



from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


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

__snake_case            ='\n    Examples:\n        ```py\n        >>> from PIL import Image\n        >>> import torch\n        >>> from diffusers import DiffusionPipeline\n        >>> from diffusers.utils import export_to_gif, load_image\n\n        >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n        >>> repo = \"openai/shap-e-img2img\"\n        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n        >>> pipe = pipe.to(device)\n\n        >>> guidance_scale = 3.0\n        >>> image_url = \"https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png\"\n        >>> image = load_image(image_url).convert(\"RGB\")\n\n        >>> images = pipe(\n        ...     image,\n        ...     guidance_scale=guidance_scale,\n        ...     num_inference_steps=64,\n        ...     frame_size=256,\n        ... ).images\n\n        >>> gif_path = export_to_gif(images[0], \"corgi_3d.gif\")\n        ```\n'



@dataclass
class  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_			=42



class  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



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

						"""simple docstring"""






						super().__init__()

						self.register_modules(
						    prior=__lowerCamelCase	,image_encoder=__lowerCamelCase	,image_processor=__lowerCamelCase	,scheduler=__lowerCamelCase	,renderer=__lowerCamelCase	,)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)  ->    Tuple:

						"""simple docstring"""






						if latents is None:
										lowerCAmelCase__  :       List[Any]				=  randn_tensor(__lowerCamelCase	,generator=__lowerCamelCase	,device=__lowerCamelCase	,dtype=__lowerCamelCase				)
						else:
										if latents.shape != shape:
														raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}"""				)
										lowerCAmelCase__  :       Any				=  latents.to(__lowerCamelCase				)

						lowerCAmelCase__  :       Optional[int]				=  latents * scheduler.init_noise_sigma
						return latents



		def 	lowerCAmelCase__   (self	,__lowerCamelCase=0				)  ->    str:

						"""simple docstring"""






						if is_accelerate_available():
										from accelerate import cpu_offload
						else:
										raise ImportError('''Please install accelerate via `pip install accelerate`'''				)

						lowerCAmelCase__  :       Union[str, Any]				=  torch.device(f"""cuda:{gpu_id}"""				)

						lowerCAmelCase__  :       Optional[Any]				=  [self.image_encoder, self.prior]
						for cpu_offloaded_model in models:
										if cpu_offloaded_model is not None:
														cpu_offload(__lowerCamelCase	,__lowerCamelCase				)



		@property
		def 	lowerCAmelCase__   (self				)  ->    Dict:

						"""simple docstring"""






						if self.device != torch.device('''meta'''				) or not hasattr(self.image_encoder	,'''_hf_hook'''				):
										return self.device
						for module in self.image_encoder.modules():
										if (
										    hasattr(__lowerCamelCase	,'''_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



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

						"""simple docstring"""






						if isinstance(__lowerCamelCase	,__lowerCamelCase				) and isinstance(image[0]	,torch.Tensor				):
										lowerCAmelCase__  :       Optional[int]				=  torch.cat(__lowerCamelCase	,axis=0				) if image[0].ndim == 4 else torch.stack(__lowerCamelCase	,axis=0				)

						if not isinstance(__lowerCamelCase	,torch.Tensor				):
										lowerCAmelCase__  :       Optional[Any]				=  self.image_processor(__lowerCamelCase	,return_tensors='''pt'''				).pixel_values[0].unsqueeze(0				)

						lowerCAmelCase__  :       Any				=  image.to(dtype=self.image_encoder.dtype	,device=__lowerCamelCase				)

						lowerCAmelCase__  :       Dict				=  self.image_encoder(__lowerCamelCase				)['''last_hidden_state''']
						lowerCAmelCase__  :       Optional[int]				=  image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

						lowerCAmelCase__  :       List[Any]				=  image_embeds.repeat_interleave(__lowerCamelCase	,dim=0				)

						if do_classifier_free_guidance:
										lowerCAmelCase__  :       Optional[int]				=  torch.zeros_like(__lowerCamelCase				)

