
'''simple docstring'''







import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModelWithProjection

from diffusers import (
    AutoencoderKL,
    DPMSolverMultistepScheduler,
    PNDMScheduler,
    StableDiffusionImageVariationPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class 					lowerCAmelCase__  (    lowerCamelCase_       ,		lowerCamelCase_       ,		lowerCamelCase_       ,		unittest.TestCase	):
					lowerCAmelCase_	  =	StableDiffusionImageVariationPipeline
					lowerCAmelCase_	  =	IMAGE_VARIATION_PARAMS
					lowerCAmelCase_	  =	IMAGE_VARIATION_BATCH_PARAMS
					lowerCAmelCase_	  =	frozenset([]	)
					# TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
					lowerCAmelCase_	  =	frozenset([]	)






					def       _snake_case       (  self						):


											"""simple docstring"""





											torch.manual_seed(0						)
											lowercase_      :	Dict						=			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							,				)
											lowercase_      :	Union[str, Any]						=			PNDMScheduler(skip_prk_steps=__SCREAMING_SNAKE_CASE						)
											torch.manual_seed(0						)
											lowercase_      :	List[Any]						=			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						)
											lowercase_      :	Union[str, Any]						=			CLIPVisionConfig(
											    hidden_size=32							,				projection_dim=32							,				intermediate_size=37							,				layer_norm_eps=1E-0_5							,				num_attention_heads=4							,				num_hidden_layers=5							,				image_size=32							,				patch_size=4							,				)
											lowercase_      :	Tuple						=			CLIPVisionModelWithProjection(__SCREAMING_SNAKE_CASE						)
											lowercase_      :	Optional[Any]						=			CLIPImageProcessor(crop_size=32							,				size=32						)

											lowercase_      :	Optional[Any]						=			{
											    '''unet''': unet,
											    '''scheduler''': scheduler,
											    '''vae''': vae,
											    '''image_encoder''': image_encoder,
											    '''feature_extractor''': feature_extractor,
											    '''safety_checker''': None,
											}
											return components






					def       _snake_case       (  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE=0						):


											"""simple docstring"""





											lowercase_      :	Tuple						=			floats_tensor((1, 3, 32, 32)							,				rng=random.Random(__SCREAMING_SNAKE_CASE						)						)
											lowercase_      :	Optional[Any]						=			image.cpu().permute(0							,				2							,				3							,				1						)[0]
											lowercase_      :	Dict						=			Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE						)						).convert('''RGB'''						).resize((32, 32)						)
											if str(__SCREAMING_SNAKE_CASE						).startswith('''mps'''						):
																	lowercase_      :	List[str]						=			torch.manual_seed(__SCREAMING_SNAKE_CASE						)
											else:
																	lowercase_      :	int						=			torch.Generator(device=__SCREAMING_SNAKE_CASE						).manual_seed(__SCREAMING_SNAKE_CASE						)
											lowercase_      :	str						=			{
											    '''image''': image,
											    '''generator''': generator,
											    '''num_inference_steps''': 2,
											    '''guidance_scale''': 6.0,
											    '''output_type''': '''numpy''',
											}
											return inputs






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	Optional[int]						=			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
											lowercase_      :	Union[str, Any]						=			self.get_dummy_components()
											lowercase_      :	str						=			StableDiffusionImageVariationPipeline(**__SCREAMING_SNAKE_CASE						)
											lowercase_      :	List[str]						=			sd_pipe.to(__SCREAMING_SNAKE_CASE						)
											sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE						)

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

											assert image.shape == (1, 64, 64, 3)
											lowercase_      :	List[Any]						=			np.array([0.5_239, 0.5_723, 0.4_796, 0.5_049, 0.5_550, 0.4_685, 0.5_329, 0.4_891, 0.4_921]						)

