



'''simple docstring'''



import argparse
import json
from pathlib import Path

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

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


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







def _lowercase (	__A							):



 '''simple docstring'''



 if "resnet-50" in model_name:
  __UpperCamelCase					   =	ResNetConfig.from_pretrained("""microsoft/resnet-50"""							)
 elif "resnet-101" in model_name:
  __UpperCamelCase					   =	ResNetConfig.from_pretrained("""microsoft/resnet-101"""							)
 else:
  raise ValueError("""Model name should include either resnet50 or resnet101"""							)

 __UpperCamelCase					   =	DetrConfig(use_timm_backbone=a__				,backbone_config=a__							)

 # set label attributes
 __UpperCamelCase					   =	"""panoptic""" in model_name
 if is_panoptic:
  __UpperCamelCase					   =	250
 else:
  __UpperCamelCase					   =	91
  __UpperCamelCase					   =	"""huggingface/label-files"""
  __UpperCamelCase					   =	"""coco-detection-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()}

 return config, is_panoptic







def _lowercase (	__A							):



 '''simple docstring'''



 __UpperCamelCase					   =	[]

 # stem
 # fmt: off
 rename_keys.append(("""backbone.0.body.conv1.weight""", """backbone.conv_encoder.model.embedder.embedder.convolution.weight""")							)
 rename_keys.append(("""backbone.0.body.bn1.weight""", """backbone.conv_encoder.model.embedder.embedder.normalization.weight""")							)
 rename_keys.append(("""backbone.0.body.bn1.bias""", """backbone.conv_encoder.model.embedder.embedder.normalization.bias""")							)
 rename_keys.append(("""backbone.0.body.bn1.running_mean""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_mean""")							)
 rename_keys.append(("""backbone.0.body.bn1.running_var""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_var""")							)
 # stages
 for stage_idx in range(len(config.backbone_config.depths							)							):
  for layer_idx in range(config.backbone_config.depths[stage_idx]							):
   # shortcut
   if layer_idx == 0:
    rename_keys.append(
        (
            f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight",
            f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight",
        )							)
    rename_keys.append(
        (
            f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight",
            f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight",
        )							)
    rename_keys.append(
        (
            f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias",
            f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias",
        )							)
    rename_keys.append(
        (
            f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean",
            f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean",
        )							)
    rename_keys.append(
        (
            f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var",
            f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var",
        )							)
   # 3 convs
   for i in range(3							):
    rename_keys.append(
        (
            f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight",
            f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight",
        )							)
    rename_keys.append(
        (
            f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight",
            f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight",
        )							)
    rename_keys.append(
        (
            f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias",
            f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias",
        )							)
    rename_keys.append(
        (
            f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean",
            f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean",
        )							)
    rename_keys.append(
        (
            f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var",
            f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var",
        )							)
    # fmt: on

