




'''simple docstring'''




import argparse
import collections

import jax
import jax.numpy as jnp
import torch
import torch.nn as nn
from clip.model import CLIP
from flax.training import checkpoints
from huggingface_hub import Repository

from transformers import (
    CLIPTokenizer,
    OwlViTConfig,
    OwlViTForObjectDetection,
    OwlViTImageProcessor,
    OwlViTModel,
    OwlViTProcessor,
)


a_   :	Optional[Any]							   = {
    '''vit_b32''': {
        '''embed_dim''': 5_12,
        '''image_resolution''': 7_68,
        '''context_length''': 16,
        '''vocab_size''': 4_94_08,
        '''vision_layers''': 12,
        '''vision_width''': 7_68,
        '''vision_patch_size''': 32,
        '''transformer_width''': 5_12,
        '''transformer_heads''': 8,
        '''transformer_layers''': 12,
    },
    '''vit_b16''': {
        '''embed_dim''': 5_12,
        '''image_resolution''': 7_68,
        '''context_length''': 16,
        '''vocab_size''': 4_94_08,
        '''vision_layers''': 12,
        '''vision_width''': 7_68,
        '''vision_patch_size''': 16,
        '''transformer_width''': 5_12,
        '''transformer_heads''': 8,
        '''transformer_layers''': 12,
    },
    '''vit_l14''': {
        '''embed_dim''': 7_68,
        '''image_resolution''': 8_40,
        '''context_length''': 16,
        '''vocab_size''': 4_94_08,
        '''vision_layers''': 24,
        '''vision_width''': 10_24,
        '''vision_patch_size''': 14,
        '''transformer_width''': 7_68,
        '''transformer_heads''': 12,
        '''transformer_layers''': 12,
    },
}





def   a_							(   __snake_case							:    Union[str, Any]     , __snake_case							:    Dict=""     , __snake_case							:    Union[str, Any]="/"     )							->		str:



       """simple docstring"""
       lowerCamelCase_							=[]

       for k, v in params.items():
              lowerCamelCase_							=parent_key + sep + k if parent_key else k

              if isinstance(snake_case__     , collections.MutableMapping     ):
                     items.extend(flatten_nested_dict(snake_case__     , snake_case__     , sep=snake_case__     ).items()     )
              else:
                     items.append((new_key, v)     )
       return dict(snake_case__     )





def   a_							(   __snake_case							:    List[Any]     )							->		Optional[Any]:



       """simple docstring"""
       return jax.tree_util.tree_map(lambda __snake_case     : x.astype(jnp.floataa     ) if x.dtype == jnp.bfloataa else x     , snake_case__     )





def   a_							(   __snake_case							:    int     , __snake_case							:    Tuple     )							->		int:



       """simple docstring"""
       lowerCamelCase_,   lowerCamelCase_,   lowerCamelCase_							=pt_attn_layer.in_proj_weight.chunk(3     , dim=0     )
       lowerCamelCase_,   lowerCamelCase_,   lowerCamelCase_							=pt_attn_layer.in_proj_bias.chunk(3     , dim=0     )

       lowerCamelCase_							=pt_attn_layer.out_proj.weight
       lowerCamelCase_							=pt_attn_layer.out_proj.bias

       lowerCamelCase_							=q_proj
       lowerCamelCase_							=q_proj_bias

       lowerCamelCase_							=k_proj
       lowerCamelCase_							=k_proj_bias

       lowerCamelCase_							=v_proj
       lowerCamelCase_							=v_proj_bias

       lowerCamelCase_							=out_proj_weights
       lowerCamelCase_							=out_proj_bias





def   a_							(   __snake_case							:    str     , __snake_case							:    int     )							->		int:



       """simple docstring"""
       copy_linear(hf_mlp.fca     , pt_mlp.c_fc     )
       copy_linear(hf_mlp.fca     , pt_mlp.c_proj     )





def   a_							(   __snake_case							:    Any     , __snake_case							:    Dict     )							->		int:



       """simple docstring"""
       lowerCamelCase_							=pt_linear.weight
       lowerCamelCase_							=pt_linear.bias





def   a_							(   __snake_case							:    Optional[int]     , __snake_case							:    Union[str, Any]     )							->		Optional[int]:



       """simple docstring"""
       # copy layer norms
       copy_linear(hf_layer.layer_norma     , pt_layer.ln_a     )
       copy_linear(hf_layer.layer_norma     , pt_layer.ln_a     )

       # copy MLP
       copy_mlp(hf_layer.mlp     , pt_layer.mlp     )

       # copy attn
       copy_attn_layer(hf_layer.self_attn     , pt_layer.attn     )





def   a_							(   __snake_case							:    Any     , __snake_case							:    Tuple     )							->		Any:



