






import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class 							_lowerCAmelCase(			__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      unittest.TestCase   ):





       """simple docstring"""







       a							:						str										=StableDiffusionDiffEditPipeline
       a							:						List[Any]										=TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""height""", """width""", """image"""} | {"""image_latents"""}
       a							:						str										=TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"""image"""} | {"""image_latents"""}
       a							:						str										=frozenset(
           []   )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
       a							:						Union[str, Any]										=frozenset([]   )





       def      _a       (   self				):
         torch.manual_seed(0				)
         UpperCamelCase_:   List[str]        =							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    ,     attention_head_dim=(2, 4)    ,     use_linear_projection=A_    ,     )
         UpperCamelCase_:   Any        =							DDIMScheduler(
             beta_start=0.0_0_0_8_5    ,     beta_end=0.0_1_2    ,     beta_schedule='scaled_linear'    ,     clip_sample=A_    ,     set_alpha_to_one=A_    ,     )
         UpperCamelCase_:   Union[str, Any]        =							DDIMInverseScheduler(
             beta_start=0.0_0_0_8_5    ,     beta_end=0.0_1_2    ,     beta_schedule='scaled_linear'    ,     clip_sample=A_    ,     set_alpha_to_zero=A_    ,     )
         torch.manual_seed(0				)
         UpperCamelCase_:   int        =							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    ,     sample_size=1_2_8    ,     )
         torch.manual_seed(0				)
         UpperCamelCase_:   Dict        =							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    ,     hidden_act='gelu'    ,     projection_dim=5_1_2    ,     )
         UpperCamelCase_:   List[Any]        =							CLIPTextModel(A_				)
         UpperCamelCase_:   str        =							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				)

         UpperCamelCase_:   str        =							{
             'unet': unet,
             'scheduler': scheduler,
             'inverse_scheduler': inverse_scheduler,
             'vae': vae,
             'text_encoder': text_encoder,
             'tokenizer': tokenizer,
             'safety_checker': None,
             'feature_extractor': None,
         }

         return components





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase=0				):
         UpperCamelCase_:   Any        =							floats_tensor((1, 1_6, 1_6)    ,     rng=random.Random(A_				)				).to(A_				)
         UpperCamelCase_:   str        =							floats_tensor((1, 2, 4, 1_6, 1_6)    ,     rng=random.Random(A_				)				).to(A_				)
         if str(A_				).startswith('mps'				):
           UpperCamelCase_:   Optional[int]        =							torch.manual_seed(A_				)
         else:
           UpperCamelCase_:   Optional[Any]        =							torch.Generator(device=A_				).manual_seed(A_				)
         UpperCamelCase_:   List[str]        =							{
             'prompt': 'a dog and a newt',
             'mask_image': mask,
             'image_latents': latents,
             'generator': generator,
             'num_inference_steps': 2,
             'inpaint_strength': 1.0,
             'guidance_scale': 6.0,
             'output_type': 'numpy',
         }

         return inputs





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase=0				):
         UpperCamelCase_:   Union[str, Any]        =							floats_tensor((1, 3, 3_2, 3_2)    ,     rng=random.Random(A_				)				).to(A_				)
         UpperCamelCase_:   str        =							image.cpu().permute(0    ,     2    ,     3    ,     1				)[0]
         UpperCamelCase_:   Dict        =							Image.fromarray(np.uinta(A_				)				).convert('RGB'				)
         if str(A_				).startswith('mps'				):
           UpperCamelCase_:   int        =							torch.manual_seed(A_				)
         else:
           UpperCamelCase_:   Any        =							torch.Generator(device=A_				).manual_seed(A_				)
         UpperCamelCase_:   Union[str, Any]        =							{
             'image': image,
             'source_prompt': 'a cat and a frog',
             'target_prompt': 'a dog and a newt',
             'generator': generator,
             'num_inference_steps': 2,
             'num_maps_per_mask': 2,
             'mask_encode_strength': 1.0,
             'guidance_scale': 6.0,
             'output_type': 'numpy',
         }

