


import argparse
import json
from pathlib import Path

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

from transformers import (
    BeitConfig,
    BeitForImageClassification,
    BeitForMaskedImageModeling,
    BeitForSemanticSegmentation,
    BeitImageProcessor,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase			:					Optional[Any]         =							logging.get_logger(__name__)
def 				UpperCamelCase_	(					__snake_case      :    Tuple						,      __snake_case      :    List[Any]=False						,      __snake_case      :    Dict=False  )->   int:




						'''simple docstring'''
						__magic_name__					=      'backbone.' if is_semantic else ''

						__magic_name__					=      []
						for i in range(config.num_hidden_layers  ):
												# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
												rename_keys.append((F'''{prefix}blocks.{i}.norm1.weight''', F'''beit.encoder.layer.{i}.layernorm_before.weight''')  )
												rename_keys.append((F'''{prefix}blocks.{i}.norm1.bias''', F'''beit.encoder.layer.{i}.layernorm_before.bias''')  )
												rename_keys.append(
												    (F'''{prefix}blocks.{i}.attn.proj.weight''', F'''beit.encoder.layer.{i}.attention.output.dense.weight''')  )
												rename_keys.append(
												    (F'''{prefix}blocks.{i}.attn.proj.bias''', F'''beit.encoder.layer.{i}.attention.output.dense.bias''')  )
												rename_keys.append((F'''{prefix}blocks.{i}.norm2.weight''', F'''beit.encoder.layer.{i}.layernorm_after.weight''')  )
												rename_keys.append((F'''{prefix}blocks.{i}.norm2.bias''', F'''beit.encoder.layer.{i}.layernorm_after.bias''')  )
												rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc1.weight''', F'''beit.encoder.layer.{i}.intermediate.dense.weight''')  )
												rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc1.bias''', F'''beit.encoder.layer.{i}.intermediate.dense.bias''')  )
												rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.weight''', F'''beit.encoder.layer.{i}.output.dense.weight''')  )
												rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.bias''', F'''beit.encoder.layer.{i}.output.dense.bias''')  )

						# projection layer + position embeddings
						rename_keys.extend(
						    [
						        (F'''{prefix}cls_token''', 'beit.embeddings.cls_token'),
						        (F'''{prefix}patch_embed.proj.weight''', 'beit.embeddings.patch_embeddings.projection.weight'),
						        (F'''{prefix}patch_embed.proj.bias''', 'beit.embeddings.patch_embeddings.projection.bias'),
						    ]  )

						if has_lm_head:
												# mask token + shared relative position bias + layernorm
												rename_keys.extend(
												    [
												        ('mask_token', 'beit.embeddings.mask_token'),
												        (
												            'rel_pos_bias.relative_position_bias_table',
												            'beit.encoder.relative_position_bias.relative_position_bias_table',
												        ),
												        (
												            'rel_pos_bias.relative_position_index',
												            'beit.encoder.relative_position_bias.relative_position_index',
												        ),
												        ('norm.weight', 'layernorm.weight'),
												        ('norm.bias', 'layernorm.bias'),
												    ]  )
						elif is_semantic:
												# semantic segmentation classification heads
												rename_keys.extend(
												    [
												        ('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
												        ('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
												        ('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
												        ('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
												    ]  )
						else:
												# layernorm + classification head
												rename_keys.extend(
												    [
												        ('fc_norm.weight', 'beit.pooler.layernorm.weight'),
												        ('fc_norm.bias', 'beit.pooler.layernorm.bias'),
												        ('head.weight', 'classifier.weight'),
												        ('head.bias', 'classifier.bias'),
												    ]  )

						return rename_keys
def 				UpperCamelCase_	(					__snake_case      :    Dict						,      __snake_case      :    Optional[Any]						,      __snake_case      :    List[Any]=False						,      __snake_case      :    Dict=False  )->   List[str]:




						'''simple docstring'''






						for i in range(config.num_hidden_layers  ):
												__magic_name__					=      'backbone.' if is_semantic else ''
												# queries, keys and values
												__magic_name__					=      state_dict.pop(F'''{prefix}blocks.{i}.attn.qkv.weight'''  )
												__magic_name__					=      state_dict.pop(F'''{prefix}blocks.{i}.attn.q_bias'''  )
												__magic_name__					=      state_dict.pop(F'''{prefix}blocks.{i}.attn.v_bias'''  )

