
import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    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',
    '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',
}
__lowerCAmelCase					   =       [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]


def    a      ( a		,						a		,						a		,						a		,						a	)     ->Optional[Any]:




			'''simple docstring'''


			for attribute in key.split('''.'''	):
						SCREAMING_SNAKE_CASE     						= getattr(a		,						a	)

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

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


def    a      ( a		,						a	)     ->str:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= []
			SCREAMING_SNAKE_CASE     						= fairseq_model.state_dict()

			SCREAMING_SNAKE_CASE     						= hf_model.feature_extractor
			SCREAMING_SNAKE_CASE     						= hf_model.adapter

			for name, value in fairseq_dict.items():
						SCREAMING_SNAKE_CASE     						= False
						if "conv_layers" in name:
									load_conv_layer(
									    a		,						a		,						a		,						a		,						hf_model.config.feat_extract_norm == '''group'''		,						)
									SCREAMING_SNAKE_CASE     						= True
						elif any(x in name for x in ['''adaptor''', '''w2v_encoder.proj.''', '''w2v_proj_ln.''']	):
									load_adapter(a		,						a		,						a		,						a	)
									SCREAMING_SNAKE_CASE     						= True
						else:
									for key, mapped_key in MAPPING.items():
												if key in name or key.split('''w2v_model.'''	)[-1] == name.split('''.'''	)[0]:
															SCREAMING_SNAKE_CASE     						= True
															if "*" in mapped_key:
																		SCREAMING_SNAKE_CASE     						= name.split(a	)[0].split('''.'''	)[-2]
																		SCREAMING_SNAKE_CASE     						= mapped_key.replace('''*'''		,						a	)
															if "weight_g" in name:
																		SCREAMING_SNAKE_CASE     						= '''weight_g'''
															elif "weight_v" in name:
																		SCREAMING_SNAKE_CASE     						= '''weight_v'''
															elif "bias" in name:
																		SCREAMING_SNAKE_CASE     						= '''bias'''
															elif "weight" in name:
																		SCREAMING_SNAKE_CASE     						= '''weight'''
															else:
																		SCREAMING_SNAKE_CASE     						= 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	)     ->Optional[int]:




			'''simple docstring'''


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


def    a      ( a		,						a		,						a		,						a	)     ->Union[str, Any]:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= full_name.split('''adaptor.'''	)[-1]
			SCREAMING_SNAKE_CASE     						= name.split('''.'''	)

			if items[1].isdigit():
						SCREAMING_SNAKE_CASE     						= int(items[1]	)
			else:
						SCREAMING_SNAKE_CASE     						= None

			if "adaptor" not in full_name:
						if "proj_ln" in full_name:
									# has to be layer norm
									if "bias" in name:
												assert (
												    value.shape == adapter.proj_layer_norm.bias.data.shape
												), F"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found."""
												SCREAMING_SNAKE_CASE     						= value
												logger.info(F"""Adapter proj layer norm bias was initialized from {full_name}."""	)
									if "weight" in name:
												assert (
												    value.shape == adapter.proj_layer_norm.weight.data.shape
												), F"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found."""
												SCREAMING_SNAKE_CASE     						= value
						else:
									# has to be projection layer
									if "bias" in name:
												assert (
												    value.shape == adapter.proj.bias.data.shape
												), F"""{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found."""
												SCREAMING_SNAKE_CASE     						= value
												logger.info(F"""Adapter proj layer bias was initialized from {full_name}."""	)
									if "weight" in name:
												assert (
												    value.shape == adapter.proj.weight.data.shape
												), F"""{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found."""
												SCREAMING_SNAKE_CASE     						= value
												logger.info(F"""Adapter proj layer weight was initialized from {full_name}."""	)
			elif isinstance(a		,						a	):
						if "bias" in name:
									assert (
									    value.shape == adapter.layers[layer_id].conv.bias.data.shape
									), F"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found."""
									SCREAMING_SNAKE_CASE     						= value
									logger.info(F"""Adapter layer {layer_id} bias was initialized from {full_name}."""	)
						elif "weight" in name:
									assert (
									    value.shape == adapter.layers[layer_id].conv.weight.data.shape
									), F"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found."""
									SCREAMING_SNAKE_CASE     						= value
									logger.info(F"""Adapter layer {layer_id} bias was initialized from {full_name}."""	)
			else:
						unused_weights.append(a	)


def    a      ( a	)     ->Dict:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE     						= emb.weight.shape
			SCREAMING_SNAKE_CASE     						= nn.Linear(a		,						a		,						bias=a	)
			SCREAMING_SNAKE_CASE     						= emb.weight.data
			return lin_layer


@torch.no_grad()
def    a      ( a		,						a		,						a		,						a		,						a		,						a		,						a		,						a		,						a		,						a		,						a		,						)     ->Dict:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= WavaVecaConfig.from_pretrained(
			    a		,						add_adapter=a		,						adapter_stride=a		,						adapter_kernel_size=a		,						use_auth_token=a		,						output_hidden_size=a		,						)
			SCREAMING_SNAKE_CASE     						= MBartConfig.from_pretrained(a	)