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






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	int						=			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
											lowercase_      :	Union[str, Any]						=			self.get_dummy_components()
											lowercase_      :	str						=			StableDiffusionImageVariationPipeline(**__SCREAMING_SNAKE_CASE						)
											lowercase_      :	List[str]						=			sd_pipe.to(__SCREAMING_SNAKE_CASE						)
											sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE						)

											lowercase_      :	List[str]						=			self.get_dummy_inputs(__SCREAMING_SNAKE_CASE						)
											lowercase_      :	Optional[Any]						=			2 * [inputs['''image''']]
											lowercase_      :	str						=			sd_pipe(**__SCREAMING_SNAKE_CASE						)

											lowercase_      :	Dict						=			output.images

											lowercase_      :	List[Any]						=			image[-1, -3:, -3:, -1]

											assert image.shape == (2, 64, 64, 3)
											lowercase_      :	Optional[int]						=			np.array([0.6_892, 0.5_637, 0.5_836, 0.5_771, 0.6_254, 0.6_409, 0.5_580, 0.5_569, 0.5_289]						)

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






					def       _snake_case       (  self						):


											"""simple docstring"""





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







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






					def       _snake_case       (  self						):


											"""simple docstring"""





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






					def       _snake_case       (  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE="cpu"							,				__SCREAMING_SNAKE_CASE=torch.floataa							,				__SCREAMING_SNAKE_CASE=0						):


											"""simple docstring"""





											lowercase_      :	Optional[Any]						=			torch.Generator(device=__SCREAMING_SNAKE_CASE						).manual_seed(__SCREAMING_SNAKE_CASE						)
											lowercase_      :	List[str]						=			load_image(
											    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'''
											    '''/stable_diffusion_imgvar/input_image_vermeer.png'''						)
											lowercase_      :	str						=			np.random.RandomState(__SCREAMING_SNAKE_CASE						).standard_normal((1, 4, 64, 64)						)
											lowercase_      :	Union[str, Any]						=			torch.from_numpy(__SCREAMING_SNAKE_CASE						).to(device=__SCREAMING_SNAKE_CASE							,				dtype=__SCREAMING_SNAKE_CASE						)
											lowercase_      :	Tuple						=			{
											    '''image''': init_image,
											    '''latents''': latents,
											    '''generator''': generator,
											    '''num_inference_steps''': 3,
											    '''guidance_scale''': 7.5,
											    '''output_type''': '''numpy''',
											}
											return inputs






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	str						=			StableDiffusionImageVariationPipeline.from_pretrained(
											    '''lambdalabs/sd-image-variations-diffusers'''							,				safety_checker=__SCREAMING_SNAKE_CASE						)
											lowercase_      :	int						=			sd_pipe.to(__SCREAMING_SNAKE_CASE						)
											sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE						)

											lowercase_      :	List[str]						=			self.get_inputs(__SCREAMING_SNAKE_CASE						)
											lowercase_      :	List[str]						=			sd_pipe(**__SCREAMING_SNAKE_CASE						).images
											lowercase_      :	List[Any]						=			image[0, -3:, -3:, -1].flatten()

											assert image.shape == (1, 5_12, 5_12, 3)
											lowercase_      :	Optional[Any]						=			np.array([0.84_491, 0.90_789, 0.75_708, 0.78_734, 0.83_485, 0.70_099, 0.66_938, 0.68_727, 0.61_379]						)
											assert np.abs(image_slice - expected_slice						).max() < 6E-3