												__magic_name__					=      in_proj_weight[
												    : config.hidden_size, :
												]
												__magic_name__					=      q_bias
												__magic_name__					=      in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												__magic_name__					=      in_proj_weight[
												    -config.hidden_size :, :
												]
												__magic_name__					=      v_bias

												# gamma_1 and gamma_2
												# we call them lambda because otherwise they are renamed when using .from_pretrained
												__magic_name__					=      state_dict.pop(F'''{prefix}blocks.{i}.gamma_1'''  )
												__magic_name__					=      state_dict.pop(F'''{prefix}blocks.{i}.gamma_2'''  )

												__magic_name__					=      gamma_a
												__magic_name__					=      gamma_a

												# relative_position bias table + index
												if not has_lm_head:
																		# each layer has its own relative position bias
																		__magic_name__					=      state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_bias_table'''  )
																		__magic_name__					=      state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_index'''  )

																		__magic_name__					=      table
																		__magic_name__					=      index
def 				UpperCamelCase_	(					__snake_case      :    List[str]						,      __snake_case      :    Dict						,      __snake_case      :    List[str]  )->   Optional[int]:




						'''simple docstring'''
						__magic_name__					=      dct.pop(__snake_case  )
						__magic_name__					=      val
def 				UpperCamelCase_	(					)->   Dict:




						'''simple docstring'''
						__magic_name__					=      'http://images.cocodataset.org/val2017/000000039769.jpg'
						__magic_name__					=      Image.open(requests.get(__snake_case						,      stream=__snake_case  ).raw  )
						return im
@torch.no_grad()
def 				UpperCamelCase_	(					__snake_case      :    Dict						,      __snake_case      :    List[Any]  )->   Any:




						'''simple docstring'''
						__magic_name__					=      BeitConfig()
						__magic_name__					=      False
						__magic_name__					=      False
						__magic_name__					=      'huggingface/label-files'
						# set config parameters based on URL
						if checkpoint_url[-9:-4] == "pt22k":
												# masked image modeling
												__magic_name__					=      True
												__magic_name__					=      True
												__magic_name__					=      True
						elif checkpoint_url[-9:-4] == "ft22k":
												# intermediate fine-tuning on ImageNet-22k
												__magic_name__					=      True
												__magic_name__					=      2_18_41
												__magic_name__					=      'imagenet-22k-id2label.json'
												__magic_name__					=      json.load(open(hf_hub_download(__snake_case						,      __snake_case						,      repo_type='dataset'  )						,      'r'  )  )
												__magic_name__					=      {int(__snake_case  ): v for k, v in idalabel.items()}
												# this dataset contains 21843 labels but the model only has 21841
												# we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
												del idalabel[92_05]
												del idalabel[1_50_27]
												__magic_name__					=      idalabel
												__magic_name__					=      {v: k for k, v in idalabel.items()}
						elif checkpoint_url[-8:-4] == "to1k":
												# fine-tuning on ImageNet-1k
												__magic_name__					=      True
												__magic_name__					=      10_00
												__magic_name__					=      'imagenet-1k-id2label.json'
												__magic_name__					=      json.load(open(hf_hub_download(__snake_case						,      __snake_case						,      repo_type='dataset'  )						,      'r'  )  )
												__magic_name__					=      {int(__snake_case  ): v for k, v in idalabel.items()}
												__magic_name__					=      idalabel
												__magic_name__					=      {v: k for k, v in idalabel.items()}
												if "384" in checkpoint_url:
																		__magic_name__					=      3_84
												if "512" in checkpoint_url:
																		__magic_name__					=      5_12
						elif "ade20k" in checkpoint_url:
												# fine-tuning
												__magic_name__					=      True
												__magic_name__					=      1_50
												__magic_name__					=      'ade20k-id2label.json'
												__magic_name__					=      json.load(open(hf_hub_download(__snake_case						,      __snake_case						,      repo_type='dataset'  )						,      'r'  )  )
												__magic_name__					=      {int(__snake_case  ): v for k, v in idalabel.items()}
												__magic_name__					=      idalabel
												__magic_name__					=      {v: k for k, v in idalabel.items()}
												__magic_name__					=      6_40
												__magic_name__					=      True
						else:
												raise ValueError('Checkpoint not supported, URL should either end with \'pt22k\', \'ft22k\', \'to1k\' or \'ade20k\''  )

