




import argparse
import os

import align
import numpy as np
import requests
import tensorflow as tf
import torch
from PIL import Image
from tokenizer import Tokenizer

from transformers import (
    AlignConfig,
    AlignModel,
    AlignProcessor,
    BertConfig,
    BertTokenizer,
    EfficientNetConfig,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowercase_      :							Optional[int]    							=					logging.get_logger(__name__)


def 				__SCREAMING_SNAKE_CASE    (     snake_case_		):

						'''simple docstring'''


						_UpperCAmelCase						=	tf.image.resize(snake_case_      ,       (346, 346)		)
						_UpperCAmelCase						=	tf.image.crop_to_bounding_box(snake_case_      ,       (346 - 289) // 2      ,       (346 - 289) // 2      ,       289      ,       289		)
						return image


def 				__SCREAMING_SNAKE_CASE    (     ):

						'''simple docstring'''


						_UpperCAmelCase						=	EfficientNetConfig.from_pretrained("google/efficientnet-b7"		)
						_UpperCAmelCase						=	289
						_UpperCAmelCase						=	640
						_UpperCAmelCase						=	{"0": "LABEL_0", "1": "LABEL_1"}
						_UpperCAmelCase						=	{"LABEL_0": 0, "LABEL_1": 1}
						_UpperCAmelCase						=	[]

						_UpperCAmelCase						=	BertConfig()
						_UpperCAmelCase						=	AlignConfig.from_text_vision_configs(
						    text_config=snake_case_      ,       vision_config=snake_case_      ,       projection_dim=640		)
						return config


def 				__SCREAMING_SNAKE_CASE    (     ):

						'''simple docstring'''


						_UpperCAmelCase						=	"http://images.cocodataset.org/val2017/000000039769.jpg"
						_UpperCAmelCase						=	Image.open(requests.get(snake_case_      ,       stream=snake_case_		).raw		)
						return im


def 				__SCREAMING_SNAKE_CASE    (     ):

						'''simple docstring'''


						_UpperCAmelCase						=	EfficientNetImageProcessor(
						    do_center_crop=snake_case_      ,       rescale_factor=1 / 127.5      ,       rescale_offset=snake_case_      ,       do_normalize=snake_case_      ,       include_top=snake_case_      ,       resample=Image.BILINEAR      ,       )
						_UpperCAmelCase						=	BertTokenizer.from_pretrained("bert-base-uncased"		)
						_UpperCAmelCase						=	64
						_UpperCAmelCase						=	AlignProcessor(image_processor=snake_case_      ,       tokenizer=snake_case_		)
						return processor


def 				__SCREAMING_SNAKE_CASE    (     snake_case_		):

						'''simple docstring'''


						_UpperCAmelCase						=	[v.split("_"		)[0].split("block"		)[1] for v in original_param_names if v.startswith("block"		)]
						_UpperCAmelCase						=	list(set(snake_case_		)		)
						_UpperCAmelCase						=	sorted(snake_case_		)
						_UpperCAmelCase						=	len(snake_case_		)
						_UpperCAmelCase						=	{b: str(snake_case_		) for b, i in zip(snake_case_      ,       range(snake_case_		)		)}

						_UpperCAmelCase						=	[]
						rename_keys.append(("stem_conv/kernel:0", "embeddings.convolution.weight")		)
						rename_keys.append(("stem_bn/gamma:0", "embeddings.batchnorm.weight")		)
						rename_keys.append(("stem_bn/beta:0", "embeddings.batchnorm.bias")		)
						rename_keys.append(("stem_bn/moving_mean:0", "embeddings.batchnorm.running_mean")		)
						rename_keys.append(("stem_bn/moving_variance:0", "embeddings.batchnorm.running_var")		)