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	int						=			0

											def callback_fn(__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						) -> None:
																	lowercase_      :	str						=			True
																	nonlocal number_of_steps
																	number_of_steps += 1
																	if step == 1:
																							lowercase_      :	Optional[int]						=			latents.detach().cpu().numpy()
																							assert latents.shape == (1, 4, 64, 64)
																							lowercase_      :	Tuple						=			latents[0, -3:, -3:, -1]
																							lowercase_      :	Any						=			np.array(
																							    [-0.1_621, 0.2_837, -0.7_979, -0.1_221, -1.3_057, 0.7_681, -2.1_191, 0.0_464, 1.6_309]						)

																							assert np.abs(latents_slice.flatten() - expected_slice						).max() < 5E-2
																	elif step == 2:
																							lowercase_      :	str						=			latents.detach().cpu().numpy()
																							assert latents.shape == (1, 4, 64, 64)
																							lowercase_      :	List[str]						=			latents[0, -3:, -3:, -1]
																							lowercase_      :	List[str]						=			np.array([0.6_299, 1.7_500, 1.1_992, -2.1_582, -1.8_994, 0.7_334, -0.7_090, 1.0_137, 1.5_273]						)

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

											lowercase_      :	List[str]						=			False

											lowercase_      :	int						=			StableDiffusionImageVariationPipeline.from_pretrained(
											    '''fusing/sd-image-variations-diffusers'''							,				safety_checker=__SCREAMING_SNAKE_CASE							,				torch_dtype=torch.floataa							,				)
											pipe.to(__SCREAMING_SNAKE_CASE						)
											pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE						)
											pipe.enable_attention_slicing()

											lowercase_      :	int						=			self.get_inputs(__SCREAMING_SNAKE_CASE							,				dtype=torch.floataa						)
											pipe(**__SCREAMING_SNAKE_CASE							,				callback=__SCREAMING_SNAKE_CASE							,				callback_steps=1						)
											assert callback_fn.has_been_called
											assert number_of_steps == inputs["num_inference_steps"]






					def       _snake_case       (  self						):


											"""simple docstring"""





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

											lowercase_      :	Dict						=			'''fusing/sd-image-variations-diffusers'''
											lowercase_      :	Tuple						=			StableDiffusionImageVariationPipeline.from_pretrained(
											    __SCREAMING_SNAKE_CASE							,				safety_checker=__SCREAMING_SNAKE_CASE							,				torch_dtype=torch.floataa						)
											lowercase_      :	Any						=			pipe.to(__SCREAMING_SNAKE_CASE						)
											pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE						)
											pipe.enable_attention_slicing(1						)
											pipe.enable_sequential_cpu_offload()

											lowercase_      :	List[Any]						=			self.get_inputs(__SCREAMING_SNAKE_CASE							,				dtype=torch.floataa						)
											lowercase_      :	Union[str, Any]						=			pipe(**__SCREAMING_SNAKE_CASE						)

											lowercase_      :	List[str]						=			torch.cuda.max_memory_allocated()
											# make sure that less than 2.6 GB is allocated
											assert mem_bytes < 2.6 * 10**9







@nightly
@require_torch_gpu
class 					lowerCAmelCase__  (    unittest.TestCase	):






					def       _snake_case       (  self						):


											"""simple docstring"""





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






					def       _snake_case       (  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE="cpu"							,				__SCREAMING_SNAKE_CASE=torch.floataa							,				__SCREAMING_SNAKE_CASE=0						):


											"""simple docstring"""





											lowercase_      :	Optional[Any]						=			torch.Generator(device=__SCREAMING_SNAKE_CASE						).manual_seed(__SCREAMING_SNAKE_CASE						)
											lowercase_      :	List[str]						=			load_image(
											    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'''
											    '''/stable_diffusion_imgvar/input_image_vermeer.png'''						)
											lowercase_      :	Dict						=			np.random.RandomState(__SCREAMING_SNAKE_CASE						).standard_normal((1, 4, 64, 64)						)
											lowercase_      :	Tuple						=			torch.from_numpy(__SCREAMING_SNAKE_CASE						).to(device=__SCREAMING_SNAKE_CASE							,				dtype=__SCREAMING_SNAKE_CASE						)
											lowercase_      :	Tuple						=			{
											    '''image''': init_image,
											    '''latents''': latents,
											    '''generator''': generator,
											    '''num_inference_steps''': 50,
											    '''guidance_scale''': 7.5,
											    '''output_type''': '''numpy''',
											}
											return inputs






