


'''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		:				Dict									=       logging.get_logger(__name__)

_A		:				List[Any]									=       {
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}



def     __lowerCAmelCase						(							snake_case	:    str						,      snake_case	:    str						,      snake_case	:    str						,      snake_case	:    Union[str, Any]						,      snake_case	:    str )       ->							Tuple:
   for attribute in key.split(""".""" ):
      __lowerCamelCase:				Any     =	getattr(snake_case						,      snake_case )

   if weight_type is not None:
      __lowerCamelCase:				Tuple     =	getattr(snake_case						,      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:				int     =	value
   elif weight_type == "weight_g":
      __lowerCamelCase:				List[str]     =	value
   elif weight_type == "weight_v":
      __lowerCamelCase:				int     =	value
   elif weight_type == "bias":
      __lowerCamelCase:				str     =	value
   else:
      __lowerCamelCase:				Optional[Any]     =	value

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



def     __lowerCAmelCase						(							snake_case	:    Any						,      snake_case	:    Any						,      snake_case	:    List[Any] )       ->							Tuple:
   __lowerCamelCase:				Any     =	[]
   __lowerCamelCase:				Dict     =	fairseq_model.state_dict()

   __lowerCamelCase:				List[str]     =	hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

   for name, value in fairseq_dict.items():
      __lowerCamelCase:				List[str]     =	False
      if "conv_layers" in name:
         load_conv_layer(
             snake_case						,      snake_case						,      snake_case						,      snake_case						,      hf_model.config.feat_extract_norm == """group"""						,      )
         __lowerCamelCase:				Dict     =	True
      else:
         for key, mapped_key in MAPPING.items():
            __lowerCamelCase:				Dict     =	"""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):
               __lowerCamelCase:				Any     =	True
               if "*" in mapped_key:
                  __lowerCamelCase:				Dict     =	name.split(snake_case )[0].split(""".""" )[-2]
                  __lowerCamelCase:				int     =	mapped_key.replace("""*"""						,      snake_case )
               if "weight_g" in name:
                  __lowerCamelCase:				int     =	"""weight_g"""
               elif "weight_v" in name:
                  __lowerCamelCase:				List[str]     =	"""weight_v"""
               elif "weight" in name:
                  __lowerCamelCase:				List[Any]     =	"""weight"""
               elif "bias" in name:
                  __lowerCamelCase:				Optional[int]     =	"""bias"""
               else:
                  __lowerCamelCase:				List[str]     =	None
               set_recursively(snake_case						,      snake_case						,      snake_case						,      snake_case						,      snake_case )
            continue
      if not is_used:
         unused_weights.append(snake_case )

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



def     __lowerCAmelCase						(							snake_case	:    Tuple						,      snake_case	:    Union[str, Any]						,      snake_case	:    Optional[Any]						,      snake_case	:    str						,      snake_case	:    List[Any] )       ->							Union[str, Any]:
   __lowerCamelCase:				List[str]     =	full_name.split("""conv_layers.""" )[-1]
   __lowerCamelCase:				List[Any]     =	name.split(""".""" )
   __lowerCamelCase:				int     =	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:				Any     =	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:				Union[str, Any]     =	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[str]     =	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:				Dict     =	value
         logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
   else:
      unused_weights.append(snake_case )



@torch.no_grad()
def     __lowerCAmelCase						(							snake_case	:    int						,      snake_case	:    Optional[Any]						,      snake_case	:    Union[str, Any]=None						,      snake_case	:    str=None						,      snake_case	:    List[Any]=True )       ->							List[str]:
   if config_path is not None:
      __lowerCamelCase:				Tuple     =	HubertConfig.from_pretrained(snake_case )
   else:
      __lowerCamelCase:				List[Any]     =	HubertConfig()

   if is_finetuned:
      if dict_path:
         __lowerCamelCase:				Dict     =	Dictionary.load(snake_case )

         # important change bos & pad token id since CTC symbol is <pad> and
         # not <s> as in fairseq
         __lowerCamelCase:				Union[str, Any]     =	target_dict.pad_index
         __lowerCamelCase:				Optional[Any]     =	target_dict.bos_index
         __lowerCamelCase:				str     =	target_dict.eos_index
         __lowerCamelCase:				Any     =	len(target_dict.symbols )
         __lowerCamelCase:				Union[str, Any]     =	os.path.join(snake_case						,      """vocab.json""" )
         if not os.path.isdir(snake_case ):
            logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(snake_case ) )
            return
         os.makedirs(snake_case						,      exist_ok=snake_case )
         with open(snake_case						,      """w"""						,      encoding="""utf-8""" ) as vocab_handle:
            json.dump(target_dict.indices						,      snake_case )
         __lowerCamelCase:				int     =	WavaVecaCTCTokenizer(
             snake_case						,      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=snake_case						,      )
         __lowerCamelCase:				Optional[int]     =	True if config.feat_extract_norm == """layer""" else False
         __lowerCamelCase:				int     =	WavaVecaFeatureExtractor(
             feature_size=1						,      sampling_rate=16000						,      padding_value=0						,      do_normalize=snake_case						,      return_attention_mask=snake_case						,      )
         __lowerCamelCase:				str     =	WavaVecaProcessor(feature_extractor=snake_case						,      tokenizer=snake_case )
         processor.save_pretrained(snake_case )

