





"""simple docstring"""


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()
a__    :   Optional[Any]    						= logging.get_logger(__name__)





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase=False		,   _UpperCAmelCase=False		)			->     Dict:

 '''simple docstring'''






 snake_case__										= "backbone." if is_semantic else ""

 snake_case__										= []
 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 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase=False		,   _UpperCAmelCase=False		)			->     Optional[Any]:

 '''simple docstring'''






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

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

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

  snake_case__										= gamma_a
  snake_case__										= gamma_a

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

   snake_case__										= table
   snake_case__										= index





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		)			->     Dict:

 '''simple docstring'''






 snake_case__										= dct.pop(_UpperCAmelCase		)
 snake_case__										= val





def 							a__	(     )			->     Any:

 '''simple docstring'''






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





@torch.no_grad()
def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		)			->     List[Any]:

 '''simple docstring'''






 snake_case__										= BeitConfig()
 snake_case__										= False
 snake_case__										= False
 snake_case__										= "huggingface/label-files"
 # set config parameters based on URL
 if checkpoint_url[-9:-4] == "pt22k":
  # masked image modeling
  snake_case__										= True
  snake_case__										= True
  snake_case__										= True
 elif checkpoint_url[-9:-4] == "ft22k":
  # intermediate fine-tuning on ImageNet-22k
  snake_case__										= True
  snake_case__										= 2_1_8_4_1
  snake_case__										= "imagenet-22k-id2label.json"
  snake_case__										= json.load(open(hf_hub_download(_UpperCAmelCase		,   _UpperCAmelCase		,   repo_type="dataset"		)		,   "r"		)		)
  snake_case__										= {int(_UpperCAmelCase		): 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[9_2_0_5]
  del idalabel[1_5_0_2_7]
  snake_case__										= idalabel
  snake_case__										= {v: k for k, v in idalabel.items()}
 elif checkpoint_url[-8:-4] == "to1k":
  # fine-tuning on ImageNet-1k
  snake_case__										= True
  snake_case__										= 1_0_0_0
  snake_case__										= "imagenet-1k-id2label.json"
  snake_case__										= json.load(open(hf_hub_download(_UpperCAmelCase		,   _UpperCAmelCase		,   repo_type="dataset"		)		,   "r"		)		)
  snake_case__										= {int(_UpperCAmelCase		): v for k, v in idalabel.items()}
  snake_case__										= idalabel
  snake_case__										= {v: k for k, v in idalabel.items()}
  if "384" in checkpoint_url:
   snake_case__										= 3_8_4
  if "512" in checkpoint_url:
   snake_case__										= 5_1_2
 elif "ade20k" in checkpoint_url:
  # fine-tuning
  snake_case__										= True
  snake_case__										= 1_5_0
  snake_case__										= "ade20k-id2label.json"
  snake_case__										= json.load(open(hf_hub_download(_UpperCAmelCase		,   _UpperCAmelCase		,   repo_type="dataset"		)		,   "r"		)		)
  snake_case__										= {int(_UpperCAmelCase		): v for k, v in idalabel.items()}
  snake_case__										= idalabel
  snake_case__										= {v: k for k, v in idalabel.items()}
  snake_case__										= 6_4_0
  snake_case__										= 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:
  snake_case__										= 1_0_2_4
  snake_case__										= 4_0_9_6
  snake_case__										= 2_4
  snake_case__										= 1_6
  if "ade20k" in checkpoint_url:
   snake_case__										= 6_4_0
   snake_case__										= [7, 1_1, 1_5, 2_3]
 else:
  raise ValueError("Should either find 'base' or 'large' in checkpoint URL"		)

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

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

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

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

 snake_case__										= image_processor(images=_UpperCAmelCase		,   return_tensors="pt"		)
 snake_case__										= encoding["pixel_values"]

 snake_case__										= model(_UpperCAmelCase		)
 snake_case__										= outputs.logits

