






"""simple docstring"""



import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase       ,		unittest.TestCase		):
							snake_case_     =							UnCLIPImageVariationPipeline
							snake_case_     =							IMAGE_VARIATION_PARAMS - {'height', 'width', 'guidance_scale'}
							snake_case_     =							IMAGE_VARIATION_BATCH_PARAMS

							snake_case_     =							[
							    'generator',
							    'return_dict',
							    'decoder_num_inference_steps',
							    'super_res_num_inference_steps',
							]
							snake_case_     =							False


							@property
							def 						_UpperCamelCase       (			self      :						List[Any]							):
													'''simple docstring'''





													return 32


							@property
							def 						_UpperCamelCase       (			self      :						Optional[int]							):
													'''simple docstring'''





													return 32


							@property
							def 						_UpperCamelCase       (			self      :						Tuple							):
													'''simple docstring'''





													return self.time_input_dim


							@property
							def 						_UpperCamelCase       (			self      :						List[str]							):
													'''simple docstring'''





													return self.time_input_dim * 4


							@property
							def 						_UpperCamelCase       (			self      :						Optional[int]							):
													'''simple docstring'''





													return 100


							@property
							def 						_UpperCamelCase       (			self      :						int							):
													'''simple docstring'''





													A__						:  Tuple		  =  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)
													return tokenizer


							@property
							def 						_UpperCamelCase       (			self      :						int							):
													'''simple docstring'''





													torch.manual_seed(0							)
													A__						:  List[str]		  =  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=1000			,    )
													return CLIPTextModelWithProjection(snake_case							)


							@property
							def 						_UpperCamelCase       (			self      :						List[str]							):
													'''simple docstring'''





													torch.manual_seed(0							)
													A__						:  List[Any]		  =  CLIPVisionConfig(
													    hidden_size=self.text_embedder_hidden_size			,    projection_dim=self.text_embedder_hidden_size			,    num_hidden_layers=5			,    num_attention_heads=4			,    image_size=32			,    intermediate_size=37			,    patch_size=1			,    )
													return CLIPVisionModelWithProjection(snake_case							)


							@property
							def 						_UpperCamelCase       (			self      :						Optional[int]							):
													'''simple docstring'''





													torch.manual_seed(0							)

													A__						:  List[str]		  =  {
													    """clip_embeddings_dim""": self.text_embedder_hidden_size,
													    """time_embed_dim""": self.time_embed_dim,
													    """cross_attention_dim""": self.cross_attention_dim,
													}

													A__						:  Dict		  =  UnCLIPTextProjModel(**snake_case							)
													return model


							@property
							def 						_UpperCamelCase       (			self      :						Dict							):
													'''simple docstring'''





													torch.manual_seed(0							)

													A__						:  int		  =  {
													    """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__						:  Optional[int]		  =  UNetaDConditionModel(**snake_case							)
													return model


							@property
							def 						_UpperCamelCase       (			self      :						Optional[Any]							):
													'''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 						_UpperCamelCase       (			self      :						Optional[int]							):
													'''simple docstring'''





													torch.manual_seed(0							)

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


							@property
							def 						_UpperCamelCase       (			self      :						Tuple							):
													'''simple docstring'''





													torch.manual_seed(1							)

													A__						:  Optional[int]		  =  UNetaDModel(**self.dummy_super_res_kwargs							)
													return model


							def 						_UpperCamelCase       (			self      :						List[str]							):
													'''simple docstring'''





													A__						:  Optional[Any]		  =  self.dummy_decoder
													A__						:  str		  =  self.dummy_text_proj
													A__						:  List[str]		  =  self.dummy_text_encoder
													A__						:  Optional[int]		  =  self.dummy_tokenizer
													A__						:  Tuple		  =  self.dummy_super_res_first
													A__						:  Dict		  =  self.dummy_super_res_last

													A__						:  Optional[int]		  =  UnCLIPScheduler(
													    variance_type="""learned_range"""			,    prediction_type="""epsilon"""			,    num_train_timesteps=1000			,    )

													A__						:  List[str]		  =  UnCLIPScheduler(
													    variance_type="""fixed_small_log"""			,    prediction_type="""epsilon"""			,    num_train_timesteps=1000			,    )

													A__						:  Dict		  =  CLIPImageProcessor(crop_size=32			,    size=32							)