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	int						=			StableDiffusionImageVariationPipeline.from_pretrained('''fusing/sd-image-variations-diffusers'''						)
											sd_pipe.to(__SCREAMING_SNAKE_CASE						)
											sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE						)

											lowercase_      :	Optional[int]						=			self.get_inputs(__SCREAMING_SNAKE_CASE						)
											lowercase_      :	Optional[int]						=			sd_pipe(**__SCREAMING_SNAKE_CASE						).images[0]

											lowercase_      :	Tuple						=			load_numpy(
											    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'''
											    '''/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy'''						)
											lowercase_      :	int						=			np.abs(expected_image - image						).max()
											assert max_diff < 1E-3






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	Union[str, Any]						=			StableDiffusionImageVariationPipeline.from_pretrained('''fusing/sd-image-variations-diffusers'''						)
											lowercase_      :	Tuple						=			DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config						)
											sd_pipe.to(__SCREAMING_SNAKE_CASE						)
											sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE						)

											lowercase_      :	Tuple						=			self.get_inputs(__SCREAMING_SNAKE_CASE						)
											lowercase_      :	Union[str, Any]						=			25
											lowercase_      :	Optional[Any]						=			sd_pipe(**__SCREAMING_SNAKE_CASE						).images[0]

											lowercase_      :	List[str]						=			load_numpy(
											    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'''
											    '''/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy'''						)
											lowercase_      :	str						=			np.abs(expected_image - image						).max()
											assert max_diff < 1E-3





'''simple docstring'''







import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_lowercase					:    Optional[int]       				=			logging.get_logger(__name__)

_lowercase					:    List[Any]       				=			{
    "post_extract_proj": "feature_projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "attention.self.query_proj": "encoder.encoder.layer.*.attention.self.query_proj",
    "attention.self.key_proj": "encoder.encoder.layer.*.attention.self.key_proj",
    "attention.self.value_proj": "encoder.encoder.layer.*.attention.self.value_proj",
    "attention.output.dense": "encoder.encoder.layer.*.attention.output.dense",
    "attention.output.LayerNorm": "encoder.encoder.layer.*.attention.output.LayerNorm",
    "intermediate.dense": "encoder.encoder.layer.*.intermediate.dense",
    "output.dense": "encoder.encoder.layer.*.output.dense",
    "output.LayerNorm": "encoder.encoder.layer.*.output.LayerNorm",
    "encoder.encoder.rel_embeddings": "encoder.encoder.rel_embeddings",
    "encoder.encoder.LayerNorm": "encoder.encoder.LayerNorm",
    "encoder.upsample.0": "encoder.upsample.projection",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					List[str] , __SCREAMING_SNAKE_CASE   :					Dict , __SCREAMING_SNAKE_CASE   :					List[Any] , __SCREAMING_SNAKE_CASE   :					int , __SCREAMING_SNAKE_CASE   :					Any				):



						"""simple docstring"""





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

						if weight_type is not None:
												lowercase_      :	Optional[int]						=			getattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE				).shape
						else:
												lowercase_      :	Optional[Any]						=			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":
												lowercase_      :	int						=			value
						elif weight_type == "weight_g":
												lowercase_      :	Dict						=			value
						elif weight_type == "weight_v":
												lowercase_      :	Dict						=			value
						elif weight_type == "bias":
												lowercase_      :	Union[str, Any]						=			value
						else:
												lowercase_      :	Optional[Any]						=			value

						logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''				)
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					Union[str, Any] , __SCREAMING_SNAKE_CASE   :					Union[str, Any] , __SCREAMING_SNAKE_CASE   :					Optional[Any]				):



						"""simple docstring"""





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

						lowercase_      :	Dict						=			hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