 # verify logits
 snake_case__										= torch.Size([1, 1_0_0_0]		)
 if checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k"		):
  snake_case__										= torch.Size([1, 1_9_6, 8_1_9_2]		)
 elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k"		):
  snake_case__										= torch.Size([1, 1_9_6, 8_1_9_2]		)
 elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22k"		):
  snake_case__										= torch.Size([1, 2_1_8_4_1]		)
  snake_case__										= torch.tensor([2.2288, 2.4671, 0.7395]		)
  snake_case__										= 2_3_9_7
 elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft22k"		):
  snake_case__										= torch.Size([1, 2_1_8_4_1]		)
  snake_case__										= torch.tensor([1.6881, -0.2787, 0.5901]		)
  snake_case__										= 2_3_9_6
 elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft1k"		):
  snake_case__										= torch.tensor([0.1241, 0.0798, -0.6569]		)
  snake_case__										= 2_8_5
 elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22kto1k"		):
  snake_case__										= torch.tensor([-1.2385, -1.0987, -1.0108]		)
  snake_case__										= 2_8_1
 elif checkpoint_url[:-4].endswith("beit_base_patch16_384_pt22k_ft22kto1k"		):
  snake_case__										= torch.tensor([-1.5303, -0.9484, -0.3147]		)
  snake_case__										= 7_6_1
 elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft1k"		):
  snake_case__										= torch.tensor([0.4610, -0.0928, 0.2086]		)
  snake_case__										= 7_6_1
 elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft22kto1k"		):
  snake_case__										= torch.tensor([-0.4804, 0.6257, -0.1837]		)
  snake_case__										= 7_6_1
 elif checkpoint_url[:-4].endswith("beit_large_patch16_384_pt22k_ft22kto1k"		):
  snake_case__										= torch.tensor([[-0.5122, 0.5117, -0.2113]]		)
  snake_case__										= 7_6_1
 elif checkpoint_url[:-4].endswith("beit_large_patch16_512_pt22k_ft22kto1k"		):
  snake_case__										= torch.tensor([-0.3062, 0.7261, 0.4852]		)
  snake_case__										= 7_6_1
 elif checkpoint_url[:-4].endswith("beit_base_patch16_640_pt22k_ft22ktoade20k"		):
  snake_case__										= (1, 1_5_0, 1_6_0, 1_6_0)
  snake_case__										= 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"		):
  snake_case__										= (1, 1_5_0, 1_6_0, 1_6_0)
  snake_case__										= 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]		,   _UpperCAmelCase		,   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]		,   _UpperCAmelCase		,   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(_UpperCAmelCase		).mkdir(exist_ok=_UpperCAmelCase		)
 print(f"Saving model to {pytorch_dump_folder_path}"		)
 model.save_pretrained(_UpperCAmelCase		)
 print(f"Saving image processor to {pytorch_dump_folder_path}"		)
 image_processor.save_pretrained(_UpperCAmelCase		)


if __name__ == "__main__":
   a__    :   List[str]    						= 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."""
   )
   a__    :   Dict    						= parser.parse_args()
   convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)








"""simple docstring"""


import types
from typing import List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


a__    :   Optional[int]    						= logging.get_logger(__name__)  # pylint: disable=invalid-name





def 							a__	(     self		,   _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		)			->     List[str]:

 '''simple docstring'''






 snake_case__										= image.to(device=_UpperCAmelCase		)
 snake_case__										= image  # take image as image_embeddings
 snake_case__										= image_embeddings.unsqueeze(1		)

 # duplicate image embeddings for each generation per prompt, using mps friendly method
 snake_case__							,      snake_case__							,      snake_case__										= image_embeddings.shape
 snake_case__										= image_embeddings.repeat(1		,   _UpperCAmelCase		,   1		)
 snake_case__										= image_embeddings.view(bs_embed * num_images_per_prompt		,   _UpperCAmelCase		,   -1		)

 if do_classifier_free_guidance:
  snake_case__										= 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
  snake_case__										= torch.cat([uncond_embeddings, image_embeddings]		)

 return image_embeddings



class    lowercase_ (   __lowerCAmelCase							):

       def __init__(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = None      ,    ):






        '''simple docstring'''

        super().__init__()

        snake_case__										= {"image_encoder": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

        snake_case__										= decoder_pipe_kwargs.get("torch_dtype"      ,    __SCREAMING_SNAKE_CASE   ) or prior.dtype

        snake_case__										= StableDiffusionImageVariationPipeline.from_pretrained(
            "lambdalabs/sd-image-variations-diffusers"      ,    **__SCREAMING_SNAKE_CASE   )