													A__						:  str		  =  self.dummy_image_encoder

													return {
													    "decoder": decoder,
													    "text_encoder": text_encoder,
													    "tokenizer": tokenizer,
													    "text_proj": text_proj,
													    "feature_extractor": feature_extractor,
													    "image_encoder": image_encoder,
													    "super_res_first": super_res_first,
													    "super_res_last": super_res_last,
													    "decoder_scheduler": decoder_scheduler,
													    "super_res_scheduler": super_res_scheduler,
													}


							def 						_UpperCamelCase       (			self      :						Optional[Any]			,    snake_case      :						Optional[Any]			,    snake_case      :						List[str]=0			,    snake_case      :						int=True							):
													'''simple docstring'''





													A__						:  Tuple		  =  floats_tensor((1, 3, 32, 32)			,    rng=random.Random(snake_case							)							).to(snake_case							)
													if str(snake_case							).startswith("""mps"""							):
																			A__						:  List[str]		  =  torch.manual_seed(snake_case							)
													else:
																			A__						:  Tuple		  =  torch.Generator(device=snake_case							).manual_seed(snake_case							)

													if pil_image:
																			A__						:  Dict		  =  input_image * 0.5 + 0.5
																			A__						:  int		  =  input_image.clamp(0			,    1							)
																			A__						:  Tuple		  =  input_image.cpu().permute(0			,    2			,    3			,    1							).float().numpy()
																			A__						:  str		  =  DiffusionPipeline.numpy_to_pil(snake_case							)[0]

													return {
													    "image": input_image,
													    "generator": generator,
													    "decoder_num_inference_steps": 2,
													    "super_res_num_inference_steps": 2,
													    "output_type": "np",
													}


							def 						_UpperCamelCase       (			self      :						Dict							):
													'''simple docstring'''





													A__						:  str		  =  """cpu"""

													A__						:  Union[str, Any]		  =  self.get_dummy_components()

													A__						:  Dict		  =  self.pipeline_class(**snake_case							)
													A__						:  Optional[Any]		  =  pipe.to(snake_case							)

													pipe.set_progress_bar_config(disable=snake_case							)

													A__						:  Any		  =  self.get_dummy_inputs(snake_case			,    pil_image=snake_case							)

													A__						:  List[str]		  =  pipe(**snake_case							)
													A__						:  Union[str, Any]		  =  output.images

													A__						:  Optional[int]		  =  self.get_dummy_inputs(snake_case			,    pil_image=snake_case							)

													A__						:  int		  =  pipe(
													    **snake_case			,    return_dict=snake_case			,    )[0]

													A__						:  Optional[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,
													        0.0002,
													        0.9997,
													        0.9997,
													        0.9969,
													        0.0023,
													        0.9997,
													        0.9969,
													        0.9970,
													    ]							)

													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 						_UpperCamelCase       (			self      :						Optional[int]							):
													'''simple docstring'''





													A__						:  Union[str, Any]		  =  """cpu"""

													A__						:  Dict		  =  self.get_dummy_components()

													A__						:  Optional[int]		  =  self.pipeline_class(**snake_case							)
													A__						:  Tuple		  =  pipe.to(snake_case							)

													pipe.set_progress_bar_config(disable=snake_case							)

													A__						:  Optional[int]		  =  self.get_dummy_inputs(snake_case			,    pil_image=snake_case							)

													A__						:  Optional[int]		  =  pipe(**snake_case							)
													A__						:  Union[str, Any]		  =  output.images

													A__						:  Tuple		  =  self.get_dummy_inputs(snake_case			,    pil_image=snake_case							)

													A__						:  Tuple		  =  pipe(
													    **snake_case			,    return_dict=snake_case			,    )[0]

													A__						:  str		  =  image[0, -3:, -3:, -1]
													A__						:  List[Any]		  =  image_from_tuple[0, -3:, -3:, -1]

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

													A__						:  Tuple		  =  np.array([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971]							)

													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 						_UpperCamelCase       (			self      :						int							):
													'''simple docstring'''





													A__						:  Dict		  =  """cpu"""

													A__						:  Tuple		  =  self.get_dummy_components()

													A__						:  int		  =  self.pipeline_class(**snake_case							)
													A__						:  Optional[int]		  =  pipe.to(snake_case							)

													pipe.set_progress_bar_config(disable=snake_case							)