       """simple docstring"""
       for hf_layer, pt_layer in zip(snake_case__     , snake_case__     ):
              copy_layer(snake_case__     , snake_case__     )





def   a_							(   __snake_case							:    List[str]     , __snake_case							:    Optional[Any]     )							->		int:



       """simple docstring"""
       # copy  embeds
       lowerCamelCase_							=pt_model.token_embedding.weight
       lowerCamelCase_							=pt_model.positional_embedding

       # copy layer norm
       copy_linear(hf_encoder.final_layer_norm     , pt_model.ln_final     )

       # copy hidden layers
       copy_layers(hf_encoder.encoder.layers     , pt_model.transformer.resblocks     )





def   a_							(   __snake_case							:    List[Any]     , __snake_case							:    int     )							->		Optional[int]:



       """simple docstring"""
       # copy projection
       lowerCamelCase_							=pt_model.text_projection.data.T

       # copy text encoder
       copy_encoder(hf_model.text_model     , snake_case__     )





def   a_							(   __snake_case							:    Tuple     , __snake_case							:    int     )							->		Any:



       """simple docstring"""
       # copy projection
       lowerCamelCase_							=pt_model.visual.proj.data.T

       # copy layer norms
       copy_linear(hf_model.vision_model.pre_layernorm     , pt_model.visual.ln_pre     )
       copy_linear(hf_model.vision_model.post_layernorm     , pt_model.visual.ln_post     )

       # copy embeds
       lowerCamelCase_							=pt_model.visual.conva.weight.data
       lowerCamelCase_							=pt_model.visual.class_embedding
       lowerCamelCase_							=pt_model.visual.positional_embedding.data

       # copy encoder
       copy_layers(hf_model.vision_model.encoder.layers     , pt_model.visual.transformer.resblocks     )





def   a_							(   __snake_case							:    List[str]     , __snake_case							:    Any     )							->		Any:



       """simple docstring"""
       lowerCamelCase_							=flatten_nested_dict(flax_params['''backbone''']['''merged_class_token''']     )

       lowerCamelCase_							=torch.from_numpy(flax_class_token_params['''scale''']     )
       lowerCamelCase_							=torch.from_numpy(flax_class_token_params['''bias''']     )
       lowerCamelCase_							=nn.Parameter(snake_case__     )
       lowerCamelCase_							=nn.Parameter(snake_case__     )





def   a_							(   __snake_case							:    Dict     , __snake_case							:    List[Any]     )							->		Dict:



       """simple docstring"""
       lowerCamelCase_							=hf_model.state_dict()
       lowerCamelCase_							={}

       # Rename class prediction head flax params to pytorch HF
       lowerCamelCase_							=flatten_nested_dict(flax_params['''class_head''']     )

       for flax_key, v in flax_class_params.items():
              lowerCamelCase_							=flax_key.replace('''/'''     , '''.'''     )
              lowerCamelCase_							=torch_key.replace('''.kernel'''     , '''.weight'''     )
              lowerCamelCase_							=torch_key.replace('''Dense_0'''     , '''dense0'''     )
              lowerCamelCase_							='''class_head.''' + torch_key

              if "weight" in torch_key and v.ndim == 2:
                     lowerCamelCase_							=v.T

              lowerCamelCase_							=nn.Parameter(torch.from_numpy(snake_case__     )     )

       # Rename box prediction box flax params to pytorch HF
       lowerCamelCase_							=flatten_nested_dict(flax_params['''obj_box_head''']     )

       for flax_key, v in flax_box_params.items():
              lowerCamelCase_							=flax_key.replace('''/'''     , '''.'''     )
              lowerCamelCase_							=torch_key.replace('''.kernel'''     , '''.weight'''     )
              lowerCamelCase_							=torch_key.replace('''_'''     , ''''''     ).lower()
              lowerCamelCase_							='''box_head.''' + torch_key

              if "weight" in torch_key and v.ndim == 2:
                     lowerCamelCase_							=v.T

              lowerCamelCase_							=nn.Parameter(torch.from_numpy(snake_case__     )     )

       # Copy flax params to PyTorch params
       for name, param in new_params.items():
              if name in pt_params.keys():
                     pt_params[name].copy_(snake_case__     )





def   a_							(   __snake_case							:    Optional[int]     , __snake_case							:    Union[str, Any]     )							->		Dict:



