


'''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, DDPMScheduler, StableDiffusionUpscalePipeline, 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


enable_full_determinism()
class _lowerCamelCase	(						unittest.TestCase							):



		'''simple docstring'''







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







		@property
		def        __lowerCAmelCase		( self		:						str      )  ->			Tuple:
			__magic_name__	:   Union[str, Any]					     =					1
			__magic_name__	:   str					     =					3
			__magic_name__	:   Any					     =					(32, 32)

			__magic_name__	:   Optional[Any]					     =					floats_tensor((batch_size, num_channels) + sizes  ,					rng=random.Random(0      )      ).to(_A      )
			return image







		@property
		def        __lowerCAmelCase		( self		:						List[str]      )  ->			Dict:
			torch.manual_seed(0      )
			__magic_name__	:   str					     =					UNetaDConditionModel(
			    block_out_channels=(32, 32, 64)  ,					layers_per_block=2  ,					sample_size=32  ,					in_channels=7  ,					out_channels=4  ,					down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D', 'CrossAttnDownBlock2D')  ,					up_block_types=('CrossAttnUpBlock2D', 'CrossAttnUpBlock2D', 'UpBlock2D')  ,					cross_attention_dim=32  ,					attention_head_dim=8  ,					use_linear_projection=_A  ,					only_cross_attention=(True, True, False)  ,					num_class_embeds=100  ,					)
			return model







		@property
		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			str:
			torch.manual_seed(0      )
			__magic_name__	:   List[Any]					     =					AutoencoderKL(
			    block_out_channels=[32, 32, 64]  ,					in_channels=3  ,					out_channels=3  ,					down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D', 'DownEncoderBlock2D']  ,					up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D']  ,					latent_channels=4  ,					)
			return model







		@property
		def        __lowerCAmelCase		( self		:						Dict      )  ->			Dict:
			torch.manual_seed(0      )
			__magic_name__	:   int					     =					CLIPTextConfig(
			    bos_token_id=0  ,					eos_token_id=2  ,					hidden_size=32  ,					intermediate_size=37  ,					layer_norm_eps=1E-05  ,					num_attention_heads=4  ,					num_hidden_layers=5  ,					pad_token_id=1  ,					vocab_size=1000  ,					hidden_act='gelu'  ,					projection_dim=512  ,					)
			return CLIPTextModel(_A      )







		def        __lowerCAmelCase		( self		:						Any      )  ->			Dict:
			__magic_name__	:   Optional[Any]					     =					'cpu'  # ensure determinism for the device-dependent torch.Generator
			__magic_name__	:   Any					     =					self.dummy_cond_unet_upscale
			__magic_name__	:   Optional[int]					     =					DDPMScheduler()
			__magic_name__	:   Dict					     =					DDIMScheduler(prediction_type='v_prediction'      )
			__magic_name__	:   Dict					     =					self.dummy_vae
			__magic_name__	:   Union[str, Any]					     =					self.dummy_text_encoder
			__magic_name__	:   List[Any]					     =					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

			__magic_name__	:   int					     =					self.dummy_image.cpu().permute(0  ,					2  ,					3  ,					1      )[0]
			__magic_name__	:   str					     =					Image.fromarray(np.uinta(_A      )      ).convert('RGB'      ).resize((64, 64)      )

			# make sure here that pndm scheduler skips prk
			__magic_name__	:   int					     =					StableDiffusionUpscalePipeline(
			    unet=_A  ,					low_res_scheduler=_A  ,					scheduler=_A  ,					vae=_A  ,					text_encoder=_A  ,					tokenizer=_A  ,					max_noise_level=350  ,					)
			__magic_name__	:   Optional[int]					     =					sd_pipe.to(_A      )
			sd_pipe.set_progress_bar_config(disable=_A      )

			__magic_name__	:   Any					     =					'A painting of a squirrel eating a burger'
			__magic_name__	:   Tuple					     =					torch.Generator(device=_A      ).manual_seed(0      )
			__magic_name__	:   List[Any]					     =					sd_pipe(
			    [prompt]  ,					image=_A  ,					generator=_A  ,					guidance_scale=6.0  ,					noise_level=20  ,					num_inference_steps=2  ,					output_type='np'  ,					)