						for b in block_names:
												_UpperCAmelCase						=	block_name_mapping[b]
												rename_keys.append((f"""block{b}_expand_conv/kernel:0""", f"""encoder.blocks.{hf_b}.expansion.expand_conv.weight""")		)
												rename_keys.append((f"""block{b}_expand_bn/gamma:0""", f"""encoder.blocks.{hf_b}.expansion.expand_bn.weight""")		)
												rename_keys.append((f"""block{b}_expand_bn/beta:0""", f"""encoder.blocks.{hf_b}.expansion.expand_bn.bias""")		)
												rename_keys.append(
												    (f"""block{b}_expand_bn/moving_mean:0""", f"""encoder.blocks.{hf_b}.expansion.expand_bn.running_mean""")		)
												rename_keys.append(
												    (f"""block{b}_expand_bn/moving_variance:0""", f"""encoder.blocks.{hf_b}.expansion.expand_bn.running_var""")		)
												rename_keys.append(
												    (f"""block{b}_dwconv/depthwise_kernel:0""", f"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight""")		)
												rename_keys.append((f"""block{b}_bn/gamma:0""", f"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight""")		)
												rename_keys.append((f"""block{b}_bn/beta:0""", f"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias""")		)
												rename_keys.append(
												    (f"""block{b}_bn/moving_mean:0""", f"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean""")		)
												rename_keys.append(
												    (f"""block{b}_bn/moving_variance:0""", f"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var""")		)

												rename_keys.append((f"""block{b}_se_reduce/kernel:0""", f"""encoder.blocks.{hf_b}.squeeze_excite.reduce.weight""")		)
												rename_keys.append((f"""block{b}_se_reduce/bias:0""", f"""encoder.blocks.{hf_b}.squeeze_excite.reduce.bias""")		)
												rename_keys.append((f"""block{b}_se_expand/kernel:0""", f"""encoder.blocks.{hf_b}.squeeze_excite.expand.weight""")		)
												rename_keys.append((f"""block{b}_se_expand/bias:0""", f"""encoder.blocks.{hf_b}.squeeze_excite.expand.bias""")		)
												rename_keys.append(
												    (f"""block{b}_project_conv/kernel:0""", f"""encoder.blocks.{hf_b}.projection.project_conv.weight""")		)
												rename_keys.append((f"""block{b}_project_bn/gamma:0""", f"""encoder.blocks.{hf_b}.projection.project_bn.weight""")		)
												rename_keys.append((f"""block{b}_project_bn/beta:0""", f"""encoder.blocks.{hf_b}.projection.project_bn.bias""")		)
												rename_keys.append(
												    (f"""block{b}_project_bn/moving_mean:0""", f"""encoder.blocks.{hf_b}.projection.project_bn.running_mean""")		)
												rename_keys.append(
												    (f"""block{b}_project_bn/moving_variance:0""", f"""encoder.blocks.{hf_b}.projection.project_bn.running_var""")		)

						_UpperCAmelCase						=	{}
						for item in rename_keys:
												if item[0] in original_param_names:
																		_UpperCAmelCase						=	"vision_model." + item[1]

