






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 CLIPImageProcessor
class 							__snake_case(       unittest.TestCase   ):



 def __init__(						self : List[str]			,		UpperCAmelCase__ : List[Any]			,		UpperCAmelCase__ : List[Any]=7			,		UpperCAmelCase__ : List[str]=3			,		UpperCAmelCase__ : str=18			,		UpperCAmelCase__ : List[str]=30			,		UpperCAmelCase__ : Any=400			,		UpperCAmelCase__ : int=True			,		UpperCAmelCase__ : Any=None			,		UpperCAmelCase__ : Optional[Any]=True			,		UpperCAmelCase__ : List[Any]=None			,		UpperCAmelCase__ : Optional[Any]=True			,		UpperCAmelCase__ : List[Any]=[0.48_145_466, 0.4_578_275, 0.40_821_073]			,		UpperCAmelCase__ : List[Any]=[0.26_862_954, 0.26_130_258, 0.27_577_711]			,		UpperCAmelCase__ : Tuple=True			,		):

    """simple docstring"""






    lowercase__    :						Optional[int]       =			size if size is not None else {"""shortest_edge""": 20}
    lowercase__    :						Any       =			crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
    lowercase__    :						str       =			parent
    lowercase__    :						Optional[int]       =			batch_size
    lowercase__    :						int       =			num_channels
    lowercase__    :						str       =			image_size
    lowercase__    :						Tuple       =			min_resolution
    lowercase__    :						int       =			max_resolution
    lowercase__    :						Dict       =			do_resize
    lowercase__    :						List[str]       =			size
    lowercase__    :						Tuple       =			do_center_crop
    lowercase__    :						int       =			crop_size
    lowercase__    :						List[Any]       =			do_normalize
    lowercase__    :						Any       =			image_mean
    lowercase__    :						Optional[Any]       =			image_std
    lowercase__    :						List[str]       =			do_convert_rgb



 def     UpperCAmelCase     (						self : Optional[int]						):

    """simple docstring"""






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







 def     UpperCAmelCase     (						self : Tuple			,		UpperCAmelCase__ : Tuple=False			,		UpperCAmelCase__ : Optional[Any]=False			,		UpperCAmelCase__ : Any=False						):

    """simple docstring"""






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

    if equal_resolution:
       lowercase__    :						Optional[Any]       =			[]
       for i in range(self.batch_size						):
          image_inputs.append(
              np.random.randint(
                  255			,		size=(self.num_channels, self.max_resolution, self.max_resolution)			,		dtype=np.uinta						)						)
    else:
       lowercase__    :						Union[str, Any]       =			[]
       for i in range(self.batch_size						):
          lowercase__  ,       lowercase__    :						List[Any]       =			np.random.choice(np.arange(self.min_resolution			,		self.max_resolution						)			,		2						)
          image_inputs.append(np.random.randint(255			,		size=(self.num_channels, width, height)			,		dtype=np.uinta						)						)

    if not numpify and not torchify:
       # PIL expects the channel dimension as last dimension
       lowercase__    :						List[str]       =			[Image.fromarray(np.moveaxis(UpperCAmelCase__			,		0			,		-1						)						) for x in image_inputs]

    if torchify:
       lowercase__    :						List[Any]       =			[torch.from_numpy(UpperCAmelCase__						) for x in image_inputs]

    return image_inputs
@require_torch
@require_vision
class 							__snake_case(       __UpperCamelCase  ,							unittest.TestCase   ):
 lowercase			:							List[str]		   =		CLIPImageProcessor if is_vision_available() else None



 def     UpperCAmelCase     (						self : Tuple						):

    """simple docstring"""






    lowercase__    :						List[str]       =			CLIPImageProcessingTester(self						)



 @property
 def     UpperCAmelCase     (						self : Union[str, Any]						):

    """simple docstring"""






    return self.image_processor_tester.prepare_image_processor_dict()



 def     UpperCAmelCase     (						self : List[Any]						):

