






"""simple docstring"""




import gc
import random
import tempfile
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu




class    lowercase_    (unittest.TestCase     ):






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







        '''simple docstring'''


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






      @property
      def 			lowerCamelCase__    (    self	)	->Union[str, Any]:







        '''simple docstring'''


        _a							=	1
        _a							=	3
        _a							=	(3_2, 3_2)

        _a							=	floats_tensor((batch_size, num_channels) + sizes     ,     rng=random.Random(0	)	).to(UpperCAmelCase_	)
        return image






      @property
      def 			lowerCamelCase__    (    self	)	->Union[str, Any]:







        '''simple docstring'''


        torch.manual_seed(0	)
        _a							=	UNetaDConditionModel(
            block_out_channels=(3_2, 6_4)     ,     layers_per_block=2     ,     sample_size=3_2     ,     in_channels=4     ,     out_channels=4     ,     down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")     ,     up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")     ,     cross_attention_dim=3_2     ,     )
        return model






      @property
      def 			lowerCamelCase__    (    self	)	->List[str]:







        '''simple docstring'''


        torch.manual_seed(0	)
        _a							=	AutoencoderKL(
            block_out_channels=[3_2, 6_4]     ,     in_channels=3     ,     out_channels=3     ,     down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]     ,     up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]     ,     latent_channels=4     ,     )
        return model






      @property
      def 			lowerCamelCase__    (    self	)	->List[Any]:







        '''simple docstring'''


        torch.manual_seed(0	)
        _a							=	CLIPTextConfig(
            bos_token_id=0     ,     eos_token_id=2     ,     hidden_size=3_2     ,     intermediate_size=3_7     ,     layer_norm_eps=1E-05     ,     num_attention_heads=4     ,     num_hidden_layers=5     ,     pad_token_id=1     ,     vocab_size=1_0_0_0     ,     )
        return CLIPTextModel(UpperCAmelCase_	)






      @property
      def 			lowerCamelCase__    (    self	)	->Optional[Any]:







        '''simple docstring'''


        def extract(*a_     ,     **a_	):




          class    lowercase_    :






                def __init__(    self	)	->Dict:







                  '''simple docstring'''


                  _a							=	torch.ones([0]	)






                def 			lowerCamelCase__    (    self     ,     a_	)	->Tuple:







                  '''simple docstring'''


                  self.pixel_values.to(UpperCAmelCase_	)
                  return self







          return Out()

        return extract






      def 			lowerCamelCase__    (    self	)	->Any:







        '''simple docstring'''


        _a							=	'cpu'  # ensure determinism for the device-dependent torch.Generator
        _a							=	self.dummy_cond_unet
        _a							=	DDIMScheduler(
            beta_start=0.00_085     ,     beta_end=0.012     ,     beta_schedule="scaled_linear"     ,     clip_sample=UpperCAmelCase_     ,     set_alpha_to_one=UpperCAmelCase_     ,     )

        _a							=	self.dummy_vae
        _a							=	self.dummy_text_encoder
        _a							=	CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

        # make sure here that pndm scheduler skips prk
        _a							=	StableDiffusionPipeline(
            unet=UpperCAmelCase_     ,     scheduler=UpperCAmelCase_     ,     vae=UpperCAmelCase_     ,     text_encoder=UpperCAmelCase_     ,     tokenizer=UpperCAmelCase_     ,     safety_checker=UpperCAmelCase_     ,     feature_extractor=self.dummy_extractor     ,     )
        _a							=	sd_pipe.to(UpperCAmelCase_	)
        sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_	)

        _a							=	'A painting of a squirrel eating a burger'

        _a							=	torch.Generator(device=UpperCAmelCase_	).manual_seed(0	)
        _a							=	sd_pipe([prompt]     ,     generator=UpperCAmelCase_     ,     guidance_scale=6.0     ,     num_inference_steps=2     ,     output_type="np"	)
        _a							=	output.images

        _a							=	torch.Generator(device=UpperCAmelCase_	).manual_seed(0	)
        _a							=	sd_pipe(
            [prompt]     ,     generator=UpperCAmelCase_     ,     guidance_scale=6.0     ,     num_inference_steps=2     ,     output_type="np"     ,     return_dict=UpperCAmelCase_     ,     )[0]