       """simple docstring"""
       for k, v in flax_attn_params.items():
              if k.startswith('''transformer'''     ):
                     lowerCamelCase_							=k.replace('''transformer.resblocks'''     , '''text_model.encoder.layers'''     )
              else:
                     lowerCamelCase_							=k.replace('''visual.transformer.resblocks'''     , '''vision_model.encoder.layers'''     )

              lowerCamelCase_							=torch_key.replace('''attn'''     , '''self_attn'''     )
              lowerCamelCase_							=torch_key.replace('''key'''     , '''k_proj'''     )
              lowerCamelCase_							=torch_key.replace('''value'''     , '''v_proj'''     )
              lowerCamelCase_							=torch_key.replace('''query'''     , '''q_proj'''     )
              lowerCamelCase_							=torch_key.replace('''out'''     , '''out_proj'''     )

              if "bias" in torch_key and v.ndim == 2:
                     lowerCamelCase_							=v.shape[0] * v.shape[1]
                     lowerCamelCase_							=v.reshape(snake_case__     )

              if "weight" in torch_key and "out" in torch_key:
                     lowerCamelCase_							=(v.shape[0] * v.shape[1], v.shape[2])
                     lowerCamelCase_							=v.reshape(snake_case__     ).T

              if "weight" in torch_key and "out" not in torch_key:
                     lowerCamelCase_							=(v.shape[0], v.shape[1] * v.shape[2])
                     lowerCamelCase_							=v.reshape(snake_case__     ).T

              # Copy flax CLIP attn params to HF PyTorch params
              lowerCamelCase_							=torch.from_numpy(snake_case__     )
              hf_backbone.state_dict()[torch_key].copy_(snake_case__     )





def   a_							(   __snake_case							:    Any     )							->		List[str]:



       """simple docstring"""
       lowerCamelCase_							={}
       lowerCamelCase_							=[]

       for k, v in params.items():
              if "attn." in k:
                     lowerCamelCase_							=k[: k.rindex('''attn.'''     ) + 5]
                     if base in processed_attn_layers:
                            continue

                     processed_attn_layers.append(snake_case__     )
                     lowerCamelCase_							=params[base + '''out.weight'''].shape[-1]
                     lowerCamelCase_							=params[base + '''out.weight'''].reshape(snake_case__     , snake_case__     ).T
                     lowerCamelCase_							=params[base + '''out.bias''']
              else:
                     lowerCamelCase_							=v
       return new_params





def   a_							(   __snake_case							:    Any     , __snake_case							:    List[str]     )							->		str:



       """simple docstring"""
       lowerCamelCase_							=CLIP(**snake_case__     )
       torch_model.eval()
       lowerCamelCase_							=torch_model.state_dict()

       lowerCamelCase_							=flatten_nested_dict(flax_params['''backbone''']['''clip''']     )
       lowerCamelCase_							={}

       for flax_key, v in flax_clip_params.items():
              lowerCamelCase_							=flax_key.replace('''/'''     , '''.'''     )
              lowerCamelCase_							=torch_key.replace('''text.token_embedding.embedding'''     , '''token_embedding.kernel'''     )

              if (
                  torch_key.startswith('''text.transformer'''     )
                  or torch_key.startswith('''text.text_projection'''     )
                  or torch_key.startswith('''text.ln_final'''     )
                  or torch_key.startswith('''text.positional_embedding'''     )
              ):
                     lowerCamelCase_							=torch_key[5:]

              lowerCamelCase_							=torch_key.replace('''text_projection.kernel'''     , '''text_projection'''     )
              lowerCamelCase_							=torch_key.replace('''visual.proj.kernel'''     , '''visual.proj'''     )
              lowerCamelCase_							=torch_key.replace('''.scale'''     , '''.weight'''     )
              lowerCamelCase_							=torch_key.replace('''.kernel'''     , '''.weight'''     )

              if "conv" in torch_key or "downsample.0.weight" in torch_key:
                     lowerCamelCase_							=v.transpose(3     , 2     , 0     , 1     )

              elif "weight" in torch_key and v.ndim == 2 and "embedding" not in torch_key:
                     # Fully connected layers are transposed, embeddings are not
                     lowerCamelCase_							=v.T

              lowerCamelCase_							=v

       lowerCamelCase_							=_convert_attn_layers(snake_case__     )
       new_torch_params.update(snake_case__     )
       lowerCamelCase_							={}

       # Copy flax CLIP backbone params to PyTorch params
       for name, param in new_torch_params.items():
              if name in torch_clip_params.keys():
                     lowerCamelCase_							=torch.from_numpy(new_torch_params[name]     )
                     torch_clip_params[name].copy_(snake_case__     )
              else:
                     lowerCamelCase_							=param

       return torch_clip_params, torch_model, attn_params





@torch.no_grad()
def   a_							(   __snake_case							:    Optional[int]     , __snake_case							:    Dict     , __snake_case							:    Union[str, Any]     , __snake_case							:    Optional[Any]     , __snake_case							:    Optional[int]=None     )							->		Optional[Any]:



