


'''simple docstring'''



import gc
import random
import unittest

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

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
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

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Optional[int]   ):
					__a			:       Optional[Any]					    =  [tensor.shape for tensor in tensor_list]
					return all(shape == shapes[0] for shape in shapes[1:]   )





class 							__UpperCamelCase				(		lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_		,      unittest.TestCase					):
	A_				=    StableDiffusionLatentUpscalePipeline
	A_				=    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
	    "height",
	    "width",
	    "cross_attention_kwargs",
	    "negative_prompt_embeds",
	    "prompt_embeds",
	}
	A_				=    PipelineTesterMixin.required_optional_params - {"num_images_per_prompt"}
	A_				=    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
	A_				=    frozenset(
	    []					)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
	A_				=    frozenset([]					)

	A_				=    True




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       int					    =  1
						__a			:       Any					    =  4
						__a			:       List[str]					    =  (16, 16)

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




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						torch.manual_seed(0							)
						__a			:       List[Any]					    =  UNetaDConditionModel(
						    act_fn='gelu'						,				attention_head_dim=8						,				norm_num_groups=__a						,				block_out_channels=[32, 32, 64, 64]						,				time_cond_proj_dim=160						,				conv_in_kernel=1						,				conv_out_kernel=1						,				cross_attention_dim=32						,				down_block_types=(
						        'KDownBlock2D',
						        'KCrossAttnDownBlock2D',
						        'KCrossAttnDownBlock2D',
						        'KCrossAttnDownBlock2D',
						    )						,				in_channels=8						,				mid_block_type=__a						,				only_cross_attention=__a						,				out_channels=5						,				resnet_time_scale_shift='scale_shift'						,				time_embedding_type='fourier'						,				timestep_post_act='gelu'						,				up_block_types=('KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KUpBlock2D')						,				)
						__a			:       Dict					    =  AutoencoderKL(
						    block_out_channels=[32, 32, 64, 64]						,				in_channels=3						,				out_channels=3						,				down_block_types=[
						        'DownEncoderBlock2D',
						        'DownEncoderBlock2D',
						        'DownEncoderBlock2D',
						        'DownEncoderBlock2D',
						    ]						,				up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D']						,				latent_channels=4						,				)
						__a			:       str					    =  EulerDiscreteScheduler(prediction_type='sample'							)
						__a			:       Optional[int]					    =  CLIPTextConfig(
						    bos_token_id=0						,				eos_token_id=2						,				hidden_size=32						,				intermediate_size=37						,				layer_norm_eps=1E-0_5						,				num_attention_heads=4						,				num_hidden_layers=5						,				pad_token_id=1						,				vocab_size=1000						,				hidden_act='quick_gelu'						,				projection_dim=512						,				)
						__a			:       Optional[Any]					    =  CLIPTextModel(__a							)
						__a			:       Any					    =  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'							)

						__a			:       Any					    =  {
						    'unet': model.eval(),
						    'vae': vae.eval(),
						    'scheduler': scheduler,
						    'text_encoder': text_encoder,
						    'tokenizer': tokenizer,
						}

						return components




	def       __UpperCAmelCase							(   self						,				__a						,				__a=0							):

						'''simple docstring'''





						if str(__a							).startswith('mps'							):
											__a			:       str					    =  torch.manual_seed(__a							)
						else:
											__a			:       Tuple					    =  torch.Generator(device=__a							).manual_seed(__a							)
						__a			:       Optional[int]					    =  {
						    'prompt': 'A painting of a squirrel eating a burger',
						    'image': self.dummy_image.cpu(),
						    'generator': generator,
						    'num_inference_steps': 2,
						    'output_type': 'numpy',
						}
						return inputs




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Optional[Any]					    =  'cpu'

						__a			:       List[Any]					    =  self.get_dummy_components()
						__a			:       Optional[int]					    =  self.pipeline_class(**__a							)
						pipe.to(__a							)
						pipe.set_progress_bar_config(disable=__a							)

