




'''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 						lowerCAmelCase_	(       UpperCAmelCase_       ,					unittest.TestCase							):







  '''simple docstring'''

  UpperCamelCase_			:      Tuple										=				UnCLIPPipeline
  UpperCamelCase_			:      Any										=				TEXT_TO_IMAGE_PARAMS - {
      """negative_prompt""",
      """height""",
      """width""",
      """negative_prompt_embeds""",
      """guidance_scale""",
      """prompt_embeds""",
      """cross_attention_kwargs""",
  }
  UpperCamelCase_			:      Tuple										=				TEXT_TO_IMAGE_BATCH_PARAMS
  UpperCamelCase_			:      List[str]										=				[
      """generator""",
      """return_dict""",
      """prior_num_inference_steps""",
      """decoder_num_inference_steps""",
      """super_res_num_inference_steps""",
  ]
  UpperCamelCase_			:      Union[str, Any]										=				False



  @property
  def 					_snake_case     ( self				:							Any	)			->		Dict:







       '''simple docstring'''



       return 32



  @property
  def 					_snake_case     ( self				:							Optional[Any]	)			->		Optional[int]:







       '''simple docstring'''



       return 32



  @property
  def 					_snake_case     ( self				:							Union[str, Any]	)			->		Tuple:







       '''simple docstring'''



       return self.time_input_dim



  @property
  def 					_snake_case     ( self				:							Optional[int]	)			->		Any:







       '''simple docstring'''



       return self.time_input_dim * 4



  @property
  def 					_snake_case     ( self				:							Tuple	)			->		Optional[int]:







       '''simple docstring'''



       return 1_00



  @property
  def 					_snake_case     ( self				:							Optional[int]	)			->		Tuple:







       '''simple docstring'''



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



  @property
  def 					_snake_case     ( self				:							Union[str, Any]	)			->		Union[str, Any]:







       '''simple docstring'''



       torch.manual_seed(0	)
       A:		List[Any]          =		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=37					,  layer_norm_eps=1E-05					,  num_attention_heads=4					,  num_hidden_layers=5					,  pad_token_id=1					,  vocab_size=10_00					,  )
       return CLIPTextModelWithProjection(SCREAMING_SNAKE_CASE_	)



  @property
  def 					_snake_case     ( self				:							str	)			->		Optional[Any]:







       '''simple docstring'''



       torch.manual_seed(0	)

       A:		str          =		{
           '''num_attention_heads''': 2,
           '''attention_head_dim''': 12,
           '''embedding_dim''': self.text_embedder_hidden_size,
           '''num_layers''': 1,
       }

       A:		Optional[Any]          =		PriorTransformer(**SCREAMING_SNAKE_CASE_	)
       return model



  @property
  def 					_snake_case     ( self				:							Any	)			->		List[Any]:







       '''simple docstring'''



       torch.manual_seed(0	)

       A:		Dict          =		{
           '''clip_embeddings_dim''': self.text_embedder_hidden_size,
           '''time_embed_dim''': self.time_embed_dim,
           '''cross_attention_dim''': self.cross_attention_dim,
       }

       A:		Any          =		UnCLIPTextProjModel(**SCREAMING_SNAKE_CASE_	)
       return model



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







       '''simple docstring'''



       torch.manual_seed(0	)

       A:		Union[str, Any]          =		{
           '''sample_size''': 32,
           # 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:		List[str]          =		UNetaDConditionModel(**SCREAMING_SNAKE_CASE_	)
       return model



  @property
  def 					_snake_case     ( self				:							Union[str, Any]	)			->		int:







       '''simple docstring'''



       return {
           "sample_size": 64,
           "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 					_snake_case     ( self				:							Any	)			->		Dict:







       '''simple docstring'''



       torch.manual_seed(0	)

       A:		int          =		UNetaDModel(**self.dummy_super_res_kwargs	)
       return model



  @property
  def 					_snake_case     ( self				:							Optional[Any]	)			->		Any:







       '''simple docstring'''



       torch.manual_seed(1	)

       A:		List[str]          =		UNetaDModel(**self.dummy_super_res_kwargs	)
       return model



  def 					_snake_case     ( self				:							Dict	)			->		Tuple:







       '''simple docstring'''



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

       A:		Optional[Any]          =		UnCLIPScheduler(
           variance_type='''fixed_small_log'''					,  prediction_type='''sample'''					,  num_train_timesteps=10_00					,  clip_sample_range=5.0					,  )

       A:		List[Any]          =		UnCLIPScheduler(
           variance_type='''learned_range'''					,  prediction_type='''epsilon'''					,  num_train_timesteps=10_00					,  )

       A:		Any          =		UnCLIPScheduler(
           variance_type='''fixed_small_log'''					,  prediction_type='''epsilon'''					,  num_train_timesteps=10_00					,  )

       A:		Optional[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 					_snake_case     ( self				:							str					,  SCREAMING_SNAKE_CASE_				:							Union[str, Any]					,  SCREAMING_SNAKE_CASE_				:							str=0	)			->		int:







       '''simple docstring'''



       if str(SCREAMING_SNAKE_CASE_	).startswith('''mps'''	):
            A:		Any          =		torch.manual_seed(SCREAMING_SNAKE_CASE_	)
       else:
            A:		Tuple          =		torch.Generator(device=SCREAMING_SNAKE_CASE_	).manual_seed(SCREAMING_SNAKE_CASE_	)
       A:		Any          =		{
           '''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 					_snake_case     ( self				:							Dict	)			->		Union[str, Any]:







       '''simple docstring'''



       A:		str          =		'''cpu'''

       A:		List[str]          =		self.get_dummy_components()

       A:		List[Any]          =		self.pipeline_class(**SCREAMING_SNAKE_CASE_	)
       A:		Tuple          =		pipe.to(SCREAMING_SNAKE_CASE_	)

       pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

       A:		Tuple          =		pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE_	)	)
       A:		Any          =		output.images

       A:		int          =		pipe(
           **self.get_dummy_inputs(SCREAMING_SNAKE_CASE_	)					,  return_dict=SCREAMING_SNAKE_CASE_					,  )[0]

       A:		Union[str, Any]          =		image[0, -3:, -3:, -1]
       A:		Optional[Any]          =		image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)

       A:		Optional[int]          =		np.array(
           [
               0.9997,
               0.9988,
               0.0028,
               0.9997,
               0.9984,
               0.9965,
               0.0029,
               0.9986,
               0.0025,
           ]	)

       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 					_snake_case     ( self				:							Tuple	)			->		Optional[int]:







       '''simple docstring'''



       A:		int          =		torch.device('''cpu'''	)






       class 						lowerCAmelCase_	:







         '''simple docstring'''

         UpperCamelCase_			:      List[Any]										=				1






       A:		Dict          =		self.get_dummy_components()

       A:		Dict          =		self.pipeline_class(**SCREAMING_SNAKE_CASE_	)
       A:		List[Any]          =		pipe.to(SCREAMING_SNAKE_CASE_	)

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

       A:		List[Any]          =		torch.Generator(device=SCREAMING_SNAKE_CASE_	).manual_seed(0	)
       A:		List[Any]          =		prior.dtype
       A:		List[str]          =		1

       A:		Dict          =		(batch_size, prior.config.embedding_dim)
       A:		List[str]          =		pipe.prepare_latents(
           SCREAMING_SNAKE_CASE_					,  dtype=SCREAMING_SNAKE_CASE_					,  device=SCREAMING_SNAKE_CASE_					,  generator=SCREAMING_SNAKE_CASE_					,  latents=SCREAMING_SNAKE_CASE_					,  scheduler=DummyScheduler()	)
       A:		Optional[int]          =		(batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
       A:		Optional[Any]          =		pipe.prepare_latents(
           SCREAMING_SNAKE_CASE_					,  dtype=SCREAMING_SNAKE_CASE_					,  device=SCREAMING_SNAKE_CASE_					,  generator=SCREAMING_SNAKE_CASE_					,  latents=SCREAMING_SNAKE_CASE_					,  scheduler=DummyScheduler()	)