 for i in range(config.encoder_layers							):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append(
      (
          f"transformer.encoder.layers.{i}.self_attn.out_proj.weight",
          f"encoder.layers.{i}.self_attn.out_proj.weight",
      )							)
  rename_keys.append(
      (f"transformer.encoder.layers.{i}.self_attn.out_proj.bias", f"encoder.layers.{i}.self_attn.out_proj.bias")							)
  rename_keys.append((f"transformer.encoder.layers.{i}.linear1.weight", f"encoder.layers.{i}.fc1.weight")							)
  rename_keys.append((f"transformer.encoder.layers.{i}.linear1.bias", f"encoder.layers.{i}.fc1.bias")							)
  rename_keys.append((f"transformer.encoder.layers.{i}.linear2.weight", f"encoder.layers.{i}.fc2.weight")							)
  rename_keys.append((f"transformer.encoder.layers.{i}.linear2.bias", f"encoder.layers.{i}.fc2.bias")							)
  rename_keys.append(
      (f"transformer.encoder.layers.{i}.norm1.weight", f"encoder.layers.{i}.self_attn_layer_norm.weight")							)
  rename_keys.append(
      (f"transformer.encoder.layers.{i}.norm1.bias", f"encoder.layers.{i}.self_attn_layer_norm.bias")							)
  rename_keys.append(
      (f"transformer.encoder.layers.{i}.norm2.weight", f"encoder.layers.{i}.final_layer_norm.weight")							)
  rename_keys.append((f"transformer.encoder.layers.{i}.norm2.bias", f"encoder.layers.{i}.final_layer_norm.bias")							)
  # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
  rename_keys.append(
      (
          f"transformer.decoder.layers.{i}.self_attn.out_proj.weight",
          f"decoder.layers.{i}.self_attn.out_proj.weight",
      )							)
  rename_keys.append(
      (f"transformer.decoder.layers.{i}.self_attn.out_proj.bias", f"decoder.layers.{i}.self_attn.out_proj.bias")							)
  rename_keys.append(
      (
          f"transformer.decoder.layers.{i}.multihead_attn.out_proj.weight",
          f"decoder.layers.{i}.encoder_attn.out_proj.weight",
      )							)
  rename_keys.append(
      (
          f"transformer.decoder.layers.{i}.multihead_attn.out_proj.bias",
          f"decoder.layers.{i}.encoder_attn.out_proj.bias",
      )							)
  rename_keys.append((f"transformer.decoder.layers.{i}.linear1.weight", f"decoder.layers.{i}.fc1.weight")							)
  rename_keys.append((f"transformer.decoder.layers.{i}.linear1.bias", f"decoder.layers.{i}.fc1.bias")							)
  rename_keys.append((f"transformer.decoder.layers.{i}.linear2.weight", f"decoder.layers.{i}.fc2.weight")							)
  rename_keys.append((f"transformer.decoder.layers.{i}.linear2.bias", f"decoder.layers.{i}.fc2.bias")							)
  rename_keys.append(
      (f"transformer.decoder.layers.{i}.norm1.weight", f"decoder.layers.{i}.self_attn_layer_norm.weight")							)
  rename_keys.append(
      (f"transformer.decoder.layers.{i}.norm1.bias", f"decoder.layers.{i}.self_attn_layer_norm.bias")							)
  rename_keys.append(
      (f"transformer.decoder.layers.{i}.norm2.weight", f"decoder.layers.{i}.encoder_attn_layer_norm.weight")							)
  rename_keys.append(
      (f"transformer.decoder.layers.{i}.norm2.bias", f"decoder.layers.{i}.encoder_attn_layer_norm.bias")							)
  rename_keys.append(
      (f"transformer.decoder.layers.{i}.norm3.weight", f"decoder.layers.{i}.final_layer_norm.weight")							)
  rename_keys.append((f"transformer.decoder.layers.{i}.norm3.bias", f"decoder.layers.{i}.final_layer_norm.bias")							)

 # convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
 rename_keys.extend(
     [
         ("""input_proj.weight""", """input_projection.weight"""),
         ("""input_proj.bias""", """input_projection.bias"""),
         ("""query_embed.weight""", """query_position_embeddings.weight"""),
         ("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
         ("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
         ("""class_embed.weight""", """class_labels_classifier.weight"""),
         ("""class_embed.bias""", """class_labels_classifier.bias"""),
         ("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
         ("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
         ("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
         ("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
         ("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
         ("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
     ]							)

 return rename_keys







def _lowercase (	__A				,__A				,__A							):



 '''simple docstring'''



 __UpperCamelCase					   =	state_dict.pop(a__							)
 __UpperCamelCase					   =	val







def _lowercase (	__A				,__A=False							):



 '''simple docstring'''



