





import argparse

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    CLIPTokenizer,
    CLIPTokenizerFast,
    VideoMAEImageProcessor,
    XCLIPConfig,
    XCLIPModel,
    XCLIPProcessor,
    XCLIPTextConfig,
    XCLIPVisionConfig,
)






def 				_UpperCAmelCase			(    A				,							A     ):




   '''simple docstring'''

   UpperCAmelCase__						=XCLIPTextConfig()

   # derive patch size from model name
   UpperCAmelCase__						=model_name.find("patch"     )
   UpperCAmelCase__						=int(model_name[start_idx + len("patch"     ) : start_idx + len("patch"     ) + 2]     )
   UpperCAmelCase__						=XCLIPVisionConfig(patch_size=A				,							num_frames=A     )

   if "large" in model_name:
      UpperCAmelCase__						=768
      UpperCAmelCase__						=3072
      UpperCAmelCase__						=12

      UpperCAmelCase__						=1024
      UpperCAmelCase__						=4096
      UpperCAmelCase__						=16
      UpperCAmelCase__						=24
      UpperCAmelCase__						=768
      UpperCAmelCase__						=3072

   if model_name == "xclip-large-patch14-16-frames":
      UpperCAmelCase__						=336

   UpperCAmelCase__						=XCLIPConfig.from_text_vision_configs(A				,							A     )

   if "large" in model_name:
      UpperCAmelCase__						=768

   return config






def 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

   if name == "token_embedding.weight":
      UpperCAmelCase__						=name.replace("token_embedding.weight"				,							"text_model.embeddings.token_embedding.weight"     )
   if name == "positional_embedding":
      UpperCAmelCase__						=name.replace("positional_embedding"				,							"text_model.embeddings.position_embedding.weight"     )
   if "ln_1" in name:
      UpperCAmelCase__						=name.replace("ln_1"				,							"layer_norm1"     )
   if "ln_2" in name:
      UpperCAmelCase__						=name.replace("ln_2"				,							"layer_norm2"     )
   if "c_fc" in name:
      UpperCAmelCase__						=name.replace("c_fc"				,							"fc1"     )
   if "c_proj" in name:
      UpperCAmelCase__						=name.replace("c_proj"				,							"fc2"     )
   if name.startswith("transformer.resblocks"     ):
      UpperCAmelCase__						=name.replace("transformer.resblocks"				,							"text_model.encoder.layers"     )
   if "attn.out_proj" in name and "message" not in name:
      UpperCAmelCase__						=name.replace("attn.out_proj"				,							"self_attn.out_proj"     )
   if "ln_final" in name:
      UpperCAmelCase__						=name.replace("ln_final"				,							"text_model.final_layer_norm"     )
   # visual encoder
   if name == "visual.class_embedding":
      UpperCAmelCase__						=name.replace("visual.class_embedding"				,							"vision_model.embeddings.class_embedding"     )
   if name == "visual.positional_embedding":
      UpperCAmelCase__						=name.replace("visual.positional_embedding"				,							"vision_model.embeddings.position_embedding.weight"     )
   if name.startswith("visual.transformer.resblocks"     ):
      UpperCAmelCase__						=name.replace("visual.transformer.resblocks"				,							"vision_model.encoder.layers"     )
   if "visual.conv1" in name:
      UpperCAmelCase__						=name.replace("visual.conv1"				,							"vision_model.embeddings.patch_embedding"     )
   if "visual.ln_pre" in name:
      UpperCAmelCase__						=name.replace("visual.ln_pre"				,							"vision_model.pre_layernorm"     )
   if "visual.ln_post" in name:
      UpperCAmelCase__						=name.replace("visual.ln_post"				,							"vision_model.post_layernorm"     )
   if "visual.proj" in name:
      UpperCAmelCase__						=name.replace("visual.proj"				,							"visual_projection.weight"     )
   if "text_projection" in name:
      UpperCAmelCase__						=name.replace("text_projection"				,							"text_projection.weight"     )
   # things on top
   if "prompts_visual_proj" in name:
      UpperCAmelCase__						=name.replace("prompts_visual_proj"				,							"prompts_visual_projection"     )
   if "prompts_visual_ln" in name:
      UpperCAmelCase__						=name.replace("prompts_visual_ln"				,							"prompts_visual_layernorm"     )
   # mit
   if name == "mit.positional_embedding":
      UpperCAmelCase__						=name.replace("positional"				,							"position"     )
   if name.startswith("mit.resblocks"     ):
      UpperCAmelCase__						=name.replace("mit.resblocks"				,							"mit.encoder.layers"     )
   # prompts generator
   if name.startswith("prompts_generator.norm"     ):
      UpperCAmelCase__						=name.replace("prompts_generator.norm"				,							"prompts_generator.layernorm"     )