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

						logger.warning(F'''Unused weights: {unused_weights}'''				)
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					List[Any] , __SCREAMING_SNAKE_CASE   :					int , __SCREAMING_SNAKE_CASE   :					int , __SCREAMING_SNAKE_CASE   :					Tuple , __SCREAMING_SNAKE_CASE   :					Optional[int]				):



						"""simple docstring"""





						lowercase_      :	int						=			full_name.split('''conv_layers.'''				)[-1]
						lowercase_      :	int						=			name.split('''.'''				)
						lowercase_      :	int						=			int(items[0]				)
						lowercase_      :	List[Any]						=			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.'''
																		)
																		lowercase_      :	int						=			value
																		logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''				)
												elif "weight" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
																		    F'''{full_name} has size {value.shape}, but'''
																		    F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
																		)
																		lowercase_      :	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."
																		)
																		lowercase_      :	int						=			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.'''
																		)
																		lowercase_      :	List[str]						=			value
																		logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''				)
						else:
												unused_weights.append(__SCREAMING_SNAKE_CASE				)
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					Any , __SCREAMING_SNAKE_CASE   :					Tuple				):



						"""simple docstring"""





						lowercase_      :	str						=			SEWDConfig()
						if is_finetuned:
												lowercase_      :	Optional[int]						=			model.wav_encoder.wav_model.cfg
						else:
												lowercase_      :	Dict						=			model.cfg

						lowercase_      :	Tuple						=			fs_config.conv_bias
						lowercase_      :	Dict						=			eval(fs_config.conv_feature_layers				)
						lowercase_      :	Union[str, Any]						=			[x[0] for x in conv_layers]
						lowercase_      :	Dict						=			[x[1] for x in conv_layers]
						lowercase_      :	List[str]						=			[x[2] for x in conv_layers]
						lowercase_      :	Any						=			'''gelu'''
						lowercase_      :	Union[str, Any]						=			'''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
						lowercase_      :	Union[str, Any]						=			0.0
						lowercase_      :	str						=			fs_config.activation_fn.name
						lowercase_      :	Union[str, Any]						=			fs_config.encoder_embed_dim
						lowercase_      :	Any						=			0.02
						lowercase_      :	Any						=			fs_config.encoder_ffn_embed_dim
						lowercase_      :	List[str]						=			1E-5
						lowercase_      :	Union[str, Any]						=			fs_config.encoder_layerdrop
						lowercase_      :	Union[str, Any]						=			fs_config.encoder_attention_heads
						lowercase_      :	Dict						=			fs_config.conv_pos_groups
						lowercase_      :	int						=			fs_config.conv_pos
						lowercase_      :	Union[str, Any]						=			len(__SCREAMING_SNAKE_CASE				)
						lowercase_      :	Optional[Any]						=			fs_config.encoder_layers
						lowercase_      :	List[Any]						=			fs_config.squeeze_factor
						# DeBERTa-specific parameters:
						lowercase_      :	Union[str, Any]						=			fs_config.max_position_embeddings
						lowercase_      :	Tuple						=			fs_config.position_buckets
						lowercase_      :	str						=			fs_config.share_att_key
						lowercase_      :	List[Any]						=			fs_config.relative_attention
						lowercase_      :	Any						=			fs_config.position_biased_input
						lowercase_      :	Union[str, Any]						=			tuple(fs_config.pos_att_type.split('''|'''				)				)
						lowercase_      :	List[str]						=			fs_config.norm_rel_ebd