										# For classifier free guidance, we need to do two forward passes.
										# Here we concatenate the unconditional and text embeddings into a single batch
										# to avoid doing two forward passes
										lowerCAmelCase__  :       Optional[Any]				=  torch.cat([negative_image_embeds, image_embeds]				)

						return image_embeds



		@torch.no_grad()
		@replace_example_docstring(__lowerCamelCase				)
		def __call__(self	,__lowerCamelCase	,__lowerCamelCase = 1	,__lowerCamelCase = 25	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = 4.0	,__lowerCamelCase = 64	,__lowerCamelCase = "pil"	,__lowerCamelCase = True	,)  ->    List[str]:

						"""simple docstring"""






						if isinstance(__lowerCamelCase	,PIL.Image.Image				):
										lowerCAmelCase__  :       Tuple				=  1
						elif isinstance(__lowerCamelCase	,torch.Tensor				):
										lowerCAmelCase__  :       Union[str, Any]				=  image.shape[0]
						elif isinstance(__lowerCamelCase	,__lowerCamelCase				) and isinstance(image[0]	,(torch.Tensor, PIL.Image.Image)				):
										lowerCAmelCase__  :       str				=  len(__lowerCamelCase				)
						else:
										raise ValueError(
										    f"""`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(__lowerCamelCase				)}"""				)

						lowerCAmelCase__  :       Dict				=  self._execution_device

						lowerCAmelCase__  :       Tuple				=  batch_size * num_images_per_prompt

						lowerCAmelCase__  :       int				=  guidance_scale > 1.0
						lowerCAmelCase__  :       int				=  self._encode_image(__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)

						# prior

						self.scheduler.set_timesteps(__lowerCamelCase	,device=__lowerCamelCase				)
						lowerCAmelCase__  :       str				=  self.scheduler.timesteps

						lowerCAmelCase__  :       Optional[int]				=  self.prior.config.num_embeddings
						lowerCAmelCase__  :       List[Any]				=  self.prior.config.embedding_dim

						lowerCAmelCase__  :       Any				=  self.prepare_latents(
						    (batch_size, num_embeddings * embedding_dim)	,image_embeds.dtype	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,self.scheduler	,)

						# YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
						lowerCAmelCase__  :       Optional[int]				=  latents.reshape(latents.shape[0]	,__lowerCamelCase	,__lowerCamelCase				)

						for i, t in enumerate(self.progress_bar(__lowerCamelCase				)				):
										# expand the latents if we are doing classifier free guidance
										lowerCAmelCase__  :       Union[str, Any]				=  torch.cat([latents] * 2				) if do_classifier_free_guidance else latents
										lowerCAmelCase__  :       Optional[int]				=  self.scheduler.scale_model_input(__lowerCamelCase	,__lowerCamelCase				)

										lowerCAmelCase__  :       Optional[Any]				=  self.prior(
										    __lowerCamelCase	,timestep=__lowerCamelCase	,proj_embedding=__lowerCamelCase	,).predicted_image_embedding

										# remove the variance
										lowerCAmelCase__		,	lowerCAmelCase__  :       List[Any]				=  noise_pred.split(
										    scaled_model_input.shape[2]	,dim=2				)  # batch_size, num_embeddings, embedding_dim

										if do_classifier_free_guidance is not None:
														lowerCAmelCase__		,	lowerCAmelCase__  :       Any				=  noise_pred.chunk(2				)
														lowerCAmelCase__  :       Optional[Any]				=  noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

										lowerCAmelCase__  :       List[Any]				=  self.scheduler.step(
										    __lowerCamelCase	,timestep=__lowerCamelCase	,sample=__lowerCamelCase	,).prev_sample

						if output_type == "latent":
										return ShapEPipelineOutput(images=__lowerCamelCase				)

						lowerCAmelCase__  :       Tuple				=  []
						for i, latent in enumerate(__lowerCamelCase				):
										print()
										lowerCAmelCase__  :       List[Any]				=  self.renderer.decode(
										    latent[None, :]	,__lowerCamelCase	,size=__lowerCamelCase	,ray_batch_size=40_96	,n_coarse_samples=64	,n_fine_samples=1_28	,)

										images.append(__lowerCamelCase				)

						lowerCAmelCase__  :       List[Any]				=  torch.stack(__lowerCamelCase				)

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

						lowerCAmelCase__  :       Optional[Any]				=  images.cpu().numpy()

						if output_type == "pil":
										lowerCAmelCase__  :       List[str]				=  [self.numpy_to_pil(__lowerCamelCase				) for image in images]