       A:		List[str]          =		(
           batch_size,
           super_res_first.config.in_channels // 2,
           super_res_first.config.sample_size,
           super_res_first.config.sample_size,
       )
       A:		str          =		pipe.prepare_latents(
           SCREAMING_SNAKE_CASE_					,  dtype=SCREAMING_SNAKE_CASE_					,  device=SCREAMING_SNAKE_CASE_					,  generator=SCREAMING_SNAKE_CASE_					,  latents=SCREAMING_SNAKE_CASE_					,  scheduler=DummyScheduler()	)

       pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

       A:		Any          =		'''this is a prompt example'''

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

       A:		Optional[int]          =		tokenizer(
           SCREAMING_SNAKE_CASE_					,  padding='''max_length'''					,  max_length=tokenizer.model_max_length					,  return_tensors='''pt'''					,  )
       A:		str          =		text_encoder(text_inputs.input_ids	)
       A:		Optional[int]          =		text_inputs.attention_mask

       A:		Any          =		torch.Generator(device=SCREAMING_SNAKE_CASE_	).manual_seed(0	)
       A:		int          =		pipe(
           generator=SCREAMING_SNAKE_CASE_					,  prior_num_inference_steps=2					,  decoder_num_inference_steps=2					,  super_res_num_inference_steps=2					,  prior_latents=SCREAMING_SNAKE_CASE_					,  decoder_latents=SCREAMING_SNAKE_CASE_					,  super_res_latents=SCREAMING_SNAKE_CASE_					,  text_model_output=SCREAMING_SNAKE_CASE_					,  text_attention_mask=SCREAMING_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 					_snake_case     ( self				:							int	)			->		str:







       '''simple docstring'''



       A:		Dict          =		torch_device == '''cpu'''

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



  @skip_mps
  def 					_snake_case     ( self				:							Any	)			->		List[Any]:







       '''simple docstring'''



       A:		Union[str, Any]          =		torch_device == '''cpu'''
       A:		Tuple          =		True
       A:		Optional[Any]          =		[
           '''prior_num_inference_steps''',
           '''decoder_num_inference_steps''',
           '''super_res_num_inference_steps''',
       ]

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



  def 					_snake_case     ( self				:							Any	)			->		str:







       '''simple docstring'''



       A:		Union[str, 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:		Dict          =		[2, 3]
            self._test_inference_batch_consistent(
                batch_sizes=SCREAMING_SNAKE_CASE_					,  additional_params_copy_to_batched_inputs=SCREAMING_SNAKE_CASE_					,  )
       else:
            self._test_inference_batch_consistent(
                additional_params_copy_to_batched_inputs=SCREAMING_SNAKE_CASE_	)



  @skip_mps
  def 					_snake_case     ( self				:							Optional[int]	)			->		List[Any]:







       '''simple docstring'''



       return super().test_dict_tuple_outputs_equivalent()



  @skip_mps
  def 					_snake_case     ( self				:							List[Any]	)			->		Any:







       '''simple docstring'''



       return super().test_save_load_local()




  @skip_mps
  def 					_snake_case     ( self				:							List[str]	)			->		int:







       '''simple docstring'''



       return super().test_save_load_optional_components()






@nightly
class 						lowerCAmelCase_	(       unittest.TestCase							):







  '''simple docstring'''

  def 					_snake_case     ( self				:							str	)			->		Dict:







       '''simple docstring'''



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




  def 					_snake_case     ( self				:							int	)			->		List[Any]:







       '''simple docstring'''



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

       A:		List[Any]          =		UnCLIPPipeline.from_pretrained('''kakaobrain/karlo-v1-alpha'''	)
       pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

       A:		Dict          =		torch.manual_seed(0	)
       A:		int          =		pipeline(
           '''horse'''					,  num_images_per_prompt=1					,  generator=SCREAMING_SNAKE_CASE_					,  output_type='''np'''					,  )