    """simple docstring"""






    lowercase__    :						Tuple       =			self.image_processing_class(**self.image_processor_dict						)
    self.assertTrue(hasattr(UpperCAmelCase__			,		"""do_resize"""						)						)
    self.assertTrue(hasattr(UpperCAmelCase__			,		"""size"""						)						)
    self.assertTrue(hasattr(UpperCAmelCase__			,		"""do_center_crop"""						)						)
    self.assertTrue(hasattr(UpperCAmelCase__			,		"""center_crop"""						)						)
    self.assertTrue(hasattr(UpperCAmelCase__			,		"""do_normalize"""						)						)
    self.assertTrue(hasattr(UpperCAmelCase__			,		"""image_mean"""						)						)
    self.assertTrue(hasattr(UpperCAmelCase__			,		"""image_std"""						)						)
    self.assertTrue(hasattr(UpperCAmelCase__			,		"""do_convert_rgb"""						)						)



 def     UpperCAmelCase     (						self : Any						):

    """simple docstring"""






    lowercase__    :						Optional[int]       =			self.image_processing_class.from_dict(self.image_processor_dict						)
    self.assertEqual(image_processor.size			,		{"""shortest_edge""": 20}						)
    self.assertEqual(image_processor.crop_size			,		{"""height""": 18, """width""": 18}						)

    lowercase__    :						Tuple       =			self.image_processing_class.from_dict(self.image_processor_dict			,		size=42			,		crop_size=84						)
    self.assertEqual(image_processor.size			,		{"""shortest_edge""": 42}						)
    self.assertEqual(image_processor.crop_size			,		{"""height""": 84, """width""": 84}						)



 def     UpperCAmelCase     (						self : str						):

    """simple docstring"""






    pass



 def     UpperCAmelCase     (						self : List[str]						):

    """simple docstring"""






    lowercase__    :						List[str]       =			self.image_processing_class(**self.image_processor_dict						)
    # create random PIL images
    lowercase__    :						Any       =			self.image_processor_tester.prepare_inputs(equal_resolution=UpperCAmelCase__						)
    for image in image_inputs:
       self.assertIsInstance(UpperCAmelCase__			,		Image.Image						)

    # Test not batched input
    lowercase__    :						Optional[Any]       =			image_processing(image_inputs[0]			,		return_tensors="""pt"""						).pixel_values
    self.assertEqual(
        encoded_images.shape			,		(
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )			,		)

    # Test batched
    lowercase__    :						List[str]       =			image_processing(UpperCAmelCase__			,		return_tensors="""pt"""						).pixel_values
    self.assertEqual(
        encoded_images.shape			,		(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )			,		)



 def     UpperCAmelCase     (						self : Tuple						):

    """simple docstring"""






    lowercase__    :						Dict       =			self.image_processing_class(**self.image_processor_dict						)
    # create random numpy tensors
    lowercase__    :						Dict       =			self.image_processor_tester.prepare_inputs(equal_resolution=UpperCAmelCase__			,		numpify=UpperCAmelCase__						)
    for image in image_inputs:
       self.assertIsInstance(UpperCAmelCase__			,		np.ndarray						)

    # Test not batched input
    lowercase__    :						str       =			image_processing(image_inputs[0]			,		return_tensors="""pt"""						).pixel_values
    self.assertEqual(
        encoded_images.shape			,		(
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )			,		)

    # Test batched
    lowercase__    :						Tuple       =			image_processing(UpperCAmelCase__			,		return_tensors="""pt"""						).pixel_values
    self.assertEqual(
        encoded_images.shape			,		(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )			,		)







 def     UpperCAmelCase     (						self : Optional[int]						):

