

'''simple docstring'''



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 		lowerCAmelCase__  ( unittest.TestCase   ):



 """simple docstring"""



 def  UpperCAmelCase__						(						self   :			List[Any]     )    ->					Any:


      """simple docstring"""




      with tempfile.TemporaryDirectory() as tmpdirname:
           # pipeline has Flax weights
           __SCREAMING_SNAKE_CASE      = FlaxDiffusionPipeline.from_pretrained(
               """hf-internal-testing/tiny-stable-diffusion-pipe"""   ,				safety_checker=__SCREAMING_SNAKE_CASE   ,				cache_dir=__SCREAMING_SNAKE_CASE     )

           __SCREAMING_SNAKE_CASE      = [t[-1] for t in os.walk(os.path.join(__SCREAMING_SNAKE_CASE   ,				os.listdir(__SCREAMING_SNAKE_CASE     )[0]   ,				"""snapshots"""     )     )]
           __SCREAMING_SNAKE_CASE      = [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 		lowerCAmelCase__  ( unittest.TestCase   ):



 """simple docstring"""



 def  UpperCAmelCase__						(						self   :			Optional[int]     )    ->					Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = FlaxStableDiffusionPipeline.from_pretrained(
          """hf-internal-testing/tiny-stable-diffusion-pipe"""   ,				safety_checker=__SCREAMING_SNAKE_CASE     )

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

      __SCREAMING_SNAKE_CASE      = jax.random.PRNGKey(0     )
      __SCREAMING_SNAKE_CASE      = 4

      __SCREAMING_SNAKE_CASE      = jax.device_count()
      __SCREAMING_SNAKE_CASE      = num_samples * [prompt]
      __SCREAMING_SNAKE_CASE      = pipeline.prepare_inputs(__SCREAMING_SNAKE_CASE     )

      # shard inputs and rng
      __SCREAMING_SNAKE_CASE      = replicate(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = jax.random.split(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = shard(__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = pipeline(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				jit=__SCREAMING_SNAKE_CASE     ).images

      assert images.shape == (num_samples, 1, 64, 64, 3)
      if jax.device_count() == 8:
           assert np.abs(np.abs(images[0, 0, :2, :2, -2:]   ,				dtype=np.floataa     ).sum() - 4.1514745     ) < 1E-3
           assert np.abs(np.abs(__SCREAMING_SNAKE_CASE   ,				dtype=np.floataa     ).sum() - 49947.875     ) < 5E-1

      __SCREAMING_SNAKE_CASE      = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]     )     )     )
      assert len(__SCREAMING_SNAKE_CASE     ) == num_samples
 def  UpperCAmelCase__						(						self   :			Dict     )    ->					Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = FlaxStableDiffusionPipeline.from_pretrained(
          """CompVis/stable-diffusion-v1-4"""   ,				revision="""flax"""   ,				safety_checker=__SCREAMING_SNAKE_CASE     )

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

      __SCREAMING_SNAKE_CASE      = jax.random.PRNGKey(0     )
      __SCREAMING_SNAKE_CASE      = 50

      __SCREAMING_SNAKE_CASE      = jax.device_count()
      __SCREAMING_SNAKE_CASE      = num_samples * [prompt]
      __SCREAMING_SNAKE_CASE      = pipeline.prepare_inputs(__SCREAMING_SNAKE_CASE     )

      # shard inputs and rng
      __SCREAMING_SNAKE_CASE      = replicate(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = jax.random.split(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = shard(__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = pipeline(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				jit=__SCREAMING_SNAKE_CASE     ).images

      assert images.shape == (num_samples, 1, 512, 512, 3)
      if jax.device_count() == 8:
           assert np.abs((np.abs(images[0, 0, :2, :2, -2:]   ,				dtype=np.floataa     ).sum() - 0.05652401)     ) < 1E-3
           assert np.abs((np.abs(__SCREAMING_SNAKE_CASE   ,				dtype=np.floataa     ).sum() - 2383808.2)     ) < 5E-1
 def  UpperCAmelCase__						(						self   :			Dict     )    ->					List[str]:


      """simple docstring"""




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

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

      __SCREAMING_SNAKE_CASE      = jax.random.PRNGKey(0     )
      __SCREAMING_SNAKE_CASE      = 50

      __SCREAMING_SNAKE_CASE      = jax.device_count()
      __SCREAMING_SNAKE_CASE      = num_samples * [prompt]
      __SCREAMING_SNAKE_CASE      = pipeline.prepare_inputs(__SCREAMING_SNAKE_CASE     )

