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

import numpy as np
import PIL
import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer

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


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

_lowerCamelCase :       str     					=    '''
    Examples:
        ```py
        >>> import torch
        >>> from diffusers import DiffusionPipeline
        >>> from diffusers.utils import export_to_gif

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

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

        >>> guidance_scale = 15.0
        >>> prompt = "a shark"

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

        >>> gif_path = export_to_gif(images[0], "shark_3d.gif")
        ```
'''




@dataclass
class      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase      ):





	'''simple docstring'''



	_UpperCAmelCase :	Union[List[List[PIL.Image.Image]], List[List[np.ndarray]]]




class      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase      ):





	'''simple docstring'''



	def __init__(						self			:					str    ,						lowercase			:					PriorTransformer    ,						lowercase			:					CLIPTextModelWithProjection    ,						lowercase			:					CLIPTokenizer    ,						lowercase			:					HeunDiscreteScheduler    ,						lowercase			:					ShapERenderer    ,						):


				'''simple docstring'''


				super().__init__()

				self.register_modules(
				    prior=lowercase    ,						text_encoder=lowercase    ,						tokenizer=lowercase    ,						scheduler=lowercase    ,						renderer=lowercase    ,						)



	def 				A							(						self			:					Optional[int]    ,						lowercase			:					Tuple    ,						lowercase			:					Any    ,						lowercase			:					Optional[int]    ,						lowercase			:					str    ,						lowercase			:					Optional[int]    ,						lowercase			:					Tuple			):


				'''simple docstring'''


				if latents is None:
							_snake_case						=    randn_tensor(lowercase    ,						generator=lowercase    ,						device=lowercase    ,						dtype=lowercase			)
				else:
							if latents.shape != shape:
										raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''			)
							_snake_case						=    latents.to(lowercase			)

				_snake_case						=    latents * scheduler.init_noise_sigma
				return latents



	def 				A							(						self			:					Union[str, Any]    ,						lowercase			:					Tuple=0			):


				'''simple docstring'''


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

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

				_snake_case						=    [self.text_encoder, self.prior]
				for cpu_offloaded_model in models:
							if cpu_offloaded_model is not None:
										cpu_offload(lowercase    ,						lowercase			)



	def 				A							(						self			:					List[str]    ,						lowercase			:					Tuple=0			):


				'''simple docstring'''


				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.'			)

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

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

				_snake_case						=    None
				for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
							_snake_case			,      _snake_case						=    cpu_offload_with_hook(lowercase    ,						lowercase    ,						prev_module_hook=lowercase			)

				if self.safety_checker is not None:
							_snake_case			,      _snake_case						=    cpu_offload_with_hook(self.safety_checker    ,						lowercase    ,						prev_module_hook=lowercase			)

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



	@property
	def 				A							(						self			:					Union[str, Any]			):


				'''simple docstring'''


				if self.device != torch.device('meta'			) or not hasattr(self.text_encoder    ,						'_hf_hook'			):
							return self.device
				for module in self.text_encoder.modules():
							if (
							    hasattr(lowercase    ,						'_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			:					Any    ,						lowercase			:					Tuple    ,						lowercase			:					Optional[Any]    ,						lowercase			:					int    ,						lowercase			:					List[Any]    ,						):


				'''simple docstring'''


				len(lowercase			) if isinstance(lowercase    ,						lowercase			) else 1

				# YiYi Notes: set pad_token_id to be 0, not sure why I can't set in the config file
				_snake_case						=    0
				# get prompt text embeddings
				_snake_case						=    self.tokenizer(
				    lowercase    ,						padding='max_length'    ,						max_length=self.tokenizer.model_max_length    ,						truncation=lowercase    ,						return_tensors='pt'    ,						)
				_snake_case						=    text_inputs.input_ids
				_snake_case						=    self.tokenizer(lowercase    ,						padding='longest'    ,						return_tensors='pt'			).input_ids

				if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(lowercase    ,						lowercase			):
							_snake_case						=    self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]			)
							logger.warning(
							    'The following part of your input was truncated because CLIP can only handle sequences up to'
							    f''' {self.tokenizer.model_max_length} tokens: {removed_text}'''			)

				_snake_case						=    self.text_encoder(text_input_ids.to(lowercase			)			)
				_snake_case						=    text_encoder_output.text_embeds

				_snake_case						=    prompt_embeds.repeat_interleave(lowercase    ,						dim=0			)
				# in Shap-E it normalize the prompt_embeds and then later rescale it
				_snake_case						=    prompt_embeds / torch.linalg.norm(lowercase    ,						dim=-1    ,						keepdim=lowercase			)

				if do_classifier_free_guidance:
							_snake_case						=    torch.zeros_like(lowercase			)

							# 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
							_snake_case						=    torch.cat([negative_prompt_embeds, prompt_embeds]			)

