





'''simple docstring'''

from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


lowercase_					= logging.get_logger(__name__)  # pylint: disable=invalid-name

lowercase_					= """
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\")
        >>> pipe_prior.to(\"cuda\")
        >>> prompt = \"red cat, 4k photo\"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> zero_image_emb = out.negative_image_embeds
        >>> pipe = KandinskyV22Pipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\")
        >>> pipe.to(\"cuda\")
        >>> image = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=50,
        ... ).images
        >>> image[0].save(\"cat.png\")
        ```
"""





def lowerCamelCase						(     __lowerCamelCase :    str					,       __lowerCamelCase :    Optional[Any]					,       __lowerCamelCase :    List[Any]=8    )						->Tuple:
    _SCREAMING_SNAKE_CASE									=			height // scale_factor**2
    if height % scale_factor**2 != 0:
        new_height += 1
    _SCREAMING_SNAKE_CASE									=			width // scale_factor**2
    if width % scale_factor**2 != 0:
        new_width += 1
    return new_height * scale_factor, new_width * scale_factor






class 					a_     (				snake_case_       ):

    '''simple docstring'''


    def __init__(		self ,		A ,		A ,		A ,		)		->   Union[str, Any]:
        super().__init__()

        self.register_modules(
            unet=A ,		scheduler=A ,		movq=A ,		)
        _SCREAMING_SNAKE_CASE									=			2 ** (len(self.movq.config.block_out_channels       ) - 1)


    def 			snake_case_(		self ,		A ,		A ,		A ,		A ,		A ,		A       )		->   Union[str, Any]:
        if latents is None:
            _SCREAMING_SNAKE_CASE									=			randn_tensor(A ,		generator=A ,		device=A ,		dtype=A       )
        else:
            if latents.shape != shape:
                raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}'       )
            _SCREAMING_SNAKE_CASE									=			latents.to(A       )

        _SCREAMING_SNAKE_CASE									=			latents * scheduler.init_noise_sigma
        return latents


    def 			snake_case_(		self ,		A=0       )		->   Dict:
        if is_accelerate_available():
            from accelerate import cpu_offload
        else:
            raise ImportError("""Please install accelerate via `pip install accelerate`"""       )

        _SCREAMING_SNAKE_CASE									=			torch.device(f'cuda:{gpu_id}'       )

        _SCREAMING_SNAKE_CASE									=			[
            self.unet,
            self.movq,
        ]
        for cpu_offloaded_model in models:
            if cpu_offloaded_model is not None:
                cpu_offload(A ,		A       )


    def 			snake_case_(		self ,		A=0       )		->   str:
        if is_accelerate_available() and is_accelerate_version(""">=""" ,		"""0.17.0.dev0"""       ):
            from accelerate import cpu_offload_with_hook
        else:
            raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""       )

        _SCREAMING_SNAKE_CASE									=			torch.device(f'cuda:{gpu_id}'       )

        if self.device.type != "cpu":
            self.to("""cpu""" ,		silence_dtype_warnings=A       )
            torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

        _SCREAMING_SNAKE_CASE									=			None
        for cpu_offloaded_model in [self.unet, self.movq]:
            _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			cpu_offload_with_hook(A ,		A ,		prev_module_hook=A       )

        # We'll offload the last model manually.
        _SCREAMING_SNAKE_CASE									=			hook


    @property
    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
    def 			snake_case_(		self       )		->   Tuple:
        if not hasattr(self.unet ,		"""_hf_hook"""       ):
            return self.device
        for module in self.unet.modules():
            if (
                hasattr(A ,		"""_hf_hook"""       )
                and hasattr(module._hf_hook ,		"""execution_device"""       )
                and module._hf_hook.execution_device is not None
            ):
                return torch.device(module._hf_hook.execution_device       )
        return self.device