													A__						:  Optional[Any]		  =  self.get_dummy_inputs(snake_case			,    pil_image=snake_case							)
													A__						:  Optional[int]		  =  [
													    pipeline_inputs["""image"""],
													    pipeline_inputs["""image"""],
													]

													A__						:  Optional[Any]		  =  pipe(**snake_case							)
													A__						:  Optional[int]		  =  output.images

													A__						:  List[Any]		  =  self.get_dummy_inputs(snake_case			,    pil_image=snake_case							)
													A__						:  List[str]		  =  [
													    tuple_pipeline_inputs["""image"""],
													    tuple_pipeline_inputs["""image"""],
													]

													A__						:  Optional[Any]		  =  pipe(
													    **snake_case			,    return_dict=snake_case			,    )[0]

													A__						:  List[Any]		  =  image[0, -3:, -3:, -1]
													A__						:  str		  =  image_from_tuple[0, -3:, -3:, -1]

													assert image.shape == (2, 64, 64, 3)

													A__						:  Optional[int]		  =  np.array(
													    [
													        0.9997,
													        0.9989,
													        0.0008,
													        0.0021,
													        0.9960,
													        0.0018,
													        0.0014,
													        0.0002,
													        0.9933,
													    ]							)

													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 						_UpperCamelCase       (			self      :						Tuple							):
													'''simple docstring'''





													A__						:  List[str]		  =  torch.device("""cpu"""							)






													class 							__SCREAMING_SNAKE_CASE						:
																				snake_case_     =							1




													A__						:  Any		  =  self.get_dummy_components()

													A__						:  List[str]		  =  self.pipeline_class(**snake_case							)
													A__						:  List[Any]		  =  pipe.to(snake_case							)

													pipe.set_progress_bar_config(disable=snake_case							)

													A__						:  Union[str, Any]		  =  torch.Generator(device=snake_case							).manual_seed(0							)
													A__						:  str		  =  pipe.decoder.dtype
													A__						:  List[Any]		  =  1

													A__						:  List[str]		  =  (
													    batch_size,
													    pipe.decoder.config.in_channels,
													    pipe.decoder.config.sample_size,
													    pipe.decoder.config.sample_size,
													)
													A__						:  List[str]		  =  pipe.prepare_latents(
													    snake_case			,    dtype=snake_case			,    device=snake_case			,    generator=snake_case			,    latents=snake_case			,    scheduler=DummyScheduler()							)

													A__						:  Dict		  =  (
													    batch_size,
													    pipe.super_res_first.config.in_channels // 2,
													    pipe.super_res_first.config.sample_size,
													    pipe.super_res_first.config.sample_size,
													)
													A__						:  str		  =  pipe.prepare_latents(
													    snake_case			,    dtype=snake_case			,    device=snake_case			,    generator=snake_case			,    latents=snake_case			,    scheduler=DummyScheduler()							)

													A__						:  List[Any]		  =  self.get_dummy_inputs(snake_case			,    pil_image=snake_case							)

													A__						:  Dict		  =  pipe(
													    **snake_case			,    decoder_latents=snake_case			,    super_res_latents=snake_case							).images

													A__						:  Any		  =  self.get_dummy_inputs(snake_case			,    pil_image=snake_case							)
													# Don't pass image, instead pass embedding
													A__						:  Union[str, Any]		  =  pipeline_inputs.pop("""image"""							)
													A__						:  List[Any]		  =  pipe.image_encoder(snake_case							).image_embeds

													A__						:  Optional[Any]		  =  pipe(
													    **snake_case			,    decoder_latents=snake_case			,    super_res_latents=snake_case			,    image_embeddings=snake_case			,    ).images

													# make sure passing text embeddings manually is identical
													assert np.abs(img_out_a - img_out_a							).max() < 1e-4


							@skip_mps
							def 						_UpperCamelCase       (			self      :						Dict							):
													'''simple docstring'''





													A__						:  Optional[Any]		  =  torch_device == """cpu"""

													# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
													A__						:  Union[str, Any]		  =  1e-2

													self._test_attention_slicing_forward_pass(
													    test_max_difference=snake_case			,    expected_max_diff=snake_case							)


							@skip_mps
							def 						_UpperCamelCase       (			self      :						List[str]							):
													'''simple docstring'''