						# size of the architecture
						if "base" in checkpoint_url:
												pass
						elif "large" in checkpoint_url:
												__magic_name__					=      10_24
												__magic_name__					=      40_96
												__magic_name__					=      24
												__magic_name__					=      16
												if "ade20k" in checkpoint_url:
																		__magic_name__					=      6_40
																		__magic_name__					=      [7, 11, 15, 23]
						else:
												raise ValueError('Should either find \'base\' or \'large\' in checkpoint URL'  )

						# load state_dict of original model, remove and rename some keys
						__magic_name__					=      torch.hub.load_state_dict_from_url(__snake_case						,      map_location='cpu'						,      check_hash=__snake_case  )
						__magic_name__					=      state_dict['model'] if 'ade20k' not in checkpoint_url else state_dict['state_dict']

						__magic_name__					=      create_rename_keys(__snake_case						,      has_lm_head=__snake_case						,      is_semantic=__snake_case  )
						for src, dest in rename_keys:
												rename_key(__snake_case						,      __snake_case						,      __snake_case  )
						read_in_q_k_v(__snake_case						,      __snake_case						,      has_lm_head=__snake_case						,      is_semantic=__snake_case  )
						if is_semantic:
												# add prefix to decoder keys
												for key, val in state_dict.copy().items():
																		__magic_name__					=      state_dict.pop(__snake_case  )
																		if key.startswith('backbone.fpn'  ):
																								__magic_name__					=      key.replace('backbone.fpn'						,      'fpn'  )
																		__magic_name__					=      val

    # load HuggingFace model
						if checkpoint_url[-9:-4] == "pt22k":
												__magic_name__					=      BeitForMaskedImageModeling(__snake_case  )
						elif "ade20k" in checkpoint_url:
												__magic_name__					=      BeitForSemanticSegmentation(__snake_case  )
						else:
												__magic_name__					=      BeitForImageClassification(__snake_case  )
						model.eval()
						model.load_state_dict(__snake_case  )

						# Check outputs on an image
						if is_semantic:
												__magic_name__					=      BeitImageProcessor(size=config.image_size						,      do_center_crop=__snake_case  )
												__magic_name__					=      load_dataset('hf-internal-testing/fixtures_ade20k'						,      split='test'  )
												__magic_name__					=      Image.open(ds[0]['file']  )
						else:
												__magic_name__					=      BeitImageProcessor(
												    size=config.image_size						,      resample=PILImageResampling.BILINEAR						,      do_center_crop=__snake_case  )
												__magic_name__					=      prepare_img()

						__magic_name__					=      image_processor(images=__snake_case						,      return_tensors='pt'  )
						__magic_name__					=      encoding['pixel_values']

