'''simple docstring'''




import argparse
import json

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

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation

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





       """simple docstring"""


       __UpperCAmelCase	:					Dict												=		384
       __UpperCAmelCase	:					Any												=		7
       if "tiny" in model_name:
              __UpperCAmelCase	:					Dict												=		96
              __UpperCAmelCase	:					str												=		(2, 2, 6, 2)
              __UpperCAmelCase	:					List[str]												=		(3, 6, 12, 24)
       elif "small" in model_name:
              __UpperCAmelCase	:					Tuple												=		96
              __UpperCAmelCase	:					Tuple												=		(2, 2, 18, 2)
              __UpperCAmelCase	:					Optional[int]												=		(3, 6, 12, 24)
       elif "base" in model_name:
              __UpperCAmelCase	:					Union[str, Any]												=		128
              __UpperCAmelCase	:					Union[str, Any]												=		(2, 2, 18, 2)
              __UpperCAmelCase	:					Tuple												=		(4, 8, 16, 32)
              __UpperCAmelCase	:					Optional[int]												=		12
              __UpperCAmelCase	:					List[Any]												=		512
       elif "large" in model_name:
              __UpperCAmelCase	:					int												=		192
              __UpperCAmelCase	:					str												=		(2, 2, 18, 2)
              __UpperCAmelCase	:					Optional[int]												=		(6, 12, 24, 48)
              __UpperCAmelCase	:					Union[str, Any]												=		12
              __UpperCAmelCase	:					str												=		768

       # set label information
       __UpperCAmelCase	:					Tuple												=		150
       __UpperCAmelCase	:					List[Any]												=		"huggingface/label-files"
       __UpperCAmelCase	:					str												=		"ade20k-id2label.json"
       __UpperCAmelCase	:					Union[str, Any]												=		json.load(open(hf_hub_download(snake_case__				,     snake_case__				,     repo_type="dataset"    )				,     "r"    )    )
       __UpperCAmelCase	:					Optional[int]												=		{int(snake_case__    ): v for k, v in idalabel.items()}
       __UpperCAmelCase	:					Optional[int]												=		{v: k for k, v in idalabel.items()}

       __UpperCAmelCase	:					List[str]												=		SwinConfig(
           embed_dim=snake_case__				,     depths=snake_case__				,     num_heads=snake_case__				,     window_size=snake_case__				,     out_features=["stage1", "stage2", "stage3", "stage4"]				,     )
       __UpperCAmelCase	:					str												=		UperNetConfig(
           backbone_config=snake_case__				,     auxiliary_in_channels=snake_case__				,     num_labels=snake_case__				,     idalabel=snake_case__				,     labelaid=snake_case__				,     )

       return config

def _lowercase						(   lowerCamelCase__    )       ->							Dict:





       """simple docstring"""


       __UpperCAmelCase	:					List[str]												=		[]

       # fmt: off
       # stem
       rename_keys.append(("backbone.patch_embed.projection.weight", "backbone.embeddings.patch_embeddings.projection.weight")    )
       rename_keys.append(("backbone.patch_embed.projection.bias", "backbone.embeddings.patch_embeddings.projection.bias")    )
       rename_keys.append(("backbone.patch_embed.norm.weight", "backbone.embeddings.norm.weight")    )
       rename_keys.append(("backbone.patch_embed.norm.bias", "backbone.embeddings.norm.bias")    )
       # stages
       for i in range(len(config.backbone_config.depths    )    ):
              for j in range(config.backbone_config.depths[i]    ):
                     rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm1.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")    )
                     rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm1.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")    )
                     rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")    )
                     rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")    )
                     rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")    )
                     rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")    )
                     rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm2.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")    )
                     rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm2.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")    )
                     rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")    )
                     rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")    )
                     rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight""")    )
                     rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias""")    )