   return name






def 				_UpperCAmelCase			(    A				,							A     ):




   '''simple docstring'''

   for key in orig_state_dict.copy().keys():
      UpperCAmelCase__						=orig_state_dict.pop(A     )

      if "attn.in_proj" in key:
         UpperCAmelCase__						=key.split("."     )
         if key.startswith("visual"     ):
            UpperCAmelCase__						=key_split[3]
            UpperCAmelCase__						=config.vision_config.hidden_size
            if "message_attn" in key:
               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:
               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:]
         elif key.startswith("mit"     ):
            UpperCAmelCase__						=key_split[2]
            UpperCAmelCase__						=config.vision_config.mit_hidden_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__						=key_split[2]
            UpperCAmelCase__						=config.text_config.hidden_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__						=rename_key(A     )
         if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
            UpperCAmelCase__						=val.T
         UpperCAmelCase__						=val

   return orig_state_dict






def 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

   if num_frames == 8:
      UpperCAmelCase__						="eating_spaghetti_8_frames.npy"
   elif num_frames == 16:
      UpperCAmelCase__						="eating_spaghetti.npy"
   elif num_frames == 32:
      UpperCAmelCase__						="eating_spaghetti_32_frames.npy"
   UpperCAmelCase__						=hf_hub_download(
       repo_id="hf-internal-testing/spaghetti-video"				,							filename=A				,							repo_type="dataset"				,							)
   UpperCAmelCase__						=np.load(A     )
   return list(A     )






def 				_UpperCAmelCase			(    A				,							A=None				,							A=False     ):




   '''simple docstring'''

   UpperCAmelCase__						={
       # fully supervised kinetics-400 checkpoints
       "xclip-base-patch32": "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth",
       "xclip-base-patch32-16-frames": (
           "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth"
       ),
       "xclip-base-patch16": "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth",
       "xclip-base-patch16-16-frames": (
           "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth"
       ),
       "xclip-large-patch14": "https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&amp;export=download&amp;confirm=t&amp;uuid=b26caedc-88e2-473e-830a-9d158b653cdb",
       "xclip-large-patch14-16-frames": "https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&amp;export=download&amp;confirm=t&amp;uuid=538fa810-e671-4050-b385-9a623f89804f",
       # fully supervised kinetics-600 checkpoints
       "xclip-base-patch16-kinetics-600": (
           "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth"
       ),
       "xclip-base-patch16-kinetics-600-16-frames": (
           "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth"
       ),
       "xclip-large-patch14-kinetics-600": "https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&amp;export=download&amp;confirm=t&amp;uuid=141d4977-4a65-44ae-864f-4b0c19f838be",
       # few shot
       "xclip-base-patch16-hmdb-2-shot": (
           "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth"
       ),
       "xclip-base-patch16-hmdb-4-shot": (
           "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth"
       ),
       "xclip-base-patch16-hmdb-8-shot": (
           "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth"
       ),
       "xclip-base-patch16-hmdb-16-shot": (
           "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth"
       ),
       "xclip-base-patch16-ucf-2-shot": (
           "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth"
       ),
       "xclip-base-patch16-ucf-4-shot": (
           "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth"
       ),
       "xclip-base-patch16-ucf-8-shot": (
           "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth"
       ),
       "xclip-base-patch16-ucf-16-shot": (
           "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth"
       ),
       # zero shot
       "xclip-base-patch16-zero-shot": "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth",
   }