         return inputs





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase=0				):
         UpperCamelCase_:   List[Any]        =							floats_tensor((1, 3, 3_2, 3_2)    ,     rng=random.Random(A_				)				).to(A_				)
         UpperCamelCase_:   List[Any]        =							image.cpu().permute(0    ,     2    ,     3    ,     1				)[0]
         UpperCamelCase_:   Optional[Any]        =							Image.fromarray(np.uinta(A_				)				).convert('RGB'				)
         if str(A_				).startswith('mps'				):
           UpperCamelCase_:   Dict        =							torch.manual_seed(A_				)
         else:
           UpperCamelCase_:   str        =							torch.Generator(device=A_				).manual_seed(A_				)
         UpperCamelCase_:   Any        =							{
             'image': image,
             'prompt': 'a cat and a frog',
             'generator': generator,
             'num_inference_steps': 2,
             'inpaint_strength': 1.0,
             'guidance_scale': 6.0,
             'decode_latents': True,
             'output_type': 'numpy',
         }
         return inputs





       def      _a       (   self				):

         if not hasattr(self.pipeline_class    ,     '_optional_components'				):
           return

         UpperCamelCase_:   List[str]        =							self.get_dummy_components()
         UpperCamelCase_:   Optional[int]        =							self.pipeline_class(**A_				)
         pipe.to(A_				)
         pipe.set_progress_bar_config(disable=A_				)

         # set all optional components to None and update pipeline config accordingly
         for optional_component in pipe._optional_components:
           setattr(A_    ,     A_    ,     A_				)
         pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}				)

         UpperCamelCase_:   List[str]        =							self.get_dummy_inputs(A_				)
         UpperCamelCase_:   Optional[Any]        =							pipe(**A_				)[0]

         with tempfile.TemporaryDirectory() as tmpdir:
           pipe.save_pretrained(A_				)
           UpperCamelCase_:   Tuple        =							self.pipeline_class.from_pretrained(A_				)
           pipe_loaded.to(A_				)
           pipe_loaded.set_progress_bar_config(disable=A_				)

         for optional_component in pipe._optional_components:
           self.assertTrue(
               getattr(A_    ,     A_				) is None    ,     f'''`{optional_component}` did not stay set to None after loading.'''    ,     )

         UpperCamelCase_:   List[Any]        =							self.get_dummy_inputs(A_				)
         UpperCamelCase_:   Any        =							pipe_loaded(**A_				)[0]

         UpperCamelCase_:   Optional[Any]        =							np.abs(output - output_loaded				).max()
         self.assertLess(A_    ,     1e-4				)





       def      _a       (   self				):
         UpperCamelCase_:   List[str]        =							'cpu'

         UpperCamelCase_:   Union[str, Any]        =							self.get_dummy_components()
         UpperCamelCase_:   str        =							self.pipeline_class(**A_				)
         pipe.to(A_				)
         pipe.set_progress_bar_config(disable=A_				)

         UpperCamelCase_:   int        =							self.get_dummy_mask_inputs(A_				)
         UpperCamelCase_:   List[str]        =							pipe.generate_mask(**A_				)
         UpperCamelCase_:   Optional[Any]        =							mask[0, -3:, -3:]

         self.assertEqual(mask.shape    ,     (1, 1_6, 1_6)				)
         UpperCamelCase_:   Any        =							np.array([0] * 9				)
         UpperCamelCase_:   List[str]        =							np.abs(mask_slice.flatten() - expected_slice				).max()
         self.assertLessEqual(A_    ,     1e-3				)
         self.assertEqual(mask[0, -3, -4]    ,     0				)





       def      _a       (   self				):
         UpperCamelCase_:   Optional[Any]        =							'cpu'

         UpperCamelCase_:   List[str]        =							self.get_dummy_components()
         UpperCamelCase_:   List[str]        =							self.pipeline_class(**A_				)
         pipe.to(A_				)
         pipe.set_progress_bar_config(disable=A_				)

