





'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class __a			( _snake_case, _snake_case, _snake_case, unittest.TestCase      ):
   __UpperCamelCase :			str						   =						StableDiffusionInstructPixaPixPipeline
   __UpperCamelCase :			Optional[int]						   =						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
   __UpperCamelCase :			Any						   =						TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   __UpperCamelCase :			List[str]						   =						IMAGE_TO_IMAGE_IMAGE_PARAMS
   __UpperCamelCase :			Dict						   =						IMAGE_TO_IMAGE_IMAGE_PARAMS





   def   UpperCAmelCase__  (       self					:      Optional[Any]     ):

      '''simple docstring'''

      torch.manual_seed(0     )
      __SCREAMING_SNAKE_CASE   				=							UNetaDConditionModel(
          block_out_channels=(32, 64)					,layers_per_block=2					,sample_size=32					,in_channels=8					,out_channels=4					,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")					,up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")					,cross_attention_dim=32					,)
      __SCREAMING_SNAKE_CASE   				=							PNDMScheduler(skip_prk_steps=lowerCamelCase     )
      torch.manual_seed(0     )
      __SCREAMING_SNAKE_CASE   				=							AutoencoderKL(
          block_out_channels=[32, 64]					,in_channels=3					,out_channels=3					,down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]					,up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]					,latent_channels=4					,)
      torch.manual_seed(0     )
      __SCREAMING_SNAKE_CASE   				=							CLIPTextConfig(
          bos_token_id=0					,eos_token_id=2					,hidden_size=32					,intermediate_size=37					,layer_norm_eps=1E-0_5					,num_attention_heads=4					,num_hidden_layers=5					,pad_token_id=1					,vocab_size=1000					,)
      __SCREAMING_SNAKE_CASE   				=							CLIPTextModel(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""     )

      __SCREAMING_SNAKE_CASE   				=							{
          """unet""": unet,
          """scheduler""": scheduler,
          """vae""": vae,
          """text_encoder""": text_encoder,
          """tokenizer""": tokenizer,
          """safety_checker""": None,
          """feature_extractor""": None,
      }
      return components





   def   UpperCAmelCase__  (       self					:      int					,lowerCamelCase					:      Optional[int]					,lowerCamelCase					:      Optional[Any]=0     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							floats_tensor((1, 3, 32, 32)					,rng=random.Random(lowerCamelCase     )     ).to(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							image.cpu().permute(0					,2					,3					,1     )[0]
      __SCREAMING_SNAKE_CASE   				=							Image.fromarray(np.uinta(lowerCamelCase     )     ).convert("""RGB"""     )
      if str(lowerCamelCase     ).startswith("""mps"""     ):
         __SCREAMING_SNAKE_CASE   				=							torch.manual_seed(lowerCamelCase     )
      else:
         __SCREAMING_SNAKE_CASE   				=							torch.Generator(device=lowerCamelCase     ).manual_seed(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							{
          """prompt""": """A painting of a squirrel eating a burger""",
          """image""": image,
          """generator""": generator,
          """num_inference_steps""": 2,
          """guidance_scale""": 6.0,
          """image_guidance_scale""": 1,
          """output_type""": """numpy""",
      }
      return inputs





   def   UpperCAmelCase__  (       self					:      Optional[Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							"""cpu"""  # ensure determinism for the device-dependent torch.Generator
      __SCREAMING_SNAKE_CASE   				=							self.get_dummy_components()
      __SCREAMING_SNAKE_CASE   				=							StableDiffusionInstructPixaPixPipeline(**lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							sd_pipe.to(lowerCamelCase     )
      sd_pipe.set_progress_bar_config(disable=lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							self.get_dummy_inputs(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							sd_pipe(**lowerCamelCase     ).images
      __SCREAMING_SNAKE_CASE   				=							image[0, -3:, -3:, -1]
      assert image.shape == (1, 32, 32, 3)
      __SCREAMING_SNAKE_CASE   				=							np.array([0.7_526, 0.3_750, 0.4_547, 0.6_117, 0.5_866, 0.5_016, 0.4_327, 0.5_642, 0.4_815]     )

      assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-3





   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							"""cpu"""  # ensure determinism for the device-dependent torch.Generator
      __SCREAMING_SNAKE_CASE   				=							self.get_dummy_components()
      __SCREAMING_SNAKE_CASE   				=							StableDiffusionInstructPixaPixPipeline(**lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							sd_pipe.to(lowerCamelCase     )
      sd_pipe.set_progress_bar_config(disable=lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							self.get_dummy_inputs(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							"""french fries"""
      __SCREAMING_SNAKE_CASE   				=							sd_pipe(**lowerCamelCase					,negative_prompt=lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							output.images
      __SCREAMING_SNAKE_CASE   				=							image[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      __SCREAMING_SNAKE_CASE   				=							np.array([0.7_511, 0.3_642, 0.4_553, 0.6_236, 0.5_797, 0.5_013, 0.4_343, 0.5_611, 0.4_831]     )

      assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-3





   def   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							"""cpu"""  # ensure determinism for the device-dependent torch.Generator
      __SCREAMING_SNAKE_CASE   				=							self.get_dummy_components()
      __SCREAMING_SNAKE_CASE   				=							StableDiffusionInstructPixaPixPipeline(**lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							sd_pipe.to(lowerCamelCase     )
      sd_pipe.set_progress_bar_config(disable=lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							self.get_dummy_inputs(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							[inputs["""prompt"""]] * 2

      __SCREAMING_SNAKE_CASE   				=							np.array(inputs["""image"""]     ).astype(np.floataa     ) / 255.0
      __SCREAMING_SNAKE_CASE   				=							torch.from_numpy(lowerCamelCase     ).unsqueeze(0     ).to(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							image / 2 + 0.5
      __SCREAMING_SNAKE_CASE   				=							image.permute(0					,3					,1					,2     )
      __SCREAMING_SNAKE_CASE   				=							image.repeat(2					,1					,1					,1     )

      __SCREAMING_SNAKE_CASE   				=							sd_pipe(**lowerCamelCase     ).images
      __SCREAMING_SNAKE_CASE   				=							image[-1, -3:, -3:, -1]

      assert image.shape == (2, 32, 32, 3)
      __SCREAMING_SNAKE_CASE   				=							np.array([0.5_812, 0.5_748, 0.5_222, 0.5_908, 0.5_695, 0.7_174, 0.6_804, 0.5_523, 0.5_579]     )

      assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-3





   def   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							"""cpu"""  # ensure determinism for the device-dependent torch.Generator
      __SCREAMING_SNAKE_CASE   				=							self.get_dummy_components()
      __SCREAMING_SNAKE_CASE   				=							EulerAncestralDiscreteScheduler(
          beta_start=0.00_085					,beta_end=0.012					,beta_schedule="""scaled_linear"""     )
      __SCREAMING_SNAKE_CASE   				=							StableDiffusionInstructPixaPixPipeline(**lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							sd_pipe.to(lowerCamelCase     )
      sd_pipe.set_progress_bar_config(disable=lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							self.get_dummy_inputs(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							sd_pipe(**lowerCamelCase     ).images
      __SCREAMING_SNAKE_CASE   				=							image[0, -3:, -3:, -1]

      __SCREAMING_SNAKE_CASE   				=							[round(lowerCamelCase					,4     ) for x in image_slice.flatten().tolist()]
      print(""",""".join([str(lowerCamelCase     ) for x in slice]     )     )

      assert image.shape == (1, 32, 32, 3)
      __SCREAMING_SNAKE_CASE   				=							np.array([0.7_417, 0.3_842, 0.4_732, 0.5_776, 0.5_891, 0.5_139, 0.4_052, 0.5_673, 0.4_986]     )

      assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-3





   def   UpperCAmelCase__  (       self					:      Optional[Any]     ):

      '''simple docstring'''

      super().test_inference_batch_single_identical(expected_max_diff=3E-3     )





   def   UpperCAmelCase__  (       self					:      Optional[Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.get_dummy_components()
      __SCREAMING_SNAKE_CASE   				=							StableDiffusionInstructPixaPixPipeline(**lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							VaeImageProcessor(do_resize=lowerCamelCase					,do_normalize=lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							pipe.to(lowerCamelCase     )
      pipe.set_progress_bar_config(disable=lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							pipe(**self.get_dummy_inputs_by_type(lowerCamelCase					,input_image_type="""pt"""     )     )[0]

      __SCREAMING_SNAKE_CASE   				=							components["""vae"""]
      __SCREAMING_SNAKE_CASE   				=							self.get_dummy_inputs_by_type(lowerCamelCase					,input_image_type="""pt"""     )

      for image_param in self.image_latents_params:
         if image_param in inputs.keys():
            __SCREAMING_SNAKE_CASE   				=							vae.encode(inputs[image_param]     ).latent_dist.mode()