        # replace `_encode_image` method
        snake_case__										= types.MethodType(_encode_image      ,    self.decoder_pipe   )

        self.register_modules(
            prior=__SCREAMING_SNAKE_CASE      ,    tokenizer=__SCREAMING_SNAKE_CASE      ,    text_encoder=__SCREAMING_SNAKE_CASE      ,    prior_scheduler=__SCREAMING_SNAKE_CASE      ,    )

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    ):






        '''simple docstring'''

        if text_model_output is None:
         snake_case__										= len(__SCREAMING_SNAKE_CASE   ) if isinstance(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ) else 1
         # get prompt text embeddings
         snake_case__										= self.tokenizer(
             __SCREAMING_SNAKE_CASE      ,    padding="max_length"      ,    max_length=self.tokenizer.model_max_length      ,    return_tensors="pt"      ,    )
         snake_case__										= text_inputs.input_ids
         snake_case__										= text_inputs.attention_mask.bool().to(__SCREAMING_SNAKE_CASE   )

         if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
          snake_case__										= self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]   )
          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__										= text_input_ids[:, : self.tokenizer.model_max_length]

         snake_case__										= self.text_encoder(text_input_ids.to(__SCREAMING_SNAKE_CASE   )   )

         snake_case__										= text_encoder_output.text_embeds
         snake_case__										= text_encoder_output.last_hidden_state

        else:
         snake_case__										= text_model_output[0].shape[0]
         snake_case__							,      snake_case__										= text_model_output[0], text_model_output[1]
         snake_case__										= text_attention_mask

        snake_case__										= text_embeddings.repeat_interleave(__SCREAMING_SNAKE_CASE      ,    dim=0   )
        snake_case__										= text_encoder_hidden_states.repeat_interleave(__SCREAMING_SNAKE_CASE      ,    dim=0   )
        snake_case__										= text_mask.repeat_interleave(__SCREAMING_SNAKE_CASE      ,    dim=0   )

        if do_classifier_free_guidance:
         snake_case__										= [""] * batch_size

         snake_case__										= self.tokenizer(
             __SCREAMING_SNAKE_CASE      ,    padding="max_length"      ,    max_length=self.tokenizer.model_max_length      ,    truncation=__SCREAMING_SNAKE_CASE      ,    return_tensors="pt"      ,    )
         snake_case__										= uncond_input.attention_mask.bool().to(__SCREAMING_SNAKE_CASE   )
         snake_case__										= self.text_encoder(uncond_input.input_ids.to(__SCREAMING_SNAKE_CASE   )   )

         snake_case__										= uncond_embeddings_text_encoder_output.text_embeds
         snake_case__										= uncond_embeddings_text_encoder_output.last_hidden_state

         # duplicate unconditional embeddings for each generation per prompt, using mps friendly method

         snake_case__										= uncond_embeddings.shape[1]
         snake_case__										= uncond_embeddings.repeat(1      ,    __SCREAMING_SNAKE_CASE   )
         snake_case__										= uncond_embeddings.view(batch_size * num_images_per_prompt      ,    __SCREAMING_SNAKE_CASE   )

         snake_case__										= uncond_text_encoder_hidden_states.shape[1]
         snake_case__										= uncond_text_encoder_hidden_states.repeat(1      ,    __SCREAMING_SNAKE_CASE      ,    1   )
         snake_case__										= uncond_text_encoder_hidden_states.view(
             batch_size * num_images_per_prompt      ,    __SCREAMING_SNAKE_CASE      ,    -1   )
         snake_case__										= uncond_text_mask.repeat_interleave(__SCREAMING_SNAKE_CASE      ,    dim=0   )

         # done duplicates

         # 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([uncond_embeddings, text_embeddings]   )
         snake_case__										= torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]   )

         snake_case__										= torch.cat([uncond_text_mask, text_mask]   )

        return text_embeddings, text_encoder_hidden_states, text_mask

       @property
       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        if self.device != torch.device("meta"   ) or not hasattr(self.prior      ,    "_hf_hook"   ):
         return self.device
        for module in self.prior.modules():
         if (
             hasattr(__SCREAMING_SNAKE_CASE      ,    "_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 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        if latents is None:
         snake_case__										= randn_tensor(__SCREAMING_SNAKE_CASE      ,    generator=__SCREAMING_SNAKE_CASE      ,    device=__SCREAMING_SNAKE_CASE      ,    dtype=__SCREAMING_SNAKE_CASE   )
        else:
         if latents.shape != shape:
          raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}"   )
         snake_case__										= latents.to(__SCREAMING_SNAKE_CASE   )

        snake_case__										= latents * scheduler.init_noise_sigma
        return latents

