'''simple docstring'''




import argparse
import json
import re
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


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






def 							snake_case__				(     _A:   List[Any]					)       ->      Any:

		'''simple docstring'''


		lowerCAmelCase     =    MobileNetVaConfig(layer_norm_eps=0.001					)

		if "_quant" in model_name:
				raise ValueError("""Quantized models are not supported."""					)

		lowerCAmelCase     =    re.match(r"""^mobilenet_v1_([^_]*)_([^_]*)$""" ,  _A					)
		if matches:
				lowerCAmelCase     =    float(matches[1]					)
				lowerCAmelCase     =    int(matches[2]					)

		# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
		# the usual 1000. The first class (index 0) is "background".
		lowerCAmelCase     =    1001
		lowerCAmelCase     =    """imagenet-1k-id2label.json"""
		lowerCAmelCase     =    """huggingface/label-files"""
		lowerCAmelCase     =    json.load(open(hf_hub_download(_A ,  _A ,  repo_type="""dataset"""					) ,  """r"""					)					)
		lowerCAmelCase     =    {int(_A					) + 1: v for k, v in idalabel.items()}
		lowerCAmelCase     =    """background"""
		lowerCAmelCase     =    idalabel
		lowerCAmelCase     =    {v: k for k, v in idalabel.items()}

		return config






def 							snake_case__				(     )       ->      Optional[int]:

		'''simple docstring'''


		lowerCAmelCase     =    """http://images.cocodataset.org/val2017/000000039769.jpg"""
		lowerCAmelCase     =    Image.open(requests.get(_A ,  stream=_A					).raw					)
		return im






@torch.no_grad()
def 							snake_case__				(     _A:   Any ,  _A:   List[Any] ,  _A:   Tuple ,  _A:   str=False					)       ->      Union[str, Any]:

		'''simple docstring'''


		lowerCAmelCase     =    get_mobilenet_va_config(_A					)

		# Load 🤗 model
		lowerCAmelCase     =    MobileNetVaForImageClassification(_A					).eval()

		# Load weights from TensorFlow checkpoint
		load_tf_weights_in_mobilenet_va(_A ,  _A ,  _A					)

		# Check outputs on an image, prepared by MobileNetV1ImageProcessor
		lowerCAmelCase     =    MobileNetVaImageProcessor(
		    crop_size={"""width""": config.image_size, """height""": config.image_size} ,  size={"""shortest_edge""": config.image_size + 32} ,  )
		lowerCAmelCase     =    image_processor(images=prepare_img() ,  return_tensors="""pt"""					)
		lowerCAmelCase     =    model(**_A					)
		lowerCAmelCase     =    outputs.logits

		assert logits.shape == (1, 1001)

		if model_name == "mobilenet_v1_1.0_224":
				lowerCAmelCase     =    torch.tensor([-4.1_739, -1.1_233, 3.1_205]					)
		elif model_name == "mobilenet_v1_0.75_192":
				lowerCAmelCase     =    torch.tensor([-3.9_440, -2.3_141, -0.3_333]					)
		else:
				lowerCAmelCase     =    None

		if expected_logits is not None:
				assert torch.allclose(logits[0, :3] ,  _A ,  atol=1e-4					)

		Path(_A					).mkdir(exist_ok=_A					)
		print(f"Saving model {model_name} to {pytorch_dump_folder_path}"					)
		model.save_pretrained(_A					)
		print(f"Saving image processor to {pytorch_dump_folder_path}"					)
		image_processor.save_pretrained(_A					)

		if push_to_hub:
				print("""Pushing to the hub..."""					)
				lowerCAmelCase     =    """google/""" + model_name
				image_processor.push_to_hub(_A					)
				model.push_to_hub(_A					)


if __name__ == "__main__":
						__lowercase        =    argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--model_name''',
						    default='''mobilenet_v1_1.0_224''',
						    type=str,
						    help='''Name of the MobileNetV1 model you\'d like to convert. Should in the form \'mobilenet_v1_<depth>_<size>\'.''',
						)
						parser.add_argument(
						    '''--checkpoint_path''', required=True, type=str, help='''Path to the original TensorFlow checkpoint (.ckpt file).'''
						)
						parser.add_argument(
						    '''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
						)
						parser.add_argument(
						    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
						)

						__lowercase        =    parser.parse_args()
						convert_movilevit_checkpoint(
						    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
						)





'''simple docstring'''




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


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

__lowercase        =    '''
    Examples:
        ```py
        >>> from PIL import Image
        >>> import torch
        >>> from diffusers import DiffusionPipeline
        >>> from diffusers.utils import export_to_gif, load_image

        >>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

        >>> repo = "openai/shap-e-img2img"
        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
        >>> pipe = pipe.to(device)