   UpperCAmelCase__						=model_to_url[model_name]
   UpperCAmelCase__						=8
   if "16-frames" in model_name:
      UpperCAmelCase__						=16
   elif "shot" in model_name:
      UpperCAmelCase__						=32

   UpperCAmelCase__						=get_xclip_config(A				,							A     )
   UpperCAmelCase__						=XCLIPModel(A     )
   model.eval()

   if "drive" in checkpoint_url:
      UpperCAmelCase__						="pytorch_model.bin"
      gdown.cached_download(A				,							A				,							quiet=A     )
      UpperCAmelCase__						=torch.load(A				,							map_location="cpu"     )["model"]
   else:
      UpperCAmelCase__						=torch.hub.load_state_dict_from_url(A     )["model"]

   UpperCAmelCase__						=convert_state_dict(A				,							A     )

   UpperCAmelCase__						=XCLIPModel(A     )
   UpperCAmelCase__			,							UpperCAmelCase__						=model.load_state_dict(A				,							strict=A     )
   assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
   model.eval()

   UpperCAmelCase__						=336 if model_name == "xclip-large-patch14-16-frames" else 224
   UpperCAmelCase__						=VideoMAEImageProcessor(size=A     )
   UpperCAmelCase__						=CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32"     )
   UpperCAmelCase__						=CLIPTokenizerFast.from_pretrained("openai/clip-vit-base-patch32"     )
   UpperCAmelCase__						=XCLIPProcessor(image_processor=A				,							tokenizer=A     )

   UpperCAmelCase__						=prepare_video(A     )
   UpperCAmelCase__						=processor(
       text=["playing sports", "eating spaghetti", "go shopping"]				,							videos=A				,							return_tensors="pt"				,							padding=A     )

   print("Shape of pixel values:"				,							inputs.pixel_values.shape     )

   with torch.no_grad():
      UpperCAmelCase__						=model(**A     )