      __SCREAMING_SNAKE_CASE   				=							pipe(**lowerCamelCase     )[0]

      __SCREAMING_SNAKE_CASE   				=							np.abs(out - out_latents_inputs     ).max()
      self.assertLess(lowerCamelCase					,1E-4					,"""passing latents as image input generate different result from passing image"""     )







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





   def   UpperCAmelCase__  (       self					:      Dict     ):

      '''simple docstring'''

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





   def   UpperCAmelCase__  (       self					:      int					,lowerCamelCase					:      int=0     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							torch.manual_seed(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							load_image(
          """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"""     )
      __SCREAMING_SNAKE_CASE   				=							{
          """prompt""": """turn him into a cyborg""",
          """image""": image,
          """generator""": generator,
          """num_inference_steps""": 3,
          """guidance_scale""": 7.5,
          """image_guidance_scale""": 1.0,
          """output_type""": """numpy""",
      }
      return inputs





   def   UpperCAmelCase__  (       self					:      Tuple     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							StableDiffusionInstructPixaPixPipeline.from_pretrained(
          """timbrooks/instruct-pix2pix"""					,safety_checker=lowerCamelCase     )
      pipe.to(lowerCamelCase     )
      pipe.set_progress_bar_config(disable=lowerCamelCase     )
      pipe.enable_attention_slicing()

      __SCREAMING_SNAKE_CASE   				=							self.get_inputs()
      __SCREAMING_SNAKE_CASE   				=							pipe(**lowerCamelCase     ).images
      __SCREAMING_SNAKE_CASE   				=							image[0, -3:, -3:, -1].flatten()

      assert image.shape == (1, 512, 512, 3)
      __SCREAMING_SNAKE_CASE   				=							np.array([0.5_902, 0.6_015, 0.6_027, 0.5_983, 0.6_092, 0.6_061, 0.5_765, 0.5_785, 0.5_555]     )

      assert np.abs(expected_slice - image_slice     ).max() < 1E-3





   def   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							StableDiffusionInstructPixaPixPipeline.from_pretrained(
          """timbrooks/instruct-pix2pix"""					,safety_checker=lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							LMSDiscreteScheduler.from_config(pipe.scheduler.config     )
      pipe.to(lowerCamelCase     )
      pipe.set_progress_bar_config(disable=lowerCamelCase     )
      pipe.enable_attention_slicing()

      __SCREAMING_SNAKE_CASE   				=							self.get_inputs()
      __SCREAMING_SNAKE_CASE   				=							pipe(**lowerCamelCase     ).images
      __SCREAMING_SNAKE_CASE   				=							image[0, -3:, -3:, -1].flatten()

      assert image.shape == (1, 512, 512, 3)
      __SCREAMING_SNAKE_CASE   				=							np.array([0.6_578, 0.6_817, 0.6_972, 0.6_761, 0.6_856, 0.6_916, 0.6_428, 0.6_516, 0.6_301]     )

      assert np.abs(expected_slice - image_slice     ).max() < 1E-3





   def   UpperCAmelCase__  (       self					:      Union[str, Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							StableDiffusionInstructPixaPixPipeline.from_pretrained(
          """timbrooks/instruct-pix2pix"""					,safety_checker=lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							DDIMScheduler.from_config(pipe.scheduler.config     )
      pipe.to(lowerCamelCase     )
      pipe.set_progress_bar_config(disable=lowerCamelCase     )
      pipe.enable_attention_slicing()

      __SCREAMING_SNAKE_CASE   				=							self.get_inputs()
      __SCREAMING_SNAKE_CASE   				=							pipe(**lowerCamelCase     ).images
      __SCREAMING_SNAKE_CASE   				=							image[0, -3:, -3:, -1].flatten()

      assert image.shape == (1, 512, 512, 3)
      __SCREAMING_SNAKE_CASE   				=							np.array([0.3_828, 0.3_834, 0.3_818, 0.3_792, 0.3_865, 0.3_752, 0.3_792, 0.3_847, 0.3_753]     )

      assert np.abs(expected_slice - image_slice     ).max() < 1E-3





   def   UpperCAmelCase__  (       self					:      Optional[Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							0