    # BERT text encoder
						_UpperCAmelCase						=	[]
						_UpperCAmelCase						=	"tf_bert_model/bert"
						_UpperCAmelCase						=	"text_model"
						for i in range(12		):
												rename_keys.append(
												    (
												        f"""{old}/encoder/layer_._{i}/attention/self/query/kernel:0""",
												        f"""{new}.encoder.layer.{i}.attention.self.query.weight""",
												    )		)
												rename_keys.append(
												    (
												        f"""{old}/encoder/layer_._{i}/attention/self/query/bias:0""",
												        f"""{new}.encoder.layer.{i}.attention.self.query.bias""",
												    )		)
												rename_keys.append(
												    (
												        f"""{old}/encoder/layer_._{i}/attention/self/key/kernel:0""",
												        f"""{new}.encoder.layer.{i}.attention.self.key.weight""",
												    )		)
												rename_keys.append(
												    (
												        f"""{old}/encoder/layer_._{i}/attention/self/key/bias:0""",
												        f"""{new}.encoder.layer.{i}.attention.self.key.bias""",
												    )		)
												rename_keys.append(
												    (
												        f"""{old}/encoder/layer_._{i}/attention/self/value/kernel:0""",
												        f"""{new}.encoder.layer.{i}.attention.self.value.weight""",
												    )		)
												rename_keys.append(
												    (
												        f"""{old}/encoder/layer_._{i}/attention/self/value/bias:0""",
												        f"""{new}.encoder.layer.{i}.attention.self.value.bias""",
												    )		)
												rename_keys.append(
												    (
												        f"""{old}/encoder/layer_._{i}/attention/output/dense/kernel:0""",
												        f"""{new}.encoder.layer.{i}.attention.output.dense.weight""",
												    )		)
												rename_keys.append(
												    (
												        f"""{old}/encoder/layer_._{i}/attention/output/dense/bias:0""",
												        f"""{new}.encoder.layer.{i}.attention.output.dense.bias""",
												    )		)
												rename_keys.append(
												    (
												        f"""{old}/encoder/layer_._{i}/attention/output/LayerNorm/gamma:0""",
												        f"""{new}.encoder.layer.{i}.attention.output.LayerNorm.weight""",
												    )		)
												rename_keys.append(
												    (
												        f"""{old}/encoder/layer_._{i}/attention/output/LayerNorm/beta:0""",
												        f"""{new}.encoder.layer.{i}.attention.output.LayerNorm.bias""",
												    )		)
												rename_keys.append(
												    (
												        f"""{old}/encoder/layer_._{i}/intermediate/dense/kernel:0""",
												        f"""{new}.encoder.layer.{i}.intermediate.dense.weight""",
												    )		)
												rename_keys.append(
												    (
												        f"""{old}/encoder/layer_._{i}/intermediate/dense/bias:0""",
												        f"""{new}.encoder.layer.{i}.intermediate.dense.bias""",
												    )		)
												rename_keys.append(
												    (f"""{old}/encoder/layer_._{i}/output/dense/kernel:0""", f"""{new}.encoder.layer.{i}.output.dense.weight""")		)
												rename_keys.append(
												    (f"""{old}/encoder/layer_._{i}/output/dense/bias:0""", f"""{new}.encoder.layer.{i}.output.dense.bias""")		)
												rename_keys.append(
												    (f"""{old}/encoder/layer_._{i}/output/LayerNorm/gamma:0""", f"""{new}.encoder.layer.{i}.output.LayerNorm.weight""")		)
												rename_keys.append(
												    (f"""{old}/encoder/layer_._{i}/output/LayerNorm/beta:0""", f"""{new}.encoder.layer.{i}.output.LayerNorm.bias""")		)

						rename_keys.append((f"""{old}/embeddings/word_embeddings/weight:0""", f"""{new}.embeddings.word_embeddings.weight""")		)
						rename_keys.append(
						    (f"""{old}/embeddings/position_embeddings/embeddings:0""", f"""{new}.embeddings.position_embeddings.weight""")		)
						rename_keys.append(
						    (f"""{old}/embeddings/token_type_embeddings/embeddings:0""", f"""{new}.embeddings.token_type_embeddings.weight""")		)
						rename_keys.append((f"""{old}/embeddings/LayerNorm/gamma:0""", f"""{new}.embeddings.LayerNorm.weight""")		)
						rename_keys.append((f"""{old}/embeddings/LayerNorm/beta:0""", f"""{new}.embeddings.LayerNorm.bias""")		)

						rename_keys.append((f"""{old}/pooler/dense/kernel:0""", f"""{new}.pooler.dense.weight""")		)
						rename_keys.append((f"""{old}/pooler/dense/bias:0""", f"""{new}.pooler.dense.bias""")		)
						rename_keys.append(("dense/kernel:0", "text_projection.weight")		)
						rename_keys.append(("dense/bias:0", "text_projection.bias")		)
						rename_keys.append(("dense/bias:0", "text_projection.bias")		)
						rename_keys.append(("temperature:0", "temperature")		)

						for item in rename_keys:
												if item[0] in original_param_names:
																		_UpperCAmelCase						=	item[1]
						return key_mapping


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_      ,       snake_case_		):

						'''simple docstring'''


						list(hf_params.keys()		)

						for key, value in tf_params.items():
												if key not in key_mapping:
																		continue

												_UpperCAmelCase						=	key_mapping[key]
												if "_conv" in key and "kernel" in key:
																		_UpperCAmelCase						=	torch.from_numpy(snake_case_		).permute(3      ,       2      ,       0      ,       1		)
												elif "embeddings" in key:
																		_UpperCAmelCase						=	torch.from_numpy(snake_case_		)
												elif "depthwise_kernel" in key:
																		_UpperCAmelCase						=	torch.from_numpy(snake_case_		).permute(2      ,       3      ,       0      ,       1		)
												elif "kernel" in key:
																		_UpperCAmelCase						=	torch.from_numpy(np.transpose(snake_case_		)		)
												elif "temperature" in key:
																		_UpperCAmelCase						=	value
												elif "bn/gamma" or "bn/beta" in key:
																		_UpperCAmelCase						=	torch.from_numpy(np.transpose(snake_case_		)		).squeeze()
												else:
																		_UpperCAmelCase						=	torch.from_numpy(snake_case_		)

