





"""simple docstring"""




import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNetaDConditionModel, UNetaDModel
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class 					_UpperCAmelCase    (  UpperCAmelCase__     ,  unittest.TestCase						):



      '''simple docstring'''
      lowercase_ :					Tuple             =  UnCLIPPipeline
      lowercase_ :					Optional[Any]             =  TEXT_TO_IMAGE_PARAMS - {
          """negative_prompt""",
          """height""",
          """width""",
          """negative_prompt_embeds""",
          """guidance_scale""",
          """prompt_embeds""",
          """cross_attention_kwargs""",
      }
      lowercase_ :					Optional[int]             =  TEXT_TO_IMAGE_BATCH_PARAMS
      lowercase_ :					Any             =  [
          """generator""",
          """return_dict""",
          """prior_num_inference_steps""",
          """decoder_num_inference_steps""",
          """super_res_num_inference_steps""",
      ]
      lowercase_ :					Union[str, Any]             =  False

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return 3_2

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return 3_2

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return self.time_input_dim

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return self.time_input_dim * 4

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return 1_0_0

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Optional[int]									=    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )
        return tokenizer

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        torch.manual_seed(0    )
        A_    :			int									=    CLIPTextConfig(
            bos_token_id=0   , eos_token_id=2   , hidden_size=self.text_embedder_hidden_size   , projection_dim=self.text_embedder_hidden_size   , intermediate_size=3_7   , layer_norm_eps=1E-05   , num_attention_heads=4   , num_hidden_layers=5   , pad_token_id=1   , vocab_size=1_0_0_0   , )
        return CLIPTextModelWithProjection(snake_case_    )

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        torch.manual_seed(0    )

        A_    :			int									=    {
            'num_attention_heads': 2,
            'attention_head_dim': 1_2,
            'embedding_dim': self.text_embedder_hidden_size,
            'num_layers': 1,
        }

        A_    :			int									=    PriorTransformer(**snake_case_    )
        return model

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        torch.manual_seed(0    )

        A_    :			Optional[Any]									=    {
            'clip_embeddings_dim': self.text_embedder_hidden_size,
            'time_embed_dim': self.time_embed_dim,
            'cross_attention_dim': self.cross_attention_dim,
        }

        A_    :			Tuple									=    UnCLIPTextProjModel(**snake_case_    )
        return model

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        torch.manual_seed(0    )

        A_    :			str									=    {
            'sample_size': 3_2,
            # RGB in channels
            'in_channels': 3,
            # Out channels is double in channels because predicts mean and variance
            'out_channels': 6,
            'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
            'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
            'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
            'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
            'layers_per_block': 1,
            'cross_attention_dim': self.cross_attention_dim,
            'attention_head_dim': 4,
            'resnet_time_scale_shift': 'scale_shift',
            'class_embed_type': 'identity',
        }

        A_    :			Tuple									=    UNetaDConditionModel(**snake_case_    )
        return model

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return {
            "sample_size": 6_4,
            "layers_per_block": 1,
            "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
            "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
            "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
            "in_channels": 6,
            "out_channels": 3,
        }

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        torch.manual_seed(0    )

        A_    :			Union[str, Any]									=    UNetaDModel(**self.dummy_super_res_kwargs    )
        return model

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        torch.manual_seed(1    )

        A_    :			Optional[int]									=    UNetaDModel(**self.dummy_super_res_kwargs    )
        return model

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			int									=    self.dummy_prior
        A_    :			Tuple									=    self.dummy_decoder
        A_    :			int									=    self.dummy_text_proj
        A_    :			Tuple									=    self.dummy_text_encoder
        A_    :			Optional[Any]									=    self.dummy_tokenizer
        A_    :			Any									=    self.dummy_super_res_first
        A_    :			Optional[Any]									=    self.dummy_super_res_last

        A_    :			Optional[int]									=    UnCLIPScheduler(
            variance_type='fixed_small_log'   , prediction_type='sample'   , num_train_timesteps=1_0_0_0   , clip_sample_range=5.0   , )

        A_    :			Tuple									=    UnCLIPScheduler(
            variance_type='learned_range'   , prediction_type='epsilon'   , num_train_timesteps=1_0_0_0   , )

        A_    :			Any									=    UnCLIPScheduler(
            variance_type='fixed_small_log'   , prediction_type='epsilon'   , num_train_timesteps=1_0_0_0   , )