			__magic_name__	:   List[Any]					     =					output.images

			__magic_name__	:   Any					     =					torch.Generator(device=_A      ).manual_seed(0      )
			__magic_name__	:   List[Any]					     =					sd_pipe(
			    [prompt]  ,					image=_A  ,					generator=_A  ,					guidance_scale=6.0  ,					noise_level=20  ,					num_inference_steps=2  ,					output_type='np'  ,					return_dict=_A  ,					)[0]

			__magic_name__	:   Dict					     =					image[0, -3:, -3:, -1]
			__magic_name__	:   Optional[Any]					     =					image_from_tuple[0, -3:, -3:, -1]

			__magic_name__	:   Union[str, Any]					     =					low_res_image.size[0] * 4
			assert image.shape == (1, expected_height_width, expected_height_width, 3)
			__magic_name__	:   int					     =					np.array([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661]      )

			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		:						List[Any]      )  ->			Union[str, Any]:
			__magic_name__	:   Union[str, Any]					     =					'cpu'  # ensure determinism for the device-dependent torch.Generator
			__magic_name__	:   List[Any]					     =					self.dummy_cond_unet_upscale
			__magic_name__	:   List[Any]					     =					DDPMScheduler()
			__magic_name__	:   str					     =					DDIMScheduler(prediction_type='v_prediction'      )
			__magic_name__	:   Tuple					     =					self.dummy_vae
			__magic_name__	:   Optional[Any]					     =					self.dummy_text_encoder
			__magic_name__	:   Dict					     =					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

			__magic_name__	:   Optional[int]					     =					self.dummy_image.cpu().permute(0  ,					2  ,					3  ,					1      )[0]
			__magic_name__	:   List[Any]					     =					Image.fromarray(np.uinta(_A      )      ).convert('RGB'      ).resize((64, 64)      )

			# make sure here that pndm scheduler skips prk
			__magic_name__	:   Dict					     =					StableDiffusionUpscalePipeline(
			    unet=_A  ,					low_res_scheduler=_A  ,					scheduler=_A  ,					vae=_A  ,					text_encoder=_A  ,					tokenizer=_A  ,					max_noise_level=350  ,					)
			__magic_name__	:   Optional[Any]					     =					sd_pipe.to(_A      )
			sd_pipe.set_progress_bar_config(disable=_A      )

			__magic_name__	:   Union[str, Any]					     =					'A painting of a squirrel eating a burger'
			__magic_name__	:   str					     =					sd_pipe(
			    2 * [prompt]  ,					image=2 * [low_res_image]  ,					guidance_scale=6.0  ,					noise_level=20  ,					num_inference_steps=2  ,					output_type='np'  ,					)
			__magic_name__	:   List[str]					     =					output.images
			assert image.shape[0] == 2

			__magic_name__	:   Union[str, Any]					     =					torch.Generator(device=_A      ).manual_seed(0      )
			__magic_name__	:   List[str]					     =					sd_pipe(
			    [prompt]  ,					image=_A  ,					generator=_A  ,					num_images_per_prompt=2  ,					guidance_scale=6.0  ,					noise_level=20  ,					num_inference_steps=2  ,					output_type='np'  ,					)
			__magic_name__	:   Tuple					     =					output.images
			assert image.shape[0] == 2







		@unittest.skipIf(torch_device != 'cuda'  ,					'This test requires a GPU'      )
		def        __lowerCAmelCase		( self		:						List[str]      )  ->			Any:
			__magic_name__	:   Optional[int]					     =					self.dummy_cond_unet_upscale
			__magic_name__	:   str					     =					DDPMScheduler()
			__magic_name__	:   Union[str, Any]					     =					DDIMScheduler(prediction_type='v_prediction'      )
			__magic_name__	:   List[Any]					     =					self.dummy_vae
			__magic_name__	:   Optional[int]					     =					self.dummy_text_encoder
			__magic_name__	:   str					     =					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

			__magic_name__	:   Tuple					     =					self.dummy_image.cpu().permute(0  ,					2  ,					3  ,					1      )[0]
			__magic_name__	:   str					     =					Image.fromarray(np.uinta(_A      )      ).convert('RGB'      ).resize((64, 64)      )

			# put models in fp16, except vae as it overflows in fp16
			__magic_name__	:   Tuple					     =					unet.half()
			__magic_name__	:   Any					     =					text_encoder.half()

			# make sure here that pndm scheduler skips prk
			__magic_name__	:   Tuple					     =					StableDiffusionUpscalePipeline(
			    unet=_A  ,					low_res_scheduler=_A  ,					scheduler=_A  ,					vae=_A  ,					text_encoder=_A  ,					tokenizer=_A  ,					max_noise_level=350  ,					)
			__magic_name__	:   Any					     =					sd_pipe.to(_A      )
			sd_pipe.set_progress_bar_config(disable=_A      )

