

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


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





def  _snake_case		(self					:     Optional[Any]    ,       snake_case_					:     Optional[Any]    ,       snake_case_					:     Dict    ,       snake_case_					:     Optional[int]    ,       snake_case_					:     Optional[Any]  )							->		Union[str, Any]:
  __UpperCAmelCase	:  Any		=     image.to(device=snake_case_  )
  __UpperCAmelCase	:  Tuple		=     image  # take image as image_embeddings
  __UpperCAmelCase	:  str		=     image_embeddings.unsqueeze(1  )

  # duplicate image embeddings for each generation per prompt, using mps friendly method
  __UpperCAmelCase,			__UpperCAmelCase,			__UpperCAmelCase	:  int		=     image_embeddings.shape
  __UpperCAmelCase	:  int		=     image_embeddings.repeat(1    ,       snake_case_    ,       1  )
  __UpperCAmelCase	:  Optional[Any]		=     image_embeddings.view(bs_embed * num_images_per_prompt    ,       snake_case_    ,       -1  )

  if do_classifier_free_guidance:
    __UpperCAmelCase	:  List[Any]		=     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
    __UpperCAmelCase	:  Union[str, Any]		=     torch.cat([uncond_embeddings, image_embeddings]  )

  return image_embeddings







class      lowerCamelCase_ (			_lowerCAmelCase):




 def __init__(			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase = None  ,  ):
   super().__init__()

   __UpperCAmelCase	:  List[Any]		=     {'image_encoder': None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

   __UpperCAmelCase	:  Optional[Any]		=     decoder_pipe_kwargs.get('torch_dtype'  ,  _UpperCAmelCase							) or prior.dtype

   __UpperCAmelCase	:  str		=     StableDiffusionImageVariationPipeline.from_pretrained(
       'lambdalabs/sd-image-variations-diffusers'  ,  **_UpperCAmelCase							)

   # replace `_encode_image` method
   __UpperCAmelCase	:  Dict		=     types.MethodType(_encode_image  ,  self.decoder_pipe							)

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




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  ):
   if text_model_output is None:
     __UpperCAmelCase	:  Any		=     len(_UpperCAmelCase							) if isinstance(_UpperCAmelCase  ,  _UpperCAmelCase							) else 1
     # get prompt text embeddings
     __UpperCAmelCase	:  str		=     self.tokenizer(
         _UpperCAmelCase  ,  padding='max_length'  ,  max_length=self.tokenizer.model_max_length  ,  return_tensors='pt'  ,  )
     __UpperCAmelCase	:  int		=     text_inputs.input_ids
     __UpperCAmelCase	:  str		=     text_inputs.attention_mask.bool().to(_UpperCAmelCase							)

     if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
       __UpperCAmelCase	:  Any		=     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}"							)
       __UpperCAmelCase	:  int		=     text_input_ids[:, : self.tokenizer.model_max_length]

     __UpperCAmelCase	:  Any		=     self.text_encoder(text_input_ids.to(_UpperCAmelCase							)							)

     __UpperCAmelCase	:  str		=     text_encoder_output.text_embeds
     __UpperCAmelCase	:  str		=     text_encoder_output.last_hidden_state

   else:
     __UpperCAmelCase	:  str		=     text_model_output[0].shape[0]
     __UpperCAmelCase,			__UpperCAmelCase	:  Any		=     text_model_output[0], text_model_output[1]
     __UpperCAmelCase	:  Optional[Any]		=     text_attention_mask

   __UpperCAmelCase	:  Optional[Any]		=     text_embeddings.repeat_interleave(_UpperCAmelCase  ,  dim=0							)
   __UpperCAmelCase	:  Tuple		=     text_encoder_hidden_states.repeat_interleave(_UpperCAmelCase  ,  dim=0							)
   __UpperCAmelCase	:  Optional[Any]		=     text_mask.repeat_interleave(_UpperCAmelCase  ,  dim=0							)

   if do_classifier_free_guidance:
     __UpperCAmelCase	:  int		=     [''] * batch_size