						__a			:       Dict					    =  self.get_dummy_inputs(__a							)
						__a			:       Tuple					    =  pipe(**__a							).images
						__a			:       List[Any]					    =  image[0, -3:, -3:, -1]

						self.assertEqual(image.shape						,				(1, 256, 256, 3)							)
						__a			:       List[str]					    =  np.array(
						    [0.47222412, 0.41921633, 0.44717434, 0.46874192, 0.42588258, 0.46150726, 0.4677534, 0.45583832, 0.48579055]							)
						__a			:       Tuple					    =  np.abs(image_slice.flatten() - expected_slice							).max()
						self.assertLessEqual(__a						,				1E-3							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						super().test_attention_slicing_forward_pass(expected_max_diff=7E-3							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						super().test_inference_batch_single_identical(expected_max_diff=7E-3							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						super().test_save_load_local(expected_max_difference=3E-3							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						super().test_save_load_optional_components(expected_max_difference=3E-3							)





	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       List[Any]					    =  [
						    'DDIMScheduler',
						    'DDPMScheduler',
						    'PNDMScheduler',
						    'HeunDiscreteScheduler',
						    'EulerAncestralDiscreteScheduler',
						    'KDPM2DiscreteScheduler',
						    'KDPM2AncestralDiscreteScheduler',
						    'DPMSolverSDEScheduler',
						]
						__a			:       Tuple					    =  self.get_dummy_components()
						__a			:       Tuple					    =  self.pipeline_class(**__a							)

						# make sure that PNDM does not need warm-up
						pipe.scheduler.register_to_config(skip_prk_steps=__a							)

						pipe.to(__a							)
						pipe.set_progress_bar_config(disable=__a							)
						__a			:       List[str]					    =  self.get_dummy_inputs(__a							)
						__a			:       Any					    =  2

						__a			:       Tuple					    =  []
						for scheduler_enum in KarrasDiffusionSchedulers:
											if scheduler_enum.name in skip_schedulers:
																# no sigma schedulers are not supported
																# no schedulers
																continue

											__a			:       Tuple					    =  getattr(__a						,				scheduler_enum.name							)
											__a			:       Optional[Any]					    =  scheduler_cls.from_config(pipe.scheduler.config							)
											__a			:       int					    =  pipe(**__a							)[0]
											outputs.append(__a							)

						assert check_same_shape(__a							)





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




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Dict					    =  torch.manual_seed(33							)

						__a			:       str					    =  StableDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'						,				torch_dtype=torch.floataa							)
						pipe.to('cuda'							)

						__a			:       str					    =  StableDiffusionLatentUpscalePipeline.from_pretrained(
						    'stabilityai/sd-x2-latent-upscaler'						,				torch_dtype=torch.floataa							)
						upscaler.to('cuda'							)

						__a			:       Union[str, Any]					    =  'a photo of an astronaut high resolution, unreal engine, ultra realistic'

						__a			:       int					    =  pipe(__a						,				generator=__a						,				output_type='latent'							).images

						__a			:       Union[str, Any]					    =  upscaler(
						    prompt=__a						,				image=__a						,				num_inference_steps=20						,				guidance_scale=0						,				generator=__a						,				output_type='np'						,				).images[0]

						__a			:       Optional[Any]					    =  load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy'							)
						assert np.abs((expected_image - image).mean()							) < 5E-2





	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Optional[Any]					    =  torch.manual_seed(33							)

						__a			:       Union[str, Any]					    =  StableDiffusionLatentUpscalePipeline.from_pretrained(
						    'stabilityai/sd-x2-latent-upscaler'						,				torch_dtype=torch.floataa							)
						upscaler.to('cuda'							)

						__a			:       Optional[int]					    =  'the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'

						__a			:       List[str]					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png'							)

						__a			:       List[str]					    =  upscaler(
						    prompt=__a						,				image=__a						,				num_inference_steps=20						,				guidance_scale=0						,				generator=__a						,				output_type='np'						,				).images[0]

						__a			:       Tuple					    =  load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy'							)
						assert np.abs((expected_image - image).max()							) < 5E-2







'''simple docstring'''



import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
__lowercase	:						Union[str, Any]       =	logging.get_logger(__name__)

__lowercase	:						Optional[int]       =	{
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    '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',
}
__lowercase	:						Optional[Any]       =	[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Tuple						,      _SCREAMING_SNAKE_CASE      :   Union[str, Any]						,      _SCREAMING_SNAKE_CASE      :   Any						,      _SCREAMING_SNAKE_CASE      :   List[str]						,      _SCREAMING_SNAKE_CASE      :   Dict   ):
					for attribute in key.split('.'   ):
										__a			:       Any					    =  getattr(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )

					if weight_type is not None:
										__a			:       List[Any]					    =  getattr(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   ).shape
					else:
										__a			:       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":
										__a			:       Tuple					    =  value
					elif weight_type == "weight_g":
										__a			:       str					    =  value
					elif weight_type == "weight_v":
										__a			:       Optional[Any]					    =  value
					elif weight_type == "bias":
										__a			:       Union[str, Any]					    =  value
					else:
										__a			:       List[Any]					    =  value