       A:		Optional[int]          =		output.images[0]

       assert image.shape == (2_56, 2_56, 3)
       assert np.abs(expected_image - image	).max() < 1E-1






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







  '''simple docstring'''

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







       '''simple docstring'''



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



  def 					_snake_case     ( self				:							List[str]	)			->		Tuple:







       '''simple docstring'''



       A:		Dict          =		load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/unclip/karlo_v1_alpha_horse_fp16.npy'''	)

       A:		List[Any]          =		UnCLIPPipeline.from_pretrained('''kakaobrain/karlo-v1-alpha'''					,  torch_dtype=torch.floataa	)
       A:		Union[str, Any]          =		pipeline.to(SCREAMING_SNAKE_CASE_	)
       pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

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

       A:		Any          =		output.images[0]

       assert image.shape == (2_56, 2_56, 3)

       assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_	)




  def 					_snake_case     ( self				:							Optional[int]	)			->		Optional[Any]:







       '''simple docstring'''



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

       A:		Any          =		UnCLIPPipeline.from_pretrained('''kakaobrain/karlo-v1-alpha'''					,  torch_dtype=torch.floataa	)
       A:		List[Any]          =		pipe.to(SCREAMING_SNAKE_CASE_	)
       pipe.set_progress_bar_config(disable=SCREAMING_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:		Optional[Any]          =		torch.cuda.max_memory_allocated()
       # make sure that less than 7 GB is allocated
       assert mem_bytes < 7 * 10**9












'''simple docstring'''


import argparse
import os
from functools import reduce

import fairseq
import torch
from datasets import load_dataset

from transformers import WavaVecaProcessor, logging
from transformers.models.dataavec.configuration_dataavec_audio import DataaVecAudioConfig

# Copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_audio.py
from transformers.models.dataavec.dataavec_audio import DataaVecAudioModel as Dummy  # noqa: F401
from transformers.models.dataavec.modeling_dataavec_audio import DataaVecAudioForCTC, DataaVecAudioModel


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

UpperCamelCase			     =					{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''models.0.layer_norm''': '''feature_projection.layer_norm''',
    '''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''',
}
UpperCamelCase			     =					[
    '''lm_head''',
]
def 					SCREAMING_SNAKE_CASE(   __lowercase		,				__lowercase		,				__lowercase		,				__lowercase		,				__lowercase	)			-> Any:
     for attribute in key.split('''.'''	):
          A:		Union[str, Any]          =		getattr(__lowercase		,				__lowercase	)

     if weight_type is not None:
          A:		Tuple          =		getattr(__lowercase		,				__lowercase	).shape
     else:
          A:		Optional[int]          =		hf_pointer.shape

     if hf_shape != value.shape:
          raise ValueError(
              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":
          A:		int          =		value
     elif weight_type == "weight_g":
          A:		Any          =		value
     elif weight_type == "weight_v":
          A:		str          =		value
     elif weight_type == "bias":
          A:		List[str]          =		value
     else:
          A:		str          =		value

     logger.info(F"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."""	)
def 					SCREAMING_SNAKE_CASE(   __lowercase		,				__lowercase		,				__lowercase	)			-> Dict:
     A:		Optional[int]          =		[]
     A:		Tuple          =		fairseq_model.state_dict()

     if not is_headless:
          A:		int          =		hf_model.dataavec_audio.feature_extractor
          A:		Dict          =		hf_model.dataavec_audio.encoder.pos_conv_embed

     else:
          A:		str          =		hf_model.feature_extractor
          A:		Tuple          =		hf_model.encoder.pos_conv_embed