			__magic_name__	:   int					     =					'A painting of a squirrel eating a burger'
			__magic_name__	:   Optional[Any]					     =					torch.manual_seed(0      )
			__magic_name__	:   List[str]					     =					sd_pipe(
			    [prompt]  ,					image=_A  ,					generator=_A  ,					num_inference_steps=2  ,					output_type='np'  ,					).images

			__magic_name__	:   Tuple					     =					low_res_image.size[0] * 4
			assert image.shape == (1, expected_height_width, expected_height_width, 3)







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



		'''simple docstring'''







		def        __lowerCAmelCase		( self		:						str      )  ->			Union[str, Any]:
			# clean up the VRAM after each test
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()







		def        __lowerCAmelCase		( self		:						int      )  ->			Dict:
			__magic_name__	:   Dict					     =					load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
			    '/sd2-upscale/low_res_cat.png'      )
			__magic_name__	:   Any					     =					load_numpy(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
			    '/upsampled_cat.npy'      )

			__magic_name__	:   Any					     =					'stabilityai/stable-diffusion-x4-upscaler'
			__magic_name__	:   Dict					     =					StableDiffusionUpscalePipeline.from_pretrained(_A      )
			pipe.to(_A      )
			pipe.set_progress_bar_config(disable=_A      )
			pipe.enable_attention_slicing()

			__magic_name__	:   Optional[int]					     =					'a cat sitting on a park bench'

			__magic_name__	:   List[Any]					     =					torch.manual_seed(0      )
			__magic_name__	:   List[Any]					     =					pipe(
			    prompt=_A  ,					image=_A  ,					generator=_A  ,					output_type='np'  ,					)
			__magic_name__	:   List[Any]					     =					output.images[0]

			assert image.shape == (512, 512, 3)
			assert np.abs(expected_image - image      ).max() < 1E-3







		def        __lowerCAmelCase		( self		:						Any      )  ->			Union[str, Any]:
			__magic_name__	:   Optional[int]					     =					load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
			    '/sd2-upscale/low_res_cat.png'      )
			__magic_name__	:   Union[str, Any]					     =					load_numpy(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
			    '/upsampled_cat_fp16.npy'      )

			__magic_name__	:   Optional[Any]					     =					'stabilityai/stable-diffusion-x4-upscaler'
			__magic_name__	:   Optional[int]					     =					StableDiffusionUpscalePipeline.from_pretrained(
			    _A  ,					torch_dtype=torch.floataa  ,					)
			pipe.to(_A      )
			pipe.set_progress_bar_config(disable=_A      )
			pipe.enable_attention_slicing()

			__magic_name__	:   str					     =					'a cat sitting on a park bench'

			__magic_name__	:   Tuple					     =					torch.manual_seed(0      )
			__magic_name__	:   Any					     =					pipe(
			    prompt=_A  ,					image=_A  ,					generator=_A  ,					output_type='np'  ,					)
			__magic_name__	:   Union[str, Any]					     =					output.images[0]

			assert image.shape == (512, 512, 3)
			assert np.abs(expected_image - image      ).max() < 5E-1







		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			List[Any]:
			torch.cuda.empty_cache()
			torch.cuda.reset_max_memory_allocated()
			torch.cuda.reset_peak_memory_stats()

			__magic_name__	:   Any					     =					load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
			    '/sd2-upscale/low_res_cat.png'      )

			__magic_name__	:   str					     =					'stabilityai/stable-diffusion-x4-upscaler'
			__magic_name__	:   int					     =					StableDiffusionUpscalePipeline.from_pretrained(
			    _A  ,					torch_dtype=torch.floataa  ,					)
			pipe.to(_A      )
			pipe.set_progress_bar_config(disable=_A      )
			pipe.enable_attention_slicing(1      )
			pipe.enable_sequential_cpu_offload()

			__magic_name__	:   List[Any]					     =					'a cat sitting on a park bench'

			__magic_name__	:   Dict					     =					torch.manual_seed(0      )
			__magic_name__	:   Dict					     =					pipe(
			    prompt=_A  ,					image=_A  ,					generator=_A  ,					num_inference_steps=5  ,					output_type='np'  ,					)