   # Verify outputs
   UpperCAmelCase__						=outputs.logits_per_video
   UpperCAmelCase__						=logits_per_video.softmax(dim=1     )
   print("Probs:"				,							A     )
   # kinetics-400
   if model_name == "xclip-base-patch32":
      UpperCAmelCase__						=torch.tensor([[0.00_19, 0.99_51, 0.00_30]]     )
   elif model_name == "xclip-base-patch32-16-frames":
      UpperCAmelCase__						=torch.tensor([[7.0_9_9_9e-0_4, 9.9_8_8_3e-0_1, 4.5_5_8_0e-0_4]]     )
   elif model_name == "xclip-base-patch16":
      UpperCAmelCase__						=torch.tensor([[0.00_83, 0.96_81, 0.02_36]]     )
   elif model_name == "xclip-base-patch16-16-frames":
      UpperCAmelCase__						=torch.tensor([[7.6_9_3_7e-0_4, 9.9_7_2_8e-0_1, 1.9_4_7_3e-0_3]]     )
   elif model_name == "xclip-large-patch14":
      UpperCAmelCase__						=torch.tensor([[0.00_62, 0.98_64, 0.00_75]]     )
   elif model_name == "xclip-large-patch14-16-frames":
      UpperCAmelCase__						=torch.tensor([[3.3_8_7_7e-0_4, 9.9_9_3_7e-0_1, 2.8_8_8_8e-0_4]]     )
   # kinetics-600
   elif model_name == "xclip-base-patch16-kinetics-600":
      UpperCAmelCase__						=torch.tensor([[0.05_55, 0.89_14, 0.05_31]]     )
   elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
      UpperCAmelCase__						=torch.tensor([[3.8_5_5_4e-0_4, 9.9_9_2_9e-0_1, 3.2_7_5_4e-0_4]]     )
   elif model_name == "xclip-large-patch14-kinetics-600":
      UpperCAmelCase__						=torch.tensor([[0.00_36, 0.99_20, 0.00_45]]     )
   # few shot
   elif model_name == "xclip-base-patch16-hmdb-2-shot":
      UpperCAmelCase__						=torch.tensor([[7.1_8_9_0e-0_6, 9.9_9_9_4e-0_1, 5.6_5_5_9e-0_5]]     )
   elif model_name == "xclip-base-patch16-hmdb-4-shot":
      UpperCAmelCase__						=torch.tensor([[1.0_3_2_0e-0_5, 9.9_9_9_3e-0_1, 6.2_4_3_5e-0_5]]     )
   elif model_name == "xclip-base-patch16-hmdb-8-shot":
      UpperCAmelCase__						=torch.tensor([[4.1_3_7_7e-0_6, 9.9_9_9_0e-0_1, 9.8_3_8_6e-0_5]]     )
   elif model_name == "xclip-base-patch16-hmdb-16-shot":
      UpperCAmelCase__						=torch.tensor([[4.1_3_4_7e-0_5, 9.9_9_6_2e-0_1, 3.3_4_1_1e-0_4]]     )
   elif model_name == "xclip-base-patch16-ucf-2-shot":
      UpperCAmelCase__						=torch.tensor([[8.5_8_5_7e-0_5, 9.9_9_2_8e-0_1, 6.3_2_9_1e-0_4]]     )
   elif model_name == "xclip-base-patch16-ucf-4-shot":
      UpperCAmelCase__						=torch.tensor([[8.5_8_5_7e-0_5, 9.9_9_2_8e-0_1, 6.3_2_9_1e-0_4]]     )
   elif model_name == "xclip-base-patch16-ucf-8-shot":
      UpperCAmelCase__						=torch.tensor([[0.00_27, 0.99_04, 0.00_70]]     )
   elif model_name == "xclip-base-patch16-ucf-16-shot":
      UpperCAmelCase__						=torch.tensor([[9.8_2_1_9e-0_4, 9.9_5_9_3e-0_1, 3.0_8_6_3e-0_3]]     )
   # zero shot
   elif model_name == "xclip-base-patch16-zero-shot":
      UpperCAmelCase__						=torch.tensor([[3.5_0_8_2e-0_4, 9.9_7_8_5e-0_1, 1.7_9_6_6e-0_3]]     )
   else:
      raise ValueError(F"""Model name {model_name} not supported"""     )
   assert torch.allclose(A				,							A				,							atol=1e-3     )
   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(A     )

   if push_to_hub:
      print("Pushing model, processor and slow tokenizer files to the hub..."     )
      model.push_to_hub(A				,							organization="nielsr"     )
      processor.push_to_hub(A				,							organization="nielsr"     )
      slow_tokenizer.push_to_hub(A				,							organization="nielsr"     )


if __name__ == "__main__":
    UpperCamelCase_												=	argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--model_name',
        default='xclip-base-patch32',
        type=str,
        help='Name of the model.',
    )
    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.'
    )

    UpperCamelCase_												=	parser.parse_args()
    convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, 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 (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
UpperCamelCase_												=	logging.get_logger(__name__)

UpperCamelCase_												=	{
    '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 				_UpperCAmelCase			(    A				,							A				,							A				,							A				,							A     ):




   '''simple docstring'''

   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 				_UpperCAmelCase			(    A				,							A				,							A     ):




   '''simple docstring'''

   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 				_UpperCAmelCase			(    A				,							A				,							A				,							A				,							A     ):




   '''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(A     )






def 				_UpperCAmelCase			(    A				,							A     ):




   '''simple docstring'''

   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 				_UpperCAmelCase			(    A				,							A				,							A=None				,							A=None				,							A=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__						=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_												=	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_												=	parser.parse_args()
    convert_sew_checkpoint(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
    )



