





import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
__UpperCamelCase:	int = logging.get_logger(__name__)

__UpperCamelCase:	Optional[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""",
    """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""",
}
__UpperCamelCase:	Optional[int] = [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : List[Any]   ,    _lowercase     : List[Any]   ,    _lowercase     : List[str]   ,    _lowercase     : Tuple   ,    _lowercase     : Optional[int]			)   ->							int:




		'''simple docstring'''
		for attribute in key.split('.'			):
				lowercase__					:      Dict      =		getattr(_lowercase   ,    _lowercase			)

		if weight_type is not None:
				lowercase__					:      List[str]      =		getattr(_lowercase   ,    _lowercase			).shape
		else:
				lowercase__					:      Optional[Any]      =		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":
				lowercase__					:      Union[str, Any]      =		value
		elif weight_type == "weight_g":
				lowercase__					:      Dict      =		value
		elif weight_type == "weight_v":
				lowercase__					:      Tuple      =		value
		elif weight_type == "bias":
				lowercase__					:      Union[str, Any]      =		value
		else:
				lowercase__					:      Dict      =		value

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


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Optional[Any]   ,    _lowercase     : Dict			)   ->							Tuple:




		'''simple docstring'''
		lowercase__					:      Any      =		[]
		lowercase__					:      int      =		fairseq_model.state_dict()

		lowercase__					:      Tuple      =		hf_model.feature_extractor

		# if encoder has different dim to decoder -> use proj_weight
		lowercase__					:      Union[str, Any]      =		None

		for name, value in fairseq_dict.items():
				lowercase__					:      List[str]      =		False
				if "conv_layers" in name:
						load_conv_layer(
						    _lowercase   ,    _lowercase   ,    _lowercase   ,    _lowercase   ,    hf_model.config.feat_extract_norm == 'group'   ,    )
						lowercase__					:      List[Any]      =		True
				elif name.split('.'			)[0] == "proj":
						lowercase__					:      Tuple      =		fairseq_model.proj
						lowercase__					:      Union[str, Any]      =		True
				else:
						for key, mapped_key in MAPPING.items():
								if key in name or key.split('w2v_model.'			)[-1] == name.split('.'			)[0]:
										lowercase__					:      Optional[int]      =		True
										if "*" in mapped_key:
												lowercase__					:      Optional[int]      =		name.split(_lowercase			)[0].split('.'			)[-2]
												lowercase__					:      Any      =		mapped_key.replace('*'   ,    _lowercase			)
										if "weight_g" in name:
												lowercase__					:      List[str]      =		'weight_g'
										elif "weight_v" in name:
												lowercase__					:      int      =		'weight_v'
										elif "bias" in name:
												lowercase__					:      List[str]      =		'bias'
										elif "weight" in name:
												lowercase__					:      Tuple      =		'weight'
										else:
												lowercase__					:      Optional[int]      =		None
										set_recursively(_lowercase   ,    _lowercase   ,    _lowercase   ,    _lowercase   ,    _lowercase			)
								continue
				if not is_used:
						unused_weights.append(_lowercase			)

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

		return proj_weight


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : List[str]   ,    _lowercase     : str   ,    _lowercase     : str   ,    _lowercase     : str   ,    _lowercase     : Any			)   ->							str:




		'''simple docstring'''
		lowercase__					:      Optional[int]      =		full_name.split('conv_layers.'			)[-1]
		lowercase__					:      int      =		name.split('.'			)
		lowercase__					:      int      =		int(items[0]			)
		lowercase__					:      str      =		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."""
						)
						lowercase__					:      Tuple      =		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."""
						)
						lowercase__					:      Tuple      =		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."
						)
						lowercase__					:      str      =		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."""
						)
						lowercase__					:      str      =		value
						logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""			)
		else:
				unused_weights.append(_lowercase			)


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Any			)   ->							List[Any]:




		'''simple docstring'''
		lowercase__   ,				lowercase__					:      Tuple      =		emb.weight.shape
		lowercase__					:      Tuple      =		nn.Linear(_lowercase   ,    _lowercase   ,    bias=_lowercase			)
		lowercase__					:      Optional[int]      =		emb.weight.data
		return lin_layer


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Tuple			)   ->							Optional[Any]:




		'''simple docstring'''
		with open(_lowercase   ,    'r'   ,    encoding='utf-8'			) as f:
				lowercase__					:      Optional[Any]      =		f.readlines()
				lowercase__					:      str      =		[line.split(' '			)[0] for line in lines]

		lowercase__					:      List[str]      =		len(_lowercase			)

		lowercase__					:      Union[str, Any]      =		{
		    '<s>': 0,
		    '<pad>': 1,
		    '</s>': 2,
		    '<unk>': 3,
		}

		vocab_dict.update(dict(zip(_lowercase   ,    range(4   ,    num_words + 4			)			)			)			)
		return vocab_dict


@torch.no_grad()
def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Dict   ,    _lowercase     : Optional[Any]   ,    _lowercase     : Tuple   ,    _lowercase     : Dict   ,    _lowercase     : Any   ,    _lowercase     : Optional[Any]   ,    _lowercase     : Optional[int]   ,    )   ->							Union[str, Any]:




		'''simple docstring'''
		lowercase__					:      List[str]      =		WavaVecaConfig.from_pretrained(_lowercase			)
		lowercase__					:      List[str]      =		SpeechaTextaConfig.from_pretrained(
		    _lowercase   ,    vocab_size=_lowercase   ,    decoder_layers=_lowercase   ,    do_stable_layer_norm=_lowercase			)

		lowercase__					:      List[str]      =		WavaVecaFeatureExtractor(
		    feature_size=1   ,    sampling_rate=16_000   ,    padding_value=0   ,    do_normalize=_lowercase   ,    return_attention_mask=_lowercase   ,    )

		lowercase__   ,				lowercase__   ,				lowercase__					:      Optional[Any]      =		fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]   ,    arg_overrides={'data': '/'.join(dict_path.split('/'			)[:-1]			)}			)
		lowercase__					:      Tuple      =		model[0].eval()

