


import os
import tempfile
import unittest

import numpy as np

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow


if is_flax_available():
				import jax
				import jax.numpy as jnp
				from flax.jax_utils import replicate
				from flax.training.common_utils import shard

				from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline



@require_flax
class 							_UpperCamelCase (						unittest.TestCase						):

			'''simple docstring'''







			def   _snake_case    (  self  : Dict							):






									'''simple docstring'''



									with tempfile.TemporaryDirectory() as tmpdirname:
															# pipeline has Flax weights
															__lowerCamelCase     :      Union[str, Any]       						=						FlaxDiffusionPipeline.from_pretrained(
															    """hf-internal-testing/tiny-stable-diffusion-pipe"""       ,  safety_checker=UpperCamelCase__       ,  cache_dir=UpperCamelCase__							)

															__lowerCamelCase     :      Optional[Any]       						=						[t[-1] for t in os.walk(os.path.join(UpperCamelCase__       ,  os.listdir(UpperCamelCase__							)[0]       ,  """snapshots"""							)							)]
															__lowerCamelCase     :      List[str]       						=						[item for sublist in all_root_files for item in sublist]

															# None of the downloaded files should be a PyTorch file even if we have some here:
															# https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
															assert not any(f.endswith(""".bin"""							) for f in files							)



@slow
@require_flax
class 							_UpperCamelCase (						unittest.TestCase						):

			'''simple docstring'''







			def   _snake_case    (  self  : List[str]							):






									'''simple docstring'''



									__lowerCamelCase						, __lowerCamelCase     :      Any       						=						FlaxStableDiffusionPipeline.from_pretrained(
									    """hf-internal-testing/tiny-stable-diffusion-pipe"""       ,  safety_checker=UpperCamelCase__							)

									__lowerCamelCase     :      Dict       						=						(
									    """A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"""
									    """ field, close up, split lighting, cinematic"""
									)

									__lowerCamelCase     :      Union[str, Any]       						=						jax.random.PRNGKey(0							)
									__lowerCamelCase     :      str       						=						4

									__lowerCamelCase     :      Optional[int]       						=						jax.device_count()
									__lowerCamelCase     :      Any       						=						num_samples * [prompt]
									__lowerCamelCase     :      Union[str, Any]       						=						pipeline.prepare_inputs(UpperCamelCase__							)

									# shard inputs and rng
									__lowerCamelCase     :      Any       						=						replicate(UpperCamelCase__							)
									__lowerCamelCase     :      int       						=						jax.random.split(UpperCamelCase__       ,  UpperCamelCase__							)
									__lowerCamelCase     :      List[str]       						=						shard(UpperCamelCase__							)

									__lowerCamelCase     :      Optional[Any]       						=						pipeline(UpperCamelCase__       ,  UpperCamelCase__       ,  UpperCamelCase__       ,  UpperCamelCase__       ,  jit=UpperCamelCase__							).images

									assert images.shape == (num_samples, 1, 6_4, 6_4, 3)
									if jax.device_count() == 8:
															assert np.abs(np.abs(images[0, 0, :2, :2, -2:]       ,  dtype=np.floataa							).sum() - 4.1_514_745							) < 1E-3
															assert np.abs(np.abs(UpperCamelCase__       ,  dtype=np.floataa							).sum() - 4_9_9_4_7.8_7_5							) < 5E-1

									__lowerCamelCase     :      Any       						=						pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]							)							)							)
									assert len(UpperCamelCase__							) == num_samples

			def   _snake_case    (  self  : Optional[int]							):






									'''simple docstring'''



									__lowerCamelCase						, __lowerCamelCase     :      Union[str, Any]       						=						FlaxStableDiffusionPipeline.from_pretrained(
									    """CompVis/stable-diffusion-v1-4"""       ,  revision="""flax"""       ,  safety_checker=UpperCamelCase__							)

									__lowerCamelCase     :      str       						=						(
									    """A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"""
									    """ field, close up, split lighting, cinematic"""
									)

									__lowerCamelCase     :      Any       						=						jax.random.PRNGKey(0							)
									__lowerCamelCase     :      Optional[int]       						=						5_0

									__lowerCamelCase     :      Optional[int]       						=						jax.device_count()
									__lowerCamelCase     :      Optional[Any]       						=						num_samples * [prompt]
									__lowerCamelCase     :      Optional[Any]       						=						pipeline.prepare_inputs(UpperCamelCase__							)

									# shard inputs and rng
									__lowerCamelCase     :      List[str]       						=						replicate(UpperCamelCase__							)
									__lowerCamelCase     :      Union[str, Any]       						=						jax.random.split(UpperCamelCase__       ,  UpperCamelCase__							)
									__lowerCamelCase     :      Optional[Any]       						=						shard(UpperCamelCase__							)