 __UpperCamelCase					   =	""""""
 if is_panoptic:
  __UpperCamelCase					   =	"""detr."""

 # first: transformer encoder
 for i in range(6							):
  # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
  __UpperCamelCase					   =	state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"							)
  __UpperCamelCase					   =	state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"							)
  # next, add query, keys and values (in that order) to the state dict
  __UpperCamelCase					   =	in_proj_weight[:256, :]
  __UpperCamelCase					   =	in_proj_bias[:256]
  __UpperCamelCase					   =	in_proj_weight[256:512, :]
  __UpperCamelCase					   =	in_proj_bias[256:512]
  __UpperCamelCase					   =	in_proj_weight[-256:, :]
  __UpperCamelCase					   =	in_proj_bias[-256:]
 # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
 for i in range(6							):
  # read in weights + bias of input projection layer of self-attention
  __UpperCamelCase					   =	state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"							)
  __UpperCamelCase					   =	state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"							)
  # next, add query, keys and values (in that order) to the state dict
  __UpperCamelCase					   =	in_proj_weight[:256, :]
  __UpperCamelCase					   =	in_proj_bias[:256]
  __UpperCamelCase					   =	in_proj_weight[256:512, :]
  __UpperCamelCase					   =	in_proj_bias[256:512]
  __UpperCamelCase					   =	in_proj_weight[-256:, :]
  __UpperCamelCase					   =	in_proj_bias[-256:]
  # read in weights + bias of input projection layer of cross-attention
  __UpperCamelCase					   =	state_dict.pop(
      f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"							)
  __UpperCamelCase					   =	state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"							)
  # next, add query, keys and values (in that order) of cross-attention to the state dict
  __UpperCamelCase					   =	in_proj_weight_cross_attn[:256, :]
  __UpperCamelCase					   =	in_proj_bias_cross_attn[:256]
  __UpperCamelCase					   =	in_proj_weight_cross_attn[256:512, :]
  __UpperCamelCase					   =	in_proj_bias_cross_attn[256:512]
  __UpperCamelCase					   =	in_proj_weight_cross_attn[-256:, :]
  __UpperCamelCase					   =	in_proj_bias_cross_attn[-256:]







def _lowercase (	):



 '''simple docstring'''



 __UpperCamelCase					   =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
 __UpperCamelCase					   =	Image.open(requests.get(a__				,stream=a__							).raw							)

 return im







@torch.no_grad()
def _lowercase (	__A				,__A=None				,__A=False							):



 '''simple docstring'''



 __UpperCamelCase   ,		__UpperCamelCase					   =	get_detr_config(a__							)

 # load original model from torch hub
 __UpperCamelCase					   =	{
     """detr-resnet-50""": """detr_resnet50""",
     """detr-resnet-101""": """detr_resnet101""",
 }
 logger.info(f"Converting model {model_name}..."							)
 __UpperCamelCase					   =	torch.hub.load("""facebookresearch/detr"""				,model_name_to_original_name[model_name]				,pretrained=a__							).eval()
 __UpperCamelCase					   =	detr.state_dict()
 # rename keys
 for src, dest in create_rename_keys(a__							):
  if is_panoptic:
   __UpperCamelCase					   =	"""detr.""" + src
  rename_key(a__				,a__				,a__							)
 # query, key and value matrices need special treatment
 read_in_q_k_v(a__				,is_panoptic=a__							)
 # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
 __UpperCamelCase					   =	"""detr.model.""" if is_panoptic else """model."""
 for key in state_dict.copy().keys():
  if is_panoptic:
   if (
       key.startswith("""detr"""							)
       and not key.startswith("""class_labels_classifier"""							)
       and not key.startswith("""bbox_predictor"""							)
   ):
    __UpperCamelCase					   =	state_dict.pop(a__							)
    __UpperCamelCase					   =	val
   elif "class_labels_classifier" in key or "bbox_predictor" in key:
    __UpperCamelCase					   =	state_dict.pop(a__							)
    __UpperCamelCase					   =	val
   elif key.startswith("""bbox_attention"""							) or key.startswith("""mask_head"""							):
    continue
   else:
    __UpperCamelCase					   =	state_dict.pop(a__							)
    __UpperCamelCase					   =	val
  else:
   if not key.startswith("""class_labels_classifier"""							) and not key.startswith("""bbox_predictor"""							):
    __UpperCamelCase					   =	state_dict.pop(a__							)
    __UpperCamelCase					   =	val

    # finally, create HuggingFace model and load state dict
 __UpperCamelCase					   =	DetrForSegmentation(a__							) if is_panoptic else DetrForObjectDetection(a__							)
 model.load_state_dict(a__							)
 model.eval()

 # verify our conversion on an image
 __UpperCamelCase					   =	"""coco_panoptic""" if is_panoptic else """coco_detection"""
 __UpperCamelCase					   =	DetrImageProcessor(format=a__							)

 __UpperCamelCase					   =	processor(images=prepare_img()				,return_tensors="""pt"""							)
 __UpperCamelCase					   =	encoding["""pixel_values"""]