     __UpperCAmelCase	:  Optional[int]		=     self.tokenizer(
         _UpperCAmelCase  ,  padding='max_length'  ,  max_length=self.tokenizer.model_max_length  ,  truncation=_UpperCAmelCase  ,  return_tensors='pt'  ,  )
     __UpperCAmelCase	:  List[str]		=     uncond_input.attention_mask.bool().to(_UpperCAmelCase							)
     __UpperCAmelCase	:  int		=     self.text_encoder(uncond_input.input_ids.to(_UpperCAmelCase							)							)

     __UpperCAmelCase	:  Optional[Any]		=     uncond_embeddings_text_encoder_output.text_embeds
     __UpperCAmelCase	:  List[str]		=     uncond_embeddings_text_encoder_output.last_hidden_state

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

     __UpperCAmelCase	:  List[str]		=     uncond_embeddings.shape[1]
     __UpperCAmelCase	:  Tuple		=     uncond_embeddings.repeat(1  ,  _UpperCAmelCase							)
     __UpperCAmelCase	:  List[str]		=     uncond_embeddings.view(batch_size * num_images_per_prompt  ,  _UpperCAmelCase							)

     __UpperCAmelCase	:  Tuple		=     uncond_text_encoder_hidden_states.shape[1]
     __UpperCAmelCase	:  Union[str, Any]		=     uncond_text_encoder_hidden_states.repeat(1  ,  _UpperCAmelCase  ,  1							)
     __UpperCAmelCase	:  Any		=     uncond_text_encoder_hidden_states.view(
         batch_size * num_images_per_prompt  ,  _UpperCAmelCase  ,  -1							)
     __UpperCAmelCase	:  int		=     uncond_text_mask.repeat_interleave(_UpperCAmelCase  ,  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
     __UpperCAmelCase	:  List[str]		=     torch.cat([uncond_embeddings, text_embeddings]							)
     __UpperCAmelCase	:  str		=     torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]							)

     __UpperCAmelCase	:  Any		=     torch.cat([uncond_text_mask, text_mask]							)

   return text_embeddings, text_encoder_hidden_states, text_mask




 @property
 def     a       (			self							):
   if self.device != torch.device('meta'							) or not hasattr(self.prior  ,  '_hf_hook'							):
     return self.device
   for module in self.prior.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     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase							):
   if latents is None:
     __UpperCAmelCase	:  Any		=     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}"							)
     __UpperCAmelCase	:  Optional[int]		=     latents.to(_UpperCAmelCase							)

   __UpperCAmelCase	:  str		=     latents * scheduler.init_noise_sigma
   return latents




 def     a       (			self  ,  _UpperCAmelCase = None							):
   self.decoder_pipe.to(_UpperCAmelCase							)
   super().to(_UpperCAmelCase							)




 @torch.no_grad()
 def __call__(			self  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = 1  ,  _UpperCAmelCase = 25  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = 4.0  ,  _UpperCAmelCase = 8.0  ,  _UpperCAmelCase = 50  ,  _UpperCAmelCase = 1  ,  _UpperCAmelCase = 0.0  ,  _UpperCAmelCase = "pil"  ,  _UpperCAmelCase = True  ,  ):
   if prompt is not None:
     if isinstance(_UpperCAmelCase  ,  _UpperCAmelCase							):
       __UpperCAmelCase	:  List[Any]		=     1
     elif isinstance(_UpperCAmelCase  ,  _UpperCAmelCase							):
       __UpperCAmelCase	:  Tuple		=     len(_UpperCAmelCase							)
     else:
       raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(_UpperCAmelCase							)}"							)
   else:
     __UpperCAmelCase	:  Tuple		=     text_model_output[0].shape[0]

   __UpperCAmelCase	:  Any		=     self._execution_device

   __UpperCAmelCase	:  Any		=     batch_size * num_images_per_prompt

   __UpperCAmelCase	:  List[str]		=     prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

   __UpperCAmelCase,			__UpperCAmelCase,			__UpperCAmelCase	:  Optional[Any]		=     self._encode_prompt(
       _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase							)