         UpperCamelCase_:   Dict        =							self.get_dummy_inversion_inputs(A_				)
         UpperCamelCase_:   Optional[int]        =							pipe.invert(**A_				).images
         UpperCamelCase_:   Tuple        =							image[0, -1, -3:, -3:]

         self.assertEqual(image.shape    ,     (2, 3_2, 3_2, 3)				)
         UpperCamelCase_:   Optional[int]        =							np.array(
             [0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9]    ,     )
         UpperCamelCase_:   Tuple        =							np.abs(image_slice.flatten() - expected_slice				).max()
         self.assertLessEqual(A_    ,     1e-3				)





       def      _a       (   self				):
         super().test_inference_batch_single_identical(expected_max_diff=5e-3				)





       def      _a       (   self				):
         UpperCamelCase_:   Union[str, Any]        =							'cpu'

         UpperCamelCase_:   Optional[Any]        =							self.get_dummy_components()

         UpperCamelCase_:   Optional[int]        =							{'beta_start': 0.0_0_0_8_5, 'beta_end': 0.0_1_2, 'beta_schedule': 'scaled_linear'}
         UpperCamelCase_:   str        =							DPMSolverMultistepScheduler(**A_				)
         UpperCamelCase_:   str        =							DPMSolverMultistepInverseScheduler(**A_				)

         UpperCamelCase_:   Dict        =							self.pipeline_class(**A_				)
         pipe.to(A_				)
         pipe.set_progress_bar_config(disable=A_				)

         UpperCamelCase_:   List[Any]        =							self.get_dummy_inversion_inputs(A_				)
         UpperCamelCase_:   Optional[int]        =							pipe.invert(**A_				).images
         UpperCamelCase_:   Dict        =							image[0, -1, -3:, -3:]

         self.assertEqual(image.shape    ,     (2, 3_2, 3_2, 3)				)
         UpperCamelCase_:   Optional[Any]        =							np.array(
             [0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9]    ,     )
         UpperCamelCase_:   int        =							np.abs(image_slice.flatten() - expected_slice				).max()
         self.assertLessEqual(A_    ,     1e-3				)







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





       """simple docstring"""







       def      _a       (   self				):
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()





       @classmethod
       def      _a       (   cls				):
         UpperCamelCase_:   Tuple        =							load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'				)

         UpperCamelCase_:   Optional[Any]        =							raw_image.convert('RGB'				).resize((7_6_8, 7_6_8)				)

         UpperCamelCase_:   Tuple        =							raw_image





       def      _a       (   self				):
         UpperCamelCase_:   str        =							torch.manual_seed(0				)

         UpperCamelCase_:   Any        =							StableDiffusionDiffEditPipeline.from_pretrained(
             'stabilityai/stable-diffusion-2-1'    ,     safety_checker=A_    ,     torch_dtype=torch.floataa				)
         UpperCamelCase_:   List[Any]        =							DDIMScheduler.from_config(pipe.scheduler.config				)
         UpperCamelCase_:   str        =							DDIMInverseScheduler.from_config(pipe.scheduler.config				)
         pipe.enable_model_cpu_offload()
         pipe.set_progress_bar_config(disable=A_				)

         UpperCamelCase_:   str        =							'a bowl of fruit'
         UpperCamelCase_:   List[str]        =							'a bowl of pears'

         UpperCamelCase_:   Dict        =							pipe.generate_mask(
             image=self.raw_image    ,     source_prompt=A_    ,     target_prompt=A_    ,     generator=A_    ,     )

         UpperCamelCase_:   List[str]        =							pipe.invert(
             prompt=A_    ,     image=self.raw_image    ,     inpaint_strength=0.7    ,     generator=A_				).latents

         UpperCamelCase_:   int        =							pipe(
             prompt=A_    ,     mask_image=A_    ,     image_latents=A_    ,     generator=A_    ,     negative_prompt=A_    ,     inpaint_strength=0.7    ,     output_type='numpy'    ,     ).images[0]