      # shard inputs and rng
      __SCREAMING_SNAKE_CASE      = replicate(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = jax.random.split(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = shard(__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = pipeline(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				jit=__SCREAMING_SNAKE_CASE     ).images

      assert images.shape == (num_samples, 1, 512, 512, 3)
      if jax.device_count() == 8:
           assert np.abs((np.abs(images[0, 0, :2, :2, -2:]   ,				dtype=np.floataa     ).sum() - 0.04003906)     ) < 1E-3
           assert np.abs((np.abs(__SCREAMING_SNAKE_CASE   ,				dtype=np.floataa     ).sum() - 2373516.75)     ) < 5E-1
 def  UpperCAmelCase__						(						self   :			Optional[int]     )    ->					Optional[int]:


      """simple docstring"""




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

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

      __SCREAMING_SNAKE_CASE      = jax.random.PRNGKey(0     )
      __SCREAMING_SNAKE_CASE      = 50

      __SCREAMING_SNAKE_CASE      = jax.device_count()
      __SCREAMING_SNAKE_CASE      = num_samples * [prompt]
      __SCREAMING_SNAKE_CASE      = pipeline.prepare_inputs(__SCREAMING_SNAKE_CASE     )

      # shard inputs and rng
      __SCREAMING_SNAKE_CASE      = replicate(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = jax.random.split(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = shard(__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = pipeline(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				jit=__SCREAMING_SNAKE_CASE     ).images

      assert images.shape == (num_samples, 1, 512, 512, 3)
      if jax.device_count() == 8:
           assert np.abs((np.abs(images[0, 0, :2, :2, -2:]   ,				dtype=np.floataa     ).sum() - 0.04003906)     ) < 1E-3
           assert np.abs((np.abs(__SCREAMING_SNAKE_CASE   ,				dtype=np.floataa     ).sum() - 2373516.75)     ) < 5E-1
 def  UpperCAmelCase__						(						self   :			Union[str, Any]     )    ->					Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = FlaxDDIMScheduler(
          beta_start=0.00085   ,				beta_end=0.012   ,				beta_schedule="""scaled_linear"""   ,				set_alpha_to_one=__SCREAMING_SNAKE_CASE   ,				steps_offset=1   ,				)

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

      __SCREAMING_SNAKE_CASE      = scheduler_state

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

      __SCREAMING_SNAKE_CASE      = jax.random.PRNGKey(0     )
      __SCREAMING_SNAKE_CASE      = 50

      __SCREAMING_SNAKE_CASE      = jax.device_count()
      __SCREAMING_SNAKE_CASE      = num_samples * [prompt]
      __SCREAMING_SNAKE_CASE      = pipeline.prepare_inputs(__SCREAMING_SNAKE_CASE     )

      # shard inputs and rng
      __SCREAMING_SNAKE_CASE      = replicate(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = jax.random.split(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = shard(__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = pipeline(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				jit=__SCREAMING_SNAKE_CASE     ).images

      assert images.shape == (num_samples, 1, 512, 512, 3)
      if jax.device_count() == 8:
           assert np.abs((np.abs(images[0, 0, :2, :2, -2:]   ,				dtype=np.floataa     ).sum() - 0.045043945)     ) < 1E-3
           assert np.abs((np.abs(__SCREAMING_SNAKE_CASE   ,				dtype=np.floataa     ).sum() - 2347693.5)     ) < 5E-1


 def  UpperCAmelCase__						(						self   :			List[str]     )    ->					Union[str, Any]:


      """simple docstring"""




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

      __SCREAMING_SNAKE_CASE      = jax.device_count()
      __SCREAMING_SNAKE_CASE      = num_samples * [prompt]
      __SCREAMING_SNAKE_CASE      = jax.random.split(jax.random.PRNGKey(0     )   ,				__SCREAMING_SNAKE_CASE     )

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

      __SCREAMING_SNAKE_CASE      = replicate(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = pipeline.prepare_inputs(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = shard(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = pipeline(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				jit=__SCREAMING_SNAKE_CASE     ).images
      assert images.shape == (num_samples, 1, 512, 512, 3)
      __SCREAMING_SNAKE_CASE      = images[2, 0, 256, 10:17, 1]

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

      __SCREAMING_SNAKE_CASE      = replicate(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = pipeline.prepare_inputs(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = shard(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = pipeline(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				jit=__SCREAMING_SNAKE_CASE     ).images
      assert images_eff.shape == (num_samples, 1, 512, 512, 3)
      __SCREAMING_SNAKE_CASE      = images[2, 0, 256, 10:17, 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









'''simple docstring'''



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

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


logging.set_verbosity_info()
UpperCAmelCase					:			List[Any]       =	logging.get_logger(__name__)