			__magic_name__	:   Dict					     =					torch.cuda.max_memory_allocated()
			# make sure that less than 2.9 GB is allocated
			assert mem_bytes < 2.9 * 10**9



'''simple docstring'''

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
lowerCAmelCase				:str        =      logging.get_logger(__name__)

lowerCAmelCase				:str        =      {
    '''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''',
    '''adapter_layer''': '''encoder.layers.*.adapter_layer''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
    '''pooling_layer.linear''': '''projector''',
    '''pooling_layer.projection''': '''classifier''',
}
lowerCAmelCase				:List[str]        =      [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''projector''',
    '''classifier''',
]





def lowerCamelCase      (      lowerCAmelCase    :     Tuple		):





	"""simple docstring"""







	__magic_name__	:   Any					     =					{}
	with open(lowerCAmelCase		,		'r'		) as file:
		for line_number, line in enumerate(lowerCAmelCase		):
			__magic_name__	:   Optional[Any]					     =					line.strip()
			if line:
				__magic_name__	:   Optional[int]					     =					line.split()
				__magic_name__	:   Any					     =					line_number
				__magic_name__	:   Union[str, Any]					     =					words[0]
				__magic_name__	:   Dict					     =					value
	return result





def lowerCamelCase      (      lowerCAmelCase    :     Optional[int]		,		lowerCAmelCase    :     List[Any]		,		lowerCAmelCase    :     Optional[int]		,		lowerCAmelCase    :     Optional[Any]		,		lowerCAmelCase    :     Tuple		):





	"""simple docstring"""







	for attribute in key.split('.'		):
		__magic_name__	:   Optional[Any]					     =					getattr(lowerCAmelCase		,		lowerCAmelCase		)

	__magic_name__	:   Tuple					     =					None
	for param_key in PARAM_MAPPING.keys():
		if full_name.endswith(lowerCAmelCase		):
			__magic_name__	:   Optional[Any]					     =					PARAM_MAPPING[full_name.split('.'		)[-1]]
			__magic_name__	:   List[Any]					     =					'param'

	if weight_type is not None and weight_type != "param":
		__magic_name__	:   List[str]					     =					getattr(lowerCAmelCase		,		lowerCAmelCase		).shape
	elif weight_type is not None and weight_type == "param":
		__magic_name__	:   Tuple					     =					hf_pointer
		for attribute in hf_param_name.split('.'		):
			__magic_name__	:   str					     =					getattr(lowerCAmelCase		,		lowerCAmelCase		)
		__magic_name__	:   Union[str, Any]					     =					shape_pointer.shape

		# let's reduce dimension
		__magic_name__	:   int					     =					value[0]
	else:
		__magic_name__	:   Optional[Any]					     =					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":
		__magic_name__	:   Optional[Any]					     =					value
	elif weight_type == "weight_g":
		__magic_name__	:   List[str]					     =					value
	elif weight_type == "weight_v":
		__magic_name__	:   Optional[int]					     =					value
	elif weight_type == "bias":
		__magic_name__	:   Optional[Any]					     =					value
	elif weight_type == "param":
		for attribute in hf_param_name.split('.'		):
			__magic_name__	:   Optional[int]					     =					getattr(lowerCAmelCase		,		lowerCAmelCase		)
		__magic_name__	:   List[str]					     =					value
	else:
		__magic_name__	:   int					     =					value

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





def lowerCamelCase      (      lowerCAmelCase    :     List[Any]		,		lowerCAmelCase    :     str		,		lowerCAmelCase    :     List[str]		,		lowerCAmelCase    :     str		,		lowerCAmelCase    :     str		):





	"""simple docstring"""







	__magic_name__	:   Optional[int]					     =					None
	for param_key in PARAM_MAPPING.keys():
		if full_name.endswith(lowerCAmelCase		):
			__magic_name__	:   List[Any]					     =					PARAM_MAPPING[full_name.split('.'		)[-1]]
			__magic_name__	:   Dict					     =					'param'

	if weight_type is not None and weight_type != "param":
		__magic_name__	:   Union[str, Any]					     =					'.'.join([key, weight_type]		)
	elif weight_type is not None and weight_type == "param":
		__magic_name__	:   Optional[Any]					     =					'.'.join([key, hf_param_name]		)
	else:
		__magic_name__	:   int					     =					key