     for name, value in fairseq_dict.items():
          A:		int          =		False
          if "conv_layers" in name:
               load_conv_layer(
                   __lowercase		,				__lowercase		,				__lowercase		,				__lowercase		,				)
               A:		List[Any]          =		True
          elif "pos_conv" in name:
               load_pos_conv_layer(
                   __lowercase		,				__lowercase		,				__lowercase		,				__lowercase		,				)
               A:		Union[str, Any]          =		True
          else:
               for key, mapped_key in MAPPING.items():
                    if not is_headless:
                         A:		str          =		'''data2vec_audio.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
                    if key in name or key.split('''w2v_model.'''	)[-1] == name.split('''.'''	)[0]:
                         A:		Optional[int]          =		True
                         if "*" in mapped_key:
                              A:		Any          =		name.split(__lowercase	)[0].split('''.'''	)[-2]
                              A:		Any          =		mapped_key.replace('''*'''		,				__lowercase	)
                         if "weight_g" in name:
                              A:		List[Any]          =		'''weight_g'''
                         elif "weight_v" in name:
                              A:		List[Any]          =		'''weight_v'''
                         elif "bias" in name:
                              A:		int          =		'''bias'''
                         elif "weight" in name:
                              # TODO: don't match quantizer.weight_proj
                              A:		int          =		'''weight'''
                         else:
                              A:		List[Any]          =		None
                         set_recursively(__lowercase		,				__lowercase		,				__lowercase		,				__lowercase		,				__lowercase	)
                    continue
          if not is_used:
               unused_weights.append(__lowercase	)

     logger.warning(F"""Unused weights: {unused_weights}"""	)
def 					SCREAMING_SNAKE_CASE(   __lowercase		,				__lowercase	)			-> str:
     A:		int          =		path.split('''.'''	)
     return reduce(__lowercase		,				__lowercase		,				__lowercase	)
def 					SCREAMING_SNAKE_CASE(   __lowercase		,				__lowercase		,				__lowercase		,				__lowercase	)			-> Optional[Any]:
     A:		Tuple          =		access_by_string(__lowercase		,				__lowercase	)
     A:		Tuple          =		hf_weight.data

     if fsq_value.shape != hf_value.shape:
          raise ValueError(F"""{full_name} has size {fsq_value.shape}, but {hf_value.shape} was found."""	)
     A:		List[Any]          =		fsq_value
     logger.info(F"""{full_name} was correctly initialized from {hf_weight_path}."""	)
def 					SCREAMING_SNAKE_CASE(   __lowercase		,				__lowercase		,				__lowercase		,				__lowercase	)			-> List[Any]:
     A:		List[Any]          =		full_name.split('''conv_layers.'''	)[-1]
     A:		List[str]          =		name.split('''.'''	)
     A:		Tuple          =		int(items[0]	)
     A:		Optional[int]          =		int(items[1]	)

     A:		List[str]          =		name.split('''.'''	)[-1]
     if type_id == 0:
          A:		Optional[Any]          =		'''conv'''
     elif type_id == 2:
          A:		List[str]          =		'''layer_norm'''
     else:
          unused_weights.append(__lowercase	)
          return

     set_weights(__lowercase		,				__lowercase		,				__lowercase		,				F"""conv_layers.{layer_id}.{layer_type}.{weight_type}"""	)
def 					SCREAMING_SNAKE_CASE(   __lowercase		,				__lowercase		,				__lowercase		,				__lowercase	)			-> Optional[Any]:
     A:		Any          =		full_name.split('''pos_conv.'''	)[-1]
     A:		Any          =		name.split('''.'''	)
     A:		int          =		int(items[0]	)
     A:		Optional[int]          =		int(items[1]	)

     A:		Optional[Any]          =		name.split('''.'''	)[-1]
     if type_id != 0:
          unused_weights.append(__lowercase	)
          return
     else:
          A:		List[str]          =		'''conv'''

     set_weights(__lowercase		,				__lowercase		,				__lowercase		,				F"""layers.{layer_id}.{layer_type}.{weight_type}"""	)
@torch.no_grad()
def 					SCREAMING_SNAKE_CASE(   __lowercase		,				__lowercase		,				__lowercase=None		,				__lowercase=None		,				__lowercase=True	)			-> Optional[Any]:
     if config_path is not None:
          A:		Tuple          =		DataaVecAudioConfig.from_pretrained(__lowercase	)
     else:
          A:		Optional[Any]          =		DataaVecAudioConfig()

     if not is_finetuned:
          # Modify final_proj layer name
          A:		Union[str, Any]          =		DataaVecAudioModel(__lowercase	)
          A:		List[Any]          =		os.path.dirname(__lowercase	)