        _a							=	image[0, -3:, -3:, -1]
        _a							=	image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 6_4, 6_4, 3)
        _a							=	np.array([0.5_756, 0.6_118, 0.5_005, 0.5_041, 0.5_471, 0.4_726, 0.4_976, 0.4_865, 0.4_864]	)

        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 			lowerCamelCase__    (    self	)	->int:







        '''simple docstring'''


        _a							=	'cpu'  # ensure determinism for the device-dependent torch.Generator
        _a							=	self.dummy_cond_unet
        _a							=	PNDMScheduler(skip_prk_steps=UpperCAmelCase_	)
        _a							=	self.dummy_vae
        _a							=	self.dummy_text_encoder
        _a							=	CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

        # make sure here that pndm scheduler skips prk
        _a							=	StableDiffusionPipeline(
            unet=UpperCAmelCase_     ,     scheduler=UpperCAmelCase_     ,     vae=UpperCAmelCase_     ,     text_encoder=UpperCAmelCase_     ,     tokenizer=UpperCAmelCase_     ,     safety_checker=UpperCAmelCase_     ,     feature_extractor=self.dummy_extractor     ,     )
        _a							=	sd_pipe.to(UpperCAmelCase_	)
        sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_	)

        _a							=	'A painting of a squirrel eating a burger'
        _a							=	torch.Generator(device=UpperCAmelCase_	).manual_seed(0	)
        _a							=	sd_pipe([prompt]     ,     generator=UpperCAmelCase_     ,     guidance_scale=6.0     ,     num_inference_steps=2     ,     output_type="np"	)

        _a							=	output.images

        _a							=	torch.Generator(device=UpperCAmelCase_	).manual_seed(0	)
        _a							=	sd_pipe(
            [prompt]     ,     generator=UpperCAmelCase_     ,     guidance_scale=6.0     ,     num_inference_steps=2     ,     output_type="np"     ,     return_dict=UpperCAmelCase_     ,     )[0]

        _a							=	image[0, -3:, -3:, -1]
        _a							=	image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 6_4, 6_4, 3)
        _a							=	np.array([0.5_125, 0.5_716, 0.4_828, 0.5_060, 0.5_650, 0.4_768, 0.5_185, 0.4_895, 0.4_993]	)

        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 			lowerCamelCase__    (    self	)	->Tuple:







        '''simple docstring'''


        _a							=	StableDiffusionPipeline.from_pretrained(
            "hf-internal-testing/tiny-stable-diffusion-lms-pipe"     ,     safety_checker=UpperCAmelCase_	)
        assert isinstance(UpperCAmelCase_     ,     UpperCAmelCase_	)
        assert isinstance(pipe.scheduler     ,     UpperCAmelCase_	)
        assert pipe.safety_checker is None

        _a							=	pipe("example prompt"     ,     num_inference_steps=2	).images[0]
        assert image is not None

        # check that there's no error when saving a pipeline with one of the models being None
        with tempfile.TemporaryDirectory() as tmpdirname:
          pipe.save_pretrained(UpperCAmelCase_	)
          _a							=	StableDiffusionPipeline.from_pretrained(UpperCAmelCase_	)

        # sanity check that the pipeline still works
        assert pipe.safety_checker is None
        _a							=	pipe("example prompt"     ,     num_inference_steps=2	).images[0]
        assert image is not None






      @unittest.skipIf(torch_device != "cuda"     ,     "This test requires a GPU"	)
      def 			lowerCamelCase__    (    self	)	->Union[str, Any]:







        '''simple docstring'''


        _a							=	self.dummy_cond_unet
        _a							=	PNDMScheduler(skip_prk_steps=UpperCAmelCase_	)
        _a							=	self.dummy_vae
        _a							=	self.dummy_text_encoder
        _a							=	CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