       """simple docstring"""
       lowerCamelCase_							=Repository(snake_case__     , clone_from=F'''google/{pytorch_dump_folder_path}'''     )
       repo.git_pull()

       if config_path is not None:
              lowerCamelCase_							=OwlViTConfig.from_pretrained(snake_case__     )
       else:
              lowerCamelCase_							=OwlViTConfig()

       lowerCamelCase_							=OwlViTModel(snake_case__     ).eval()
       lowerCamelCase_							=OwlViTForObjectDetection(snake_case__     ).eval()

       copy_text_model_and_projection(snake_case__     , snake_case__     )
       copy_vision_model_and_projection(snake_case__     , snake_case__     )
       lowerCamelCase_							=pt_backbone.logit_scale
       copy_flax_attn_params(snake_case__     , snake_case__     )

       lowerCamelCase_							=hf_backbone
       copy_class_merge_token(snake_case__     , snake_case__     )
       copy_class_box_heads(snake_case__     , snake_case__     )

       # Save HF model
       hf_model.save_pretrained(repo.local_dir     )

       # Initialize image processor
       lowerCamelCase_							=OwlViTImageProcessor(
           size=config.vision_config.image_size     , crop_size=config.vision_config.image_size     )
       # Initialize tokenizer
       lowerCamelCase_							=CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''     , pad_token='''!'''     , model_max_length=16     )

       # Initialize processor
       lowerCamelCase_							=OwlViTProcessor(image_processor=snake_case__     , tokenizer=snake_case__     )
       image_processor.save_pretrained(repo.local_dir     )
       processor.save_pretrained(repo.local_dir     )

       repo.git_add()
       repo.git_commit('''Upload model and processor'''     )
       repo.git_push()


if __name__ == "__main__":
 a_   :	int							   = argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--owlvit_version""",
     default=None,
     type=str,
     required=True,
     help="""OWL-ViT model name [clip_b16, clip_b32, clip_l14].""",
 )
 parser.add_argument(
     """--owlvit_checkpoint""", default=None, type=str, required=True, help="""Path to flax model checkpoint."""
 )
 parser.add_argument("""--hf_config""", default=None, type=str, required=True, help="""Path to HF model config.""")
 parser.add_argument(
     """--pytorch_dump_folder_path""", default="""hf_model""", type=str, help="""Path to the output PyTorch model."""
 )
 a_   :	str							   = parser.parse_args()

 # Initialize PyToch clip model
 a_   :	Union[str, Any]							   = args.owlvit_version
 if model_name == "clip_b16":
  a_   :	Union[str, Any]							   = CONFIGS['''vit_b16''']
 elif model_name == "clip_b32":
  a_   :	Optional[Any]							   = CONFIGS['''vit_b32''']
 elif model_name == "clip_l14":
  a_   :	str							   = CONFIGS['''vit_l14''']

 # Load from checkpoint and convert params to float-32
 a_   :	List[str]							   = checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)['''optimizer''']['''target''']
 a_   :	Dict							   = jax.tree_util.tree_map(lambda x: x.astype(jnp.floataa) if x.dtype == jnp.bfloataa else x, variables)
 del variables

 # Convert CLIP backbone
 a_   :	Any							   = convert_clip_backbone(flax_params, torch_config)

 convert_owlvit_checkpoint(clip_pt, flax_params, attn_params, args.pytorch_dump_folder_path, args.hf_config)










'''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[Any]							   = logging.get_logger(__name__)  # pylint: disable=invalid-name





def   a_							(   self							:    Union[str, Any]     , __snake_case							:    List[str]     , __snake_case							:    List[str]     , __snake_case							:    Dict     , __snake_case							:    str     )							->		str:



       """simple docstring"""
       lowerCamelCase_							=image.to(device=__snake_case     )
       lowerCamelCase_							=image  # take image as image_embeddings
       lowerCamelCase_							=image_embeddings.unsqueeze(1     )

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

       if do_classifier_free_guidance:
              lowerCamelCase_							=torch.zeros_like(__snake_case     )

              # 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([uncond_embeddings, image_embeddings]     )

       return image_embeddings


class 						__UpperCamelCase (				lowerCamelCase__       ):