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






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Dict						,      _SCREAMING_SNAKE_CASE      :   Optional[Any]   ):
					__a			:       int					    =  []
					__a			:       List[str]					    =  fairseq_model.state_dict()

					__a			:       Tuple					    =  hf_model.feature_extractor

					# if encoder has different dim to decoder -> use proj_weight
					__a			:       int					    =  None

					for name, value in fairseq_dict.items():
										__a			:       List[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'						,      )
															__a			:       List[str]					    =  True
										elif name.split('.'   )[0] == "proj":
															__a			:       Tuple					    =  fairseq_model.proj
															__a			:       str					    =  True
										else:
															for key, mapped_key in MAPPING.items():
																				if key in name or key.split('w2v_model.'   )[-1] == name.split('.'   )[0]:
																									__a			:       List[Any]					    =  True
																									if "*" in mapped_key:
																														__a			:       str					    =  name.split(_SCREAMING_SNAKE_CASE   )[0].split('.'   )[-2]
																														__a			:       int					    =  mapped_key.replace('*'						,      _SCREAMING_SNAKE_CASE   )
																									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			:       Optional[Any]					    =  'bias'
																									elif "weight" in name:
																														__a			:       Tuple					    =  'weight'
																									else:
																														__a			:       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}"""   )

					return proj_weight






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Any						,      _SCREAMING_SNAKE_CASE      :   Dict						,      _SCREAMING_SNAKE_CASE      :   Optional[int]						,      _SCREAMING_SNAKE_CASE      :   List[Any]						,      _SCREAMING_SNAKE_CASE      :   Optional[Any]   ):
					__a			:       List[str]					    =  full_name.split('conv_layers.'   )[-1]
					__a			:       Any					    =  name.split('.'   )
					__a			:       List[str]					    =  int(items[0]   )
					__a			:       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."""
															)
															__a			:       List[str]					    =  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."""
															)
															__a			:       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:
															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."
															)
															__a			:       Tuple					    =  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."""
															)
															__a			:       List[Any]					    =  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   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int   ):
					__a    ,				__a			:       List[str]					    =  emb.weight.shape
					__a			:       str					    =  nn.Linear(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      bias=_SCREAMING_SNAKE_CASE   )
					__a			:       Optional[int]					    =  emb.weight.data
					return lin_layer






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Any   ):
					with open(_SCREAMING_SNAKE_CASE						,      'r'						,      encoding='utf-8'   ) as f:
										__a			:       Union[str, Any]					    =  f.readlines()
										__a			:       Tuple					    =  [line.split(' '   )[0] for line in lines]

					__a			:       int					    =  len(_SCREAMING_SNAKE_CASE   )

					__a			:       List[Any]					    =  {
					    '<s>': 0,
					    '<pad>': 1,
					    '</s>': 2,
					    '<unk>': 3,
					}

					vocab_dict.update(dict(zip(_SCREAMING_SNAKE_CASE						,      range(4						,      num_words + 4   )   )   )   )
					return vocab_dict






@torch.no_grad()
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[Any]						,      _SCREAMING_SNAKE_CASE      :   List[Any]						,      _SCREAMING_SNAKE_CASE      :   str						,      _SCREAMING_SNAKE_CASE      :   Any						,      _SCREAMING_SNAKE_CASE      :   str						,      _SCREAMING_SNAKE_CASE      :   Any						,      _SCREAMING_SNAKE_CASE      :   Optional[int]						,      ):
					__a			:       Optional[int]					    =  WavaVecaConfig.from_pretrained(_SCREAMING_SNAKE_CASE   )
					__a			:       Any					    =  SpeechaTextaConfig.from_pretrained(
					    _SCREAMING_SNAKE_CASE						,      vocab_size=_SCREAMING_SNAKE_CASE						,      decoder_layers=_SCREAMING_SNAKE_CASE						,      do_stable_layer_norm=_SCREAMING_SNAKE_CASE   )