              if i < 3:
                     rename_keys.append((f"""backbone.stages.{i}.downsample.reduction.weight""", f"""backbone.encoder.layers.{i}.downsample.reduction.weight""")    )
                     rename_keys.append((f"""backbone.stages.{i}.downsample.norm.weight""", f"""backbone.encoder.layers.{i}.downsample.norm.weight""")    )
                     rename_keys.append((f"""backbone.stages.{i}.downsample.norm.bias""", f"""backbone.encoder.layers.{i}.downsample.norm.bias""")    )
              rename_keys.append((f"""backbone.norm{i}.weight""", f"""backbone.hidden_states_norms.stage{i+1}.weight""")    )
              rename_keys.append((f"""backbone.norm{i}.bias""", f"""backbone.hidden_states_norms.stage{i+1}.bias""")    )

       # decode head
       rename_keys.extend(
           [
               ("decode_head.conv_seg.weight", "decode_head.classifier.weight"),
               ("decode_head.conv_seg.bias", "decode_head.classifier.bias"),
               ("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"),
               ("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.bias"),
           ]    )
       # fmt: on

       return rename_keys

def _lowercase						(   lowerCamelCase__				,     lowerCamelCase__				,     lowerCamelCase__    )       ->							Optional[int]:





       """simple docstring"""


       __UpperCAmelCase	:					Tuple												=		dct.pop(snake_case__    )
       __UpperCAmelCase	:					str												=		val

def _lowercase						(   lowerCamelCase__				,     lowerCamelCase__    )       ->							Optional[Any]:





       """simple docstring"""


       __UpperCAmelCase	:					Optional[int]												=		[int(backbone_config.embed_dim * 2**i    ) for i in range(len(backbone_config.depths    )    )]
       for i in range(len(backbone_config.depths    )    ):
              __UpperCAmelCase	:					str												=		num_features[i]
              for j in range(backbone_config.depths[i]    ):
                     # fmt: off
                     # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
                     __UpperCAmelCase	:					Dict												=		state_dict.pop(f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight"""    )
                     __UpperCAmelCase	:					Optional[Any]												=		state_dict.pop(f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias"""    )
                     # next, add query, keys and values (in that order) to the state dict
                     __UpperCAmelCase	:					str												=		in_proj_weight[:dim, :]
                     __UpperCAmelCase	:					List[str]												=		in_proj_bias[: dim]
                     __UpperCAmelCase	:					List[Any]												=		in_proj_weight[
                         dim : dim * 2, :
                     ]
                     __UpperCAmelCase	:					Union[str, Any]												=		in_proj_bias[
                         dim : dim * 2
                     ]
                     __UpperCAmelCase	:					int												=		in_proj_weight[
                         -dim :, :
                     ]
                     __UpperCAmelCase	:					Dict												=		in_proj_bias[-dim :]
                     # fmt: on

def _lowercase						(   lowerCamelCase__    )       ->							Dict:





       """simple docstring"""


       __UpperCAmelCase					,			__UpperCAmelCase	:					Union[str, Any]												=		x.shape
       __UpperCAmelCase	:					Union[str, Any]												=		x.reshape(snake_case__				,     4				,     in_channel // 4    )
       __UpperCAmelCase	:					Union[str, Any]												=		x[:, [0, 2, 1, 3], :].transpose(1				,     2    ).reshape(snake_case__				,     snake_case__    )
       return x

def _lowercase						(   lowerCamelCase__    )       ->							Any:





       """simple docstring"""


       __UpperCAmelCase					,			__UpperCAmelCase	:					int												=		x.shape
       __UpperCAmelCase	:					Union[str, Any]												=		x.reshape(snake_case__				,     in_channel // 4				,     4    )
       __UpperCAmelCase	:					Optional[Any]												=		x[:, :, [0, 2, 1, 3]].transpose(1				,     2    ).reshape(snake_case__				,     snake_case__    )

       return x

def _lowercase						(   lowerCamelCase__    )       ->							Tuple:





       """simple docstring"""