									__lowerCamelCase     :      Optional[int]       						=						pipeline(UpperCamelCase__       ,  UpperCamelCase__       ,  UpperCamelCase__       ,  UpperCamelCase__       ,  jit=UpperCamelCase__							).images

									assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
									if jax.device_count() == 8:
															assert np.abs((np.abs(images[0, 0, :2, :2, -2:]       ,  dtype=np.floataa							).sum() - 0.05_652_401)							) < 1E-3
															assert np.abs((np.abs(UpperCamelCase__       ,  dtype=np.floataa							).sum() - 2_3_8_3_8_0_8.2)							) < 5E-1

			def   _snake_case    (  self  : Any							):






									'''simple docstring'''



									__lowerCamelCase						, __lowerCamelCase     :      Any       						=						FlaxStableDiffusionPipeline.from_pretrained(
									    """CompVis/stable-diffusion-v1-4"""       ,  revision="""bf16"""       ,  dtype=jnp.bfloataa       ,  safety_checker=UpperCamelCase__							)

									__lowerCamelCase     :      Tuple       						=						(
									    """A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"""
									    """ field, close up, split lighting, cinematic"""
									)

									__lowerCamelCase     :      List[Any]       						=						jax.random.PRNGKey(0							)
									__lowerCamelCase     :      Union[str, Any]       						=						5_0

									__lowerCamelCase     :      Optional[int]       						=						jax.device_count()
									__lowerCamelCase     :      Any       						=						num_samples * [prompt]
									__lowerCamelCase     :      Tuple       						=						pipeline.prepare_inputs(UpperCamelCase__							)

									# shard inputs and rng
									__lowerCamelCase     :      List[Any]       						=						replicate(UpperCamelCase__							)
									__lowerCamelCase     :      int       						=						jax.random.split(UpperCamelCase__       ,  UpperCamelCase__							)
									__lowerCamelCase     :      int       						=						shard(UpperCamelCase__							)

									__lowerCamelCase     :      int       						=						pipeline(UpperCamelCase__       ,  UpperCamelCase__       ,  UpperCamelCase__       ,  UpperCamelCase__       ,  jit=UpperCamelCase__							).images

									assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
									if jax.device_count() == 8:
															assert np.abs((np.abs(images[0, 0, :2, :2, -2:]       ,  dtype=np.floataa							).sum() - 0.04_003_906)							) < 1E-3
															assert np.abs((np.abs(UpperCamelCase__       ,  dtype=np.floataa							).sum() - 2_3_7_3_5_1_6.7_5)							) < 5E-1

			def   _snake_case    (  self  : Tuple							):






									'''simple docstring'''



									__lowerCamelCase						, __lowerCamelCase     :      Tuple       						=						FlaxStableDiffusionPipeline.from_pretrained(
									    """CompVis/stable-diffusion-v1-4"""       ,  revision="""bf16"""       ,  dtype=jnp.bfloataa							)

									__lowerCamelCase     :      Tuple       						=						(
									    """A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"""
									    """ field, close up, split lighting, cinematic"""
									)

									__lowerCamelCase     :      int       						=						jax.random.PRNGKey(0							)
									__lowerCamelCase     :      int       						=						5_0

									__lowerCamelCase     :      Tuple       						=						jax.device_count()
									__lowerCamelCase     :      Optional[Any]       						=						num_samples * [prompt]
									__lowerCamelCase     :      str       						=						pipeline.prepare_inputs(UpperCamelCase__							)

									# shard inputs and rng
									__lowerCamelCase     :      List[str]       						=						replicate(UpperCamelCase__							)
									__lowerCamelCase     :      Union[str, Any]       						=						jax.random.split(UpperCamelCase__       ,  UpperCamelCase__							)
									__lowerCamelCase     :      Tuple       						=						shard(UpperCamelCase__							)

									__lowerCamelCase     :      Optional[Any]       						=						pipeline(UpperCamelCase__       ,  UpperCamelCase__       ,  UpperCamelCase__       ,  UpperCamelCase__       ,  jit=UpperCamelCase__							).images

									assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
									if jax.device_count() == 8:
															assert np.abs((np.abs(images[0, 0, :2, :2, -2:]       ,  dtype=np.floataa							).sum() - 0.04_003_906)							) < 1E-3
															assert np.abs((np.abs(UpperCamelCase__       ,  dtype=np.floataa							).sum() - 2_3_7_3_5_1_6.7_5)							) < 5E-1

			def   _snake_case    (  self  : Tuple							):