 __UpperCamelCase					   =	detr(a__							)
 __UpperCamelCase					   =	model(a__							)

 assert torch.allclose(outputs.logits				,original_outputs["""pred_logits"""]				,atol=1E-3							)
 assert torch.allclose(outputs.pred_boxes				,original_outputs["""pred_boxes"""]				,atol=1E-3							)
 if is_panoptic:
  assert torch.allclose(outputs.pred_masks				,original_outputs["""pred_masks"""]				,atol=1E-4							)
 print("""Looks ok!"""							)

 if pytorch_dump_folder_path is not None:
  # Save model and image processor
  logger.info(f"Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."							)
  Path(a__							).mkdir(exist_ok=a__							)
  model.save_pretrained(a__							)
  processor.save_pretrained(a__							)

 if push_to_hub:
  # Upload model and image processor to the hub
  logger.info("""Uploading PyTorch model and image processor to the hub..."""							)
  model.push_to_hub(f"nielsr/{model_name}"							)
  processor.push_to_hub(f"nielsr/{model_name}"							)


if __name__ == "__main__":
 a__    :      Optional[int]      				=      argparse.ArgumentParser()

 parser.add_argument(
     '--model_name',
     default='detr-resnet-50',
     type=str,
     choices=['detr-resnet-50', 'detr-resnet-101'],
     help='Name of the DETR model you\'d like to convert.',
 )
 parser.add_argument(
     '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
 )
 parser.add_argument('--push_to_hub', action='store_true', help='Whether to push the model to the hub or not.')
 a__    :      List[str]      				=      parser.parse_args()
 convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








'''simple docstring'''



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
UpperCAmelCase					:			List[Any]       =	logging.get_logger(__name__)

UpperCAmelCase					:			Optional[Any]       =	{
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'adapter_layer': 'encoder.layers.*.adapter_layer',
    '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',
    'pooling_layer.linear': 'projector',
    'pooling_layer.projection': 'classifier',
}
UpperCAmelCase					:			Any       =	[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]







def   a__			(    a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = {}
     with open(a__ ,       """r"""		) as file:
          for line_number, line in enumerate(a__		):
               __SCREAMING_SNAKE_CASE      = line.strip()
               if line:
                    __SCREAMING_SNAKE_CASE      = line.split()
                    __SCREAMING_SNAKE_CASE      = line_number
                    __SCREAMING_SNAKE_CASE      = words[0]
                    __SCREAMING_SNAKE_CASE      = value
     return result







def   a__			(    a__ ,       a__ ,       a__ ,       a__ ,       a__		):




     """simple docstring"""


     for attribute in key.split("""."""		):
          __SCREAMING_SNAKE_CASE      = getattr(a__ ,       a__		)

     __SCREAMING_SNAKE_CASE      = None
     for param_key in PARAM_MAPPING.keys():
          if full_name.endswith(a__		):
               __SCREAMING_SNAKE_CASE      = PARAM_MAPPING[full_name.split("""."""		)[-1]]
               __SCREAMING_SNAKE_CASE      = """param"""

     if weight_type is not None and weight_type != "param":
          __SCREAMING_SNAKE_CASE      = getattr(a__ ,       a__		).shape
     elif weight_type is not None and weight_type == "param":
          __SCREAMING_SNAKE_CASE      = hf_pointer
          for attribute in hf_param_name.split("""."""		):
               __SCREAMING_SNAKE_CASE      = getattr(a__ ,       a__		)
          __SCREAMING_SNAKE_CASE      = shape_pointer.shape

          # let's reduce dimension
          __SCREAMING_SNAKE_CASE      = value[0]
     else:
          __SCREAMING_SNAKE_CASE      = hf_pointer.shape

     if hf_shape != value.shape:
          raise ValueError(
              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":
          __SCREAMING_SNAKE_CASE      = value
     elif weight_type == "weight_g":
          __SCREAMING_SNAKE_CASE      = value
     elif weight_type == "weight_v":
          __SCREAMING_SNAKE_CASE      = value
     elif weight_type == "bias":
          __SCREAMING_SNAKE_CASE      = value
     elif weight_type == "param":
          for attribute in hf_param_name.split("""."""		):
               __SCREAMING_SNAKE_CASE      = getattr(a__ ,       a__		)
          __SCREAMING_SNAKE_CASE      = value
     else:
          __SCREAMING_SNAKE_CASE      = value