						__magic_name__					=      model(__snake_case  )
						__magic_name__					=      outputs.logits

						# verify logits
						__magic_name__					=      torch.Size([1, 10_00]  )
						if checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k'  ):
												__magic_name__					=      torch.Size([1, 1_96, 81_92]  )
						elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k'  ):
												__magic_name__					=      torch.Size([1, 1_96, 81_92]  )
						elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22k'  ):
												__magic_name__					=      torch.Size([1, 2_18_41]  )
												__magic_name__					=      torch.tensor([2.2288, 2.4671, 0.7395]  )
												__magic_name__					=      23_97
						elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22k'  ):
												__magic_name__					=      torch.Size([1, 2_18_41]  )
												__magic_name__					=      torch.tensor([1.6881, -0.2787, 0.5901]  )
												__magic_name__					=      23_96
						elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft1k'  ):
												__magic_name__					=      torch.tensor([0.1241, 0.0798, -0.6569]  )
												__magic_name__					=      2_85
						elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22kto1k'  ):
												__magic_name__					=      torch.tensor([-1.2385, -1.0987, -1.0108]  )
												__magic_name__					=      2_81
						elif checkpoint_url[:-4].endswith('beit_base_patch16_384_pt22k_ft22kto1k'  ):
												__magic_name__					=      torch.tensor([-1.5303, -0.9484, -0.3147]  )
												__magic_name__					=      7_61
						elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft1k'  ):
												__magic_name__					=      torch.tensor([0.4610, -0.0928, 0.2086]  )
												__magic_name__					=      7_61
						elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22kto1k'  ):
												__magic_name__					=      torch.tensor([-0.4804, 0.6257, -0.1837]  )
												__magic_name__					=      7_61
						elif checkpoint_url[:-4].endswith('beit_large_patch16_384_pt22k_ft22kto1k'  ):
												__magic_name__					=      torch.tensor([[-0.5122, 0.5117, -0.2113]]  )
												__magic_name__					=      7_61
						elif checkpoint_url[:-4].endswith('beit_large_patch16_512_pt22k_ft22kto1k'  ):
												__magic_name__					=      torch.tensor([-0.3062, 0.7261, 0.4852]  )
												__magic_name__					=      7_61
						elif checkpoint_url[:-4].endswith('beit_base_patch16_640_pt22k_ft22ktoade20k'  ):
												__magic_name__					=      (1, 1_50, 1_60, 1_60)
												__magic_name__					=      torch.tensor(
												    [
												        [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
												        [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
												        [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
												    ]  )
						elif checkpoint_url[:-4].endswith('beit_large_patch16_640_pt22k_ft22ktoade20k'  ):
												__magic_name__					=      (1, 1_50, 1_60, 1_60)
												__magic_name__					=      torch.tensor(
												    [
												        [[-4.3305, -2.3049, -3.0161], [-2.9591, -1.5305, -2.2251], [-3.4198, -1.8004, -2.9062]],
												        [[-5.8922, -3.7435, -4.3978], [-4.2063, -2.7872, -3.4755], [-4.2791, -3.1874, -4.1681]],
												        [[0.9895, 4.3467, 4.7663], [4.2476, 5.6830, 6.1518], [4.5550, 6.2495, 6.5154]],
												    ]  )
						else:
												raise ValueError('Can\'t verify logits as model is not supported'  )

						assert logits.shape == expected_shape, "Shape of logits not as expected"
						if not has_lm_head:
												if is_semantic:
																		assert torch.allclose(
																		    logits[0, :3, :3, :3]						,      __snake_case						,      atol=1E-3  ), "First elements of logits not as expected"
												else:
																		print('Predicted class idx:'						,      logits.argmax(-1  ).item()  )
																		assert torch.allclose(
																		    logits[0, :3]						,      __snake_case						,      atol=1E-3  ), "First elements of logits not as expected"
																		assert logits.argmax(-1  ).item() == expected_class_idx, "Predicted class index not as expected"

						Path(__snake_case  ).mkdir(exist_ok=__snake_case  )
						print(F'''Saving model to {pytorch_dump_folder_path}'''  )
						model.save_pretrained(__snake_case  )
						print(F'''Saving image processor to {pytorch_dump_folder_path}'''  )
						image_processor.save_pretrained(__snake_case  )


if __name__ == "__main__":
					UpperCamelCase			:					Optional[Any]         =							argparse.ArgumentParser()

					parser.add_argument(
					    '''--checkpoint_url''',
					    default='''https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth''',
					    type=str,
					    help='''URL to the original PyTorch checkpoint (.pth file).''',
					)
					parser.add_argument(
					    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
					)
					UpperCamelCase			:					Any         =							parser.parse_args()
					convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)








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


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

snake_case__   :				Any						=	'\n    Examples:\n        ```py\n        >>> import torch\n        >>> from diffusers import DiffusionPipeline\n        >>> from diffusers.utils import export_to_gif\n\n        >>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")\n\n        >>> repo = "openai/shap-e"\n        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n        >>> pipe = pipe.to(device)\n\n        >>> guidance_scale = 15.0\n        >>> prompt = "a shark"\n\n        >>> images = pipe(\n        ...     prompt,\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], "shark_3d.gif")\n        ```\n'