												# Replace HF parameters with original TF model parameters
												hf_params[hf_key].copy_(snake_case_		)


@torch.no_grad()
def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_		):

						'''simple docstring'''


						_UpperCAmelCase						=	64
						_UpperCAmelCase						=	Tokenizer(snake_case_		)
						_UpperCAmelCase						=	align.Align("efficientnet-b7"      ,       "bert-base"      ,       640      ,       snake_case_      ,       tok.get_vocab_size()		)
						original_model.compile()
						original_model.load_weights(snake_case_		)

						_UpperCAmelCase						=	original_model.trainable_variables
						_UpperCAmelCase						=	original_model.non_trainable_variables
						_UpperCAmelCase						=	{param.name: param.numpy() for param in tf_params}
						for param in tf_non_train_params:
												_UpperCAmelCase						=	param.numpy()
						_UpperCAmelCase						=	list(tf_params.keys()		)

						# Load HuggingFace model
						_UpperCAmelCase						=	get_align_config()
						_UpperCAmelCase						=	AlignModel(snake_case_		).eval()
						_UpperCAmelCase						=	hf_model.state_dict()

						# Create src-to-dst parameter name mapping dictionary
						print("Converting parameters..."		)
						_UpperCAmelCase						=	rename_keys(snake_case_		)
						replace_params(snake_case_      ,       snake_case_      ,       snake_case_		)

						# Initialize processor
						_UpperCAmelCase						=	get_processor()
						_UpperCAmelCase						=	processor(
						    images=prepare_img()      ,       text="A picture of a cat"      ,       padding="max_length"      ,       max_length=64      ,       return_tensors="pt"		)

						# HF model inference
						hf_model.eval()
						with torch.no_grad():
												_UpperCAmelCase						=	hf_model(**snake_case_		)

						_UpperCAmelCase						=	outputs.image_embeds.detach().numpy()
						_UpperCAmelCase						=	outputs.text_embeds.detach().numpy()

						# Original model inference
						_UpperCAmelCase						=	False
						_UpperCAmelCase						=	EfficientNetImageProcessor(
						    do_center_crop=snake_case_      ,       do_rescale=snake_case_      ,       do_normalize=snake_case_      ,       include_top=snake_case_      ,       resample=Image.BILINEAR      ,       )
						_UpperCAmelCase						=	tf_image_processor(images=prepare_img()      ,       return_tensors="tf"      ,       data_format="channels_last"		)["pixel_values"]
						_UpperCAmelCase						=	tok(tf.constant(["A picture of a cat"]		)		)

						_UpperCAmelCase						=	original_model.image_encoder(snake_case_      ,       training=snake_case_		)
						_UpperCAmelCase						=	original_model.text_encoder(snake_case_      ,       training=snake_case_		)

						_UpperCAmelCase						=	tf.nn.la_normalize(snake_case_      ,       axis=-1		)
						_UpperCAmelCase						=	tf.nn.la_normalize(snake_case_      ,       axis=-1		)

						# Check whether original and HF model outputs match  -> np.allclose
						if not np.allclose(snake_case_      ,       snake_case_      ,       atol=1e-3		):
												raise ValueError("The predicted image features are not the same."		)
						if not np.allclose(snake_case_      ,       snake_case_      ,       atol=1e-3		):
												raise ValueError("The predicted text features are not the same."		)
						print("Model outputs match!"		)

						if save_model:
												# Create folder to save model
												if not os.path.isdir(snake_case_		):
																		os.mkdir(snake_case_		)
												# Save converted model and image processor
												hf_model.save_pretrained(snake_case_		)
												processor.save_pretrained(snake_case_		)

						if push_to_hub:
												# Push model and image processor to hub
												print("Pushing converted ALIGN to the hub..."		)
												processor.push_to_hub("align-base"		)
												hf_model.push_to_hub("align-base"		)