      __lowerCamelCase:				Optional[Any]     =	HubertForCTC(snake_case )
   else:
      __lowerCamelCase:				str     =	HubertModel(snake_case )

   if is_finetuned:
      __lowerCamelCase:				List[Any]     =	fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]						,      arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} )
   else:
      __lowerCamelCase:				str     =	fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )

   __lowerCamelCase:				List[Any]     =	model[0].eval()

   recursively_load_weights(snake_case						,      snake_case						,      snake_case )

   hf_wavavec.save_pretrained(snake_case )


if __name__ == "__main__":
   _A		:				Dict									=       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		:				Dict									=       parser.parse_args()
   convert_hubert_checkpoint(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
   )






import gc
import random
import unittest

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

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

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


enable_full_determinism()



class        a					(			_UpperCAmelCase     ,_UpperCAmelCase     ,unittest.TestCase   ):
     UpperCAmelCase__ :  Tuple						      =	CycleDiffusionPipeline
     UpperCAmelCase__ :  Optional[Any]						      =	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
         "negative_prompt",
         "height",
         "width",
         "negative_prompt_embeds",
     }
     UpperCAmelCase__ :  Union[str, Any]						      =	PipelineTesterMixin.required_optional_params - {"latents"}
     UpperCAmelCase__ :  Optional[int]						      =	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"source_prompt"}   )
     UpperCAmelCase__ :  Optional[int]						      =	IMAGE_TO_IMAGE_IMAGE_PARAMS
     UpperCAmelCase__ :  Optional[Any]						      =	IMAGE_TO_IMAGE_IMAGE_PARAMS





     def  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        torch.manual_seed(0							)
        __lowerCamelCase:				Tuple     =	UNetaDConditionModel(
            block_out_channels=(32, 64)    ,	layers_per_block=2    ,	sample_size=32    ,	in_channels=4    ,	out_channels=4    ,	down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")    ,	up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")    ,	cross_attention_dim=32    ,	)
        __lowerCamelCase:				Dict     =	DDIMScheduler(
            beta_start=0.0_0085    ,	beta_end=0.012    ,	beta_schedule="""scaled_linear"""    ,	num_train_timesteps=1000    ,	clip_sample=SCREAMING_SNAKE_CASE_    ,	set_alpha_to_one=SCREAMING_SNAKE_CASE_    ,	)
        torch.manual_seed(0							)
        __lowerCamelCase:				Tuple     =	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							)
        __lowerCamelCase:				str     =	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    ,	)
        __lowerCamelCase:				int     =	CLIPTextModel(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Tuple     =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)

        __lowerCamelCase:				Optional[int]     =	{
            """unet""": unet,
            """scheduler""": scheduler,
            """vae""": vae,
            """text_encoder""": text_encoder,
            """tokenizer""": tokenizer,
            """safety_checker""": None,
            """feature_extractor""": None,
        }
        return components





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				str=0							):
        __lowerCamelCase:				Optional[Any]     =	floats_tensor((1, 3, 32, 32)    ,	rng=random.Random(SCREAMING_SNAKE_CASE_							)							).to(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				List[str]     =	image / 2 + 0.5
        if str(SCREAMING_SNAKE_CASE_							).startswith("""mps"""							):
           __lowerCamelCase:				int     =	torch.manual_seed(SCREAMING_SNAKE_CASE_							)
        else:
           __lowerCamelCase:				Tuple     =	torch.Generator(device=SCREAMING_SNAKE_CASE_							).manual_seed(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Any     =	{
            """prompt""": """An astronaut riding an elephant""",
            """source_prompt""": """An astronaut riding a horse""",
            """image""": image,
            """generator""": generator,
            """num_inference_steps""": 2,
            """eta""": 0.1,
            """strength""": 0.8,
            """guidance_scale""": 3,
            """source_guidance_scale""": 1,
            """output_type""": """numpy""",
        }
        return inputs





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        __lowerCamelCase:				Tuple     =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator

        __lowerCamelCase:				Union[str, Any]     =	self.get_dummy_components()
        __lowerCamelCase:				Union[str, Any]     =	CycleDiffusionPipeline(**SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				List[Any]     =	pipe.to(SCREAMING_SNAKE_CASE_							)
        pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				List[Any]     =	self.get_dummy_inputs(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Dict     =	pipe(**SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Union[str, Any]     =	output.images

        __lowerCamelCase:				int     =	images[0, -3:, -3:, -1]

        assert images.shape == (1, 32, 32, 3)
        __lowerCamelCase:				Dict     =	np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179]							)