   # prior

   self.prior_scheduler.set_timesteps(_UpperCAmelCase  ,  device=_UpperCAmelCase							)
   __UpperCAmelCase	:  Dict		=     self.prior_scheduler.timesteps

   __UpperCAmelCase	:  Tuple		=     self.prior.config.embedding_dim

   __UpperCAmelCase	:  Dict		=     self.prepare_latents(
       (batch_size, embedding_dim)  ,  text_embeddings.dtype  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  self.prior_scheduler  ,  )

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

     __UpperCAmelCase	:  Union[str, Any]		=     self.prior(
         _UpperCAmelCase  ,  timestep=_UpperCAmelCase  ,  proj_embedding=_UpperCAmelCase  ,  encoder_hidden_states=_UpperCAmelCase  ,  attention_mask=_UpperCAmelCase  ,  ).predicted_image_embedding

     if do_classifier_free_guidance:
       __UpperCAmelCase,			__UpperCAmelCase	:  List[str]		=     predicted_image_embedding.chunk(2							)
       __UpperCAmelCase	:  Optional[Any]		=     predicted_image_embedding_uncond + prior_guidance_scale * (
           predicted_image_embedding_text - predicted_image_embedding_uncond
       )

     if i + 1 == prior_timesteps_tensor.shape[0]:
       __UpperCAmelCase	:  int		=     None
     else:
       __UpperCAmelCase	:  Any		=     prior_timesteps_tensor[i + 1]

     __UpperCAmelCase	:  Union[str, Any]		=     self.prior_scheduler.step(
         _UpperCAmelCase  ,  timestep=_UpperCAmelCase  ,  sample=_UpperCAmelCase  ,  generator=_UpperCAmelCase  ,  prev_timestep=_UpperCAmelCase  ,  ).prev_sample

   __UpperCAmelCase	:  Union[str, Any]		=     self.prior.post_process_latents(_UpperCAmelCase							)

   __UpperCAmelCase	:  Any		=     prior_latents

   __UpperCAmelCase	:  List[Any]		=     self.decoder_pipe(
       image=_UpperCAmelCase  ,  height=_UpperCAmelCase  ,  width=_UpperCAmelCase  ,  num_inference_steps=_UpperCAmelCase  ,  guidance_scale=_UpperCAmelCase  ,  generator=_UpperCAmelCase  ,  output_type=_UpperCAmelCase  ,  return_dict=_UpperCAmelCase  ,  num_images_per_prompt=_UpperCAmelCase  ,  eta=_UpperCAmelCase  ,  )
   return output










import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  ):


						'''simple docstring'''




						lowerCAmelCase	:   List[str]					     =					json.load(open(SCREAMING_SNAKE_CASE__			,"""r"""  )  )
						lowerCAmelCase	:   Tuple					     =					GPTNeoConfig(
						    hidden_size=config_json["""n_embd"""]			,num_layers=config_json["""n_layer"""]			,num_heads=config_json["""n_head"""]			,attention_types=config_json["""attention_types"""]			,max_position_embeddings=config_json["""n_positions"""]			,resid_dropout=config_json["""res_dropout"""]			,embed_dropout=config_json["""embed_dropout"""]			,attention_dropout=config_json["""attn_dropout"""]			,)
						print(F"""Building PyTorch model from configuration: {config}"""  )
						lowerCAmelCase	:   str					     =					GPTNeoForCausalLM(SCREAMING_SNAKE_CASE__  )

						# Load weights from tf checkpoint
						load_tf_weights_in_gpt_neo(SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  )

						# Save pytorch-model
						print(F"""Save PyTorch model to {pytorch_dump_path}"""  )
						model.save_pretrained(SCREAMING_SNAKE_CASE__  )


if __name__ == "__main__":
		lowerCAmelCase					:				Optional[Any]   	=argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained mesh-tf model. \n'
		        'This specifies the model architecture.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		lowerCAmelCase					:				str   	=parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