if __name__ == "__main__":
				lowercase_      :							str    							=					argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--checkpoint_path',
				    default='./weights/model-weights',
				    type=str,
				    help='Path to the pretrained TF ALIGN checkpoint.',
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path',
				    default='hf_model',
				    type=str,
				    help='Path to the output PyTorch model directory.',
				)
				parser.add_argument('--save_model', action='store_true', help='Save model to local')
				parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')

				lowercase_      :							int    							=					parser.parse_args()
				convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)










import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
lowercase_      :							Dict    							=					logging.get_logger(__name__)

lowercase_      :							str    							=					{
    'post_extract_proj': 'feature_projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'attention.self.query_proj': 'encoder.encoder.layer.*.attention.self.query_proj',
    'attention.self.key_proj': 'encoder.encoder.layer.*.attention.self.key_proj',
    'attention.self.value_proj': 'encoder.encoder.layer.*.attention.self.value_proj',
    'attention.output.dense': 'encoder.encoder.layer.*.attention.output.dense',
    'attention.output.LayerNorm': 'encoder.encoder.layer.*.attention.output.LayerNorm',
    'intermediate.dense': 'encoder.encoder.layer.*.intermediate.dense',
    'output.dense': 'encoder.encoder.layer.*.output.dense',
    'output.LayerNorm': 'encoder.encoder.layer.*.output.LayerNorm',
    'encoder.encoder.rel_embeddings': 'encoder.encoder.rel_embeddings',
    'encoder.encoder.LayerNorm': 'encoder.encoder.LayerNorm',
    'encoder.upsample.0': 'encoder.upsample.projection',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_		):

						'''simple docstring'''


						for attribute in key.split("."		):
												_UpperCAmelCase						=	getattr(snake_case_      ,       snake_case_		)

						if weight_type is not None:
												_UpperCAmelCase						=	getattr(snake_case_      ,       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 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_      ,       snake_case_		):

						'''simple docstring'''


						_UpperCAmelCase						=	[]
						_UpperCAmelCase						=	fairseq_model.state_dict()

						_UpperCAmelCase						=	hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

						for name, value in fairseq_dict.items():
												_UpperCAmelCase						=	False
												if "conv_layers" in name:
																		load_conv_layer(
																		    snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_      ,       hf_model.config.feat_extract_norm == "group"      ,       )
																		_UpperCAmelCase						=	True
												else:
																		for key, mapped_key in MAPPING.items():
																								_UpperCAmelCase						=	"sew_d." + mapped_key if (is_finetuned and mapped_key != "lm_head") else mapped_key

																								if key in name or key.split("w2v_model."		)[-1] == name.split("."		)[0]:
																														_UpperCAmelCase						=	True
																														if "*" in mapped_key:
																																				_UpperCAmelCase						=	name.split(snake_case_		)[0].split("."		)[-2]
																																				if not layer_index.isnumeric():
																																										continue
																																				_UpperCAmelCase						=	mapped_key.replace("*"      ,       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(snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_		)
																								continue
												if not is_used:
																		unused_weights.append(snake_case_		)

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


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_		):

						'''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(snake_case_		)


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_		):

						'''simple docstring'''


						_UpperCAmelCase						=	SEWDConfig()
						if is_finetuned:
												_UpperCAmelCase						=	model.wav_encoder.wav_model.cfg
						else:
												_UpperCAmelCase						=	model.cfg

						_UpperCAmelCase						=	fs_config.conv_bias
						_UpperCAmelCase						=	eval(fs_config.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						=	0.0
						_UpperCAmelCase						=	fs_config.activation_fn.name
						_UpperCAmelCase						=	fs_config.encoder_embed_dim
						_UpperCAmelCase						=	0.02
						_UpperCAmelCase						=	fs_config.encoder_ffn_embed_dim
						_UpperCAmelCase						=	1e-5
						_UpperCAmelCase						=	fs_config.encoder_layerdrop
						_UpperCAmelCase						=	fs_config.encoder_attention_heads
						_UpperCAmelCase						=	fs_config.conv_pos_groups
						_UpperCAmelCase						=	fs_config.conv_pos
						_UpperCAmelCase						=	len(snake_case_		)
						_UpperCAmelCase						=	fs_config.encoder_layers
						_UpperCAmelCase						=	fs_config.squeeze_factor
						# DeBERTa-specific parameters:
						_UpperCAmelCase						=	fs_config.max_position_embeddings
						_UpperCAmelCase						=	fs_config.position_buckets
						_UpperCAmelCase						=	fs_config.share_att_key
						_UpperCAmelCase						=	fs_config.relative_attention
						_UpperCAmelCase						=	fs_config.position_biased_input
						_UpperCAmelCase						=	tuple(fs_config.pos_att_type.split("|"		)		)
						_UpperCAmelCase						=	fs_config.norm_rel_ebd