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



              """simple docstring"""







              super().__init__()

              lowerCamelCase_							={'''image_encoder''': None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

              lowerCamelCase_							=decoder_pipe_kwargs.get('''torch_dtype''',	lowerCAmelCase						) or prior.dtype

              lowerCamelCase_							=StableDiffusionImageVariationPipeline.from_pretrained(
                  '''lambdalabs/sd-image-variations-diffusers''',	**lowerCAmelCase						)

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

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






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase = None,	lowerCAmelCase = None,	):



              """simple docstring"""







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

                     if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                            lowerCamelCase_							=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}'''						)
                            lowerCamelCase_							=text_input_ids[:, : self.tokenizer.model_max_length]

                     lowerCamelCase_							=self.text_encoder(text_input_ids.to(lowerCAmelCase						)						)

                     lowerCamelCase_							=text_encoder_output.text_embeds
                     lowerCamelCase_							=text_encoder_output.last_hidden_state

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

              lowerCamelCase_							=text_embeddings.repeat_interleave(lowerCAmelCase,	dim=0						)
              lowerCamelCase_							=text_encoder_hidden_states.repeat_interleave(lowerCAmelCase,	dim=0						)
              lowerCamelCase_							=text_mask.repeat_interleave(lowerCAmelCase,	dim=0						)

              if do_classifier_free_guidance:
                     lowerCamelCase_							=[''''''] * batch_size

                     lowerCamelCase_							=self.tokenizer(
                         lowerCAmelCase,	padding='''max_length''',	max_length=self.tokenizer.model_max_length,	truncation=lowerCAmelCase,	return_tensors='''pt''',	)
                     lowerCamelCase_							=uncond_input.attention_mask.bool().to(lowerCAmelCase						)
                     lowerCamelCase_							=self.text_encoder(uncond_input.input_ids.to(lowerCAmelCase						)						)

                     lowerCamelCase_							=uncond_embeddings_text_encoder_output.text_embeds
                     lowerCamelCase_							=uncond_embeddings_text_encoder_output.last_hidden_state

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

                     lowerCamelCase_							=uncond_embeddings.shape[1]
                     lowerCamelCase_							=uncond_embeddings.repeat(1,	lowerCAmelCase						)
                     lowerCamelCase_							=uncond_embeddings.view(batch_size * num_images_per_prompt,	lowerCAmelCase						)

                     lowerCamelCase_							=uncond_text_encoder_hidden_states.shape[1]
                     lowerCamelCase_							=uncond_text_encoder_hidden_states.repeat(1,	lowerCAmelCase,	1						)
                     lowerCamelCase_							=uncond_text_encoder_hidden_states.view(
                         batch_size * num_images_per_prompt,	lowerCAmelCase,	-1						)
                     lowerCamelCase_							=uncond_text_mask.repeat_interleave(lowerCAmelCase,	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
                     lowerCamelCase_							=torch.cat([uncond_embeddings, text_embeddings]						)
                     lowerCamelCase_							=torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]						)

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

              return text_embeddings, text_encoder_hidden_states, text_mask






       @property
       def   lowercase__  (							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(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   lowercase__  (							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   lowercase__  (							self,	lowerCAmelCase = None						):



              """simple docstring"""







              self.decoder_pipe.to(lowerCAmelCase						)
              super().to(lowerCAmelCase						)






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



              """simple docstring"""







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

              lowerCamelCase_							=self._execution_device

              lowerCamelCase_							=batch_size * num_images_per_prompt

              lowerCamelCase_							=prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

              lowerCamelCase_,   lowerCamelCase_,   lowerCamelCase_							=self._encode_prompt(
                  lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						)

              # prior

              self.prior_scheduler.set_timesteps(lowerCAmelCase,	device=lowerCAmelCase						)
              lowerCamelCase_							=self.prior_scheduler.timesteps

              lowerCamelCase_							=self.prior.config.embedding_dim

              lowerCamelCase_							=self.prepare_latents(
                  (batch_size, embedding_dim),	text_embeddings.dtype,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	self.prior_scheduler,	)

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

                     lowerCamelCase_							=self.prior(
                         lowerCAmelCase,	timestep=lowerCAmelCase,	proj_embedding=lowerCAmelCase,	encoder_hidden_states=lowerCAmelCase,	attention_mask=lowerCAmelCase,	).predicted_image_embedding

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

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

                     lowerCamelCase_							=self.prior_scheduler.step(
                         lowerCAmelCase,	timestep=lowerCAmelCase,	sample=lowerCAmelCase,	generator=lowerCAmelCase,	prev_timestep=lowerCAmelCase,	).prev_sample

              lowerCamelCase_							=self.prior.post_process_latents(lowerCAmelCase						)

              lowerCamelCase_							=prior_latents

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




