





import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification




def    A_(			A   ):
	UpperCAmelCase_								=     SwinvaConfig()
	UpperCAmelCase_								=     swinva_name.split("""_"""   )

	UpperCAmelCase_								=     name_split[1]
	if "to" in name_split[3]:
		UpperCAmelCase_								=     int(name_split[3][-3:]   )
	else:
		UpperCAmelCase_								=     int(name_split[3]   )
	if "to" in name_split[2]:
		UpperCAmelCase_								=     int(name_split[2][-2:]   )
	else:
		UpperCAmelCase_								=     int(name_split[2][6:]   )

	if model_size == "tiny":
		UpperCAmelCase_								=     96
		UpperCAmelCase_								=     (2, 2, 6, 2)
		UpperCAmelCase_								=     (3, 6, 12, 24)
	elif model_size == "small":
		UpperCAmelCase_								=     96
		UpperCAmelCase_								=     (2, 2, 18, 2)
		UpperCAmelCase_								=     (3, 6, 12, 24)
	elif model_size == "base":
		UpperCAmelCase_								=     128
		UpperCAmelCase_								=     (2, 2, 18, 2)
		UpperCAmelCase_								=     (4, 8, 16, 32)
	else:
		UpperCAmelCase_								=     192
		UpperCAmelCase_								=     (2, 2, 18, 2)
		UpperCAmelCase_								=     (6, 12, 24, 48)

	if "to" in swinva_name:
		UpperCAmelCase_								=     (12, 12, 12, 6)

	if ("22k" in swinva_name) and ("to" not in swinva_name):
		UpperCAmelCase_								=     21841
		UpperCAmelCase_								=     """huggingface/label-files"""
		UpperCAmelCase_								=     """imagenet-22k-id2label.json"""
		UpperCAmelCase_								=     json.load(open(hf_hub_download(A				,      A				,      repo_type="""dataset"""   )				,      """r"""   )   )
		UpperCAmelCase_								=     {int(A   ): v for k, v in idalabel.items()}
		UpperCAmelCase_								=     idalabel
		UpperCAmelCase_								=     {v: k for k, v in idalabel.items()}

	else:
		UpperCAmelCase_								=     1000
		UpperCAmelCase_								=     """huggingface/label-files"""
		UpperCAmelCase_								=     """imagenet-1k-id2label.json"""
		UpperCAmelCase_								=     json.load(open(hf_hub_download(A				,      A				,      repo_type="""dataset"""   )				,      """r"""   )   )
		UpperCAmelCase_								=     {int(A   ): v for k, v in idalabel.items()}
		UpperCAmelCase_								=     idalabel
		UpperCAmelCase_								=     {v: k for k, v in idalabel.items()}

	UpperCAmelCase_								=     img_size
	UpperCAmelCase_								=     num_classes
	UpperCAmelCase_								=     embed_dim
	UpperCAmelCase_								=     depths
	UpperCAmelCase_								=     num_heads
	UpperCAmelCase_								=     window_size

	return config




def    A_(			A   ):
	if "patch_embed.proj" in name:
		UpperCAmelCase_								=     name.replace("""patch_embed.proj"""				,      """embeddings.patch_embeddings.projection"""   )
	if "patch_embed.norm" in name:
		UpperCAmelCase_								=     name.replace("""patch_embed.norm"""				,      """embeddings.norm"""   )
	if "layers" in name:
		UpperCAmelCase_								=     """encoder.""" + name
	if "attn.proj" in name:
		UpperCAmelCase_								=     name.replace("""attn.proj"""				,      """attention.output.dense"""   )
	if "attn" in name:
		UpperCAmelCase_								=     name.replace("""attn"""				,      """attention.self"""   )
	if "norm1" in name:
		UpperCAmelCase_								=     name.replace("""norm1"""				,      """layernorm_before"""   )
	if "norm2" in name:
		UpperCAmelCase_								=     name.replace("""norm2"""				,      """layernorm_after"""   )
	if "mlp.fc1" in name:
		UpperCAmelCase_								=     name.replace("""mlp.fc1"""				,      """intermediate.dense"""   )
	if "mlp.fc2" in name:
		UpperCAmelCase_								=     name.replace("""mlp.fc2"""				,      """output.dense"""   )
	if "q_bias" in name:
		UpperCAmelCase_								=     name.replace("""q_bias"""				,      """query.bias"""   )
	if "k_bias" in name:
		UpperCAmelCase_								=     name.replace("""k_bias"""				,      """key.bias"""   )
	if "v_bias" in name:
		UpperCAmelCase_								=     name.replace("""v_bias"""				,      """value.bias"""   )
	if "cpb_mlp" in name:
		UpperCAmelCase_								=     name.replace("""cpb_mlp"""				,      """continuous_position_bias_mlp"""   )
	if name == "norm.weight":
		UpperCAmelCase_								=     """layernorm.weight"""
	if name == "norm.bias":
		UpperCAmelCase_								=     """layernorm.bias"""

	if "head" in name:
		UpperCAmelCase_								=     name.replace("""head"""				,      """classifier"""   )
	else:
		UpperCAmelCase_								=     """swinv2.""" + name

	return name




def    A_(			A				,      A   ):
	for key in orig_state_dict.copy().keys():
		UpperCAmelCase_								=     orig_state_dict.pop(A   )

		if "mask" in key:
			continue
		elif "qkv" in key:
			UpperCAmelCase_								=     key.split("""."""   )
			UpperCAmelCase_								=     int(key_split[1]   )
			UpperCAmelCase_								=     int(key_split[3]   )
			UpperCAmelCase_								=     model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

			if "weight" in key:
				UpperCAmelCase_								=     val[:dim, :]
				UpperCAmelCase_								=     val[dim : dim * 2, :]
				UpperCAmelCase_								=     val[-dim:, :]
			else:
				UpperCAmelCase_								=     val[:dim]
				UpperCAmelCase_								=     val[
				    dim : dim * 2
				]
				UpperCAmelCase_								=     val[-dim:]
		else:
			UpperCAmelCase_								=     val

	return orig_state_dict




def    A_(			A				,      A   ):
	UpperCAmelCase_								=     timm.create_model(A				,      pretrained=A   )
	timm_model.eval()