													A__						:  Tuple		  =  torch_device == """cpu"""
													A__						:  Any		  =  True
													A__						:  str		  =  [
													    """decoder_num_inference_steps""",
													    """super_res_num_inference_steps""",
													]

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


							def 						_UpperCamelCase       (			self      :						Dict							):
													'''simple docstring'''





													A__						:  int		  =  [
													    """decoder_num_inference_steps""",
													    """super_res_num_inference_steps""",
													]

													if torch_device == "mps":
																			# TODO: MPS errors with larger batch sizes
																			A__						:  int		  =  [2, 3]
																			self._test_inference_batch_consistent(
																			    batch_sizes=snake_case			,    additional_params_copy_to_batched_inputs=snake_case			,    )
													else:
																			self._test_inference_batch_consistent(
																			    additional_params_copy_to_batched_inputs=snake_case							)


							@skip_mps
							def 						_UpperCamelCase       (			self      :						Tuple							):
													'''simple docstring'''





													return super().test_dict_tuple_outputs_equivalent()


							@skip_mps
							def 						_UpperCamelCase       (			self      :						Dict							):
													'''simple docstring'''





													return super().test_save_load_local()


							@skip_mps
							def 						_UpperCamelCase       (			self      :						Optional[Any]							):
													'''simple docstring'''





													return super().test_save_load_optional_components()






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


							def 						_UpperCamelCase       (			self      :						Dict							):
													'''simple docstring'''





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


							def 						_UpperCamelCase       (			self      :						Optional[Any]							):
													'''simple docstring'''





													A__						:  str		  =  load_image(
													    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png"""							)
													A__						:  Dict		  =  load_numpy(
													    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
													    """/unclip/karlo_v1_alpha_cat_variation_fp16.npy"""							)

													A__						:  List[Any]		  =  UnCLIPImageVariationPipeline.from_pretrained(
													    """kakaobrain/karlo-v1-alpha-image-variations"""			,    torch_dtype=torch.floataa							)
													A__						:  Optional[int]		  =  pipeline.to(snake_case							)
													pipeline.set_progress_bar_config(disable=snake_case							)

													A__						:  str		  =  torch.Generator(device="""cpu"""							).manual_seed(0							)
													A__						:  Optional[Any]		  =  pipeline(
													    snake_case			,    generator=snake_case			,    output_type="""np"""			,    )

													A__						:  Optional[Any]		  =  output.images[0]

													assert image.shape == (256, 256, 3)

													assert_mean_pixel_difference(snake_case			,    snake_case			,    15							)














"""simple docstring"""



import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


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

A_    =							{
    '''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''',
}
A_    =							[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Optional[Any],						UpperCAmelCase__       :					int,						UpperCAmelCase__       :					List[str],						UpperCAmelCase__       :					Tuple,						UpperCAmelCase__       :					List[str]      )		->str:
						for attribute in key.split("""."""      ):
												A__						:  Any		  =  getattr(UpperCAmelCase__,						UpperCAmelCase__      )

						if weight_type is not None:
												A__						:  Union[str, Any]		  =  getattr(UpperCAmelCase__,						UpperCAmelCase__      ).shape
						else:
												A__						:  Optional[int]		  =  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__						:  List[Any]		  =  value
						elif weight_type == "weight_g":
												A__						:  Any		  =  value
						elif weight_type == "weight_v":
												A__						:  Optional[int]		  =  value
						elif weight_type == "bias":
												A__						:  Any		  =  value
						else:
												A__						:  Any		  =  value

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




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Optional[Any],						UpperCAmelCase__       :					Any      )		->Union[str, Any]:
						A__						:  Dict		  =  []
						A__						:  int		  =  fairseq_model.state_dict()