				# Rescale the features to have unit variance
				_snake_case						=    math.sqrt(prompt_embeds.shape[1]			) * prompt_embeds

				return prompt_embeds





	@torch.no_grad()
	@replace_example_docstring(lowercase			)
	def __call__(						self			:					List[str]    ,						lowercase			:					str    ,						lowercase			:					int = 1    ,						lowercase			:					int = 25    ,						lowercase			:					Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,						lowercase			:					Optional[torch.FloatTensor] = None    ,						lowercase			:					float = 4.0    ,						lowercase			:					int = 64    ,						lowercase			:					Optional[str] = "pil"    ,						lowercase			:					bool = True    ,						):


				'''simple docstring'''


				if isinstance(lowercase    ,						lowercase			):
							_snake_case						=    1
				elif isinstance(lowercase    ,						lowercase			):
							_snake_case						=    len(lowercase			)
				else:
							raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(lowercase			)}'''			)

				_snake_case						=    self._execution_device

				_snake_case						=    batch_size * num_images_per_prompt

				_snake_case						=    guidance_scale > 1.0
				_snake_case						=    self._encode_prompt(lowercase    ,						lowercase    ,						lowercase    ,						lowercase			)

				# prior

				self.scheduler.set_timesteps(lowercase    ,						device=lowercase			)
				_snake_case						=    self.scheduler.timesteps

				_snake_case						=    self.prior.config.num_embeddings
				_snake_case						=    self.prior.config.embedding_dim

				_snake_case						=    self.prepare_latents(
				    (batch_size, num_embeddings * embedding_dim)    ,						prompt_embeds.dtype    ,						lowercase    ,						lowercase    ,						lowercase    ,						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
				_snake_case						=    latents.reshape(latents.shape[0]    ,						lowercase    ,						lowercase			)

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

							_snake_case						=    self.prior(
							    lowercase    ,						timestep=lowercase    ,						proj_embedding=lowercase    ,						).predicted_image_embedding

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

							if do_classifier_free_guidance is not None:
										_snake_case			,      _snake_case						=    noise_pred.chunk(2			)
										_snake_case						=    noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

							_snake_case						=    self.scheduler.step(
							    lowercase    ,						timestep=lowercase    ,						sample=lowercase    ,						).prev_sample

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

				_snake_case						=    []
				for i, latent in enumerate(lowercase			):
							_snake_case						=    self.renderer.decode(
							    latent[None, :]    ,						lowercase    ,						size=lowercase    ,						ray_batch_size=4_096    ,						n_coarse_samples=64    ,						n_fine_samples=128    ,						)
							images.append(lowercase			)

				_snake_case						=    torch.stack(lowercase			)

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

				_snake_case						=    images.cpu().numpy()

				if output_type == "pil":
							_snake_case						=    [self.numpy_to_pil(lowercase			) 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=lowercase			)

import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def  a_	( __lowercase     :			Optional[Any] ,      __lowercase     :			Union[str, Any] ,      __lowercase     :			str				)				->						Any:
			# Construct configuration depending on size
			_snake_case						=    {'small': (512, 8, 24), 'medium': (1_024, 8, 36), 'large': (1_536, 16, 48)}
			_snake_case			,      _snake_case			,      _snake_case						=    MODELS[model_size]  # set model hyperparameters
			_snake_case						=    ImageGPTConfig(n_embd=__lowercase ,      n_layer=__lowercase ,      n_head=__lowercase				)
			_snake_case						=    ImageGPTForCausalLM(__lowercase				)

			# Load weights from numpy
			load_tf_weights_in_imagegpt(__lowercase ,      __lowercase ,      __lowercase				)

			# Save pytorch-model
			_snake_case						=    pytorch_dump_folder_path + '/' + WEIGHTS_NAME
			_snake_case						=    pytorch_dump_folder_path + '/' + CONFIG_NAME
			print(f'''Save PyTorch model to {pytorch_weights_dump_path}'''				)
			torch.save(model.state_dict() ,      __lowercase				)
			print(f'''Save configuration file to {pytorch_config_dump_path}'''				)
			with open(__lowercase ,      'w' ,      encoding='utf-8'				) as f:
						f.write(config.to_json_string()				)


if __name__ == "__main__":
			_lowerCamelCase :       List[Any]     					=    argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--imagegpt_checkpoint_path''',
			    default=None,
			    type=str,
			    required=True,
			    help='''Path to the TensorFlow checkpoint path.''',
			)
			parser.add_argument(
			    '''--model_size''',
			    default=None,
			    type=str,
			    required=True,
			    help='''Size of the model (can be either \'small\', \'medium\' or \'large\').''',
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
			)
			_lowerCamelCase :       Optional[Any]     					=    parser.parse_args()
			convert_imagegpt_checkpoint_to_pytorch(
			    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
			)