        A_    :			int									=    {
            'prior': prior,
            'decoder': decoder,
            'text_proj': text_proj,
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'super_res_first': super_res_first,
            'super_res_last': super_res_last,
            'prior_scheduler': prior_scheduler,
            'decoder_scheduler': decoder_scheduler,
            'super_res_scheduler': super_res_scheduler,
        }

        return components

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_=0    ):

        """simple docstring"""

        if str(snake_case_    ).startswith('mps'    ):
          A_    :			Tuple									=    torch.manual_seed(snake_case_    )
        else:
          A_    :			Dict									=    torch.Generator(device=snake_case_    ).manual_seed(snake_case_    )
        A_    :			Optional[int]									=    {
            'prompt': 'horse',
            'generator': generator,
            'prior_num_inference_steps': 2,
            'decoder_num_inference_steps': 2,
            'super_res_num_inference_steps': 2,
            'output_type': 'numpy',
        }
        return inputs

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			List[Any]									=    'cpu'

        A_    :			Optional[Any]									=    self.get_dummy_components()

        A_    :			Any									=    self.pipeline_class(**snake_case_    )
        A_    :			Dict									=    pipe.to(snake_case_    )

        pipe.set_progress_bar_config(disable=snake_case_    )

        A_    :			List[str]									=    pipe(**self.get_dummy_inputs(snake_case_    )    )
        A_    :			int									=    output.images

        A_    :			Union[str, Any]									=    pipe(
            **self.get_dummy_inputs(snake_case_    )   , return_dict=snake_case_   , )[0]

        A_    :			Tuple									=    image[0, -3:, -3:, -1]
        A_    :			Any									=    image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 6_4, 6_4, 3)

        A_    :			Union[str, Any]									=    np.array(
            [
                0.99_97,
                0.99_88,
                0.00_28,
                0.99_97,
                0.99_84,
                0.99_65,
                0.00_29,
                0.99_86,
                0.00_25,
            ]    )

        assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1E-2

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			List[Any]									=    torch.device('cpu'    )

        class 					_UpperCAmelCase    :



              '''simple docstring'''
              lowercase_ :					Any             =  1


        A_    :			List[Any]									=    self.get_dummy_components()

        A_    :			List[str]									=    self.pipeline_class(**snake_case_    )
        A_    :			Any									=    pipe.to(snake_case_    )

        A_    :			int									=    components['prior']
        A_    :			Optional[int]									=    components['decoder']
        A_    :			Any									=    components['super_res_first']
        A_    :			List[Any]									=    components['tokenizer']
        A_    :			List[str]									=    components['text_encoder']

        A_    :			Any									=    torch.Generator(device=snake_case_    ).manual_seed(0    )
        A_    :			Optional[Any]									=    prior.dtype
        A_    :			Dict									=    1

        A_    :			Any									=    (batch_size, prior.config.embedding_dim)
        A_    :			List[Any]									=    pipe.prepare_latents(
            snake_case_   , dtype=snake_case_   , device=snake_case_   , generator=snake_case_   , latents=snake_case_   , scheduler=DummyScheduler()    )
        A_    :			Tuple									=    (batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
        A_    :			Any									=    pipe.prepare_latents(
            snake_case_   , dtype=snake_case_   , device=snake_case_   , generator=snake_case_   , latents=snake_case_   , scheduler=DummyScheduler()    )

        A_    :			Any									=    (
            batch_size,
            super_res_first.config.in_channels // 2,
            super_res_first.config.sample_size,
            super_res_first.config.sample_size,
        )
        A_    :			Union[str, Any]									=    pipe.prepare_latents(
            snake_case_   , dtype=snake_case_   , device=snake_case_   , generator=snake_case_   , latents=snake_case_   , scheduler=DummyScheduler()    )

        pipe.set_progress_bar_config(disable=snake_case_    )

        A_    :			Union[str, Any]									=    'this is a prompt example'

        A_    :			int									=    torch.Generator(device=snake_case_    ).manual_seed(0    )
        A_    :			str									=    pipe(
            [prompt]   , generator=snake_case_   , prior_num_inference_steps=2   , decoder_num_inference_steps=2   , super_res_num_inference_steps=2   , prior_latents=snake_case_   , decoder_latents=snake_case_   , super_res_latents=snake_case_   , output_type='np'   , )
        A_    :			Any									=    output.images

        A_    :			Any									=    tokenizer(
            snake_case_   , padding='max_length'   , max_length=tokenizer.model_max_length   , return_tensors='pt'   , )
        A_    :			List[str]									=    text_encoder(text_inputs.input_ids    )
        A_    :			List[str]									=    text_inputs.attention_mask