         UpperCamelCase_:   Tuple        =							(
             np.array(
                 load_image(
                     'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                     '/diffedit/pears.png'				).resize((7_6_8, 7_6_8)				)				)
             / 2_5_5
         )
         assert np.abs((expected_image - image).max()				) < 5e-1





       def      _a       (   self				):
         UpperCamelCase_:   List[str]        =							torch.manual_seed(0				)

         UpperCamelCase_:   str        =							StableDiffusionDiffEditPipeline.from_pretrained(
             'stabilityai/stable-diffusion-2-1'    ,     safety_checker=A_    ,     torch_dtype=torch.floataa				)
         UpperCamelCase_:   int        =							DPMSolverMultistepScheduler.from_config(pipe.scheduler.config				)
         UpperCamelCase_:   Union[str, Any]        =							DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config				)
         pipe.enable_model_cpu_offload()
         pipe.set_progress_bar_config(disable=A_				)

         UpperCamelCase_:   str        =							'a bowl of fruit'
         UpperCamelCase_:   Union[str, Any]        =							'a bowl of pears'

         UpperCamelCase_:   Tuple        =							pipe.generate_mask(
             image=self.raw_image    ,     source_prompt=A_    ,     target_prompt=A_    ,     generator=A_    ,     )

         UpperCamelCase_:   int        =							pipe.invert(
             prompt=A_    ,     image=self.raw_image    ,     inpaint_strength=0.7    ,     generator=A_    ,     num_inference_steps=2_5    ,     ).latents

         UpperCamelCase_:   List[Any]        =							pipe(
             prompt=A_    ,     mask_image=A_    ,     image_latents=A_    ,     generator=A_    ,     negative_prompt=A_    ,     inpaint_strength=0.7    ,     num_inference_steps=2_5    ,     output_type='numpy'    ,     ).images[0]

         UpperCamelCase_:   int        =							(
             np.array(
                 load_image(
                     'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                     '/diffedit/pears.png'				).resize((7_6_8, 7_6_8)				)				)
             / 2_5_5
         )
         assert np.abs((expected_image - image).max()				) < 5e-1






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()
_A					:				List[str]				=			logging.get_logger(__name__)

_A					:				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""",
    """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""",
}
_A					:				Optional[int]				=			[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """projector""",
    """classifier""",
]


def 							__snake_case							(    lowerCAmelCase_	)				->						Tuple:
    SCREAMING_SNAKE_CASE__											=			{}
    with open(lowerCAmelCase_ ,							'''r'''	) as file:
        for line_number, line in enumerate(lowerCAmelCase_	):
            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 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Optional[Any]:
    for attribute in key.split('''.'''	):
        SCREAMING_SNAKE_CASE__											=			getattr(lowerCAmelCase_ ,							lowerCAmelCase_	)

    SCREAMING_SNAKE_CASE__											=			None
    for param_key in PARAM_MAPPING.keys():
        if full_name.endswith(lowerCAmelCase_	):
            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(lowerCAmelCase_ ,							lowerCAmelCase_	).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(lowerCAmelCase_ ,							lowerCAmelCase_	)
        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(lowerCAmelCase_ ,							lowerCAmelCase_	)
        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 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						List[str]:
    SCREAMING_SNAKE_CASE__											=			None
    for param_key in PARAM_MAPPING.keys():
        if full_name.endswith(lowerCAmelCase_	):
            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]


_A					:				Union[str, 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 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_=None ,							lowerCAmelCase_=None	)				->						Tuple:
    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(lowerCAmelCase_	)[0].split('''.'''	)[-2]
                SCREAMING_SNAKE_CASE__											=			mapped_key.replace('''*''' ,							lowerCAmelCase_	)
            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(lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)
            else:
                set_recursively(lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)
            return is_used
    return is_used