    @torch.no_grad()
    @replace_example_docstring(A       )
    def __call__(		self ,		A ,		A ,		A = 512 ,		A = 512 ,		A = 100 ,		A = 4.0 ,		A = 1 ,		A = None ,		A = None ,		A = "pil" ,		A = True ,		)		->   List[str]:
        _SCREAMING_SNAKE_CASE									=			self._execution_device

        _SCREAMING_SNAKE_CASE									=			guidance_scale > 1.0

        if isinstance(A ,		A       ):
            _SCREAMING_SNAKE_CASE									=			torch.cat(A ,		dim=0       )
        _SCREAMING_SNAKE_CASE									=			image_embeds.shape[0] * num_images_per_prompt
        if isinstance(A ,		A       ):
            _SCREAMING_SNAKE_CASE									=			torch.cat(A ,		dim=0       )

        if do_classifier_free_guidance:
            _SCREAMING_SNAKE_CASE									=			image_embeds.repeat_interleave(A ,		dim=0       )
            _SCREAMING_SNAKE_CASE									=			negative_image_embeds.repeat_interleave(A ,		dim=0       )

        _SCREAMING_SNAKE_CASE									=			torch.cat([negative_image_embeds, image_embeds] ,		dim=0       ).to(dtype=self.unet.dtype ,		device=A       )

        self.scheduler.set_timesteps(A ,		device=A       )
        _SCREAMING_SNAKE_CASE									=			self.scheduler.timesteps

        _SCREAMING_SNAKE_CASE									=			self.unet.config.in_channels

        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			downscale_height_and_width(A ,		A ,		self.movq_scale_factor       )

        # create initial latent
        _SCREAMING_SNAKE_CASE									=			self.prepare_latents(
            (batch_size, num_channels_latents, height, width) ,		image_embeds.dtype ,		A ,		A ,		A ,		self.scheduler ,		)

        for i, t in enumerate(self.progress_bar(A       )       ):
            # expand the latents if we are doing classifier free guidance
            _SCREAMING_SNAKE_CASE									=			torch.cat([latents] * 2       ) if do_classifier_free_guidance else latents

            _SCREAMING_SNAKE_CASE									=			{"""image_embeds""": image_embeds}
            _SCREAMING_SNAKE_CASE									=			self.unet(
                sample=A ,		timestep=A ,		encoder_hidden_states=A ,		added_cond_kwargs=A ,		return_dict=A ,		)[0]

            if do_classifier_free_guidance:
                _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			noise_pred.split(latents.shape[1] ,		dim=1       )
                _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			noise_pred.chunk(2       )
                _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			variance_pred.chunk(2       )
                _SCREAMING_SNAKE_CASE									=			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                _SCREAMING_SNAKE_CASE									=			torch.cat([noise_pred, variance_pred_text] ,		dim=1       )

            if not (
                hasattr(self.scheduler.config ,		"""variance_type"""       )
                and self.scheduler.config.variance_type in ["learned", "learned_range"]
            ):
                _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			noise_pred.split(latents.shape[1] ,		dim=1       )

            # compute the previous noisy sample x_t -> x_t-1
            _SCREAMING_SNAKE_CASE									=			self.scheduler.step(
                A ,		A ,		A ,		generator=A ,		)[0]
        # post-processing
        _SCREAMING_SNAKE_CASE									=			self.movq.decode(A ,		force_not_quantize=A       )["""sample"""]

        if output_type not in ["pt", "np", "pil"]:
            raise ValueError(f'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'       )

        if output_type in ["np", "pil"]:
            _SCREAMING_SNAKE_CASE									=			image * 0.5 + 0.5
            _SCREAMING_SNAKE_CASE									=			image.clamp(0 ,		1       )
            _SCREAMING_SNAKE_CASE									=			image.cpu().permute(0 ,		2 ,		3 ,		1       ).float().numpy()

        if output_type == "pil":
            _SCREAMING_SNAKE_CASE									=			self.numpy_to_pil(A       )

        if not return_dict:
            return (image,)

        return ImagePipelineOutput(images=A       )










'''simple docstring'''