        # put models in fp16
        _a							=	unet.half()
        _a							=	vae.half()
        _a							=	bert.half()

        # make sure here that pndm scheduler skips prk
        _a							=	StableDiffusionPipeline(
            unet=UpperCAmelCase_     ,     scheduler=UpperCAmelCase_     ,     vae=UpperCAmelCase_     ,     text_encoder=UpperCAmelCase_     ,     tokenizer=UpperCAmelCase_     ,     safety_checker=UpperCAmelCase_     ,     feature_extractor=self.dummy_extractor     ,     )
        _a							=	sd_pipe.to(UpperCAmelCase_	)
        sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_	)

        _a							=	'A painting of a squirrel eating a burger'
        _a							=	sd_pipe([prompt]     ,     num_inference_steps=2     ,     output_type="np"	).images

        assert image.shape == (1, 6_4, 6_4, 3)







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






      def 			lowerCamelCase__    (    self	)	->Optional[Any]:







        '''simple docstring'''


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






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







        '''simple docstring'''


        _a							=	StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"     ,     safety_checker=UpperCAmelCase_	)
        _a							=	LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config	)
        _a							=	sd_pipe.to(UpperCAmelCase_	)
        sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_	)

        _a							=	(
            'portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle'
            ' coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with'
            ' anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and'
            ' children from bahnhof zoo, detailed '
        )
        _a							=	4_0_0_3_6_6_0_3_4_6
        _a							=	7

        # without safety guidance (sld_guidance_scale = 0)
        _a							=	torch.manual_seed(UpperCAmelCase_	)
        _a							=	sd_pipe(
            [prompt]     ,     generator=UpperCAmelCase_     ,     guidance_scale=UpperCAmelCase_     ,     num_inference_steps=5_0     ,     output_type="np"     ,     width=5_1_2     ,     height=5_1_2     ,     sld_guidance_scale=0     ,     )

        _a							=	output.images
        _a							=	image[0, -3:, -3:, -1]
        _a							=	[0.2_278, 0.2_231, 0.2_249, 0.2_333, 0.2_303, 0.1_885, 0.2_273, 0.2_144, 0.2_176]

        assert image.shape == (1, 5_1_2, 5_1_2, 3)

        assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2

        # without safety guidance (strong configuration)
        _a							=	torch.manual_seed(UpperCAmelCase_	)
        _a							=	sd_pipe(
            [prompt]     ,     generator=UpperCAmelCase_     ,     guidance_scale=UpperCAmelCase_     ,     num_inference_steps=5_0     ,     output_type="np"     ,     width=5_1_2     ,     height=5_1_2     ,     sld_guidance_scale=2_0_0_0     ,     sld_warmup_steps=7     ,     sld_threshold=0.025     ,     sld_momentum_scale=0.5     ,     sld_mom_beta=0.7     ,     )

        _a							=	output.images
        _a							=	image[0, -3:, -3:, -1]
        _a							=	[0.2_383, 0.2_276, 0.236, 0.2_192, 0.2_186, 0.2_053, 0.1_971, 0.1_901, 0.1_719]

        assert image.shape == (1, 5_1_2, 5_1_2, 3)

        assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2






      def 			lowerCamelCase__    (    self	)	->Optional[int]:







        '''simple docstring'''


        _a							=	StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"     ,     safety_checker=UpperCAmelCase_	)
        _a							=	LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config	)
        _a							=	sd_pipe.to(UpperCAmelCase_	)
        sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_	)

        _a							=	'padme amidala taking a bath artwork, safe for work, no nudity'
        _a							=	2_7_3_4_9_7_1_7_5_5
        _a							=	7

        _a							=	torch.manual_seed(UpperCAmelCase_	)
        _a							=	sd_pipe(
            [prompt]     ,     generator=UpperCAmelCase_     ,     guidance_scale=UpperCAmelCase_     ,     num_inference_steps=5_0     ,     output_type="np"     ,     width=5_1_2     ,     height=5_1_2     ,     sld_guidance_scale=0     ,     )