					__a			:       Dict					    =  WavaVecaFeatureExtractor(
					    feature_size=1						,      sampling_rate=16_000						,      padding_value=0						,      do_normalize=_SCREAMING_SNAKE_CASE						,      return_attention_mask=_SCREAMING_SNAKE_CASE						,      )

					__a    ,				__a    ,				__a			:       List[Any]					    =  fairseq.checkpoint_utils.load_model_ensemble_and_task(
					    [checkpoint_path]						,      arg_overrides={'data': '/'.join(dict_path.split('/'   )[:-1]   )}   )
					__a			:       Optional[int]					    =  model[0].eval()

					# set weights for wav2vec2 encoder
					__a			:       Tuple					    =  WavaVecaModel(_SCREAMING_SNAKE_CASE   )
					__a			:       int					    =  recursively_load_weights_wavaveca(model.encoder						,      _SCREAMING_SNAKE_CASE   )

					__a			:       Dict					    =  SpeechaTextaForCausalLM(_SCREAMING_SNAKE_CASE   )
					__a    ,				__a			:       Optional[int]					    =  hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()						,      strict=_SCREAMING_SNAKE_CASE   )

					# set output linear layer
					unexpected_keys.remove('embed_out'   )
					__a			:       Optional[Any]					    =  nn.Parameter(model.decoder.embed_out.detach()   )

					# layer norm is init to identity matrix so leaving it is fine
					logger.warning(F"""The following keys are missing when loading the decoder weights: {missing_keys}"""   )
					logger.warning(F"""The following keys are unexpected when loading the decoder weights: {unexpected_keys}"""   )

					__a			:       Tuple					    =  SpeechEncoderDecoderModel(encoder=_SCREAMING_SNAKE_CASE						,      decoder=_SCREAMING_SNAKE_CASE   )
					__a			:       int					    =  False

					# add projection layer
					__a			:       str					    =  nn.Parameter(projection_layer.weight   )
					__a			:       Any					    =  nn.Parameter(projection_layer.bias   )

					__a			:       str					    =  create_vocab_dict(_SCREAMING_SNAKE_CASE   )

					with open(os.path.join(_SCREAMING_SNAKE_CASE						,      'vocab.json'   )						,      'w'   ) as fp:
										json.dump(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )

					__a			:       Optional[Any]					    =  SpeechaTextaTokenizer(os.path.join(_SCREAMING_SNAKE_CASE						,      'vocab.json'   )   )
					tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE   )

					__a			:       Union[str, Any]					    =  hf_wavavec.config.to_dict()
					__a			:       Tuple					    =  tokenizer.pad_token_id
					__a			:       Optional[int]					    =  tokenizer.bos_token_id
					__a			:       Union[str, Any]					    =  tokenizer.eos_token_id
					__a			:       Tuple					    =  'speech_to_text_2'
					__a			:       Tuple					    =  'wav2vec2'

					__a			:       List[str]					    =  SpeechEncoderDecoderConfig.from_dict(_SCREAMING_SNAKE_CASE   )

					hf_wavavec.save_pretrained(_SCREAMING_SNAKE_CASE   )
					feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
	__lowercase	:						Dict       =	argparse.ArgumentParser()
	parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
	parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
	parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
	parser.add_argument(
	    '--encoder_config_path',
	    default='facebook/wav2vec2-large-lv60',
	    type=str,
	    help='Path to hf encoder wav2vec2 checkpoint config',
	)
	parser.add_argument(
	    '--decoder_config_path',
	    default='facebook/s2t-small-mustc-en-fr-st',
	    type=str,
	    help='Path to hf decoder s2t checkpoint config',
	)
	parser.add_argument('--vocab_size', default=1_02_24, type=int, help='Vocab size of decoder')
	parser.add_argument('--num_decoder_layers', default=7, type=int, help='Number of decoder layers')

	__lowercase	:						Tuple       =	parser.parse_args()
	convert_wavaveca_checkpoint(
	    args.checkpoint_path,
	    args.pytorch_dump_folder_path,
	    args.dict_path,
	    encoder_config_path=args.encoder_config_path,
	    decoder_config_path=args.decoder_config_path,
	    vocab_size=args.vocab_size,
	    num_decoder_layers=args.num_decoder_layers,
	)