									'''simple docstring'''



									__lowerCamelCase     :      str       						=						FlaxDDIMScheduler(
									    beta_start=0.00_085       ,  beta_end=0.012       ,  beta_schedule="""scaled_linear"""       ,  set_alpha_to_one=UpperCamelCase__       ,  steps_offset=1       ,  )

									__lowerCamelCase						, __lowerCamelCase     :      Tuple       						=						FlaxStableDiffusionPipeline.from_pretrained(
									    """CompVis/stable-diffusion-v1-4"""       ,  revision="""bf16"""       ,  dtype=jnp.bfloataa       ,  scheduler=UpperCamelCase__       ,  safety_checker=UpperCamelCase__       ,  )
									__lowerCamelCase     :      Tuple       						=						scheduler.create_state()

									__lowerCamelCase     :      Tuple       						=						scheduler_state

									__lowerCamelCase     :      Union[str, Any]       						=						(
									    """A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"""
									    """ field, close up, split lighting, cinematic"""
									)

									__lowerCamelCase     :      Optional[Any]       						=						jax.random.PRNGKey(0							)
									__lowerCamelCase     :      Optional[Any]       						=						5_0

									__lowerCamelCase     :      Union[str, Any]       						=						jax.device_count()
									__lowerCamelCase     :      Union[str, Any]       						=						num_samples * [prompt]
									__lowerCamelCase     :      str       						=						pipeline.prepare_inputs(UpperCamelCase__							)

									# shard inputs and rng
									__lowerCamelCase     :      Any       						=						replicate(UpperCamelCase__							)
									__lowerCamelCase     :      int       						=						jax.random.split(UpperCamelCase__       ,  UpperCamelCase__							)
									__lowerCamelCase     :      int       						=						shard(UpperCamelCase__							)

									__lowerCamelCase     :      Dict       						=						pipeline(UpperCamelCase__       ,  UpperCamelCase__       ,  UpperCamelCase__       ,  UpperCamelCase__       ,  jit=UpperCamelCase__							).images

									assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
									if jax.device_count() == 8:
															assert np.abs((np.abs(images[0, 0, :2, :2, -2:]       ,  dtype=np.floataa							).sum() - 0.045_043_945)							) < 1E-3
															assert np.abs((np.abs(UpperCamelCase__       ,  dtype=np.floataa							).sum() - 2_3_4_7_6_9_3.5)							) < 5E-1


			def   _snake_case    (  self  : Tuple							):






									'''simple docstring'''



									__lowerCamelCase     :      Dict       						=						(
									    """A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"""
									    """ field, close up, split lighting, cinematic"""
									)

									__lowerCamelCase     :      Union[str, Any]       						=						jax.device_count()
									__lowerCamelCase     :      Any       						=						num_samples * [prompt]
									__lowerCamelCase     :      Dict       						=						jax.random.split(jax.random.PRNGKey(0							)       ,  UpperCamelCase__							)

									__lowerCamelCase						, __lowerCamelCase     :      Any       						=						FlaxStableDiffusionPipeline.from_pretrained(
									    """CompVis/stable-diffusion-v1-4"""       ,  revision="""bf16"""       ,  dtype=jnp.bfloataa       ,  safety_checker=UpperCamelCase__       ,  )

									__lowerCamelCase     :      str       						=						replicate(UpperCamelCase__							)
									__lowerCamelCase     :      Union[str, Any]       						=						pipeline.prepare_inputs(UpperCamelCase__							)
									__lowerCamelCase     :      int       						=						shard(UpperCamelCase__							)
									__lowerCamelCase     :      Any       						=						pipeline(UpperCamelCase__       ,  UpperCamelCase__       ,  UpperCamelCase__       ,  jit=UpperCamelCase__							).images
									assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
									__lowerCamelCase     :      Union[str, Any]       						=						images[2, 0, 2_5_6, 1_0:1_7, 1]

									# With memory efficient attention
									__lowerCamelCase						, __lowerCamelCase     :      int       						=						FlaxStableDiffusionPipeline.from_pretrained(
									    """CompVis/stable-diffusion-v1-4"""       ,  revision="""bf16"""       ,  dtype=jnp.bfloataa       ,  safety_checker=UpperCamelCase__       ,  use_memory_efficient_attention=UpperCamelCase__       ,  )