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





     @unittest.skipIf(torch_device != """cuda"""    ,	"""This test requires a GPU"""							)
     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]							):
        __lowerCamelCase:				Optional[int]     =	self.get_dummy_components()
        for name, module in components.items():
           if hasattr(SCREAMING_SNAKE_CASE_    ,	"""half"""							):
              __lowerCamelCase:				Tuple     =	module.half()
        __lowerCamelCase:				Union[str, Any]     =	CycleDiffusionPipeline(**SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Optional[Any]     =	pipe.to(SCREAMING_SNAKE_CASE_							)
        pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Tuple     =	self.get_dummy_inputs(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				str     =	pipe(**SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				int     =	output.images

        __lowerCamelCase:				Optional[int]     =	images[0, -3:, -3:, -1]

        assert images.shape == (1, 32, 32, 3)
        __lowerCamelCase:				List[str]     =	np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116]							)

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





     @skip_mps
     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        return super().test_save_load_local()





     @unittest.skip("""non-deterministic pipeline"""							)
     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        return super().test_inference_batch_single_identical()





     @skip_mps
     def  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        return super().test_dict_tuple_outputs_equivalent()





     @skip_mps
     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        return super().test_save_load_optional_components()







     @skip_mps
     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        return super().test_attention_slicing_forward_pass()



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





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]							):
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]							):
        __lowerCamelCase:				Any     =	load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/cycle-diffusion/black_colored_car.png"""							)
        __lowerCamelCase:				Tuple     =	load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy"""							)
        __lowerCamelCase:				str     =	init_image.resize((512, 512)							)

        __lowerCamelCase:				Dict     =	"""CompVis/stable-diffusion-v1-4"""
        __lowerCamelCase:				Optional[int]     =	DDIMScheduler.from_pretrained(SCREAMING_SNAKE_CASE_    ,	subfolder="""scheduler"""							)
        __lowerCamelCase:				List[Any]     =	CycleDiffusionPipeline.from_pretrained(
            SCREAMING_SNAKE_CASE_    ,	scheduler=SCREAMING_SNAKE_CASE_    ,	safety_checker=SCREAMING_SNAKE_CASE_    ,	torch_dtype=torch.floataa    ,	revision="""fp16"""							)

        pipe.to(SCREAMING_SNAKE_CASE_							)
        pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_							)
        pipe.enable_attention_slicing()

        __lowerCamelCase:				List[Any]     =	"""A black colored car"""
        __lowerCamelCase:				List[Any]     =	"""A blue colored car"""

        __lowerCamelCase:				List[Any]     =	torch.manual_seed(0							)
        __lowerCamelCase:				Any     =	pipe(
            prompt=SCREAMING_SNAKE_CASE_    ,	source_prompt=SCREAMING_SNAKE_CASE_    ,	image=SCREAMING_SNAKE_CASE_    ,	num_inference_steps=100    ,	eta=0.1    ,	strength=0.85    ,	guidance_scale=3    ,	source_guidance_scale=1    ,	generator=SCREAMING_SNAKE_CASE_    ,	output_type="""np"""    ,	)
        __lowerCamelCase:				Dict     =	output.images

        # the values aren't exactly equal, but the images look the same visually
        assert np.abs(image - expected_image							).max() < 5E-1







     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]							):
        __lowerCamelCase:				Optional[int]     =	load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/cycle-diffusion/black_colored_car.png"""							)
        __lowerCamelCase:				Tuple     =	load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy"""							)
        __lowerCamelCase:				List[str]     =	init_image.resize((512, 512)							)

        __lowerCamelCase:				List[Any]     =	"""CompVis/stable-diffusion-v1-4"""
        __lowerCamelCase:				Optional[Any]     =	DDIMScheduler.from_pretrained(SCREAMING_SNAKE_CASE_    ,	subfolder="""scheduler"""							)
        __lowerCamelCase:				Dict     =	CycleDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE_    ,	scheduler=SCREAMING_SNAKE_CASE_    ,	safety_checker=SCREAMING_SNAKE_CASE_							)

        pipe.to(SCREAMING_SNAKE_CASE_							)
        pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_							)
        pipe.enable_attention_slicing()

        __lowerCamelCase:				Optional[int]     =	"""A black colored car"""
        __lowerCamelCase:				int     =	"""A blue colored car"""

        __lowerCamelCase:				str     =	torch.manual_seed(0							)
        __lowerCamelCase:				Optional[Any]     =	pipe(
            prompt=SCREAMING_SNAKE_CASE_    ,	source_prompt=SCREAMING_SNAKE_CASE_    ,	image=SCREAMING_SNAKE_CASE_    ,	num_inference_steps=100    ,	eta=0.1    ,	strength=0.85    ,	guidance_scale=3    ,	source_guidance_scale=1    ,	generator=SCREAMING_SNAKE_CASE_    ,	output_type="""np"""    ,	)
        __lowerCamelCase:				Any     =	output.images

        assert np.abs(image - expected_image							).max() < 2E-2