UpperCAmelCase					:			Optional[Any]       =	{
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'adapter_layer': 'encoder.layers.*.adapter_layer',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
    'pooling_layer.linear': 'projector',
    'pooling_layer.projection': 'classifier',
}
UpperCAmelCase					:			Any       =	[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]







def   a__			(    a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = {}
     with open(a__ ,       """r"""		) as file:
          for line_number, line in enumerate(a__		):
               __SCREAMING_SNAKE_CASE      = line.strip()
               if line:
                    __SCREAMING_SNAKE_CASE      = line.split()
                    __SCREAMING_SNAKE_CASE      = line_number
                    __SCREAMING_SNAKE_CASE      = words[0]
                    __SCREAMING_SNAKE_CASE      = value
     return result







def   a__			(    a__ ,       a__ ,       a__ ,       a__ ,       a__		):




     """simple docstring"""


     for attribute in key.split("""."""		):
          __SCREAMING_SNAKE_CASE      = getattr(a__ ,       a__		)

     __SCREAMING_SNAKE_CASE      = None
     for param_key in PARAM_MAPPING.keys():
          if full_name.endswith(a__		):
               __SCREAMING_SNAKE_CASE      = PARAM_MAPPING[full_name.split("""."""		)[-1]]
               __SCREAMING_SNAKE_CASE      = """param"""

     if weight_type is not None and weight_type != "param":
          __SCREAMING_SNAKE_CASE      = getattr(a__ ,       a__		).shape
     elif weight_type is not None and weight_type == "param":
          __SCREAMING_SNAKE_CASE      = hf_pointer
          for attribute in hf_param_name.split("""."""		):
               __SCREAMING_SNAKE_CASE      = getattr(a__ ,       a__		)
          __SCREAMING_SNAKE_CASE      = shape_pointer.shape

          # let's reduce dimension
          __SCREAMING_SNAKE_CASE      = value[0]
     else:
          __SCREAMING_SNAKE_CASE      = hf_pointer.shape

     if hf_shape != value.shape:
          raise ValueError(
              F'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
              F' {value.shape} for {full_name}'		)

     if weight_type == "weight":
          __SCREAMING_SNAKE_CASE      = value
     elif weight_type == "weight_g":
          __SCREAMING_SNAKE_CASE      = value
     elif weight_type == "weight_v":
          __SCREAMING_SNAKE_CASE      = value
     elif weight_type == "bias":
          __SCREAMING_SNAKE_CASE      = value
     elif weight_type == "param":
          for attribute in hf_param_name.split("""."""		):
               __SCREAMING_SNAKE_CASE      = getattr(a__ ,       a__		)
          __SCREAMING_SNAKE_CASE      = value
     else:
          __SCREAMING_SNAKE_CASE      = value

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







def   a__			(    a__ ,       a__ ,       a__ ,       a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = None
     for param_key in PARAM_MAPPING.keys():
          if full_name.endswith(a__		):
               __SCREAMING_SNAKE_CASE      = PARAM_MAPPING[full_name.split("""."""		)[-1]]
               __SCREAMING_SNAKE_CASE      = """param"""

     if weight_type is not None and weight_type != "param":
          __SCREAMING_SNAKE_CASE      = """.""".join([key, weight_type]		)
     elif weight_type is not None and weight_type == "param":
          __SCREAMING_SNAKE_CASE      = """.""".join([key, hf_param_name]		)
     else:
          __SCREAMING_SNAKE_CASE      = key

     __SCREAMING_SNAKE_CASE      = value if """lm_head""" in full_key else value[0]


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







def   a__			(    a__ ,       a__ ,       a__=None ,       a__=None		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = False
     for key, mapped_key in MAPPING.items():
          __SCREAMING_SNAKE_CASE      = """wav2vec2.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
          if key in name or key.split("""w2v_model."""		)[-1] == name.split("""."""		)[0]:
               __SCREAMING_SNAKE_CASE      = True
               if "*" in mapped_key:
                    __SCREAMING_SNAKE_CASE      = name.split(a__		)[0].split("""."""		)[-2]
                    __SCREAMING_SNAKE_CASE      = mapped_key.replace("""*""" ,       a__		)
               if "weight_g" in name:
                    __SCREAMING_SNAKE_CASE      = """weight_g"""
               elif "weight_v" in name:
                    __SCREAMING_SNAKE_CASE      = """weight_v"""
               elif "bias" in name:
                    __SCREAMING_SNAKE_CASE      = """bias"""
               elif "weight" in name:
                    # TODO: don't match quantizer.weight_proj
                    __SCREAMING_SNAKE_CASE      = """weight"""
               else:
                    __SCREAMING_SNAKE_CASE      = None
               if hf_dict is not None:
                    rename_dict(a__ ,       a__ ,       a__ ,       a__ ,       a__		)
               else:
                    set_recursively(a__ ,       a__ ,       a__ ,       a__ ,       a__		)
               return is_used
     return is_used







def   a__			(    a__ ,       a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = []
     __SCREAMING_SNAKE_CASE      = fairseq_model.state_dict()