									__lowerCamelCase     :      Any       						=						replicate(UpperCamelCase__							)
									__lowerCamelCase     :      Optional[Any]       						=						pipeline.prepare_inputs(UpperCamelCase__							)
									__lowerCamelCase     :      Union[str, Any]       						=						shard(UpperCamelCase__							)
									__lowerCamelCase     :      int       						=						pipeline(UpperCamelCase__       ,  UpperCamelCase__       ,  UpperCamelCase__       ,  jit=UpperCamelCase__							).images
									assert images_eff.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
									__lowerCamelCase     :      List[Any]       						=						images[2, 0, 2_5_6, 1_0:1_7, 1]

									# I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
									# over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
									assert abs(slice_eff - slice							).max() < 1E-2







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()
__UpperCamelCase    :						Optional[Any]     =							logging.get_logger(__name__)

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





def   _UpperCAmelCase       (     UpperCAmelCase      :			Optional[int]						,	UpperCAmelCase      :			List[str]						,	UpperCAmelCase      :			Optional[int]						,	UpperCAmelCase      :			Optional[Any]						,	UpperCAmelCase      :			Any      ):
      """simple docstring"""




      for attribute in key.split("""."""      ):
            __lowerCamelCase     :      Any       						=						getattr(UpperCAmelCase						,	UpperCAmelCase      )

      if weight_type is not None:
            __lowerCamelCase     :      List[Any]       						=						getattr(UpperCAmelCase						,	UpperCAmelCase      ).shape
      else:
            __lowerCamelCase     :      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":
            __lowerCamelCase     :      List[Any]       						=						value
      elif weight_type == "weight_g":
            __lowerCamelCase     :      Tuple       						=						value
      elif weight_type == "weight_v":
            __lowerCamelCase     :      List[Any]       						=						value
      elif weight_type == "bias":
            __lowerCamelCase     :      Optional[int]       						=						value
      else:
            __lowerCamelCase     :      Dict       						=						value

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





def   _UpperCAmelCase       (     UpperCAmelCase      :			List[Any]						,	UpperCAmelCase      :			Optional[Any]      ):
      """simple docstring"""




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

      __lowerCamelCase     :      Optional[Any]       						=						hf_model.feature_extractor
      __lowerCamelCase     :      List[str]       						=						hf_model.adapter

      for name, value in fairseq_dict.items():
            __lowerCamelCase     :      List[str]       						=						False
            if "conv_layers" in name:
                  load_conv_layer(
                      UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	hf_model.config.feat_extract_norm == """group"""						,	)
                  __lowerCamelCase     :      Any       						=						True
            elif any(x in name for x in ["""adaptor""", """w2v_encoder.proj.""", """w2v_proj_ln."""]      ):
                  load_adapter(UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase      )
                  __lowerCamelCase     :      Dict       						=						True
            else:
                  for key, mapped_key in MAPPING.items():
                        if key in name or key.split("""w2v_model."""      )[-1] == name.split("""."""      )[0]:
                              __lowerCamelCase     :      Union[str, Any]       						=						True
                              if "*" in mapped_key:
                                    __lowerCamelCase     :      int       						=						name.split(UpperCAmelCase      )[0].split("""."""      )[-2]
                                    __lowerCamelCase     :      Dict       						=						mapped_key.replace("""*"""						,	UpperCAmelCase      )
                              if "weight_g" in name:
                                    __lowerCamelCase     :      int       						=						"""weight_g"""
                              elif "weight_v" in name:
                                    __lowerCamelCase     :      List[str]       						=						"""weight_v"""
                              elif "bias" in name:
                                    __lowerCamelCase     :      str       						=						"""bias"""
                              elif "weight" in name:
                                    __lowerCamelCase     :      List[str]       						=						"""weight"""
                              else:
                                    __lowerCamelCase     :      int       						=						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   _UpperCAmelCase       (     UpperCAmelCase      :			Dict						,	UpperCAmelCase      :			Optional[int]						,	UpperCAmelCase      :			Any						,	UpperCAmelCase      :			Union[str, Any]						,	UpperCAmelCase      :			Union[str, Any]      ):
      """simple docstring"""




      __lowerCamelCase     :      List[str]       						=						full_name.split("""conv_layers."""      )[-1]
      __lowerCamelCase     :      Tuple       						=						name.split("""."""      )
      __lowerCamelCase     :      Tuple       						=						int(items[0]      )
      __lowerCamelCase     :      str       						=						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."""
                  )
                  __lowerCamelCase     :      Dict       						=						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."""
                  )
                  __lowerCamelCase     :      List[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."
                  )
                  __lowerCamelCase     :      Any       						=						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."""
                  )
                  __lowerCamelCase     :      str       						=						value
                  logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""      )
      else:
            unused_weights.append(UpperCAmelCase      )





def   _UpperCAmelCase       (     UpperCAmelCase      :			str						,	UpperCAmelCase      :			int						,	UpperCAmelCase      :			List[Any]						,	UpperCAmelCase      :			List[Any]      ):
      """simple docstring"""