			# load model
			SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE     						= fairseq.checkpoint_utils.load_model_ensemble_and_task(
			    [checkpoint_path]		,						arg_overrides={
			        '''config_yaml''': config_yaml_path,
			        '''data''': '''/'''.join(dict_path.split('''/'''	)[:-1]	),
			        '''w2v_path''': checkpoint_path,
			        '''load_pretrained_decoder_from''': None,
			    }		,						)
			SCREAMING_SNAKE_CASE     						= model[0].eval()

			# load feature extractor
			SCREAMING_SNAKE_CASE     						= WavaVecaFeatureExtractor.from_pretrained(a		,						use_auth_token=a	)

			# set weights for wav2vec2 encoder
			SCREAMING_SNAKE_CASE     						= WavaVecaModel(a	)

			recursively_load_weights_wavaveca(model.encoder		,						a	)

			# load decoder weights
			SCREAMING_SNAKE_CASE     						= MBartForCausalLM(a	)
			SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE     						= hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()		,						strict=a	)
			logger.warning(F"""The following keys are missing when loading the decoder weights: {missing_keys}"""	)
			logger.warning(F"""The following keys are unexpected when loading the decoder weights: {unexpected_keys}"""	)

			SCREAMING_SNAKE_CASE     						= SpeechEncoderDecoderModel(encoder=a		,						decoder=a	)
			SCREAMING_SNAKE_CASE     						= False

			SCREAMING_SNAKE_CASE     						= MBartaaTokenizer(a	)
			tokenizer.save_pretrained(a	)

			SCREAMING_SNAKE_CASE     						= hf_wavavec.config.to_dict()
			SCREAMING_SNAKE_CASE     						= tokenizer.pad_token_id
			SCREAMING_SNAKE_CASE     						= tokenizer.bos_token_id
			SCREAMING_SNAKE_CASE     						= tokenizer.eos_token_id
			SCREAMING_SNAKE_CASE     						= '''mbart50'''
			SCREAMING_SNAKE_CASE     						= '''wav2vec2'''

			SCREAMING_SNAKE_CASE     						= tokenizer.eos_token_id
			SCREAMING_SNAKE_CASE     						= 25_0004
			SCREAMING_SNAKE_CASE     						= tokenizer.eos_token_id

			SCREAMING_SNAKE_CASE     						= SpeechEncoderDecoderConfig.from_dict(a	)

			hf_wavavec.save_pretrained(a	)
			feature_extractor.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_yaml_path', default=None, type=str, help='Path to yaml file of fine-tuned model')
							parser.add_argument(
							    '--encoder_config_path',
							    default='facebook/wav2vec2-xls-r-1b',
							    type=str,
							    help='Path to hf encoder wav2vec2 checkpoint config',
							)
							parser.add_argument(
							    '--decoder_config_path',
							    default='facebook/mbart-large-50-one-to-many-mmt',
							    type=str,
							    help='Path to hf decoder checkpoint config',
							)
							parser.add_argument('--add_adapter', default=True, type=bool, help='whethere to add model adapter layers')
							parser.add_argument('--adapter_stride', default=2, type=int, help='stride of adapter layers')
							parser.add_argument('--adapter_kernel_size', default=3, type=int, help='kernel size of adapter layers')
							parser.add_argument('--encoder_output_dim', default=1_0_2_4, type=int, help='encoder output dim')
							parser.add_argument('--start_token_id', default=2_5_0_0_0_4, type=int, help='`decoder_start_token_id` of model config')

							__lowerCAmelCase					   =       parser.parse_args()
							convert_wavaveca_checkpoint(
							    args.checkpoint_path,
							    args.pytorch_dump_folder_path,
							    args.dict_path,
							    args.config_yaml_path,
							    encoder_config_path=args.encoder_config_path,
							    decoder_config_path=args.decoder_config_path,
							    add_adapter=args.add_adapter,
							    adapter_kernel_size=args.adapter_kernel_size,
							    adapter_stride=args.adapter_stride,
							    decoder_start_token_id=args.start_token_id,
							    encoder_output_dim=args.encoder_output_dim,
							)

from PIL import Image


def    a      ( a	)     ->Image:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE     						= image.size
			SCREAMING_SNAKE_CASE     						= 0
			SCREAMING_SNAKE_CASE     						= image.load()
			for i in range(a	):
						for j in range(a	):
									SCREAMING_SNAKE_CASE     						= pixels[j, i]
									mean += pixel
			mean //= width * height

			for j in range(a	):
						for i in range(a	):
									SCREAMING_SNAKE_CASE     						= 255 if pixels[i, j] > mean else 0
			return image


if __name__ == "__main__":
							__lowerCAmelCase					   =       mean_threshold(Image.open('path_to_image').convert('L'))
							image.save('output_image_path')