	__magic_name__	:   int					     =					value if 'lm_head' in full_key else value[0]


lowerCAmelCase				:int        =      {
    '''W_a''': '''linear_1.weight''',
    '''W_b''': '''linear_2.weight''',
    '''b_a''': '''linear_1.bias''',
    '''b_b''': '''linear_2.bias''',
    '''ln_W''': '''norm.weight''',
    '''ln_b''': '''norm.bias''',
}





def lowerCamelCase      (      lowerCAmelCase    :     Optional[Any]		,		lowerCAmelCase    :     List[Any]		,		lowerCAmelCase    :     Tuple=None		,		lowerCAmelCase    :     Tuple=None		):





	"""simple docstring"""







	__magic_name__	:   Dict					     =					False
	for key, mapped_key in MAPPING.items():
		__magic_name__	:   int					     =					'wav2vec2.' + 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]:
			__magic_name__	:   Union[str, Any]					     =					True
			if "*" in mapped_key:
				__magic_name__	:   List[Any]					     =					name.split(lowerCAmelCase		)[0].split('.'		)[-2]
				__magic_name__	:   List[str]					     =					mapped_key.replace('*'		,		lowerCAmelCase		)
			if "weight_g" in name:
				__magic_name__	:   str					     =					'weight_g'
			elif "weight_v" in name:
				__magic_name__	:   Optional[int]					     =					'weight_v'
			elif "bias" in name:
				__magic_name__	:   int					     =					'bias'
			elif "weight" in name:
				# TODO: don't match quantizer.weight_proj
				__magic_name__	:   List[str]					     =					'weight'
			else:
				__magic_name__	:   Any					     =					None
			if hf_dict is not None:
				rename_dict(lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		)
			else:
				set_recursively(lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		)
			return is_used
	return is_used





def lowerCamelCase      (      lowerCAmelCase    :     Union[str, Any]		,		lowerCAmelCase    :     Optional[int]		,		lowerCAmelCase    :     Dict		):





	"""simple docstring"""







	__magic_name__	:   Union[str, Any]					     =					[]
	__magic_name__	:   Any					     =					fairseq_model.state_dict()

	__magic_name__	:   Optional[int]					     =					hf_model.wavaveca.feature_extractor

	for name, value in fairseq_dict.items():
		__magic_name__	:   Optional[Any]					     =					False
		if "conv_layers" in name:
			load_conv_layer(
			    lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		hf_model.config.feat_extract_norm == 'group'		,		)
			__magic_name__	:   Optional[int]					     =					True
		else:
			__magic_name__	:   str					     =					load_wavaveca_layer(lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		)
		if not is_used:
			unused_weights.append(lowerCAmelCase		)

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





def lowerCamelCase      (      lowerCAmelCase    :     List[str]		,		lowerCAmelCase    :     Dict		,		lowerCAmelCase    :     Any		,		lowerCAmelCase    :     Any		,		lowerCAmelCase    :     int		):





	"""simple docstring"""







	__magic_name__	:   Any					     =					full_name.split('conv_layers.'		)[-1]
	__magic_name__	:   int					     =					name.split('.'		)
	__magic_name__	:   Any					     =					int(items[0]		)
	__magic_name__	:   Any					     =					int(items[1]		)

	if type_id == 0:
		if "bias" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
				raise ValueError(
				    f'{full_name} has size {value.shape}, but'
				    f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'		)
			__magic_name__	:   Union[str, Any]					     =					value
			logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'		)
		elif "weight" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
				raise ValueError(
				    f'{full_name} has size {value.shape}, but'
				    f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'		)
			__magic_name__	:   str					     =					value
			logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'		)
	elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
		if "bias" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
				raise ValueError(
				    f'{full_name} has size {value.shape}, but'
				    f' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'		)
			__magic_name__	:   Optional[int]					     =					value
			logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'		)
		elif "weight" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
				raise ValueError(
				    f'{full_name} has size {value.shape}, but'
				    f' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'		)
			__magic_name__	:   Dict					     =					value
			logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'		)
	else:
		unused_weights.append(lowerCAmelCase		)





@torch.no_grad()
def lowerCamelCase      (      lowerCAmelCase    :     Tuple		,		lowerCAmelCase    :     Optional[int]		,		lowerCAmelCase    :     Dict=None		,		lowerCAmelCase    :     Any=None		,		lowerCAmelCase    :     Optional[Any]=True		,		lowerCAmelCase    :     Any=False		):