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Optional[int]:
    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(
                lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							hf_model.config.feat_extract_norm == '''group''' ,							)
            SCREAMING_SNAKE_CASE__											=			True
        else:
            SCREAMING_SNAKE_CASE__											=			load_wavaveca_layer(lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)
        if not is_used:
            unused_weights.append(lowerCAmelCase_	)

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


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Any:
    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(lowerCAmelCase_	)


@torch.no_grad()
def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_=None ,							lowerCAmelCase_=None ,							lowerCAmelCase_=True ,							lowerCAmelCase_=False	)				->						int:
    if config_path is not None:
        SCREAMING_SNAKE_CASE__											=			WavaVecaConfig.from_pretrained(lowerCAmelCase_	)
    else:
        SCREAMING_SNAKE_CASE__											=			WavaVecaConfig()

    if is_seq_class:
        SCREAMING_SNAKE_CASE__											=			read_txt_into_dict(lowerCAmelCase_	)
        SCREAMING_SNAKE_CASE__											=			idalabel
        SCREAMING_SNAKE_CASE__											=			WavaVecaForSequenceClassification(lowerCAmelCase_	)
        SCREAMING_SNAKE_CASE__											=			WavaVecaFeatureExtractor(
            feature_size=1 ,							sampling_rate=1_6_0_0_0 ,							padding_value=0 ,							do_normalize=lowerCAmelCase_ ,							return_attention_mask=lowerCAmelCase_ ,							)
        feature_extractor.save_pretrained(lowerCAmelCase_	)

    elif is_finetuned:
        if dict_path:
            SCREAMING_SNAKE_CASE__											=			Dictionary.load(lowerCAmelCase_	)

            # 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(lowerCAmelCase_ ,							'''vocab.json'''	)
            if not os.path.isdir(lowerCAmelCase_	):
                logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(lowerCAmelCase_	)	)
                return
            os.makedirs(lowerCAmelCase_ ,							exist_ok=lowerCAmelCase_	)
            SCREAMING_SNAKE_CASE__											=			target_dict.indices

            # fairseq has the <pad> and <s> switched
            SCREAMING_SNAKE_CASE__											=			0
            SCREAMING_SNAKE_CASE__											=			1
            with open(lowerCAmelCase_ ,							'''w''' ,							encoding='''utf-8'''	) as vocab_handle:
                json.dump(lowerCAmelCase_ ,							lowerCAmelCase_	)
            SCREAMING_SNAKE_CASE__											=			WavaVecaCTCTokenizer(
                lowerCAmelCase_ ,							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=lowerCAmelCase_ ,							)
            SCREAMING_SNAKE_CASE__											=			True if config.feat_extract_norm == '''layer''' else False
            SCREAMING_SNAKE_CASE__											=			WavaVecaFeatureExtractor(
                feature_size=1 ,							sampling_rate=1_6_0_0_0 ,							padding_value=0 ,							do_normalize=lowerCAmelCase_ ,							return_attention_mask=lowerCAmelCase_ ,							)
            SCREAMING_SNAKE_CASE__											=			WavaVecaProcessor(feature_extractor=lowerCAmelCase_ ,							tokenizer=lowerCAmelCase_	)
            processor.save_pretrained(lowerCAmelCase_	)

        SCREAMING_SNAKE_CASE__											=			WavaVecaForCTC(lowerCAmelCase_	)
    else:
        SCREAMING_SNAKE_CASE__											=			WavaVecaForPreTraining(lowerCAmelCase_	)

    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(lowerCAmelCase_	)

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] ,							task=lowerCAmelCase_	)

    SCREAMING_SNAKE_CASE__											=			model[0].eval()

    recursively_load_weights(lowerCAmelCase_ ,							lowerCAmelCase_ ,							not is_finetuned	)

    hf_wavavec.save_pretrained(lowerCAmelCase_	)


if __name__ == "__main__":
  _A					:				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""",
  )
  _A					:				List[str]				=			parser.parse_args()

  _A					:				List[str]				=			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,
  )