						# take care of any params that are overridden by the Wav2VecCtc model
						if is_finetuned:
												_UpperCAmelCase						=	model.cfg
												_UpperCAmelCase						=	fs_config.final_dropout
												_UpperCAmelCase						=	fs_config.layerdrop
						_UpperCAmelCase						=	fs_config.activation_dropout
						_UpperCAmelCase						=	fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
						_UpperCAmelCase						=	fs_config.attention_dropout
						_UpperCAmelCase						=	fs_config.dropout_input
						_UpperCAmelCase						=	fs_config.dropout
						_UpperCAmelCase						=	fs_config.mask_channel_length
						_UpperCAmelCase						=	fs_config.mask_channel_prob
						_UpperCAmelCase						=	fs_config.mask_length
						_UpperCAmelCase						=	fs_config.mask_prob

						_UpperCAmelCase						=	"Wav2Vec2FeatureExtractor"
						_UpperCAmelCase						=	"Wav2Vec2CTCTokenizer"

						return config


@torch.no_grad()
def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_      ,       snake_case_=None      ,       snake_case_=None      ,       snake_case_=True		):

						'''simple docstring'''


						if is_finetuned:
												_UpperCAmelCase				,					_UpperCAmelCase				,					_UpperCAmelCase						=	fairseq.checkpoint_utils.load_model_ensemble_and_task(
												    [checkpoint_path]      ,       arg_overrides={"data": "/".join(dict_path.split("/"		)[:-1]		)}		)
						else:
												_UpperCAmelCase				,					_UpperCAmelCase				,					_UpperCAmelCase						=	fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]		)

						if config_path is not None:
												_UpperCAmelCase						=	SEWDConfig.from_pretrained(snake_case_		)
						else:
												_UpperCAmelCase						=	convert_config(model[0]      ,       snake_case_		)
						_UpperCAmelCase						=	model[0].eval()

						_UpperCAmelCase						=	True if config.feat_extract_norm == "layer" else False
						_UpperCAmelCase						=	WavaVecaFeatureExtractor(
						    feature_size=1      ,       sampling_rate=1_6000      ,       padding_value=0      ,       do_normalize=snake_case_      ,       return_attention_mask=snake_case_      ,       )

						if is_finetuned:
												if dict_path:
																		_UpperCAmelCase						=	Dictionary.load(snake_case_		)

																		# important change bos & pad token id since CTC symbol is <pad> and
																		# not <s> as in fairseq
																		_UpperCAmelCase						=	target_dict.pad_index
																		_UpperCAmelCase						=	target_dict.bos_index
																		_UpperCAmelCase						=	target_dict.pad_index
																		_UpperCAmelCase						=	target_dict.bos_index
																		_UpperCAmelCase						=	target_dict.eos_index
																		_UpperCAmelCase						=	len(target_dict.symbols		)
																		_UpperCAmelCase						=	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_		)
																		with open(snake_case_      ,       "w"      ,       encoding="utf-8"		) as vocab_handle:
																								json.dump(target_dict.indices      ,       snake_case_		)
																		_UpperCAmelCase						=	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_      ,       )
																		_UpperCAmelCase						=	WavaVecaProcessor(feature_extractor=snake_case_      ,       tokenizer=snake_case_		)
																		processor.save_pretrained(snake_case_		)

												_UpperCAmelCase						=	SEWDForCTC(snake_case_		)
						else:
												_UpperCAmelCase						=	SEWDModel(snake_case_		)
												feature_extractor.save_pretrained(snake_case_		)

						recursively_load_weights(snake_case_      ,       snake_case_      ,       snake_case_		)

						hf_model.save_pretrained(snake_case_		)


if __name__ == "__main__":
				lowercase_      :							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(
				    '--is_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
				)
				lowercase_      :							Tuple    							=					parser.parse_args()
				convert_sew_checkpoint(
				    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
				)