     __SCREAMING_SNAKE_CASE      = hf_model.wavaveca.feature_extractor

     for name, value in fairseq_dict.items():
          __SCREAMING_SNAKE_CASE      = False
          if "conv_layers" in name:
               load_conv_layer(
                   a__ ,       a__ ,       a__ ,       a__ ,       hf_model.config.feat_extract_norm == """group""" ,       )
               __SCREAMING_SNAKE_CASE      = True
          else:
               __SCREAMING_SNAKE_CASE      = load_wavaveca_layer(a__ ,       a__ ,       a__		)
          if not is_used:
               unused_weights.append(a__		)

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







def   a__			(    a__ ,       a__ ,       a__ ,       a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = full_name.split("""conv_layers."""		)[-1]
     __SCREAMING_SNAKE_CASE      = name.split("""."""		)
     __SCREAMING_SNAKE_CASE      = int(items[0]		)
     __SCREAMING_SNAKE_CASE      = int(items[1]		)

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







@torch.no_grad()
def   a__			(    a__ ,       a__ ,       a__=None ,       a__=None ,       a__=True ,       a__=False		):




     """simple docstring"""


     if config_path is not None:
          __SCREAMING_SNAKE_CASE      = WavaVecaConfig.from_pretrained(a__		)
     else:
          __SCREAMING_SNAKE_CASE      = WavaVecaConfig()

     if is_seq_class:
          __SCREAMING_SNAKE_CASE      = read_txt_into_dict(a__		)
          __SCREAMING_SNAKE_CASE      = idalabel
          __SCREAMING_SNAKE_CASE      = WavaVecaForSequenceClassification(a__		)
          __SCREAMING_SNAKE_CASE      = WavaVecaFeatureExtractor(
              feature_size=1 ,       sampling_rate=1_60_00 ,       padding_value=0 ,       do_normalize=a__ ,       return_attention_mask=a__ ,       )
          feature_extractor.save_pretrained(a__		)

     elif is_finetuned:
          if dict_path:
               __SCREAMING_SNAKE_CASE      = Dictionary.load(a__		)

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

               # fairseq has the <pad> and <s> switched
               __SCREAMING_SNAKE_CASE      = 0
               __SCREAMING_SNAKE_CASE      = 1
               with open(a__ ,       """w""" ,       encoding="""utf-8"""		) as vocab_handle:
                    json.dump(a__ ,       a__		)
               __SCREAMING_SNAKE_CASE      = WavaVecaCTCTokenizer(
                   a__ ,       unk_token=target_dict.unk_word ,       pad_token=target_dict.pad_word ,       bos_token=target_dict.bos_word ,       eos_token=target_dict.eos_word ,       word_delimiter_token="""|""" ,       do_lower_case=a__ ,       )
               __SCREAMING_SNAKE_CASE      = True if config.feat_extract_norm == """layer""" else False
               __SCREAMING_SNAKE_CASE      = WavaVecaFeatureExtractor(
                   feature_size=1 ,       sampling_rate=1_60_00 ,       padding_value=0 ,       do_normalize=a__ ,       return_attention_mask=a__ ,       )
               __SCREAMING_SNAKE_CASE      = WavaVecaProcessor(feature_extractor=a__ ,       tokenizer=a__		)
               processor.save_pretrained(a__		)

          __SCREAMING_SNAKE_CASE      = WavaVecaForCTC(a__		)
     else:
          __SCREAMING_SNAKE_CASE      = WavaVecaForPreTraining(a__		)

     if is_finetuned or is_seq_class:
          __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = fairseq.checkpoint_utils.load_model_ensemble_and_task(
              [checkpoint_path] ,       arg_overrides={"""data""": """/""".join(dict_path.split("""/"""		)[:-1]		)}		)
     else:
          __SCREAMING_SNAKE_CASE      = argparse.Namespace(task="""audio_pretraining"""		)
          __SCREAMING_SNAKE_CASE      = fairseq.tasks.setup_task(a__		)

          __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] ,       task=a__		)

     __SCREAMING_SNAKE_CASE      = model[0].eval()

     recursively_load_weights(a__ ,       a__ ,       not is_finetuned		)

     hf_wavavec.save_pretrained(a__		)


if __name__ == "__main__":
  UpperCAmelCase					:			int       =	argparse.ArgumentParser()
  parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
  parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
  parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
  parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
  parser.add_argument(
      '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
  )
  parser.add_argument(
      '--is_seq_class',
      action='store_true',
      help='Whether the model to convert is a fine-tuned sequence classification model or not',
  )
  UpperCAmelCase					:			Optional[Any]       =	parser.parse_args()

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