      def callback_fn(lowerCamelCase					:      int					,lowerCamelCase					:      int					,lowerCamelCase					:      torch.FloatTensor     ) -> None:
         __SCREAMING_SNAKE_CASE   				=							True
         nonlocal number_of_steps
         number_of_steps += 1
         if step == 1:
            __SCREAMING_SNAKE_CASE   				=							latents.detach().cpu().numpy()
            assert latents.shape == (1, 4, 64, 64)
            __SCREAMING_SNAKE_CASE   				=							latents[0, -3:, -3:, -1]
            __SCREAMING_SNAKE_CASE   				=							np.array([-0.2_463, -0.4_644, -0.9_756, 1.5_176, 1.4_414, 0.7_866, 0.9_897, 0.8_521, 0.7_983]     )

            assert np.abs(latents_slice.flatten() - expected_slice     ).max() < 5E-2
         elif step == 2:
            __SCREAMING_SNAKE_CASE   				=							latents.detach().cpu().numpy()
            assert latents.shape == (1, 4, 64, 64)
            __SCREAMING_SNAKE_CASE   				=							latents[0, -3:, -3:, -1]
            __SCREAMING_SNAKE_CASE   				=							np.array([-0.2_644, -0.4_626, -0.9_653, 1.5_176, 1.4_551, 0.7_686, 0.9_805, 0.8_452, 0.8_115]     )

            assert np.abs(latents_slice.flatten() - expected_slice     ).max() < 5E-2

      __SCREAMING_SNAKE_CASE   				=							False

      __SCREAMING_SNAKE_CASE   				=							StableDiffusionInstructPixaPixPipeline.from_pretrained(
          """timbrooks/instruct-pix2pix"""					,safety_checker=lowerCamelCase					,torch_dtype=torch.floataa     )
      __SCREAMING_SNAKE_CASE   				=							pipe.to(lowerCamelCase     )
      pipe.set_progress_bar_config(disable=lowerCamelCase     )
      pipe.enable_attention_slicing()

      __SCREAMING_SNAKE_CASE   				=							self.get_inputs()
      pipe(**lowerCamelCase					,callback=lowerCamelCase					,callback_steps=1     )
      assert callback_fn.has_been_called
      assert number_of_steps == 3





   def   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

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

      __SCREAMING_SNAKE_CASE   				=							StableDiffusionInstructPixaPixPipeline.from_pretrained(
          """timbrooks/instruct-pix2pix"""					,safety_checker=lowerCamelCase					,torch_dtype=torch.floataa     )
      __SCREAMING_SNAKE_CASE   				=							pipe.to(lowerCamelCase     )
      pipe.set_progress_bar_config(disable=lowerCamelCase     )
      pipe.enable_attention_slicing(1     )
      pipe.enable_sequential_cpu_offload()

      __SCREAMING_SNAKE_CASE   				=							self.get_inputs()
      __SCREAMING_SNAKE_CASE   				=							pipe(**lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							torch.cuda.max_memory_allocated()
      # make sure that less than 2.2 GB is allocated
      assert mem_bytes < 2.2 * 10**9





   def   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.get_inputs()
      # resize to resolution that is divisible by 8 but not 16 or 32
      __SCREAMING_SNAKE_CASE   				=							inputs["""image"""].resize((504, 504)     )

      __SCREAMING_SNAKE_CASE   				=							"""timbrooks/instruct-pix2pix"""
      __SCREAMING_SNAKE_CASE   				=							StableDiffusionInstructPixaPixPipeline.from_pretrained(
          lowerCamelCase					,safety_checker=lowerCamelCase					,)
      pipe.to(lowerCamelCase     )
      pipe.set_progress_bar_config(disable=lowerCamelCase     )
      pipe.enable_attention_slicing()

      __SCREAMING_SNAKE_CASE   				=							pipe(**lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							output.images[0]

      __SCREAMING_SNAKE_CASE   				=							image[255:258, 383:386, -1]

      assert image.shape == (504, 504, 3)
      __SCREAMING_SNAKE_CASE   				=							np.array([0.2_726, 0.2_529, 0.2_664, 0.2_655, 0.2_641, 0.2_642, 0.2_591, 0.2_649, 0.2_590]     )

      assert np.abs(image_slice.flatten() - expected_slice     ).max() < 5E-3












'''simple docstring'''

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    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",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Dict:

   '''simple docstring'''







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

   if weight_type is not None:
      __SCREAMING_SNAKE_CASE   				=							getattr(__UpperCAmelCase				,						__UpperCAmelCase   ).shape
   else:
      __SCREAMING_SNAKE_CASE   				=							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   				=							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
   else:
      __SCREAMING_SNAKE_CASE   				=							value