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







def   a__			(    a__ ,       a__ ,       a__ ,       a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = None
     for param_key in PARAM_MAPPING.keys():
          if full_name.endswith(a__		):
               __SCREAMING_SNAKE_CASE      = PARAM_MAPPING[full_name.split("""."""		)[-1]]
               __SCREAMING_SNAKE_CASE      = """param"""

     if weight_type is not None and weight_type != "param":
          __SCREAMING_SNAKE_CASE      = """.""".join([key, weight_type]		)
     elif weight_type is not None and weight_type == "param":
          __SCREAMING_SNAKE_CASE      = """.""".join([key, hf_param_name]		)
     else:
          __SCREAMING_SNAKE_CASE      = key

     __SCREAMING_SNAKE_CASE      = value if """lm_head""" in full_key else value[0]


UpperCAmelCase					:			Any       =	{
    'W_a': 'linear_1.weight',
    'W_b': 'linear_2.weight',
    'b_a': 'linear_1.bias',
    'b_b': 'linear_2.bias',
    'ln_W': 'norm.weight',
    'ln_b': 'norm.bias',
}







def   a__			(    a__ ,       a__ ,       a__=None ,       a__=None		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = False
     for key, mapped_key in MAPPING.items():
          __SCREAMING_SNAKE_CASE      = """wav2vec2.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
          if key in name or key.split("""w2v_model."""		)[-1] == name.split("""."""		)[0]:
               __SCREAMING_SNAKE_CASE      = True
               if "*" in mapped_key:
                    __SCREAMING_SNAKE_CASE      = name.split(a__		)[0].split("""."""		)[-2]
                    __SCREAMING_SNAKE_CASE      = mapped_key.replace("""*""" ,       a__		)
               if "weight_g" in name:
                    __SCREAMING_SNAKE_CASE      = """weight_g"""
               elif "weight_v" in name:
                    __SCREAMING_SNAKE_CASE      = """weight_v"""
               elif "bias" in name:
                    __SCREAMING_SNAKE_CASE      = """bias"""
               elif "weight" in name:
                    # TODO: don't match quantizer.weight_proj
                    __SCREAMING_SNAKE_CASE      = """weight"""
               else:
                    __SCREAMING_SNAKE_CASE      = None
               if hf_dict is not None:
                    rename_dict(a__ ,       a__ ,       a__ ,       a__ ,       a__		)
               else:
                    set_recursively(a__ ,       a__ ,       a__ ,       a__ ,       a__		)
               return is_used
     return is_used







def   a__			(    a__ ,       a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = []
     __SCREAMING_SNAKE_CASE      = fairseq_model.state_dict()

     __SCREAMING_SNAKE_CASE      = hf_model.wavaveca.feature_extractor

     for name, value in fairseq_dict.items():
          __SCREAMING_SNAKE_CASE      = False
          if "conv_layers" in name:
               load_conv_layer(
                   a__ ,       a__ ,       a__ ,       a__ ,       hf_model.config.feat_extract_norm == """group""" ,       )
               __SCREAMING_SNAKE_CASE      = True
          else:
               __SCREAMING_SNAKE_CASE      = load_wavaveca_layer(a__ ,       a__ ,       a__		)
          if not is_used:
               unused_weights.append(a__		)

     logger.warning(F'Unused weights: {unused_weights}'		)







def   a__			(    a__ ,       a__ ,       a__ ,       a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = full_name.split("""conv_layers."""		)[-1]
     __SCREAMING_SNAKE_CASE      = name.split("""."""		)
     __SCREAMING_SNAKE_CASE      = int(items[0]		)
     __SCREAMING_SNAKE_CASE      = int(items[1]		)

     if type_id == 0:
          if "bias" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                    raise ValueError(
                        F'{full_name} has size {value.shape}, but'
                        F' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'		)
               __SCREAMING_SNAKE_CASE      = value
               logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'		)
          elif "weight" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                    raise ValueError(
                        F'{full_name} has size {value.shape}, but'
                        F' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'		)
               __SCREAMING_SNAKE_CASE      = 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:
               if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                    raise ValueError(
                        F'{full_name} has size {value.shape}, but'
                        F' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'		)
               __SCREAMING_SNAKE_CASE      = value
               logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'		)
          elif "weight" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                    raise ValueError(
                        F'{full_name} has size {value.shape}, but'
                        F' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'		)
               __SCREAMING_SNAKE_CASE      = value
               logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'		)
     else:
          unused_weights.append(a__		)