@dataclass
class A_   (     _lowerCamelCase  ):
   lowerCAmelCase__  	=					42





class A_   (     _lowerCamelCase  ):




   def __init__(self      :List[str]       ,  _UpperCamelCase      :PriorTransformer       ,  _UpperCamelCase      :CLIPTextModelWithProjection       ,  _UpperCamelCase      :CLIPTokenizer       ,  _UpperCamelCase      :HeunDiscreteScheduler       ,  _UpperCamelCase      :ShapERenderer       ,  )->   Tuple:
         super().__init__()

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




   def _lowerCAmelCase (self      :Any       ,  _UpperCamelCase      :str       ,  _UpperCamelCase      :Optional[int]       ,  _UpperCamelCase      :Union[str, Any]       ,  _UpperCamelCase      :Optional[int]       ,  _UpperCamelCase      :str       ,  _UpperCamelCase      :Union[str, Any]					)->   Any:
         if latents is None:
               __A			  =							randn_tensor(_UpperCamelCase       ,  generator=_UpperCamelCase       ,  device=_UpperCamelCase       ,  dtype=_UpperCamelCase					)
         else:
               if latents.shape != shape:
                     raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}"""					)
               __A			  =							latents.to(_UpperCamelCase					)

         __A			  =							latents * scheduler.init_noise_sigma
         return latents




   def _lowerCAmelCase (self      :Union[str, Any]       ,  _UpperCamelCase      :Dict=0					)->   Optional[Any]:
         if is_accelerate_available():
               from accelerate import cpu_offload
         else:
               raise ImportError('''Please install accelerate via `pip install accelerate`'''					)

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

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




   def _lowerCAmelCase (self      :Tuple       ,  _UpperCamelCase      :Dict=0					)->   Tuple:
         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.'''					)

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

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

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

         if self.safety_checker is not None:
               __A  ,		__A			  =							cpu_offload_with_hook(self.safety_checker       ,  _UpperCamelCase       ,  prev_module_hook=_UpperCamelCase					)

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




   @property
   def _lowerCAmelCase (self      :int					)->   int:
         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(_UpperCamelCase       ,  '''_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      :str       ,  _UpperCamelCase      :Optional[Any]       ,  _UpperCamelCase      :Optional[int]       ,  _UpperCamelCase      :Any       ,  _UpperCamelCase      :int       ,  )->   Dict:
         len(_UpperCamelCase					) if isinstance(_UpperCamelCase       ,  _UpperCamelCase					) else 1

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

         if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(_UpperCamelCase       ,  _UpperCamelCase					):
               __A			  =							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}"""					)

         __A			  =							self.text_encoder(text_input_ids.to(_UpperCamelCase					)					)
         __A			  =							text_encoder_output.text_embeds

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

         if do_classifier_free_guidance:
               __A			  =							torch.zeros_like(_UpperCamelCase					)

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

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

         return prompt_embeds






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

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

         __A			  =							self._execution_device

         __A			  =							batch_size * num_images_per_prompt

         __A			  =							guidance_scale > 1.0
         __A			  =							self._encode_prompt(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase					)

         # prior

         self.scheduler.set_timesteps(_UpperCamelCase       ,  device=_UpperCamelCase					)
         __A			  =							self.scheduler.timesteps

         __A			  =							self.prior.config.num_embeddings
         __A			  =							self.prior.config.embedding_dim

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

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

               __A			  =							self.prior(
                   _UpperCamelCase       ,  timestep=_UpperCamelCase       ,  proj_embedding=_UpperCamelCase       ,  ).predicted_image_embedding

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

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

               __A			  =							self.scheduler.step(
                   _UpperCamelCase       ,  timestep=_UpperCamelCase       ,  sample=_UpperCamelCase       ,  ).prev_sample

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

         __A			  =							[]
         for i, latent in enumerate(_UpperCamelCase					):
               __A			  =							self.renderer.decode(
                   latent[None, :]       ,  _UpperCamelCase       ,  size=_UpperCamelCase       ,  ray_batch_size=4096       ,  n_coarse_samples=64       ,  n_fine_samples=128       ,  )
               images.append(_UpperCamelCase					)

         __A			  =							torch.stack(_UpperCamelCase					)

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

         __A			  =							images.cpu().numpy()

         if output_type == "pil":
               __A			  =							[self.numpy_to_pil(_UpperCamelCase					) 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=_UpperCamelCase					)