	"""simple docstring"""







	if config_path is not None:
		__magic_name__	:   int					     =					WavaVecaConfig.from_pretrained(lowerCAmelCase		)
	else:
		__magic_name__	:   List[str]					     =					WavaVecaConfig()

	if is_seq_class:
		__magic_name__	:   Any					     =					read_txt_into_dict(lowerCAmelCase		)
		__magic_name__	:   Optional[Any]					     =					idalabel
		__magic_name__	:   Union[str, Any]					     =					WavaVecaForSequenceClassification(lowerCAmelCase		)
		__magic_name__	:   Optional[int]					     =					WavaVecaFeatureExtractor(
		    feature_size=1		,		sampling_rate=1_6000		,		padding_value=0		,		do_normalize=lowerCAmelCase		,		return_attention_mask=lowerCAmelCase		,		)
		feature_extractor.save_pretrained(lowerCAmelCase		)

	elif is_finetuned:
		if dict_path:
			__magic_name__	:   str					     =					Dictionary.load(lowerCAmelCase		)

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			__magic_name__	:   Dict					     =					target_dict.pad_index
			__magic_name__	:   Union[str, Any]					     =					target_dict.bos_index
			__magic_name__	:   Union[str, Any]					     =					target_dict.eos_index
			__magic_name__	:   Union[str, Any]					     =					len(target_dict.symbols		)
			__magic_name__	:   Dict					     =					os.path.join(lowerCAmelCase		,		'vocab.json'		)
			if not os.path.isdir(lowerCAmelCase		):
				logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(lowerCAmelCase		)		)
				return
			os.makedirs(lowerCAmelCase		,		exist_ok=lowerCAmelCase		)
			__magic_name__	:   List[Any]					     =					target_dict.indices

			# fairseq has the <pad> and <s> switched
			__magic_name__	:   Any					     =					0
			__magic_name__	:   Optional[int]					     =					1
			with open(lowerCAmelCase		,		'w'		,		encoding='utf-8'		) as vocab_handle:
				json.dump(lowerCAmelCase		,		lowerCAmelCase		)
			__magic_name__	:   List[str]					     =					WavaVecaCTCTokenizer(
			    lowerCAmelCase		,		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=lowerCAmelCase		,		)
			__magic_name__	:   Tuple					     =					True if config.feat_extract_norm == 'layer' else False
			__magic_name__	:   Optional[Any]					     =					WavaVecaFeatureExtractor(
			    feature_size=1		,		sampling_rate=1_6000		,		padding_value=0		,		do_normalize=lowerCAmelCase		,		return_attention_mask=lowerCAmelCase		,		)
			__magic_name__	:   List[Any]					     =					WavaVecaProcessor(feature_extractor=lowerCAmelCase		,		tokenizer=lowerCAmelCase		)
			processor.save_pretrained(lowerCAmelCase		)

		__magic_name__	:   Dict					     =					WavaVecaForCTC(lowerCAmelCase		)
	else:
		__magic_name__	:   Tuple					     =					WavaVecaForPreTraining(lowerCAmelCase		)

	if is_finetuned or is_seq_class:
		__magic_name__     ,    __magic_name__     ,    __magic_name__	:   str					     =					fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]		,		arg_overrides={'data': '/'.join(dict_path.split('/'		)[:-1]		)}		)
	else:
		__magic_name__	:   Optional[Any]					     =					argparse.Namespace(task='audio_pretraining'		)
		__magic_name__	:   Dict					     =					fairseq.tasks.setup_task(lowerCAmelCase		)

		__magic_name__     ,    __magic_name__     ,    __magic_name__	:   Optional[Any]					     =					fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]		,		task=lowerCAmelCase		)

	__magic_name__	:   Any					     =					model[0].eval()

	recursively_load_weights(lowerCAmelCase		,		lowerCAmelCase		,		not is_finetuned		)

	hf_wavavec.save_pretrained(lowerCAmelCase		)


if __name__ == "__main__":
							lowerCAmelCase				:str        =      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'''
							)
							parser.add_argument(
							    '''--is_seq_class''',
							    action='''store_true''',
							    help='''Whether the model to convert is a fine-tuned sequence classification model or not''',
							)
							lowerCAmelCase				:Dict        =      parser.parse_args()

							lowerCAmelCase				:Tuple        =      not args.not_finetuned and not args.is_seq_class
							convert_wavaveca_checkpoint(
							    args.checkpoint_path,
							    args.pytorch_dump_folder_path,
							    args.config_path,
							    args.dict_path,
							    is_finetuned,
							    args.is_seq_class,
							)