeling_flax_{lowercase_model_name}.py'      )

							shutil.move(
							    F'{directory}/{lowercase_model_name}.md'  ,F'{path_to_transformer_root}/docs/source/en/model_doc/{lowercase_model_name}.md'  ,)

							shutil.move(
							    F'{directory}/tokenization_{lowercase_model_name}.py'  ,F'{model_dir}/tokenization_{lowercase_model_name}.py'  ,)

							shutil.move(
							    F'{directory}/tokenization_fast_{lowercase_model_name}.py'  ,F'{model_dir}/tokenization_{lowercase_model_name}_fast.py'  ,)

							from os import fdopen, remove
							from shutil import copymode, move
							from tempfile import mkstemp

							def replace(A_     :      str  ,A_     :      str  ,A_     :      List[str]      ):
								# Create temp file
								A   ,      A						   =   mkstemp()
								A						   =   False
								with fdopen(A_  ,'w'      ) as new_file:
									with open(A_      ) as old_file:
										for line in old_file:
											new_file.write(A_      )
											if line_to_copy_below in line:
												A						   =   True
												for line_to_copy in lines_to_copy:
													new_file.write(A_      )

								if not line_found:
									raise ValueError(F'Line {line_to_copy_below} was not found in file.'      )

								# Copy the file permissions from the old file to the new file
								copymode(A_  ,A_      )
								# Remove original file
								remove(A_      )
								# Move new file
								move(A_  ,A_      )

							def skip_units(A_     :      Dict      ):
								return (
								    ("generating PyTorch" in line and not output_pytorch)
								    or ("generating TensorFlow" in line and not output_tensorflow)
								    or ("generating Flax" in line and not output_flax)
								)

							def replace_in_files(A_     :      Tuple      ):
								with open(A_      ) as datafile:
									A						   =   []
									A						   =   False
									A						   =   False
									for line in datafile:
										if "# To replace in: " in line and "##" not in line:
											A						   =   line.split('"'      )[1]
											A						   =   skip_units(A_      )
										elif "# Below: " in line and "##" not in line:
											A						   =   line.split('"'      )[1]
											A						   =   skip_units(A_      )
										elif "# End." in line and "##" not in line:
											if not skip_file and not skip_snippet:
												replace(A_  ,A_  ,A_      )

											A						   =   []
										elif "# Replace with" in line and "##" not in line:
											A						   =   []
										elif "##" not in line:
											lines_to_copy.append(A_      )

								remove(A_      )

							replace_in_files(F'{directory}/to_replace_{lowercase_model_name}.py'      )
							os.rmdir(A_      )