        >>> guidance_scale = 3.0
        >>> image_url = "https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png"
        >>> image = load_image(image_url).convert("RGB")

        >>> images = pipe(
        ...     image,
        ...     guidance_scale=guidance_scale,
        ...     num_inference_steps=64,
        ...     frame_size=256,
        ... ).images

        >>> gif_path = export_to_gif(images[0], "corgi_3d.gif")
        ```
'''






@dataclass
class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	Union[PIL.Image.Image, np.ndarray]







class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			def __init__(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       ):




					"""simple docstring"""


					super().__init__()

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



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase):




					"""simple docstring"""


					if latents is None:
							lowerCAmelCase     =    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     =    latents.to(__lowerCAmelCase)

					lowerCAmelCase     =    latents * scheduler.init_noise_sigma
					return latents



			def 				a_			(  self			,       __lowerCAmelCase=0):




					"""simple docstring"""


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

					lowerCAmelCase     =    torch.device(f"cuda:{gpu_id}")

					lowerCAmelCase     =    [self.image_encoder, self.prior]
					for cpu_offloaded_model in models:
							if cpu_offloaded_model is not None:
									cpu_offload(__lowerCAmelCase			,       __lowerCAmelCase)



			@property
			def 				a_			(  self):




					"""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 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       ):




					"""simple docstring"""


					if isinstance(__lowerCAmelCase			,       __lowerCAmelCase) and isinstance(image[0]			,       torch.Tensor):
							lowerCAmelCase     =    torch.cat(__lowerCAmelCase			,       axis=0) if image[0].ndim == 4 else torch.stack(__lowerCAmelCase			,       axis=0)

					if not isinstance(__lowerCAmelCase			,       torch.Tensor):
							lowerCAmelCase     =    self.image_processor(__lowerCAmelCase			,       return_tensors="""pt""").pixel_values[0].unsqueeze(0)

					lowerCAmelCase     =    image.to(dtype=self.image_encoder.dtype			,       device=__lowerCAmelCase)

					lowerCAmelCase     =    self.image_encoder(__lowerCAmelCase)["""last_hidden_state"""]
					lowerCAmelCase     =    image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

					lowerCAmelCase     =    image_embeds.repeat_interleave(__lowerCAmelCase			,       dim=0)

					if do_classifier_free_guidance:
							lowerCAmelCase     =    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     =    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			,       ):




					"""simple docstring"""


					if isinstance(__lowerCAmelCase			,       PIL.Image.Image):
							lowerCAmelCase     =    1
					elif isinstance(__lowerCAmelCase			,       torch.Tensor):
							lowerCAmelCase     =    image.shape[0]
					elif isinstance(__lowerCAmelCase			,       __lowerCAmelCase) and isinstance(image[0]			,       (torch.Tensor, PIL.Image.Image)):
							lowerCAmelCase     =    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     =    self._execution_device

					lowerCAmelCase     =    batch_size * num_images_per_prompt

					lowerCAmelCase     =    guidance_scale > 1.0
					lowerCAmelCase     =    self._encode_image(__lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase)

					# prior

					self.scheduler.set_timesteps(__lowerCAmelCase			,       device=__lowerCAmelCase)
					lowerCAmelCase     =    self.scheduler.timesteps

					lowerCAmelCase     =    self.prior.config.num_embeddings
					lowerCAmelCase     =    self.prior.config.embedding_dim

					lowerCAmelCase     =    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     =    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     =    torch.cat([latents] * 2) if do_classifier_free_guidance else latents
							lowerCAmelCase     =    self.scheduler.scale_model_input(__lowerCAmelCase			,       __lowerCAmelCase)

							lowerCAmelCase     =    self.prior(
							    __lowerCAmelCase			,       timestep=__lowerCAmelCase			,       proj_embedding=__lowerCAmelCase			,       ).predicted_image_embedding

							# remove the variance
							lowerCAmelCase   ,	lowerCAmelCase     =    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     =    noise_pred.chunk(2)
									lowerCAmelCase     =    noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

							lowerCAmelCase     =    self.scheduler.step(
							    __lowerCAmelCase			,       timestep=__lowerCAmelCase			,       sample=__lowerCAmelCase			,       ).prev_sample

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

					lowerCAmelCase     =    []
					for i, latent in enumerate(__lowerCAmelCase):
							print()
							lowerCAmelCase     =    self.renderer.decode(
							    latent[None, :]			,       __lowerCAmelCase			,       size=__lowerCAmelCase			,       ray_batch_size=4096			,       n_coarse_samples=64			,       n_fine_samples=128			,       )

							images.append(__lowerCAmelCase)

					lowerCAmelCase     =    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     =    images.cpu().numpy()

					if output_type == "pil":
							lowerCAmelCase     =    [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)