						A__						:  Dict		  =  hf_model.feature_extractor
						A__						:  Any		  =  hf_model.adapter

						for name, value in fairseq_dict.items():
												A__						:  Optional[int]		  =  False
												if "conv_layers" in name:
																		load_conv_layer(
																		    UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__,						hf_model.config.feat_extract_norm == """group""",						)
																		A__						:  Union[str, Any]		  =  True
												elif any(x in name for x in ["""adaptor""", """w2v_encoder.proj.""", """w2v_proj_ln."""]      ):
																		load_adapter(UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__      )
																		A__						:  int		  =  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__						:  Union[str, Any]		  =  name.split(UpperCAmelCase__      )[0].split("""."""      )[-2]
																																				A__						:  Union[str, Any]		  =  mapped_key.replace("""*""",						UpperCAmelCase__      )
																														if "weight_g" in name:
																																				A__						:  List[str]		  =  """weight_g"""
																														elif "weight_v" in name:
																																				A__						:  Optional[Any]		  =  """weight_v"""
																														elif "bias" in name:
																																				A__						:  Optional[int]		  =  """bias"""
																														elif "weight" in name:
																																				A__						:  int		  =  """weight"""
																														else:
																																				A__						:  Optional[Any]		  =  None
																														set_recursively(UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__      )
																								continue
												if not is_used:
																		unused_weights.append(UpperCAmelCase__      )

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




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




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Tuple,						UpperCAmelCase__       :					Tuple,						UpperCAmelCase__       :					List[str],						UpperCAmelCase__       :					Dict      )		->str:
						A__						:  Tuple		  =  full_name.split("""adaptor."""      )[-1]
						A__						:  Optional[int]		  =  name.split("""."""      )

						if items[1].isdigit():
												A__						:  Optional[Any]		  =  int(items[1]      )
						else:
												A__						:  Any		  =  None

						if "adaptor" not in full_name:
												if "proj_ln" in full_name:
																		# has to be layer norm
																		if "bias" in name:
																								assert (
																								    value.shape == adapter.proj_layer_norm.bias.data.shape
																								), f'{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.'
																								A__						:  Union[str, Any]		  =  value
																								logger.info(f'Adapter proj layer norm bias was initialized from {full_name}.'      )
																		if "weight" in name:
																								assert (
																								    value.shape == adapter.proj_layer_norm.weight.data.shape
																								), f'{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.'
																								A__						:  Tuple		  =  value
												else:
																		# has to be projection layer
																		if "bias" in name:
																								assert (
																								    value.shape == adapter.proj.bias.data.shape
																								), f'{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.'
																								A__						:  str		  =  value
																								logger.info(f'Adapter proj layer bias was initialized from {full_name}.'      )
																		if "weight" in name:
																								assert (
																								    value.shape == adapter.proj.weight.data.shape
																								), f'{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.'
																								A__						:  Union[str, Any]		  =  value
																								logger.info(f'Adapter proj layer weight was initialized from {full_name}.'      )
						elif isinstance(UpperCAmelCase__,						UpperCAmelCase__      ):
												if "bias" in name:
																		assert (
																		    value.shape == adapter.layers[layer_id].conv.bias.data.shape
																		), f'{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.'
																		A__						:  Dict		  =  value
																		logger.info(f'Adapter layer {layer_id} bias was initialized from {full_name}.'      )
												elif "weight" in name:
																		assert (
																		    value.shape == adapter.layers[layer_id].conv.weight.data.shape
																		), f'{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.'
																		A__						:  Optional[int]		  =  value
																		logger.info(f'Adapter layer {layer_id} bias was initialized from {full_name}.'      )
						else:
												unused_weights.append(UpperCAmelCase__      )




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Union[str, Any]      )		->List[Any]:
						A__  ,  A__						:  Union[str, Any]		  =  emb.weight.shape
						A__						:  List[str]		  =  nn.Linear(UpperCAmelCase__,						UpperCAmelCase__,						bias=UpperCAmelCase__      )
						A__						:  List[Any]		  =  emb.weight.data
						return lin_layer




@torch.no_grad()
def      _lowerCAmelCase		(							UpperCAmelCase__       :					Optional[int],						UpperCAmelCase__       :					Any,						UpperCAmelCase__       :					Optional[int],						UpperCAmelCase__       :					Tuple,						UpperCAmelCase__       :					Optional[int],						UpperCAmelCase__       :					Dict,						UpperCAmelCase__       :					Union[str, Any],						UpperCAmelCase__       :					List[Any],						UpperCAmelCase__       :					int,						UpperCAmelCase__       :					Optional[int],						UpperCAmelCase__       :					Dict,						)		->str:
						A__						:  Tuple		  =  WavaVecaConfig.from_pretrained(
						    UpperCAmelCase__,						add_adapter=UpperCAmelCase__,						adapter_stride=UpperCAmelCase__,						adapter_kernel_size=UpperCAmelCase__,						use_auth_token=UpperCAmelCase__,						output_hidden_size=UpperCAmelCase__,						)
						A__						:  List[Any]		  =  MBartConfig.from_pretrained(UpperCAmelCase__      )