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE = None   ):






        '''simple docstring'''

        self.decoder_pipe.to(__SCREAMING_SNAKE_CASE   )
        super().to(__SCREAMING_SNAKE_CASE   )

       @torch.no_grad()
       def __call__(	self      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = 1      ,    __SCREAMING_SNAKE_CASE = 25      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = 4.0      ,    __SCREAMING_SNAKE_CASE = 8.0      ,    __SCREAMING_SNAKE_CASE = 50      ,    __SCREAMING_SNAKE_CASE = 1      ,    __SCREAMING_SNAKE_CASE = 0.0      ,    __SCREAMING_SNAKE_CASE = "pil"      ,    __SCREAMING_SNAKE_CASE = True      ,    ):






        '''simple docstring'''

        if prompt is not None:
         if isinstance(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ):
          snake_case__										= 1
         elif isinstance(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ):
          snake_case__										= len(__SCREAMING_SNAKE_CASE   )
         else:
          raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(__SCREAMING_SNAKE_CASE   )}"   )
        else:
         snake_case__										= text_model_output[0].shape[0]

        snake_case__										= self._execution_device

        snake_case__										= batch_size * num_images_per_prompt

        snake_case__										= prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

        snake_case__							,      snake_case__							,      snake_case__										= self._encode_prompt(
            __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   )

        # prior

        self.prior_scheduler.set_timesteps(__SCREAMING_SNAKE_CASE      ,    device=__SCREAMING_SNAKE_CASE   )
        snake_case__										= self.prior_scheduler.timesteps

        snake_case__										= self.prior.config.embedding_dim

        snake_case__										= self.prepare_latents(
            (batch_size, embedding_dim)      ,    text_embeddings.dtype      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    self.prior_scheduler      ,    )

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

         snake_case__										= self.prior(
             __SCREAMING_SNAKE_CASE      ,    timestep=__SCREAMING_SNAKE_CASE      ,    proj_embedding=__SCREAMING_SNAKE_CASE      ,    encoder_hidden_states=__SCREAMING_SNAKE_CASE      ,    attention_mask=__SCREAMING_SNAKE_CASE      ,    ).predicted_image_embedding

         if do_classifier_free_guidance:
          snake_case__							,      snake_case__										= predicted_image_embedding.chunk(2   )
          snake_case__										= predicted_image_embedding_uncond + prior_guidance_scale * (
              predicted_image_embedding_text - predicted_image_embedding_uncond
          )

         if i + 1 == prior_timesteps_tensor.shape[0]:
          snake_case__										= None
         else:
          snake_case__										= prior_timesteps_tensor[i + 1]

         snake_case__										= self.prior_scheduler.step(
             __SCREAMING_SNAKE_CASE      ,    timestep=__SCREAMING_SNAKE_CASE      ,    sample=__SCREAMING_SNAKE_CASE      ,    generator=__SCREAMING_SNAKE_CASE      ,    prev_timestep=__SCREAMING_SNAKE_CASE      ,    ).prev_sample

        snake_case__										= self.prior.post_process_latents(__SCREAMING_SNAKE_CASE   )

        snake_case__										= prior_latents

        snake_case__										= self.decoder_pipe(
            image=__SCREAMING_SNAKE_CASE      ,    height=__SCREAMING_SNAKE_CASE      ,    width=__SCREAMING_SNAKE_CASE      ,    num_inference_steps=__SCREAMING_SNAKE_CASE      ,    guidance_scale=__SCREAMING_SNAKE_CASE      ,    generator=__SCREAMING_SNAKE_CASE      ,    output_type=__SCREAMING_SNAKE_CASE      ,    return_dict=__SCREAMING_SNAKE_CASE      ,    num_images_per_prompt=__SCREAMING_SNAKE_CASE      ,    eta=__SCREAMING_SNAKE_CASE      ,    )
        return output