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

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Union[str, Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							[]
   __SCREAMING_SNAKE_CASE   				=							fairseq_model.state_dict()

   __SCREAMING_SNAKE_CASE   				=							hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

   for name, value in fairseq_dict.items():
      __SCREAMING_SNAKE_CASE   				=							False
      if "conv_layers" in name:
         load_conv_layer(
             __UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						hf_model.config.feat_extract_norm == """group"""				,						)
         __SCREAMING_SNAKE_CASE   				=							True
      else:
         for key, mapped_key in MAPPING.items():
            __SCREAMING_SNAKE_CASE   				=							"""hubert.""" + mapped_key if (is_finetuned and mapped_key != """lm_head""") else mapped_key

            if key in name or (key.split("""w2v_model."""   )[-1] == name.split("""."""   )[0] and not is_finetuned):
               __SCREAMING_SNAKE_CASE   				=							True
               if "*" in mapped_key:
                  __SCREAMING_SNAKE_CASE   				=							name.split(__UpperCAmelCase   )[0].split("""."""   )[-2]
                  __SCREAMING_SNAKE_CASE   				=							mapped_key.replace("""*"""				,						__UpperCAmelCase   )
               if "weight_g" in name:
                  __SCREAMING_SNAKE_CASE   				=							"""weight_g"""
               elif "weight_v" in name:
                  __SCREAMING_SNAKE_CASE   				=							"""weight_v"""
               elif "weight" in name:
                  __SCREAMING_SNAKE_CASE   				=							"""weight"""
               elif "bias" in name:
                  __SCREAMING_SNAKE_CASE   				=							"""bias"""
               else:
                  __SCREAMING_SNAKE_CASE   				=							None
               set_recursively(__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )
            continue
      if not is_used:
         unused_weights.append(__UpperCAmelCase   )

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

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Union[str, Any]:

   '''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:
         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   				=							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   				=							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   				=							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   				=							value
         logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""   )
   else:
      unused_weights.append(__UpperCAmelCase   )

@torch.no_grad()
def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase=None				,						__UpperCAmelCase=None				,						__UpperCAmelCase=True   )    ->       Tuple:

   '''simple docstring'''







   if config_path is not None:
      __SCREAMING_SNAKE_CASE   				=							HubertConfig.from_pretrained(__UpperCAmelCase   )
   else:
      __SCREAMING_SNAKE_CASE   				=							HubertConfig()

   if is_finetuned:
      if dict_path:
         __SCREAMING_SNAKE_CASE   				=							Dictionary.load(__UpperCAmelCase   )

         # 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(__UpperCAmelCase				,						"""vocab.json"""   )
         if not os.path.isdir(__UpperCAmelCase   ):
            logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(__UpperCAmelCase   )   )
            return
         os.makedirs(__UpperCAmelCase				,						exist_ok=__UpperCAmelCase   )
         with open(__UpperCAmelCase				,						"""w"""				,						encoding="""utf-8"""   ) as vocab_handle:
            json.dump(target_dict.indices				,						__UpperCAmelCase   )
         __SCREAMING_SNAKE_CASE   				=							WavaVecaCTCTokenizer(
             __UpperCAmelCase				,						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=__UpperCAmelCase				,						)
         __SCREAMING_SNAKE_CASE   				=							True if config.feat_extract_norm == """layer""" else False
         __SCREAMING_SNAKE_CASE   				=							WavaVecaFeatureExtractor(
             feature_size=1				,						sampling_rate=16000				,						padding_value=0				,						do_normalize=__UpperCAmelCase				,						return_attention_mask=__UpperCAmelCase				,						)
         __SCREAMING_SNAKE_CASE   				=							WavaVecaProcessor(feature_extractor=__UpperCAmelCase				,						tokenizer=__UpperCAmelCase   )
         processor.save_pretrained(__UpperCAmelCase   )

      __SCREAMING_SNAKE_CASE   				=							HubertForCTC(__UpperCAmelCase   )
   else:
      __SCREAMING_SNAKE_CASE   				=							HubertModel(__UpperCAmelCase   )

   if is_finetuned:
      __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				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]   )

   __SCREAMING_SNAKE_CASE   				=							model[0].eval()

   recursively_load_weights(__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )

   hf_wavavec.save_pretrained(__UpperCAmelCase   )


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("--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"
    )
    a   	=    parser.parse_args()
    convert_hubert_checkpoint(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
    )