		# set weights for wav2vec2 encoder
		lowercase__					:      int      =		WavaVecaModel(_lowercase			)
		lowercase__					:      Tuple      =		recursively_load_weights_wavaveca(model.encoder   ,    _lowercase			)

		lowercase__					:      Tuple      =		SpeechaTextaForCausalLM(_lowercase			)
		lowercase__   ,				lowercase__					:      Tuple      =		hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()   ,    strict=_lowercase			)

		# set output linear layer
		unexpected_keys.remove('embed_out'			)
		lowercase__					:      Dict      =		nn.Parameter(model.decoder.embed_out.detach()			)

		# layer norm is init to identity matrix so leaving it is fine
		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}"""			)

		lowercase__					:      Tuple      =		SpeechEncoderDecoderModel(encoder=_lowercase   ,    decoder=_lowercase			)
		lowercase__					:      Any      =		False

		# add projection layer
		lowercase__					:      Union[str, Any]      =		nn.Parameter(projection_layer.weight			)
		lowercase__					:      List[str]      =		nn.Parameter(projection_layer.bias			)

		lowercase__					:      Dict      =		create_vocab_dict(_lowercase			)

		with open(os.path.join(_lowercase   ,    'vocab.json'			)   ,    'w'			) as fp:
				json.dump(_lowercase   ,    _lowercase			)

		lowercase__					:      int      =		SpeechaTextaTokenizer(os.path.join(_lowercase   ,    'vocab.json'			)			)
		tokenizer.save_pretrained(_lowercase			)

		lowercase__					:      Optional[int]      =		hf_wavavec.config.to_dict()
		lowercase__					:      str      =		tokenizer.pad_token_id
		lowercase__					:      Any      =		tokenizer.bos_token_id
		lowercase__					:      Union[str, Any]      =		tokenizer.eos_token_id
		lowercase__					:      Dict      =		'speech_to_text_2'
		lowercase__					:      List[Any]      =		'wav2vec2'

		lowercase__					:      Dict      =		SpeechEncoderDecoderConfig.from_dict(_lowercase			)

		hf_wavavec.save_pretrained(_lowercase			)
		feature_extractor.save_pretrained(_lowercase			)


if __name__ == "__main__":
			__UpperCamelCase:	List[Any] = 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(
			    """--encoder_config_path""",
			    default="""facebook/wav2vec2-large-lv60""",
			    type=str,
			    help="""Path to hf encoder wav2vec2 checkpoint config""",
			)
			parser.add_argument(
			    """--decoder_config_path""",
			    default="""facebook/s2t-small-mustc-en-fr-st""",
			    type=str,
			    help="""Path to hf decoder s2t checkpoint config""",
			)
			parser.add_argument("""--vocab_size""", default=1_0_2_2_4, type=int, help="""Vocab size of decoder""")
			parser.add_argument("""--num_decoder_layers""", default=7, type=int, help="""Number of decoder layers""")

			__UpperCamelCase:	Dict = parser.parse_args()
			convert_wavaveca_checkpoint(
			    args.checkpoint_path,
			    args.pytorch_dump_folder_path,
			    args.dict_path,
			    encoder_config_path=args.encoder_config_path,
			    decoder_config_path=args.decoder_config_path,
			    vocab_size=args.vocab_size,
			    num_decoder_layers=args.num_decoder_layers,
			)









import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey


class       __lowerCAmelCase					:


	'''simple docstring'''





	def __init__(						self:						List[str],					lowerCamelCase_:						Optional[Any],					lowerCamelCase_:						int,					lowerCamelCase_:						int			):
			if dst_width < 0 or dst_height < 0:
					raise ValueError('Destination width/height should be > 0'			)

			lowercase__					:      str      =		img
			lowercase__					:      Tuple      =		img.shape[1]
			lowercase__					:      str      =		img.shape[0]
			lowercase__					:      Tuple      =		dst_width
			lowercase__					:      Any      =		dst_height

			lowercase__					:      Tuple      =		self.src_w / self.dst_w
			lowercase__					:      Union[str, Any]      =		self.src_h / self.dst_h

			lowercase__					:      List[Any]      =		(
			    np.ones((self.dst_h, self.dst_w, 3),					np.uinta			) * 255
			)

	def    snake_case__(						self:						Dict			):
			for i in range(self.dst_h			):
					for j in range(self.dst_w			):
							lowercase__					:      Tuple      =		self.img[self.get_y(lowerCamelCase_			)][self.get_x(lowerCamelCase_			)]

	def    snake_case__(						self:						List[str],					lowerCamelCase_:						int			):
			return int(self.ratio_x * x			)





	def    snake_case__(						self:						int,					lowerCamelCase_:						int			):
			return int(self.ratio_y * y			)


if __name__ == "__main__":
			__UpperCamelCase,	__UpperCamelCase:	List[Any] = 8_0_0, 6_0_0
			__UpperCamelCase:	Optional[Any] = imread("""image_data/lena.jpg""", 1)
			__UpperCamelCase:	Union[str, Any] = NearestNeighbour(im, dst_w, dst_h)
			n.process()

			imshow(
			    F'Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}', n.output
			)
			waitKey(0)
			destroyAllWindows()