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

lowercase_					= {
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}





def lowerCamelCase						(     __lowerCamelCase :    Dict					,       __lowerCamelCase :    Optional[int]					,       __lowerCamelCase :    int					,       __lowerCamelCase :    Union[str, Any]					,       __lowerCamelCase :    List[Any]    )						->Union[str, Any]:
    for attribute in key.split("""."""    ):
        _SCREAMING_SNAKE_CASE									=			getattr(__lowerCamelCase					,       __lowerCamelCase    )

    if weight_type is not None:
        _SCREAMING_SNAKE_CASE									=			getattr(__lowerCamelCase					,       __lowerCamelCase    ).shape
    else:
        _SCREAMING_SNAKE_CASE									=			hf_pointer.shape

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

    if weight_type == "weight":
        _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
    else:
        _SCREAMING_SNAKE_CASE									=			value

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





def lowerCamelCase						(     __lowerCamelCase :    Optional[int]					,       __lowerCamelCase :    Optional[Any]					,       __lowerCamelCase :    List[Any]    )						->Any:
    _SCREAMING_SNAKE_CASE									=			[]
    _SCREAMING_SNAKE_CASE									=			fairseq_model.state_dict()

    _SCREAMING_SNAKE_CASE									=			hf_model.hubert.feature_extractor if is_finetuned else hf_model.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:
            for key, mapped_key in MAPPING.items():
                _SCREAMING_SNAKE_CASE									=			"""hubert.""" + mapped_key if (is_finetuned and mapped_key != """lm_head""") else mapped_key

                if key in name or (key.split("""w2v_model."""    )[-1] == name.split("""."""    )[0] and not is_finetuned):
                    _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 "weight" in name:
                        _SCREAMING_SNAKE_CASE									=			"""weight"""
                    elif "bias" in name:
                        _SCREAMING_SNAKE_CASE									=			"""bias"""
                    else:
                        _SCREAMING_SNAKE_CASE									=			None
                    set_recursively(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )
                continue
        if not is_used:
            unused_weights.append(__lowerCamelCase    )

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





def lowerCamelCase						(     __lowerCamelCase :    Dict					,       __lowerCamelCase :    Any					,       __lowerCamelCase :    List[Any]					,       __lowerCamelCase :    Optional[int]					,       __lowerCamelCase :    Union[str, Any]    )						->Union[str, 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:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                F'{full_name} has size {value.shape}, but'
                F' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
            )
            _SCREAMING_SNAKE_CASE									=			value
            logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'    )
        elif "weight" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                F'{full_name} has size {value.shape}, but'
                F' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
            )
            _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:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                F'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
                " found."
            )
            _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:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                F'{full_name} has size {value.shape}, but'
                F' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
            )
            _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 lowerCamelCase						(     __lowerCamelCase :    str					,       __lowerCamelCase :    str					,       __lowerCamelCase :    List[Any]=None					,       __lowerCamelCase :    Optional[int]=None					,       __lowerCamelCase :    Union[str, Any]=True    )						->Optional[int]:
    if config_path is not None:
        _SCREAMING_SNAKE_CASE									=			HubertConfig.from_pretrained(__lowerCamelCase    )
    else:
        _SCREAMING_SNAKE_CASE									=			HubertConfig()

    if 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    )
            with open(__lowerCamelCase					,       """w"""					,       encoding="""utf-8"""    ) as vocab_handle:
                json.dump(target_dict.indices					,       __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_6000					,       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									=			HubertForCTC(__lowerCamelCase    )
    else:
        _SCREAMING_SNAKE_CASE									=			HubertModel(__lowerCamelCase    )

    if is_finetuned:
        _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				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]    )

    _SCREAMING_SNAKE_CASE									=			model[0].eval()

    recursively_load_weights(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )

    hf_wavavec.save_pretrained(__lowerCamelCase    )


if __name__ == "__main__":
      lowercase_					= 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"""
      )
      lowercase_					= parser.parse_args()
      convert_hubert_checkpoint(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
      )