       __UpperCAmelCase	:					Tuple												=		x.shape[0]
       __UpperCAmelCase	:					Dict												=		x.reshape(4				,     in_channel // 4    )
       __UpperCAmelCase	:					Union[str, Any]												=		x[[0, 2, 1, 3], :].transpose(0				,     1    ).reshape(snake_case__    )
       return x

def _lowercase						(   lowerCamelCase__    )       ->							Optional[int]:





       """simple docstring"""


       __UpperCAmelCase	:					List[Any]												=		x.shape[0]
       __UpperCAmelCase	:					int												=		x.reshape(in_channel // 4				,     4    )
       __UpperCAmelCase	:					Any												=		x[:, [0, 2, 1, 3]].transpose(0				,     1    ).reshape(snake_case__    )
       return x

def _lowercase						(   lowerCamelCase__				,     lowerCamelCase__				,     lowerCamelCase__    )       ->							int:





       """simple docstring"""


       __UpperCAmelCase	:					Any												=		{
           "upernet-swin-tiny": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth",
           "upernet-swin-small": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth",
           "upernet-swin-base": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth",
           "upernet-swin-large": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth",
       }
       __UpperCAmelCase	:					List[Any]												=		model_name_to_url[model_name]
       __UpperCAmelCase	:					Optional[Any]												=		torch.hub.load_state_dict_from_url(snake_case__				,     map_location="cpu"				,     file_name=snake_case__    )[
           "state_dict"
       ]

       for name, param in state_dict.items():
              print(snake_case__				,     param.shape    )

       __UpperCAmelCase	:					List[Any]												=		get_upernet_config(snake_case__    )
       __UpperCAmelCase	:					int												=		UperNetForSemanticSegmentation(snake_case__    )
       model.eval()

       # replace "bn" => "batch_norm"
       for key in state_dict.copy().keys():
              __UpperCAmelCase	:					List[Any]												=		state_dict.pop(snake_case__    )
              if "bn" in key:
                     __UpperCAmelCase	:					List[Any]												=		key.replace("bn"				,     "batch_norm"    )
              __UpperCAmelCase	:					Optional[Any]												=		val

       # rename keys
       __UpperCAmelCase	:					Optional[Any]												=		create_rename_keys(snake_case__    )
       for src, dest in rename_keys:
              rename_key(snake_case__				,     snake_case__				,     snake_case__    )
       read_in_q_k_v(snake_case__				,     config.backbone_config    )

       # fix downsample parameters
       for key, value in state_dict.items():
              if "downsample" in key:
                     if "reduction" in key:
                            __UpperCAmelCase	:					str												=		reverse_correct_unfold_reduction_order(snake_case__    )
                     if "norm" in key:
                            __UpperCAmelCase	:					List[Any]												=		reverse_correct_unfold_norm_order(snake_case__    )

       model.load_state_dict(snake_case__    )

       # verify on image
       __UpperCAmelCase	:					Union[str, Any]												=		"https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"
       __UpperCAmelCase	:					List[str]												=		Image.open(requests.get(snake_case__				,     stream=snake_case__    ).raw    ).convert("RGB"    )

       __UpperCAmelCase	:					Tuple												=		SegformerImageProcessor()
       __UpperCAmelCase	:					Optional[Any]												=		processor(snake_case__				,     return_tensors="pt"    ).pixel_values

       with torch.no_grad():
              __UpperCAmelCase	:					Optional[int]												=		model(snake_case__    )
              __UpperCAmelCase	:					Optional[int]												=		outputs.logits