    """simple docstring"""






    lowercase__    :						List[Any]       =			self.image_processing_class(**self.image_processor_dict						)
    # create random PyTorch tensors
    lowercase__    :						List[Any]       =			self.image_processor_tester.prepare_inputs(equal_resolution=UpperCAmelCase__			,		torchify=UpperCAmelCase__						)
    for image in image_inputs:
       self.assertIsInstance(UpperCAmelCase__			,		torch.Tensor						)

    # Test not batched input
    lowercase__    :						Union[str, Any]       =			image_processing(image_inputs[0]			,		return_tensors="""pt"""						).pixel_values
    self.assertEqual(
        encoded_images.shape			,		(
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )			,		)

    # Test batched
    lowercase__    :						Dict       =			image_processing(UpperCAmelCase__			,		return_tensors="""pt"""						).pixel_values
    self.assertEqual(
        encoded_images.shape			,		(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )			,		)
@require_torch
@require_vision
class 							__snake_case(       __UpperCamelCase  ,							unittest.TestCase   ):
 lowercase			:							Union[str, Any]		   =		CLIPImageProcessor if is_vision_available() else None



 def     UpperCAmelCase     (						self : int						):

    """simple docstring"""






    lowercase__    :						List[Any]       =			CLIPImageProcessingTester(self			,		num_channels=4						)
    lowercase__    :						Union[str, Any]       =			3



 @property
 def     UpperCAmelCase     (						self : Optional[int]						):

    """simple docstring"""






    return self.image_processor_tester.prepare_image_processor_dict()



 def     UpperCAmelCase     (						self : Tuple						):

    """simple docstring"""






    lowercase__    :						Any       =			self.image_processing_class(**self.image_processor_dict						)
    self.assertTrue(hasattr(UpperCAmelCase__			,		"""do_resize"""						)						)
    self.assertTrue(hasattr(UpperCAmelCase__			,		"""size"""						)						)
    self.assertTrue(hasattr(UpperCAmelCase__			,		"""do_center_crop"""						)						)
    self.assertTrue(hasattr(UpperCAmelCase__			,		"""center_crop"""						)						)
    self.assertTrue(hasattr(UpperCAmelCase__			,		"""do_normalize"""						)						)
    self.assertTrue(hasattr(UpperCAmelCase__			,		"""image_mean"""						)						)
    self.assertTrue(hasattr(UpperCAmelCase__			,		"""image_std"""						)						)
    self.assertTrue(hasattr(UpperCAmelCase__			,		"""do_convert_rgb"""						)						)



 def     UpperCAmelCase     (						self : Tuple						):

    """simple docstring"""






    pass







 def     UpperCAmelCase     (						self : str						):

    """simple docstring"""






    lowercase__    :						Any       =			self.image_processing_class(**self.image_processor_dict						)
    # create random PIL images
    lowercase__    :						Optional[int]       =			self.image_processor_tester.prepare_inputs(equal_resolution=UpperCAmelCase__						)
    for image in image_inputs:
       self.assertIsInstance(UpperCAmelCase__			,		Image.Image						)

    # Test not batched input
    lowercase__    :						Tuple       =			image_processing(image_inputs[0]			,		return_tensors="""pt"""						).pixel_values
    self.assertEqual(
        encoded_images.shape			,		(
            1,
            self.expected_encoded_image_num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )			,		)

    # Test batched
    lowercase__    :						Any       =			image_processing(UpperCAmelCase__			,		return_tensors="""pt"""						).pixel_values
    self.assertEqual(
        encoded_images.shape			,		(
            self.image_processor_tester.batch_size,
            self.expected_encoded_image_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()
a 			=							logging.get_logger(__name__)

a 			=							{
    '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    a__    (	A		,					A		,					A		,					A		,					A       ):
   for attribute in key.split("""."""       ):
      lowercase__    :						Dict       =			getattr(A		,					A       )

   if weight_type is not None:
      lowercase__    :						List[Any]       =			getattr(A		,					A       ).shape
   else:
      lowercase__    :						Tuple       =			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":
      lowercase__    :						Dict       =			value
   elif weight_type == "weight_g":
      lowercase__    :						Any       =			value
   elif weight_type == "weight_v":
      lowercase__    :						Dict       =			value
   elif weight_type == "bias":
      lowercase__    :						Any       =			value
   else:
      lowercase__    :						Tuple       =			value

   logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''       )
def    a__    (	A		,					A       ):
   lowercase__    :						str       =			[]
   lowercase__    :						int       =			fairseq_model.state_dict()

   lowercase__    :						Tuple       =			hf_model.feature_extractor

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

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

   logger.warning(F'''Unused weights: {unused_weights}'''       )
def    a__    (	A		,					A		,					A		,					A		,					A       ):
   lowercase__    :						Optional[int]       =			full_name.split("""conv_layers."""       )[-1]
   lowercase__    :						int       =			name.split("""."""       )
   lowercase__    :						Optional[Any]       =			int(items[0]       )
   lowercase__    :						Optional[int]       =			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.'''
         )
         lowercase__    :						int       =			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.'''
         )
         lowercase__    :						str       =			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."
         )
         lowercase__    :						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.'''
         )
         lowercase__    :						int       =			value
         logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''       )
   else:
      unused_weights.append(A       )
def    a__    (	A       ):
   lowercase__    :						Optional[Any]       =			HubertConfig()
   lowercase__    :						List[str]       =			model.config

   lowercase__    :						int       =			fs_config.activation_dropout
   lowercase__    :						Tuple       =			False
   lowercase__    :						Optional[Any]       =			fs_config.attention_dropout
   lowercase__    :						str       =			False
   lowercase__    :						List[Any]       =			eval(fs_config.extractor_conv_feature_layers       )
   lowercase__    :						Union[str, Any]       =			[x[0] for x in conv_layers]
   lowercase__    :						str       =			[x[1] for x in conv_layers]
   lowercase__    :						str       =			[x[2] for x in conv_layers]
   lowercase__    :						Any       =			"""gelu"""
   lowercase__    :						Optional[int]       =			"""layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
   lowercase__    :						Optional[int]       =			False
   lowercase__    :						Tuple       =			0.0
   lowercase__    :						Optional[Any]       =			0.0
   lowercase__    :						Optional[Any]       =			fs_config.activation_fn
   lowercase__    :						Optional[Any]       =			fs_config.dropout
   lowercase__    :						Any       =			fs_config.encoder_embed_dim
   lowercase__    :						List[Any]       =			0.0_2
   lowercase__    :						List[Any]       =			fs_config.encoder_ffn_embed_dim
   lowercase__    :						List[Any]       =			1e-5
   lowercase__    :						Dict       =			0.0
   lowercase__    :						List[Any]       =			fs_config.encoder_attention_heads
   lowercase__    :						List[str]       =			fs_config.conv_pos_groups
   lowercase__    :						List[Any]       =			fs_config.conv_pos
   lowercase__    :						Union[str, Any]       =			len(A       )
   lowercase__    :						List[str]       =			fs_config.encoder_layers

   return config
@torch.no_grad()
def    a__    (	A		,					A=None       ):
   lowercase__    :						List[str]       =			distilhubert().model.model

   if config_path is not None:
      lowercase__    :						List[str]       =			HubertConfig.from_pretrained(A       )
   else:
      lowercase__    :						List[str]       =			convert_config(A       )
   lowercase__    :						List[Any]       =			model.eval()

   lowercase__    :						Optional[Any]       =			WavaVecaFeatureExtractor(
       feature_size=1		,					sampling_rate=16_000		,					padding_value=0		,					do_normalize=A		,					return_attention_mask=A		,					)
   lowercase__    :						Union[str, Any]       =			HubertModel(A       )

   recursively_load_weights(A		,					A       )

   feature_extractor.save_pretrained(A       )
   hf_model.save_pretrained(A       )


if __name__ == "__main__":
 a 			=							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')
 a 			=							parser.parse_args()
 convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)