						# take care of any params that are overridden by the Wav2VecCtc model
						if is_finetuned:
												lowercase_      :	List[Any]						=			model.cfg
												lowercase_      :	List[str]						=			fs_config.final_dropout
												lowercase_      :	Union[str, Any]						=			fs_config.layerdrop
						lowercase_      :	Optional[int]						=			fs_config.activation_dropout
						lowercase_      :	Dict						=			fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
						lowercase_      :	int						=			fs_config.attention_dropout
						lowercase_      :	Union[str, Any]						=			fs_config.dropout_input
						lowercase_      :	Any						=			fs_config.dropout
						lowercase_      :	List[str]						=			fs_config.mask_channel_length
						lowercase_      :	Union[str, Any]						=			fs_config.mask_channel_prob
						lowercase_      :	str						=			fs_config.mask_length
						lowercase_      :	List[Any]						=			fs_config.mask_prob

						lowercase_      :	Optional[int]						=			'''Wav2Vec2FeatureExtractor'''
						lowercase_      :	Dict						=			'''Wav2Vec2CTCTokenizer'''

						return config
@torch.no_grad()
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					Optional[Any] , __SCREAMING_SNAKE_CASE   :					Any , __SCREAMING_SNAKE_CASE   :					int=None , __SCREAMING_SNAKE_CASE   :					List[Any]=None , __SCREAMING_SNAKE_CASE   :					Tuple=True				):



						"""simple docstring"""





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

						if config_path is not None:
												lowercase_      :	Union[str, Any]						=			SEWDConfig.from_pretrained(__SCREAMING_SNAKE_CASE				)
						else:
												lowercase_      :	List[str]						=			convert_config(model[0] , __SCREAMING_SNAKE_CASE				)
						lowercase_      :	Tuple						=			model[0].eval()

						lowercase_      :	Any						=			True if config.feat_extract_norm == '''layer''' else False
						lowercase_      :	List[Any]						=			WavaVecaFeatureExtractor(
						    feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=__SCREAMING_SNAKE_CASE , return_attention_mask=__SCREAMING_SNAKE_CASE , )

						if is_finetuned:
												if dict_path:
																		lowercase_      :	Optional[int]						=			Dictionary.load(__SCREAMING_SNAKE_CASE				)

																		# important change bos & pad token id since CTC symbol is <pad> and
																		# not <s> as in fairseq
																		lowercase_      :	int						=			target_dict.pad_index
																		lowercase_      :	List[Any]						=			target_dict.bos_index
																		lowercase_      :	Dict						=			target_dict.pad_index
																		lowercase_      :	Dict						=			target_dict.bos_index
																		lowercase_      :	List[str]						=			target_dict.eos_index
																		lowercase_      :	Dict						=			len(target_dict.symbols				)
																		lowercase_      :	Optional[Any]						=			os.path.join(__SCREAMING_SNAKE_CASE , '''vocab.json'''				)
																		if not os.path.isdir(__SCREAMING_SNAKE_CASE				):
																								logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(__SCREAMING_SNAKE_CASE				)				)
																								return
																		os.makedirs(__SCREAMING_SNAKE_CASE , exist_ok=__SCREAMING_SNAKE_CASE				)
																		with open(__SCREAMING_SNAKE_CASE , '''w''' , encoding='''utf-8'''				) as vocab_handle:
																								json.dump(target_dict.indices , __SCREAMING_SNAKE_CASE				)
																		lowercase_      :	List[str]						=			WavaVecaCTCTokenizer(
																		    __SCREAMING_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=__SCREAMING_SNAKE_CASE , )
																		lowercase_      :	Optional[int]						=			WavaVecaProcessor(feature_extractor=__SCREAMING_SNAKE_CASE , tokenizer=__SCREAMING_SNAKE_CASE				)
																		processor.save_pretrained(__SCREAMING_SNAKE_CASE				)

												lowercase_      :	int						=			SEWDForCTC(__SCREAMING_SNAKE_CASE				)
						else:
												lowercase_      :	Union[str, Any]						=			SEWDModel(__SCREAMING_SNAKE_CASE				)
												feature_extractor.save_pretrained(__SCREAMING_SNAKE_CASE				)

						recursively_load_weights(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE				)

						hf_model.save_pretrained(__SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
							_lowercase					:    List[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(
							    "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
							)
							_lowercase					:    str       				=			parser.parse_args()
							convert_sew_checkpoint(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
							)