       print(logits.shape    )
       print("First values of logits:"				,     logits[0, 0, :3, :3]    )
       # assert values
       if model_name == "upernet-swin-tiny":
              __UpperCAmelCase	:					int												=		torch.tensor(
                  [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]    )
       elif model_name == "upernet-swin-small":
              __UpperCAmelCase	:					Tuple												=		torch.tensor(
                  [[-7.1921, -7.1921, -6.9532], [-7.1921, -7.1921, -6.9532], [-7.0908, -7.0908, -6.8534]]    )
       elif model_name == "upernet-swin-base":
              __UpperCAmelCase	:					Dict												=		torch.tensor(
                  [[-6.5851, -6.5851, -6.4330], [-6.5851, -6.5851, -6.4330], [-6.4763, -6.4763, -6.3254]]    )
       elif model_name == "upernet-swin-large":
              __UpperCAmelCase	:					Optional[Any]												=		torch.tensor(
                  [[-7.5297, -7.5297, -7.3802], [-7.5297, -7.5297, -7.3802], [-7.4044, -7.4044, -7.2586]]    )
       print("Logits:"				,     outputs.logits[0, 0, :3, :3]    )
       assert torch.allclose(outputs.logits[0, 0, :3, :3]				,     snake_case__				,     atol=1e-4    )
       print("Looks ok!"    )

       if pytorch_dump_folder_path is not None:
              print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""    )
              model.save_pretrained(snake_case__    )
              print(f"""Saving processor to {pytorch_dump_folder_path}"""    )
              processor.save_pretrained(snake_case__    )

       if push_to_hub:
              print(f"""Pushing model and processor for {model_name} to hub"""    )
              model.push_to_hub(f"""openmmlab/{model_name}"""    )
              processor.push_to_hub(f"""openmmlab/{model_name}"""    )


if __name__ == "__main__":
   _a :						List[str]							= argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--model_name",
       default="upernet-swin-tiny",
       type=str,
       choices=[f"""upernet-swin-{size}""" for size in ["tiny", "small", "base", "large"]],
       help="Name of the Swin + UperNet 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."
   )
   parser.add_argument(
       "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
   )

   _a :						List[str]							= parser.parse_args()
   convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)











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()
_lowerCAmelCase	:   str    =   logging.get_logger(__name__)

_lowerCAmelCase	:   str    =   {
    "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	:   List[str]    =   [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__					)	->     Any:



				"""simple docstring"""





				for attribute in key.split('.'					):
								lowerCAmelCase__		  =  getattr(snake_case__    ,			snake_case__					)

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

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







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     List[Any]:



				"""simple docstring"""





				lowerCAmelCase__		  =  []
				lowerCAmelCase__		  =  fairseq_model.state_dict()

				lowerCAmelCase__		  =  hf_model.feature_extractor

				# if encoder has different dim to decoder -> use proj_weight
				lowerCAmelCase__		  =  None

				for name, value in fairseq_dict.items():
								lowerCAmelCase__		  =  False
								if "conv_layers" in name:
												load_conv_layer(
												    snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			hf_model.config.feat_extract_norm == 'group'    ,			)
												lowerCAmelCase__		  =  True
								elif name.split('.'					)[0] == "proj":
												lowerCAmelCase__		  =  fairseq_model.proj
												lowerCAmelCase__		  =  True
								else:
												for key, mapped_key in MAPPING.items():
																if key in name or key.split('w2v_model.'					)[-1] == name.split('.'					)[0]:
																				lowerCAmelCase__		  =  True
																				if "*" in mapped_key:
																								lowerCAmelCase__		  =  name.split(snake_case__					)[0].split('.'					)[-2]
																								lowerCAmelCase__		  =  mapped_key.replace('*'    ,			snake_case__					)
																				if "weight_g" in name:
																								lowerCAmelCase__		  =  'weight_g'
																				elif "weight_v" in name:
																								lowerCAmelCase__		  =  'weight_v'
																				elif "bias" in name:
																								lowerCAmelCase__		  =  'bias'
																				elif "weight" in name:
																								lowerCAmelCase__		  =  'weight'
																				else:
																								lowerCAmelCase__		  =  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}'					)

				return proj_weight







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__					)	->     List[Any]:



				"""simple docstring"""





				lowerCAmelCase__		  =  full_name.split('conv_layers.'					)[-1]
				lowerCAmelCase__		  =  name.split('.'					)
				lowerCAmelCase__		  =  int(items[0]					)
				lowerCAmelCase__		  =  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.'
												)
												lowerCAmelCase__		  =  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.'
												)
												lowerCAmelCase__		  =  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."
												)
												lowerCAmelCase__		  =  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.'
												)
												lowerCAmelCase__		  =  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     UpperCAmelCase_		(					snake_case__					)	->     str:



				"""simple docstring"""





				lowerCAmelCase__  ,   lowerCAmelCase__		  =  emb.weight.shape
				lowerCAmelCase__		  =  nn.Linear(snake_case__    ,			snake_case__    ,			bias=snake_case__					)
				lowerCAmelCase__		  =  emb.weight.data
				return lin_layer







def     UpperCAmelCase_		(					snake_case__					)	->     Any:



				"""simple docstring"""





				with open(snake_case__    ,			'r'    ,			encoding='utf-8'					) as f:
								lowerCAmelCase__		  =  f.readlines()
								lowerCAmelCase__		  =  [line.split(' '					)[0] for line in lines]

				lowerCAmelCase__		  =  len(snake_case__					)

				lowerCAmelCase__		  =  {
				    '<s>': 0,
				    '<pad>': 1,
				    '</s>': 2,
				    '<unk>': 3,
				}

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







@torch.no_grad()
def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			)	->     Any:



				"""simple docstring"""





				lowerCAmelCase__		  =  WavaVecaConfig.from_pretrained(snake_case__					)
				lowerCAmelCase__		  =  SpeechaTextaConfig.from_pretrained(
				    snake_case__    ,			vocab_size=snake_case__    ,			decoder_layers=snake_case__    ,			do_stable_layer_norm=snake_case__					)

				lowerCAmelCase__		  =  WavaVecaFeatureExtractor(
				    feature_size=1    ,			sampling_rate=16000    ,			padding_value=0    ,			do_normalize=snake_case__    ,			return_attention_mask=snake_case__    ,			)

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

				# set weights for wav2vec2 encoder
				lowerCAmelCase__		  =  WavaVecaModel(snake_case__					)
				lowerCAmelCase__		  =  recursively_load_weights_wavaveca(model.encoder    ,			snake_case__					)

				lowerCAmelCase__		  =  SpeechaTextaForCausalLM(snake_case__					)
				lowerCAmelCase__  ,   lowerCAmelCase__		  =  hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()    ,			strict=snake_case__					)

				# set output linear layer
				unexpected_keys.remove('embed_out'					)
				lowerCAmelCase__		  =  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}'					)

				lowerCAmelCase__		  =  SpeechEncoderDecoderModel(encoder=snake_case__    ,			decoder=snake_case__					)
				lowerCAmelCase__		  =  False

				# add projection layer
				lowerCAmelCase__		  =  nn.Parameter(projection_layer.weight					)
				lowerCAmelCase__		  =  nn.Parameter(projection_layer.bias					)

				lowerCAmelCase__		  =  create_vocab_dict(snake_case__					)

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

				lowerCAmelCase__		  =  SpeechaTextaTokenizer(os.path.join(snake_case__    ,			'vocab.json'					)					)
				tokenizer.save_pretrained(snake_case__					)

				lowerCAmelCase__		  =  hf_wavavec.config.to_dict()
				lowerCAmelCase__		  =  tokenizer.pad_token_id
				lowerCAmelCase__		  =  tokenizer.bos_token_id
				lowerCAmelCase__		  =  tokenizer.eos_token_id
				lowerCAmelCase__		  =  'speech_to_text_2'
				lowerCAmelCase__		  =  'wav2vec2'

				lowerCAmelCase__		  =  SpeechEncoderDecoderConfig.from_dict(snake_case__					)

				hf_wavavec.save_pretrained(snake_case__					)
				feature_extractor.save_pretrained(snake_case__					)


if __name__ == "__main__":
	_lowerCAmelCase	:   int    =   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")

	_lowerCAmelCase	:   Optional[int]    =   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,
	)