          A:		Union[str, Any]          =		torch.load(__lowercase	)
          A:		int          =		state_dict['''model'''].pop('''final_proj.0.weight'''	)
          A:		str          =		state_dict['''model'''].pop('''final_proj.0.bias'''	)
          A:		Any          =		os.path.join(__lowercase		,				'''converted.pt'''	)
          torch.save(__lowercase		,				__lowercase	)
     else:
          A:		Tuple          =		DataaVecAudioForCTC(__lowercase	)
          A:		Optional[int]          =		checkpoint_path

     def load_dataavec(__lowercase	):
          A     ,    A     ,    A:		List[Any]          =		fairseq.checkpoint_utils.load_model_ensemble_and_task([path]	)
          return model[0].eval()

     A:		Any          =		load_dataavec(__lowercase	)

     recursively_load_weights(__lowercase		,				__lowercase		,				not is_finetuned	)

     A:		Optional[int]          =		WavaVecaProcessor.from_pretrained('''facebook/wav2vec2-large-lv60'''	)

     A:		Optional[Any]          =		load_dataset('''patrickvonplaten/librispeech_asr_dummy'''		,				'''clean'''		,				split='''validation'''	)
     A:		Optional[int]          =		[x['''array'''] for x in ds[:4]['''audio''']]

     A:		Dict          =		processor(__lowercase		,				return_tensors='''pt'''		,				padding=__lowercase	)

     A:		str          =		inputs.input_values
     A:		str          =		inputs.attention_mask
     #    input_values = inputs.input_values[:, :-1]
     #    attention_mask = inputs.attention_mask[:, :-1]

     hf_wavavec.eval()
     model.eval()
     if is_finetuned:
          A:		Any          =		model(source=__lowercase		,				padding_mask=(1 - attention_mask)		,				mask=__lowercase		,				features_only=__lowercase	)[
              '''encoder_out'''
          ].transpose(0		,				1	)
          A:		int          =		hf_wavavec(__lowercase		,				attention_mask=__lowercase	)['''logits''']

          A:		str          =		torch.argmax(__lowercase		,				dim=-1	)
          A:		Any          =		processor.batch_decode(__lowercase	)

          print(F"""Expected Output: {ds[:4]['text']}, Pred: {output_string}"""	)
     else:
          A:		int          =		model(source=__lowercase		,				padding_mask=(1 - attention_mask)		,				mask=__lowercase		,				features_only=__lowercase	)[
              '''layer_results'''
          ][-1][0].transpose(0		,				1	)
          A:		List[Any]          =		hf_wavavec(__lowercase		,				attention_mask=__lowercase	)['''last_hidden_state''']

     print(our_output.shape		,				their_output.shape	)
     A:		List[Any]          =		torch.max(torch.abs(our_output - their_output	)	).item()
     print(F"""max_absolute_diff = {max_absolute_diff}"""	)  # ~ 1e-7
     A:		Optional[int]          =		torch.allclose(__lowercase		,				__lowercase		,				atol=1E-3	)
     print('''Do both models output the same tensors?'''		,				'''🔥''' if success else '''💩'''	)
     if not success:
          raise Exception('''Something went wRoNg'''	)

     hf_wavavec.save_pretrained(__lowercase	)

     if is_finetuned:
          processor.save_pretrained(__lowercase	)
     else:
          processor.feature_extractor.save_pretrained(__lowercase	)


if __name__ == "__main__":
     UpperCamelCase			     =					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'''
     )
     UpperCamelCase			     =					parser.parse_args()
     convert_wavaveca_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
     )






