






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import TvltImageProcessor





def 						A_					(						lowercase_	,  lowercase_=1_0	,  lowercase_=1_0	,  lowercase_=False	,  lowercase_=False			)				->List[str]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE				    =		[]
 for i in range(image_processor_tester.num_frames			):
  video.append(np.random.randint(2_5_5	,  size=(image_processor_tester.num_channels, width, height)	,  dtype=np.uinta			)			)

 if not numpify and not torchify:
  # PIL expects the channel dimension as last dimension
  SCREAMING_SNAKE_CASE				    =		[Image.fromarray(np.moveaxis(lowercase_	,  0	,  -1			)			) for frame in video]

 if torchify:
  SCREAMING_SNAKE_CASE				    =		[torch.from_numpy(lowercase_			) for frame in video]

 return video





def 						A_					(						lowercase_	,  lowercase_=False	,  lowercase_=False	,  lowercase_=False			)				->Any:







 """simple docstring"""

 assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

 SCREAMING_SNAKE_CASE				    =		[]
 for i in range(image_processor_tester.batch_size			):
  if equal_resolution:
   SCREAMING_SNAKE_CASE				    =		SCREAMING_SNAKE_CASE				    =		image_processor_tester.max_resolution
  else:
   SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		np.random.choice(
       np.arange(image_processor_tester.min_resolution	,  image_processor_tester.max_resolution			)	,  2			)
   SCREAMING_SNAKE_CASE				    =		prepare_video(
       image_processor_tester=lowercase_	,  width=lowercase_	,  height=lowercase_	,  numpify=lowercase_	,  torchify=lowercase_	,  )
  video_inputs.append(lowercase_			)

 return video_inputs
class 	a_(		unittest.TestCase	):



     """simple docstring"""



     def __init__(		self					:	Any  ,  lowerCAmelCase__					:	int  ,  lowerCAmelCase__					:	int=7  ,  lowerCAmelCase__					:	Dict=3  ,  lowerCAmelCase__					:	Optional[int]=4  ,  lowerCAmelCase__					:	Any=1_8  ,  lowerCAmelCase__					:	Dict=3_0  ,  lowerCAmelCase__					:	Union[str, Any]=4_0_0  ,  lowerCAmelCase__					:	List[Any]=True  ,  lowerCAmelCase__					:	int=None  ,  lowerCAmelCase__					:	Optional[Any]=True  ,  lowerCAmelCase__					:	int=[0.5, 0.5, 0.5]  ,  lowerCAmelCase__					:	Tuple=[0.5, 0.5, 0.5]  ,  lowerCAmelCase__					:	Dict=True  ,  lowerCAmelCase__					:	Union[str, Any]=None  ,  )							->	Any:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		size if size is not None else {'shortest_edge': 1_8}
      SCREAMING_SNAKE_CASE				    =		crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}

      SCREAMING_SNAKE_CASE				    =		parent
      SCREAMING_SNAKE_CASE				    =		batch_size
      SCREAMING_SNAKE_CASE				    =		num_channels
      SCREAMING_SNAKE_CASE				    =		num_frames
      SCREAMING_SNAKE_CASE				    =		image_size
      SCREAMING_SNAKE_CASE				    =		min_resolution
      SCREAMING_SNAKE_CASE				    =		max_resolution
      SCREAMING_SNAKE_CASE				    =		do_resize
      SCREAMING_SNAKE_CASE				    =		size
      SCREAMING_SNAKE_CASE				    =		do_normalize
      SCREAMING_SNAKE_CASE				    =		image_mean
      SCREAMING_SNAKE_CASE				    =		image_std
      SCREAMING_SNAKE_CASE				    =		do_center_crop
      SCREAMING_SNAKE_CASE				    =		crop_size





     def     __UpperCamelCase						(		self					:	Optional[Any])							->	Optional[Any]:

      """simple docstring"""
      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
      }

@require_torch
@require_vision
class 	a_(		lowercase__			,							unittest.TestCase	):



     """simple docstring"""



     __snake_case		:				int  					=TvltImageProcessor if is_vision_available() else None





     def     __UpperCamelCase						(		self					:	str)							->	Dict:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		TvltImageProcessorTester(self)





     @property
     def     __UpperCamelCase						(		self					:	List[str])							->	Tuple:

      """simple docstring"""
      return self.image_processor_tester.prepare_image_processor_dict()





     def     __UpperCamelCase						(		self					:	List[Any])							->	Optional[Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		self.image_processing_class(**self.image_processor_dict)
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'image_mean'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'image_std'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'do_normalize'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'do_resize'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'do_center_crop'))
      self.assertTrue(hasattr(lowerCAmelCase__  ,  'size'))





     def     __UpperCamelCase						(		self					:	Tuple)							->	int:

      """simple docstring"""
      # Initialize image_processor
      SCREAMING_SNAKE_CASE				    =		self.image_processing_class(**self.image_processor_dict)
      # create random PIL videos
      SCREAMING_SNAKE_CASE				    =		prepare_video_inputs(self.image_processor_tester  ,  equal_resolution=lowerCAmelCase__)
      for video in video_inputs:
       self.assertIsInstance(lowerCAmelCase__  ,  lowerCAmelCase__)
       self.assertIsInstance(video[0]  ,  Image.Image)

      # Test not batched input
      SCREAMING_SNAKE_CASE				    =		image_processor(video_inputs[0]  ,  return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_videos.shape  ,  (
              1,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )  ,  )

      # Test batched
      SCREAMING_SNAKE_CASE				    =		image_processor(lowerCAmelCase__  ,  return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_videos.shape  ,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )  ,  )





     def     __UpperCamelCase						(		self					:	str)							->	Any:

      """simple docstring"""
      # Initialize image_processor
      SCREAMING_SNAKE_CASE				    =		self.image_processing_class(**self.image_processor_dict)
      # create random numpy tensors
      SCREAMING_SNAKE_CASE				    =		prepare_video_inputs(self.image_processor_tester  ,  equal_resolution=lowerCAmelCase__  ,  numpify=lowerCAmelCase__)
      for video in video_inputs:
       self.assertIsInstance(lowerCAmelCase__  ,  lowerCAmelCase__)
       self.assertIsInstance(video[0]  ,  np.ndarray)

      # Test not batched input
      SCREAMING_SNAKE_CASE				    =		image_processor(video_inputs[0]  ,  return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_videos.shape  ,  (
              1,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )  ,  )

      # Test batched
      SCREAMING_SNAKE_CASE				    =		image_processor(lowerCAmelCase__  ,  return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_videos.shape  ,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )  ,  )





     def     __UpperCamelCase						(		self					:	Tuple)							->	Optional[Any]:

      """simple docstring"""
      # Initialize image_processor
      SCREAMING_SNAKE_CASE				    =		self.image_processing_class(**self.image_processor_dict)
      # create random PyTorch tensors
      SCREAMING_SNAKE_CASE				    =		prepare_video_inputs(self.image_processor_tester  ,  equal_resolution=lowerCAmelCase__  ,  torchify=lowerCAmelCase__)
      for video in video_inputs:
       self.assertIsInstance(lowerCAmelCase__  ,  lowerCAmelCase__)
       self.assertIsInstance(video[0]  ,  torch.Tensor)

      # Test not batched input
      SCREAMING_SNAKE_CASE				    =		image_processor(video_inputs[0]  ,  return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_videos.shape  ,  (
              1,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )  ,  )

      # Test batched
      SCREAMING_SNAKE_CASE				    =		image_processor(lowerCAmelCase__  ,  return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_videos.shape  ,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )  ,  )









import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

_lowerCamelCase         =	{
    """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""",
    """mask_emb""": """masked_spec_embed""",
}






def 						_lowerCAmelCase    (					__lowerCamelCase :  Union[str, Any]		,						__lowerCamelCase :  Optional[int]		,						__lowerCamelCase :  List[Any]		,						__lowerCamelCase :  str		,						__lowerCamelCase :  Tuple  ):



 """simple docstring"""





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

 if weight_type is not None:
  __SCREAMING_SNAKE_CASE							:     List[Any]							=						getattr(__lowerCamelCase		,						__lowerCamelCase  ).shape
 else:
  __SCREAMING_SNAKE_CASE							:     Dict							=						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":
  __SCREAMING_SNAKE_CASE							:     str							=						value
 elif weight_type == "weight_g":
  __SCREAMING_SNAKE_CASE							:     str							=						value
 elif weight_type == "weight_v":
  __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						value
 elif weight_type == "bias":
  __SCREAMING_SNAKE_CASE							:     int							=						value
 else:
  __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						value

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






def 						_lowerCAmelCase    (					__lowerCamelCase :  int		,						__lowerCamelCase :  Tuple  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Dict							=						[]
 __SCREAMING_SNAKE_CASE							:     str							=						fairseq_model.state_dict()

 __SCREAMING_SNAKE_CASE							:     List[Any]							=						hf_model.feature_extractor

 for name, value in fairseq_dict.items():
  __SCREAMING_SNAKE_CASE							:     str							=						False
  if "conv_layers" in name:
   load_conv_layer(
       __lowerCamelCase		,						__lowerCamelCase		,						__lowerCamelCase		,						__lowerCamelCase		,						hf_model.config.feat_extract_norm == "group"		,						)
   __SCREAMING_SNAKE_CASE							:     Optional[int]							=						True
  else:
   for key, mapped_key in MAPPING.items():
    __SCREAMING_SNAKE_CASE							:     List[str]							=						mapped_key