	UpperCAmelCase_								=     get_swinva_config(A   )
	UpperCAmelCase_								=     SwinvaForImageClassification(A   )
	model.eval()

	UpperCAmelCase_								=     convert_state_dict(timm_model.state_dict()				,      A   )
	model.load_state_dict(A   )

	UpperCAmelCase_								=     """http://images.cocodataset.org/val2017/000000039769.jpg"""

	UpperCAmelCase_								=     AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swinva_name.replace("""_"""				,      """-"""   )   )   )
	UpperCAmelCase_								=     Image.open(requests.get(A				,      stream=A   ).raw   )
	UpperCAmelCase_								=     image_processor(images=A				,      return_tensors="""pt"""   )

	UpperCAmelCase_								=     timm_model(inputs["""pixel_values"""]   )
	UpperCAmelCase_								=     model(**A   ).logits

	assert torch.allclose(A				,      A				,      atol=1E-3   )

	print(f"""Saving model {swinva_name} to {pytorch_dump_folder_path}"""   )
	model.save_pretrained(A   )

	print(f"""Saving image processor to {pytorch_dump_folder_path}"""   )
	image_processor.save_pretrained(A   )

	model.push_to_hub(
	    repo_path_or_name=Path(A				,      A   )				,      organization="""nandwalritik"""				,      commit_message="""Add model"""				,      )


if __name__ == "__main__":
				UpperCamelCase__   : Tuple					  = argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    """--swinv2_name""",
				    default="""swinv2_tiny_patch4_window8_256""",
				    type=str,
				    help="""Name of the Swinv2 timm model you'd like to convert.""",
				)
				parser.add_argument(
				    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
				)

				UpperCamelCase__   : Tuple					  = parser.parse_args()
				convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)









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 (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
UpperCamelCase__   : str					  = logging.get_logger(__name__)

UpperCamelCase__   : Any					  = {
    """post_extract_proj""": """feature_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.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    A_(			A				,      A				,      A				,      A				,      A   ):
	for attribute in key.split("""."""   ):
		UpperCAmelCase_								=     getattr(A				,      A   )

	if weight_type is not None:
		UpperCAmelCase_								=     getattr(A				,      A   ).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_(			A				,      A				,      A   ):
	UpperCAmelCase_								=     []
	UpperCAmelCase_								=     fairseq_model.state_dict()

	UpperCAmelCase_								=     hf_model.sew.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(
			    A				,      A				,      A				,      A				,      hf_model.config.feat_extract_norm == """group"""				,      )
			UpperCAmelCase_								=     True
		else:
			for key, mapped_key in MAPPING.items():
				UpperCAmelCase_								=     """sew.""" + 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(A   )[0].split("""."""   )[-2]
						UpperCAmelCase_								=     mapped_key.replace("""*"""				,      A   )
					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(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   ):
	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(A   )




def    A_(			A				,      A   ):
	UpperCAmelCase_								=     SEWConfig()
	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(A   )
	UpperCAmelCase_								=     fs_config.encoder_layers
	UpperCAmelCase_								=     fs_config.squeeze_factor

	# 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    A_(			A				,      A				,      A=None				,      A=None				,      A=True   ):

	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_								=     SEWConfig.from_pretrained(A   )
	else:
		UpperCAmelCase_								=     convert_config(model[0]				,      A   )
	UpperCAmelCase_								=     model[0].eval()

	UpperCAmelCase_								=     True if config.feat_extract_norm == """layer""" else False
	UpperCAmelCase_								=     WavaVecaFeatureExtractor(
	    feature_size=1				,      sampling_rate=16000				,      padding_value=0				,      do_normalize=A				,      return_attention_mask=A				,      )

	if is_finetuned:
		if dict_path:
			UpperCAmelCase_								=     Dictionary.load(A   )

			# 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(A				,      """vocab.json"""   )
			if not os.path.isdir(A   ):
				logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(A   )   )
				return
			os.makedirs(A				,      exist_ok=A   )
			with open(A				,      """w"""				,      encoding="""utf-8"""   ) as vocab_handle:
				json.dump(target_dict.indices				,      A   )
			UpperCAmelCase_								=     WavaVecaCTCTokenizer(
			    A				,      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=A				,      )
			UpperCAmelCase_								=     WavaVecaProcessor(feature_extractor=A				,      tokenizer=A   )
			processor.save_pretrained(A   )

		UpperCAmelCase_								=     SEWForCTC(A   )
	else:
		UpperCAmelCase_								=     SEWModel(A   )
		feature_extractor.save_pretrained(A   )

	recursively_load_weights(A				,      A				,      A   )

	hf_model.save_pretrained(A   )


if __name__ == "__main__":
				UpperCamelCase__   : 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"""
				)
				UpperCamelCase__   : Union[str, Any]					  = parser.parse_args()
				convert_sew_checkpoint(
				    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
				)