      __lowerCamelCase     :      Union[str, Any]       						=						full_name.split("""adaptor."""      )[-1]
      __lowerCamelCase     :      Any       						=						name.split("""."""      )

      if items[1].isdigit():
            __lowerCamelCase     :      Dict       						=						int(items[1]      )
      else:
            __lowerCamelCase     :      List[str]       						=						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."""
                        __lowerCamelCase     :      str       						=						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."""
                        __lowerCamelCase     :      str       						=						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."""
                        __lowerCamelCase     :      Optional[int]       						=						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."""
                        __lowerCamelCase     :      List[str]       						=						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."""
                  __lowerCamelCase     :      int       						=						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."""
                  __lowerCamelCase     :      Tuple       						=						value
                  logger.info(f"""Adapter layer {layer_id} bias was initialized from {full_name}."""      )
      else:
            unused_weights.append(UpperCAmelCase      )





def   _UpperCAmelCase       (     UpperCAmelCase      :			List[Any]      ):
      """simple docstring"""




      __lowerCamelCase						, __lowerCamelCase     :      Tuple       						=						emb.weight.shape
      __lowerCamelCase     :      Any       						=						nn.Linear(UpperCAmelCase						,	UpperCAmelCase						,	bias=UpperCAmelCase      )
      __lowerCamelCase     :      List[Any]       						=						emb.weight.data
      return lin_layer





@torch.no_grad()
def   _UpperCAmelCase       (     UpperCAmelCase      :			Optional[Any]						,	UpperCAmelCase      :			List[str]						,	UpperCAmelCase      :			List[Any]						,	UpperCAmelCase      :			str						,	UpperCAmelCase      :			int						,	UpperCAmelCase      :			Dict						,	UpperCAmelCase      :			Tuple						,	UpperCAmelCase      :			Optional[Any]						,	UpperCAmelCase      :			int						,	UpperCAmelCase      :			int						,	UpperCAmelCase      :			Optional[Any]						,	):
      """simple docstring"""




      __lowerCamelCase     :      int       						=						WavaVecaConfig.from_pretrained(
          UpperCAmelCase						,	add_adapter=UpperCAmelCase						,	adapter_stride=UpperCAmelCase						,	adapter_kernel_size=UpperCAmelCase						,	use_auth_token=UpperCAmelCase						,	output_hidden_size=UpperCAmelCase						,	)
      __lowerCamelCase     :      Optional[int]       						=						MBartConfig.from_pretrained(UpperCAmelCase      )

      # load model
      __lowerCamelCase						, __lowerCamelCase						, __lowerCamelCase     :      Dict       						=						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,
          }						,	)
      __lowerCamelCase     :      Union[str, Any]       						=						model[0].eval()

      # load feature extractor
      __lowerCamelCase     :      Any       						=						WavaVecaFeatureExtractor.from_pretrained(UpperCAmelCase						,	use_auth_token=UpperCAmelCase      )

      # set weights for wav2vec2 encoder
      __lowerCamelCase     :      Tuple       						=						WavaVecaModel(UpperCAmelCase      )

      recursively_load_weights_wavaveca(model.encoder						,	UpperCAmelCase      )

      # load decoder weights
      __lowerCamelCase     :      Dict       						=						MBartForCausalLM(UpperCAmelCase      )
      __lowerCamelCase						, __lowerCamelCase     :      int       						=						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}"""      )

      __lowerCamelCase     :      List[Any]       						=						SpeechEncoderDecoderModel(encoder=UpperCAmelCase						,	decoder=UpperCAmelCase      )
      __lowerCamelCase     :      Union[str, Any]       						=						False

      __lowerCamelCase     :      List[str]       						=						MBartaaTokenizer(UpperCAmelCase      )
      tokenizer.save_pretrained(UpperCAmelCase      )

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

      __lowerCamelCase     :      List[str]       						=						tokenizer.eos_token_id
      __lowerCamelCase     :      Optional[int]       						=						250_004
      __lowerCamelCase     :      Dict       						=						tokenizer.eos_token_id

      __lowerCamelCase     :      Optional[Any]       						=						SpeechEncoderDecoderConfig.from_dict(UpperCAmelCase      )

      hf_wavavec.save_pretrained(UpperCAmelCase      )
      feature_extractor.save_pretrained(UpperCAmelCase      )


if __name__ == "__main__":
    __UpperCamelCase    :						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('--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=250004, type=int, help='`decoder_start_token_id` of model config')

    __UpperCamelCase    :						int     =							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,
    )