        _a							=	output.images
        _a							=	image[0, -3:, -3:, -1]
        _a							=	[0.3_502, 0.3_622, 0.3_396, 0.3_642, 0.3_478, 0.3_318, 0.35, 0.3_348, 0.3_297]

        assert image.shape == (1, 5_1_2, 5_1_2, 3)

        assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2

        _a							=	torch.manual_seed(UpperCAmelCase_	)
        _a							=	sd_pipe(
            [prompt]     ,     generator=UpperCAmelCase_     ,     guidance_scale=UpperCAmelCase_     ,     num_inference_steps=5_0     ,     output_type="np"     ,     width=5_1_2     ,     height=5_1_2     ,     sld_guidance_scale=2_0_0_0     ,     sld_warmup_steps=7     ,     sld_threshold=0.025     ,     sld_momentum_scale=0.5     ,     sld_mom_beta=0.7     ,     )

        _a							=	output.images
        _a							=	image[0, -3:, -3:, -1]
        _a							=	[0.5_531, 0.5_206, 0.4_895, 0.5_156, 0.5_182, 0.4_751, 0.4_802, 0.4_803, 0.4_443]

        assert image.shape == (1, 5_1_2, 5_1_2, 3)

        assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2






      def 			lowerCamelCase__    (    self	)	->int:







        '''simple docstring'''


        _a							=	StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"	)
        _a							=	sd_pipe.to(UpperCAmelCase_	)
        sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_	)

        _a							=	(
            'the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'
            ' leyendecker'
        )
        _a							=	1_0_4_4_3_5_5_2_3_4
        _a							=	1_2

        _a							=	torch.manual_seed(UpperCAmelCase_	)
        _a							=	sd_pipe(
            [prompt]     ,     generator=UpperCAmelCase_     ,     guidance_scale=UpperCAmelCase_     ,     num_inference_steps=5_0     ,     output_type="np"     ,     width=5_1_2     ,     height=5_1_2     ,     sld_guidance_scale=0     ,     )

        _a							=	output.images
        _a							=	image[0, -3:, -3:, -1]
        _a							=	np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	)

        assert image.shape == (1, 5_1_2, 5_1_2, 3)

        assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-7

        _a							=	torch.manual_seed(UpperCAmelCase_	)
        _a							=	sd_pipe(
            [prompt]     ,     generator=UpperCAmelCase_     ,     guidance_scale=UpperCAmelCase_     ,     num_inference_steps=5_0     ,     output_type="np"     ,     width=5_1_2     ,     height=5_1_2     ,     sld_guidance_scale=2_0_0_0     ,     sld_warmup_steps=7     ,     sld_threshold=0.025     ,     sld_momentum_scale=0.5     ,     sld_mom_beta=0.7     ,     )

        _a							=	output.images
        _a							=	image[0, -3:, -3:, -1]
        _a							=	np.array([0.5_818, 0.6_285, 0.6_835, 0.6_019, 0.625, 0.6_754, 0.6_096, 0.6_334, 0.6_561]	)
        assert image.shape == (1, 5_1_2, 5_1_2, 3)

        assert np.abs(image_slice.flatten() - expected_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							       =    logging.get_logger(__name__)

UpperCamelCase							       =    {
    """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							       =    [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """projector""",
    """classifier""",
]






def        lowerCAmelCase (      UpperCamelCase_:							List[str]					)       ->							List[Any]:







  '''simple docstring'''







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






def        lowerCAmelCase (      UpperCamelCase_:							List[str]     , UpperCamelCase_:							Dict     , UpperCamelCase_:							List[str]     , UpperCamelCase_:							Union[str, Any]     , UpperCamelCase_:							Union[str, Any]					)       ->							Union[str, Any]:







  '''simple docstring'''







  for attribute in key.split("."					):
    _a							=	getattr(UpperCamelCase_     , UpperCamelCase_					)

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

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

    # let's reduce dimension
    _a							=	value[0]
  else:
    _a							=	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":
    _a							=	value
  elif weight_type == "weight_g":
    _a							=	value
  elif weight_type == "weight_v":
    _a							=	value
  elif weight_type == "bias":
    _a							=	value
  elif weight_type == "param":
    for attribute in hf_param_name.split("."					):
      _a							=	getattr(UpperCamelCase_     , UpperCamelCase_					)
    _a							=	value
  else:
    _a							=	value

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






def        lowerCAmelCase (      UpperCamelCase_:							Union[str, Any]     , UpperCamelCase_:							Dict     , UpperCamelCase_:							Tuple     , UpperCamelCase_:							Any     , UpperCamelCase_:							Union[str, Any]					)       ->							str:







  '''simple docstring'''







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

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

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


UpperCamelCase							       =    {
    """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        lowerCAmelCase (      UpperCamelCase_:							str     , UpperCamelCase_:							Union[str, Any]     , UpperCamelCase_:							int=None     , UpperCamelCase_:							Optional[int]=None					)       ->							Tuple:







  '''simple docstring'''







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






def        lowerCAmelCase (      UpperCamelCase_:							Dict     , UpperCamelCase_:							Dict     , UpperCamelCase_:							Dict					)       ->							Dict:







  '''simple docstring'''







  _a							=	[]
  _a							=	fairseq_model.state_dict()

  _a							=	hf_model.wavaveca.feature_extractor

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

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






def        lowerCAmelCase (      UpperCamelCase_:							Union[str, Any]     , UpperCamelCase_:							List[Any]     , UpperCamelCase_:							int     , UpperCamelCase_:							Optional[int]     , UpperCamelCase_:							Optional[int]					)       ->							Dict:







  '''simple docstring'''







  _a							=	full_name.split("conv_layers."					)[-1]
  _a							=	name.split("."					)
  _a							=	int(items[0]					)
  _a							=	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.'''					)
      _a							=	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.'''					)
      _a							=	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.'''					)
      _a							=	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.'''					)
      _a							=	value
      logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''					)
  else:
    unused_weights.append(UpperCamelCase_					)






@torch.no_grad()
def        lowerCAmelCase (      UpperCamelCase_:							Tuple     , UpperCamelCase_:							List[str]     , UpperCamelCase_:							int=None     , UpperCamelCase_:							int=None     , UpperCamelCase_:							Optional[Any]=True     , UpperCamelCase_:							Any=False					)       ->							Optional[Any]:







  '''simple docstring'''







  if config_path is not None:
    _a							=	WavaVecaConfig.from_pretrained(UpperCamelCase_					)
  else:
    _a							=	WavaVecaConfig()

  if is_seq_class:
    _a							=	read_txt_into_dict(UpperCamelCase_					)
    _a							=	idalabel
    _a							=	WavaVecaForSequenceClassification(UpperCamelCase_					)
    _a							=	WavaVecaFeatureExtractor(
        feature_size=1     , sampling_rate=16000     , padding_value=0     , do_normalize=UpperCamelCase_     , return_attention_mask=UpperCamelCase_     , )
    feature_extractor.save_pretrained(UpperCamelCase_					)

  elif is_finetuned:
    if dict_path:
      _a							=	Dictionary.load(UpperCamelCase_					)

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

      # fairseq has the <pad> and <s> switched
      _a							=	0
      _a							=	1
      with open(UpperCamelCase_     , "w"     , encoding="utf-8"					) as vocab_handle:
        json.dump(UpperCamelCase_     , UpperCamelCase_					)
      _a							=	WavaVecaCTCTokenizer(
          UpperCamelCase_     , 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=UpperCamelCase_     , )
      _a							=	True if config.feat_extract_norm == "layer" else False
      _a							=	WavaVecaFeatureExtractor(
          feature_size=1     , sampling_rate=16000     , padding_value=0     , do_normalize=UpperCamelCase_     , return_attention_mask=UpperCamelCase_     , )
      _a							=	WavaVecaProcessor(feature_extractor=UpperCamelCase_     , tokenizer=UpperCamelCase_					)
      processor.save_pretrained(UpperCamelCase_					)

    _a							=	WavaVecaForCTC(UpperCamelCase_					)
  else:
    _a							=	WavaVecaForPreTraining(UpperCamelCase_					)

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

    _a			,	_a			,	_a							=	fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]     , task=UpperCamelCase_					)

  _a							=	model[0].eval()

  recursively_load_weights(UpperCamelCase_     , UpperCamelCase_     , not is_finetuned					)

  hf_wavavec.save_pretrained(UpperCamelCase_					)


if __name__ == "__main__":
     UpperCamelCase							       =    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							       =    parser.parse_args()

     UpperCamelCase							       =    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,
     )