						# load model
						A__  ,  A__  ,  A__						:  str		  =  fairseq.checkpoint_utils.load_model_ensemble_and_task(
						    [checkpoint_path],						arg_overrides={
						        """config_yaml""": config_yaml_path,
						        """data""": """/""".join(dict_path.split("""/"""      )[:-1]      ),
						        """w2v_path""": checkpoint_path,
						        """load_pretrained_decoder_from""": None,
						    },						)
						A__						:  List[Any]		  =  model[0].eval()

						# load feature extractor
						A__						:  Any		  =  WavaVecaFeatureExtractor.from_pretrained(UpperCAmelCase__,						use_auth_token=UpperCAmelCase__      )

						# set weights for wav2vec2 encoder
						A__						:  Dict		  =  WavaVecaModel(UpperCAmelCase__      )

						recursively_load_weights_wavaveca(model.encoder,						UpperCAmelCase__      )

						# load decoder weights
						A__						:  Any		  =  MBartForCausalLM(UpperCAmelCase__      )
						A__  ,  A__						:  Union[str, Any]		  =  hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict(),						strict=UpperCAmelCase__      )
						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__						:  Dict		  =  SpeechEncoderDecoderModel(encoder=UpperCAmelCase__,						decoder=UpperCAmelCase__      )
						A__						:  Optional[Any]		  =  False

						A__						:  Optional[Any]		  =  MBartaaTokenizer(UpperCAmelCase__      )
						tokenizer.save_pretrained(UpperCAmelCase__      )

						A__						:  Dict		  =  hf_wavavec.config.to_dict()
						A__						:  List[Any]		  =  tokenizer.pad_token_id
						A__						:  Optional[Any]		  =  tokenizer.bos_token_id
						A__						:  List[Any]		  =  tokenizer.eos_token_id
						A__						:  Tuple		  =  """mbart50"""
						A__						:  List[str]		  =  """wav2vec2"""

						A__						:  Optional[int]		  =  tokenizer.eos_token_id
						A__						:  int		  =  2_5_0_0_0_4
						A__						:  Dict		  =  tokenizer.eos_token_id

						A__						:  str		  =  SpeechEncoderDecoderConfig.from_dict(UpperCAmelCase__      )

						hf_wavavec.save_pretrained(UpperCAmelCase__      )
						feature_extractor.save_pretrained(UpperCAmelCase__      )


if __name__ == "__main__":
						A_    =							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_yaml_path''', default=None, type=str, help='''Path to yaml file of fine-tuned model''')
						parser.add_argument(
						    '''--encoder_config_path''',
						    default='''facebook/wav2vec2-xls-r-1b''',
						    type=str,
						    help='''Path to hf encoder wav2vec2 checkpoint config''',
						)
						parser.add_argument(
						    '''--decoder_config_path''',
						    default='''facebook/mbart-large-50-one-to-many-mmt''',
						    type=str,
						    help='''Path to hf decoder checkpoint config''',
						)
						parser.add_argument('''--add_adapter''', default=True, type=bool, help='''whethere to add model adapter layers''')
						parser.add_argument('''--adapter_stride''', default=2, type=int, help='''stride of adapter layers''')
						parser.add_argument('''--adapter_kernel_size''', default=3, type=int, help='''kernel size of adapter layers''')
						parser.add_argument('''--encoder_output_dim''', default=1024, type=int, help='''encoder output dim''')
						parser.add_argument('''--start_token_id''', default=25_0004, type=int, help='''`decoder_start_token_id` of model config''')

						A_    =							parser.parse_args()
						convert_wavaveca_checkpoint(
						    args.checkpoint_path,
						    args.pytorch_dump_folder_path,
						    args.dict_path,
						    args.config_yaml_path,
						    encoder_config_path=args.encoder_config_path,
						    decoder_config_path=args.decoder_config_path,
						    add_adapter=args.add_adapter,
						    adapter_kernel_size=args.adapter_kernel_size,
						    adapter_stride=args.adapter_stride,
						    decoder_start_token_id=args.start_token_id,
						    encoder_output_dim=args.encoder_output_dim,
						)