						# Offload last model to CPU
						if hasattr(self	,'''final_offload_hook'''				) and self.final_offload_hook is not None:
										self.final_offload_hook.offload()

						if not return_dict:
										return (images,)

						return ShapEPipelineOutput(images=__lowerCamelCase				)






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__snake_case       :	Dict            =logging.get_logger(__name__)

__snake_case       :	Union[str, 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',
    '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': 'ctc_proj',
    'mask_emb': 'masked_spec_embed',
}
__snake_case       :	Tuple            =[
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Dict ,lowerCamelCase_	:	Union[str, Any] ,lowerCamelCase_	:	int ,lowerCamelCase_	:	int ,lowerCamelCase_	:	Optional[Any] ,lowerCamelCase_	:	Optional[int]):





				'''simple docstring'''

				for attribute in key.split('''.'''):
								if is_finetuned:
												if attribute in ["quantizer", "project_q", "project_hid"]:
																# those layers are only relevant for pretraining and should be dropped
																return

												if attribute == "ctc_proj":
																# we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
																lowerCAmelCase__  :       str				=  '''lm_head'''

								lowerCAmelCase__  :       List[str]				=  getattr(lowerCamelCase_ ,lowerCamelCase_)

				if weight_type is not None:
								lowerCAmelCase__  :       Union[str, Any]				=  getattr(lowerCamelCase_ ,lowerCamelCase_).shape
				else:
								lowerCAmelCase__  :       Union[str, Any]				=  hf_pointer.shape

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

				if weight_type == "weight":
								lowerCAmelCase__  :       str				=  value
				elif weight_type == "weight_g":
								lowerCAmelCase__  :       Any				=  value
				elif weight_type == "weight_v":
								lowerCAmelCase__  :       Tuple				=  value
				elif weight_type == "bias":
								lowerCAmelCase__  :       int				=  value
				else:
								lowerCAmelCase__  :       Tuple				=  value

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






def 			lowerCAmelCase__	(					lowerCamelCase_	:	List[Any] ,lowerCamelCase_	:	Dict ,lowerCamelCase_	:	Optional[int]):





				'''simple docstring'''

				lowerCAmelCase__  :       Tuple				=  []
				lowerCAmelCase__  :       Optional[Any]				=  fairseq_model.state_dict()

				lowerCAmelCase__  :       str				=  hf_model.unispeech.feature_extractor

				for name, value in fairseq_dict.items():
								lowerCAmelCase__  :       Union[str, Any]				=  False
								if "conv_layers" in name:
												load_conv_layer(
												    lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,hf_model.config.feat_extract_norm == '''group''' ,)
												lowerCAmelCase__  :       Tuple				=  True
								else:
												for key, mapped_key in MAPPING.items():
																lowerCAmelCase__  :       List[str]				=  '''unispeech.''' + 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]:
																				lowerCAmelCase__  :       Dict				=  True
																				if "*" in mapped_key:
																								lowerCAmelCase__  :       Tuple				=  name.split(lowerCamelCase_)[0].split('''.''')[-2]
																								lowerCAmelCase__  :       str				=  mapped_key.replace('''*''' ,lowerCamelCase_)
																				if "weight_g" in name:
																								lowerCAmelCase__  :       List[str]				=  '''weight_g'''
																				elif "weight_v" in name:
																								lowerCAmelCase__  :       Tuple				=  '''weight_v'''
																				elif "bias" in name:
																								lowerCAmelCase__  :       Any				=  '''bias'''
																				elif "weight" in name:
																								# TODO: don't match quantizer.weight_proj
																								lowerCAmelCase__  :       Union[str, Any]				=  '''weight'''
																				else:
																								lowerCAmelCase__  :       Dict				=  None
																				set_recursively(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_)
																continue
								if not is_used:
												unused_weights.append(lowerCamelCase_)

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






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Tuple ,lowerCamelCase_	:	Dict ,lowerCamelCase_	:	List[Any] ,lowerCamelCase_	:	Tuple ,lowerCamelCase_	:	Optional[int]):