        A_    :			int									=    torch.Generator(device=snake_case_    ).manual_seed(0    )
        A_    :			Optional[int]									=    pipe(
            generator=snake_case_   , prior_num_inference_steps=2   , decoder_num_inference_steps=2   , super_res_num_inference_steps=2   , prior_latents=snake_case_   , decoder_latents=snake_case_   , super_res_latents=snake_case_   , text_model_output=snake_case_   , text_attention_mask=snake_case_   , output_type='np'   , )[0]

        # make sure passing text embeddings manually is identical
        assert np.abs(image - image_from_text    ).max() < 1E-4

      @skip_mps
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			int									=    torch_device == 'cpu'

        self._test_attention_slicing_forward_pass(test_max_difference=snake_case_   , expected_max_diff=0.01    )

      @skip_mps
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			int									=    torch_device == 'cpu'
        A_    :			Optional[Any]									=    True
        A_    :			List[str]									=    [
            'prior_num_inference_steps',
            'decoder_num_inference_steps',
            'super_res_num_inference_steps',
        ]

        self._test_inference_batch_single_identical(
            test_max_difference=snake_case_   , relax_max_difference=snake_case_   , additional_params_copy_to_batched_inputs=snake_case_   , )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Optional[Any]									=    [
            'prior_num_inference_steps',
            'decoder_num_inference_steps',
            'super_res_num_inference_steps',
        ]

        if torch_device == "mps":
          # TODO: MPS errors with larger batch sizes
          A_    :			List[str]									=    [2, 3]
          self._test_inference_batch_consistent(
              batch_sizes=snake_case_   , additional_params_copy_to_batched_inputs=snake_case_   , )
        else:
          self._test_inference_batch_consistent(
              additional_params_copy_to_batched_inputs=snake_case_    )

      @skip_mps
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return super().test_dict_tuple_outputs_equivalent()

      @skip_mps
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return super().test_save_load_local()

      @skip_mps
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return super().test_save_load_optional_components()


@nightly
class 					_UpperCAmelCase    (  unittest.TestCase						):



      '''simple docstring'''

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Optional[int]									=    load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/unclip/karlo_v1_alpha_horse_cpu.npy'    )

        A_    :			str									=    UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'    )
        pipeline.set_progress_bar_config(disable=snake_case_    )

        A_    :			Any									=    torch.manual_seed(0    )
        A_    :			str									=    pipeline(
            'horse'   , num_images_per_prompt=1   , generator=snake_case_   , output_type='np'   , )

        A_    :			int									=    output.images[0]

        assert image.shape == (2_5_6, 2_5_6, 3)
        assert np.abs(expected_image - image    ).max() < 1E-1


@slow
@require_torch_gpu
class 					_UpperCAmelCase    (  unittest.TestCase						):



      '''simple docstring'''

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Optional[int]									=    load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/unclip/karlo_v1_alpha_horse_fp16.npy'    )

        A_    :			int									=    UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'   , torch_dtype=torch.floataa    )
        A_    :			str									=    pipeline.to(snake_case_    )
        pipeline.set_progress_bar_config(disable=snake_case_    )

        A_    :			List[str]									=    torch.Generator(device='cpu'    ).manual_seed(0    )
        A_    :			Any									=    pipeline(
            'horse'   , generator=snake_case_   , output_type='np'   , )

        A_    :			List[str]									=    output.images[0]

        assert image.shape == (2_5_6, 2_5_6, 3)

        assert_mean_pixel_difference(snake_case_   , snake_case_    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        A_    :			List[Any]									=    UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'   , torch_dtype=torch.floataa    )
        A_    :			Dict									=    pipe.to(snake_case_    )
        pipe.set_progress_bar_config(disable=snake_case_    )
        pipe.enable_attention_slicing()
        pipe.enable_sequential_cpu_offload()

        A_    :			Union[str, Any]									=    pipe(
            'horse'   , num_images_per_prompt=1   , prior_num_inference_steps=2   , decoder_num_inference_steps=2   , super_res_num_inference_steps=2   , output_type='np'   , )

        A_    :			str									=    torch.cuda.max_memory_allocated()
        # make sure that less than 7 GB is allocated
        assert mem_bytes < 7 * 1_0**9


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					(			SCREAMING_SNAKE_CASE						:     List[str]       ,   SCREAMING_SNAKE_CASE						:     List[Any]       ,   SCREAMING_SNAKE_CASE						:     List[str]       ,   SCREAMING_SNAKE_CASE						:     Optional[int]       ,   SCREAMING_SNAKE_CASE						:     List[Any]						)       ->       str:

 """simple docstring"""






 for attribute in key.split('.'						):
  __lowerCAmelCase:  Optional[int]        = getattr(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)

 if weight_type is not None:
  __lowerCAmelCase:  Dict        = getattr(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						).shape
 else:
  __lowerCAmelCase:  str        = hf_pointer.shape

 assert hf_shape == value.shape, (
     f'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
     f''' {value.shape} for {full_name}'''
 )

 if weight_type == "weight":
  __lowerCAmelCase:  str        = value
 elif weight_type == "weight_g":
  __lowerCAmelCase:  List[str]        = value
 elif weight_type == "weight_v":
  __lowerCAmelCase:  List[Any]        = value
 elif weight_type == "bias":
  __lowerCAmelCase:  Optional[int]        = value
 else:
  __lowerCAmelCase:  int        = value

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







def _a					(			SCREAMING_SNAKE_CASE						:     List[Any]       ,   SCREAMING_SNAKE_CASE						:     Any						)       ->       str:

 """simple docstring"""






 __lowerCAmelCase:  Union[str, Any]        = []
 __lowerCAmelCase:  Optional[int]        = fairseq_model.state_dict()

 __lowerCAmelCase:  int        = hf_model.feature_extractor

 for name, value in fairseq_dict.items():
  __lowerCAmelCase:  List[str]        = False
  if "conv_layers" in name:
   load_conv_layer(
       SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   hf_model.config.feat_extract_norm == 'group'       ,   )
   __lowerCAmelCase:  List[str]        = True
  else:
   for key, mapped_key in MAPPING.items():
    __lowerCAmelCase:  List[Any]        = mapped_key

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

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







def _a					(			SCREAMING_SNAKE_CASE						:     Union[str, Any]       ,   SCREAMING_SNAKE_CASE						:     Optional[int]       ,   SCREAMING_SNAKE_CASE						:     List[Any]       ,   SCREAMING_SNAKE_CASE						:     Tuple       ,   SCREAMING_SNAKE_CASE						:     List[str]						)       ->       Optional[int]:

 """simple docstring"""






 __lowerCAmelCase:  Dict        = full_name.split('conv_layers.'						)[-1]
 __lowerCAmelCase:  Optional[Any]        = name.split('.'						)
 __lowerCAmelCase:  List[str]        = int(items[0]						)
 __lowerCAmelCase:  str        = int(items[1]						)

 if type_id == 0:
  if "bias" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
       f'''{full_name} has size {value.shape}, but'''
       f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
   )
   __lowerCAmelCase:  Optional[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.'''
   )
   __lowerCAmelCase:  Tuple        = value
   logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''						)
 elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
  if "bias" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
       f'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
       " found."
   )
   __lowerCAmelCase:  List[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.'''
   )
   __lowerCAmelCase:  Any        = value
   logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''						)
 else:
  unused_weights.append(SCREAMING_SNAKE_CASE						)







def _a					(			SCREAMING_SNAKE_CASE						:     str						)       ->       str:

 """simple docstring"""






 __lowerCAmelCase:  str        = HubertConfig()
 __lowerCAmelCase:  Any        = model.config

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

 return config







@torch.no_grad()
def _a					(			SCREAMING_SNAKE_CASE						:     Optional[int]       ,   SCREAMING_SNAKE_CASE						:     List[Any]=None						)       ->       Optional[int]:

 """simple docstring"""






 __lowerCAmelCase:  List[str]        = distilhubert().model.model

 if config_path is not None:
  __lowerCAmelCase:  Dict        = HubertConfig.from_pretrained(SCREAMING_SNAKE_CASE						)
 else:
  __lowerCAmelCase:  Union[str, Any]        = convert_config(SCREAMING_SNAKE_CASE						)
 __lowerCAmelCase:  Any        = model.eval()

 __lowerCAmelCase:  Tuple        = WavaVecaFeatureExtractor(
     feature_size=1       ,   sampling_rate=1_60_00       ,   padding_value=0       ,   do_normalize=SCREAMING_SNAKE_CASE       ,   return_attention_mask=SCREAMING_SNAKE_CASE       ,   )
 __lowerCAmelCase:  Tuple        = HubertModel(SCREAMING_SNAKE_CASE						)

 recursively_load_weights(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)

 feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE						)
 hf_model.save_pretrained(SCREAMING_SNAKE_CASE						)


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)