@torch.no_grad()
def   a__			(    a__ ,       a__ ,       a__=None ,       a__=None ,       a__=True ,       a__=False		):




     """simple docstring"""


     if config_path is not None:
          __SCREAMING_SNAKE_CASE      = WavaVecaConfig.from_pretrained(a__		)
     else:
          __SCREAMING_SNAKE_CASE      = WavaVecaConfig()

     if is_seq_class:
          __SCREAMING_SNAKE_CASE      = read_txt_into_dict(a__		)
          __SCREAMING_SNAKE_CASE      = idalabel
          __SCREAMING_SNAKE_CASE      = WavaVecaForSequenceClassification(a__		)
          __SCREAMING_SNAKE_CASE      = WavaVecaFeatureExtractor(
              feature_size=1 ,       sampling_rate=1_60_00 ,       padding_value=0 ,       do_normalize=a__ ,       return_attention_mask=a__ ,       )
          feature_extractor.save_pretrained(a__		)

     elif is_finetuned:
          if dict_path:
               __SCREAMING_SNAKE_CASE      = Dictionary.load(a__		)

               # important change bos & pad token id since CTC symbol is <pad> and
               # not <s> as in fairseq
               __SCREAMING_SNAKE_CASE      = target_dict.pad_index
               __SCREAMING_SNAKE_CASE      = target_dict.bos_index
               __SCREAMING_SNAKE_CASE      = target_dict.eos_index
               __SCREAMING_SNAKE_CASE      = len(target_dict.symbols		)
               __SCREAMING_SNAKE_CASE      = 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__		)
               __SCREAMING_SNAKE_CASE      = target_dict.indices

               # fairseq has the <pad> and <s> switched
               __SCREAMING_SNAKE_CASE      = 0
               __SCREAMING_SNAKE_CASE      = 1
               with open(a__ ,       """w""" ,       encoding="""utf-8"""		) as vocab_handle:
                    json.dump(a__ ,       a__		)
               __SCREAMING_SNAKE_CASE      = 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__ ,       )
               __SCREAMING_SNAKE_CASE      = True if config.feat_extract_norm == """layer""" else False
               __SCREAMING_SNAKE_CASE      = WavaVecaFeatureExtractor(
                   feature_size=1 ,       sampling_rate=1_60_00 ,       padding_value=0 ,       do_normalize=a__ ,       return_attention_mask=a__ ,       )
               __SCREAMING_SNAKE_CASE      = WavaVecaProcessor(feature_extractor=a__ ,       tokenizer=a__		)
               processor.save_pretrained(a__		)

          __SCREAMING_SNAKE_CASE      = WavaVecaForCTC(a__		)
     else:
          __SCREAMING_SNAKE_CASE      = WavaVecaForPreTraining(a__		)

     if is_finetuned or is_seq_class:
          __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = fairseq.checkpoint_utils.load_model_ensemble_and_task(
              [checkpoint_path] ,       arg_overrides={"""data""": """/""".join(dict_path.split("""/"""		)[:-1]		)}		)
     else:
          __SCREAMING_SNAKE_CASE      = argparse.Namespace(task="""audio_pretraining"""		)
          __SCREAMING_SNAKE_CASE      = fairseq.tasks.setup_task(a__		)

          __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] ,       task=a__		)

     __SCREAMING_SNAKE_CASE      = model[0].eval()

     recursively_load_weights(a__ ,       a__ ,       not is_finetuned		)

     hf_wavavec.save_pretrained(a__		)


if __name__ == "__main__":
  UpperCAmelCase					:			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('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
  parser.add_argument(
      '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
  )
  parser.add_argument(
      '--is_seq_class',
      action='store_true',
      help='Whether the model to convert is a fine-tuned sequence classification model or not',
  )
  UpperCAmelCase					:			Optional[Any]       =	parser.parse_args()

  UpperCAmelCase					:			int       =	not args.not_finetuned and not args.is_seq_class
  convert_wavaveca_checkpoint(
      args.checkpoint_path,
      args.pytorch_dump_folder_path,
      args.config_path,
      args.dict_path,
      is_finetuned,
      args.is_seq_class,
  )