    if key in name:
     __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						True
     if "*" in mapped_key:
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						name.split(__lowerCamelCase  )[0].split("."  )[-2]
      __SCREAMING_SNAKE_CASE							:     int							=						mapped_key.replace("*"		,						__lowerCamelCase  )
     if "weight_g" in name:
      __SCREAMING_SNAKE_CASE							:     int							=						"weight_g"
     elif "weight_v" in name:
      __SCREAMING_SNAKE_CASE							:     str							=						"weight_v"
     elif "weight" in name:
      __SCREAMING_SNAKE_CASE							:     Tuple							=						"weight"
     elif "bias" in name:
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						"bias"
     else:
      __SCREAMING_SNAKE_CASE							:     int							=						None
     set_recursively(__lowerCamelCase		,						__lowerCamelCase		,						__lowerCamelCase		,						__lowerCamelCase		,						__lowerCamelCase  )
    continue
  if not is_used:
   unused_weights.append(__lowerCamelCase  )

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






def 						_lowerCAmelCase    (					__lowerCamelCase :  List[Any]		,						__lowerCamelCase :  Optional[Any]		,						__lowerCamelCase :  str		,						__lowerCamelCase :  str		,						__lowerCamelCase :  Union[str, Any]  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     int							=						full_name.split("conv_layers."  )[-1]
 __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						name.split("."  )
 __SCREAMING_SNAKE_CASE							:     Dict							=						int(items[0]  )
 __SCREAMING_SNAKE_CASE							:     Dict							=						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."""
   )
   __SCREAMING_SNAKE_CASE							:     Dict							=						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."""
   )
   __SCREAMING_SNAKE_CASE							:     int							=						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."
   )
   __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						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."""
   )
   __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						value
   logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""  )
 else:
  unused_weights.append(__lowerCamelCase  )






def 						_lowerCAmelCase    (					__lowerCamelCase :  List[Any]  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						HubertConfig()
 __SCREAMING_SNAKE_CASE							:     List[str]							=						model.config

 __SCREAMING_SNAKE_CASE							:     str							=						fs_config.activation_dropout
 __SCREAMING_SNAKE_CASE							:     Any							=						False
 __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						fs_config.attention_dropout
 __SCREAMING_SNAKE_CASE							:     List[str]							=						False
 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						eval(fs_config.extractor_conv_feature_layers  )
 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						[x[0] for x in conv_layers]
 __SCREAMING_SNAKE_CASE							:     Any							=						[x[1] for x in conv_layers]
 __SCREAMING_SNAKE_CASE							:     Dict							=						[x[2] for x in conv_layers]
 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						"gelu"
 __SCREAMING_SNAKE_CASE							:     List[Any]							=						"layer" if fs_config.extractor_mode == "layer_norm" else "group"
 __SCREAMING_SNAKE_CASE							:     Any							=						False
 __SCREAMING_SNAKE_CASE							:     Tuple							=						0.0
 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						0.0
 __SCREAMING_SNAKE_CASE							:     Any							=						fs_config.activation_fn
 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						fs_config.dropout
 __SCREAMING_SNAKE_CASE							:     List[Any]							=						fs_config.encoder_embed_dim
 __SCREAMING_SNAKE_CASE							:     int							=						0.02
 __SCREAMING_SNAKE_CASE							:     Tuple							=						fs_config.encoder_ffn_embed_dim
 __SCREAMING_SNAKE_CASE							:     List[Any]							=						1E-5
 __SCREAMING_SNAKE_CASE							:     Any							=						0.0
 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						fs_config.encoder_attention_heads
 __SCREAMING_SNAKE_CASE							:     Dict							=						fs_config.conv_pos_groups
 __SCREAMING_SNAKE_CASE							:     Tuple							=						fs_config.conv_pos
 __SCREAMING_SNAKE_CASE							:     Tuple							=						len(__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						fs_config.encoder_layers

 return config






@torch.no_grad()
def 						_lowerCAmelCase    (					__lowerCamelCase :  Any		,						__lowerCamelCase :  Optional[Any]=None  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Dict							=						distilhubert().model.model

 if config_path is not None:
  __SCREAMING_SNAKE_CASE							:     List[Any]							=						HubertConfig.from_pretrained(__lowerCamelCase  )
 else:
  __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						convert_config(__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						model.eval()

 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						WavaVecaFeatureExtractor(
     feature_size=1		,						sampling_rate=16000		,						padding_value=0		,						do_normalize=__lowerCamelCase		,						return_attention_mask=__lowerCamelCase		,						)
 __SCREAMING_SNAKE_CASE							:     List[Any]							=						HubertModel(__lowerCamelCase  )

 recursively_load_weights(__lowerCamelCase		,						__lowerCamelCase  )

 feature_extractor.save_pretrained(__lowerCamelCase  )
 hf_model.save_pretrained(__lowerCamelCase  )


if __name__ == "__main__":
      _lowerCamelCase         =	argparse.ArgumentParser()
      parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
      parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
      _lowerCamelCase         =	parser.parse_args()
      convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)