				'''simple docstring'''

				lowerCAmelCase__  :       Tuple				=  full_name.split('''conv_layers.''')[-1]
				lowerCAmelCase__  :       Tuple				=  name.split('''.''')
				lowerCAmelCase__  :       Any				=  int(items[0])
				lowerCAmelCase__  :       int				=  int(items[1])

				if type_id == 0:
								if "bias" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
												    f"""{full_name} has size {value.shape}, but"""
												    f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
												)
												lowerCAmelCase__  :       List[Any]				=  value
												logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""")
								elif "weight" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
												    f"""{full_name} has size {value.shape}, but"""
												    f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
												)
												lowerCAmelCase__  :       List[Any]				=  value
												logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""")
				elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
								if "bias" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
												    f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
												    " found."
												)
												lowerCAmelCase__  :       Optional[int]				=  value
												logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""")
								elif "weight" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
												    f"""{full_name} has size {value.shape}, but"""
												    f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
												)
												lowerCAmelCase__  :       str				=  value
												logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""")
				else:
								unused_weights.append(lowerCamelCase_)






@torch.no_grad()
def 			lowerCAmelCase__	(					lowerCamelCase_	:	List[str] ,lowerCamelCase_	:	Union[str, Any] ,lowerCamelCase_	:	str=None ,lowerCamelCase_	:	List[Any]=None ,lowerCamelCase_	:	List[str]=True):





				'''simple docstring'''

				if config_path is not None:
								lowerCAmelCase__  :       Optional[Any]				=  UniSpeechConfig.from_pretrained(lowerCamelCase_)
				else:
								lowerCAmelCase__  :       List[str]				=  UniSpeechConfig()

				if is_finetuned:
								if dict_path:
												lowerCAmelCase__  :       Dict				=  Dictionary.load_from_json(lowerCamelCase_)

												# important change bos & pad token id since CTC symbol is <pad> and
												# not <s> as in fairseq
												lowerCAmelCase__  :       Any				=  target_dict.pad_index
												lowerCAmelCase__  :       str				=  target_dict.bos_index
												lowerCAmelCase__  :       Any				=  target_dict.eos_index
												lowerCAmelCase__  :       List[Any]				=  len(target_dict.symbols)
												lowerCAmelCase__  :       Union[str, Any]				=  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_)
												lowerCAmelCase__  :       str				=  target_dict.indices

												# fairseq has the <pad> and <s> switched
												lowerCAmelCase__  :       Optional[int]				=  42
												lowerCAmelCase__  :       List[Any]				=  43
												with open(lowerCamelCase_ ,'''w''' ,encoding='''utf-8''') as vocab_handle:
																json.dump(lowerCamelCase_ ,lowerCamelCase_)
												lowerCAmelCase__  :       Dict				=  WavaVecaPhonemeCTCTokenizer(
												    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_ ,)
												lowerCAmelCase__  :       Optional[Any]				=  True if config.feat_extract_norm == '''layer''' else False
												lowerCAmelCase__  :       Tuple				=  WavaVecaFeatureExtractor(
												    feature_size=1 ,sampling_rate=16000 ,padding_value=0 ,do_normalize=lowerCamelCase_ ,return_attention_mask=lowerCamelCase_ ,)
												lowerCAmelCase__  :       Any				=  WavaVecaProcessor(feature_extractor=lowerCamelCase_ ,tokenizer=lowerCamelCase_)
												processor.save_pretrained(lowerCamelCase_)

								lowerCAmelCase__  :       Union[str, Any]				=  UniSpeechForCTC(lowerCamelCase_)
				else:
								lowerCAmelCase__  :       List[Any]				=  UniSpeechForPreTraining(lowerCamelCase_)

				if is_finetuned:
								lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__  :       List[str]				=  fairseq.checkpoint_utils.load_model_ensemble_and_task(
								    [checkpoint_path] ,arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''')[:-1]), '''w2v_path''': checkpoint_path})
				else:
								lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__  :       Any				=  fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path])

				lowerCAmelCase__  :       Dict				=  model[0].eval()

				recursively_load_weights(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_)

				hf_unispeech.save_pretrained(lowerCamelCase_)


if __name__ == "__main__":
							__snake_case       :	Optional[Any]            =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'
							)
							__snake_case       :	List[Any]            =parser.parse_args()
							convert_unispeech_checkpoint(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
							)

