



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


snake_case     :    Any						=    logging.get_logger(__name__)  # pylint: disable=invalid-name
def 						snake_case__							(       self				,  __lowercase				,  __lowercase				,  __lowercase				,  __lowercase							)		->		Optional[Any]:







						"""simple docstring"""

						A__						:				Optional[Any]         =			image.to(device=__lowercase							)
						A__						:				Optional[Any]         =			image  # take image as image_embeddings
						A__						:				List[str]         =			image_embeddings.unsqueeze(1							)

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

						if do_classifier_free_guidance:
												A__						:				str         =			torch.zeros_like(__lowercase							)

												# For classifier free guidance, we need to do two forward passes.
												# Here we concatenate the unconditional and text embeddings into a single batch
												# to avoid doing two forward passes
												A__						:				Any         =			torch.cat([uncond_embeddings, image_embeddings]							)

						return image_embeddings




class 				lowerCAmelCase__	(						UpperCamelCase    ):







					def __init__(							self   :  Dict      ,		_A   :  PriorTransformer      ,		_A   :  CLIPTokenizer      ,		_A   :  CLIPTextModelWithProjection      ,		_A   :  UnCLIPScheduler      ,		_A   :  Optional[dict] = None      ,		):
											super().__init__()

											A__						:				List[str]         =			{"image_encoder": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

											A__						:				Optional[Any]         =			decoder_pipe_kwargs.get("torch_dtype"      ,		_A) or prior.dtype

											A__						:				Union[str, Any]         =			StableDiffusionImageVariationPipeline.from_pretrained(
											    "lambdalabs/sd-image-variations-diffusers"      ,		**_A)

											# replace `_encode_image` method
											A__						:				Optional[Any]         =			types.MethodType(_encode_image      ,		self.decoder_pipe)

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







					def 			_lowercase      (							self   :  Union[str, Any]      ,		_A   :  List[Any]      ,		_A   :  Any      ,		_A   :  Dict      ,		_A   :  Union[str, Any]      ,		_A   :  Optional[Union[CLIPTextModelOutput, Tuple]] = None      ,		_A   :  Optional[torch.Tensor] = None      ,		):
											if text_model_output is None:
																	A__						:				Union[str, Any]         =			len(_A) if isinstance(_A      ,		_A) else 1
																	# get prompt text embeddings
																	A__						:				int         =			self.tokenizer(
																	    _A      ,		padding="max_length"      ,		max_length=self.tokenizer.model_max_length      ,		return_tensors="pt"      ,		)
																	A__						:				str         =			text_inputs.input_ids
																	A__						:				Union[str, Any]         =			text_inputs.attention_mask.bool().to(_A)

																	if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																							A__						:				List[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}')
																							A__						:				Union[str, Any]         =			text_input_ids[:, : self.tokenizer.model_max_length]

																	A__						:				Union[str, Any]         =			self.text_encoder(text_input_ids.to(_A))

																	A__						:				Any         =			text_encoder_output.text_embeds
																	A__						:				Tuple         =			text_encoder_output.last_hidden_state

											else:
																	A__						:				Tuple         =			text_model_output[0].shape[0]
																	A__     ,							A__						:				str         =			text_model_output[0], text_model_output[1]
																	A__						:				List[Any]         =			text_attention_mask

											A__						:				Union[str, Any]         =			text_embeddings.repeat_interleave(_A      ,		dim=0)
											A__						:				Union[str, Any]         =			text_encoder_hidden_states.repeat_interleave(_A      ,		dim=0)
											A__						:				Optional[Any]         =			text_mask.repeat_interleave(_A      ,		dim=0)

											if do_classifier_free_guidance:
																	A__						:				List[str]         =			[""] * batch_size

																	A__						:				int         =			self.tokenizer(
																	    _A      ,		padding="max_length"      ,		max_length=self.tokenizer.model_max_length      ,		truncation=_A      ,		return_tensors="pt"      ,		)
																	A__						:				int         =			uncond_input.attention_mask.bool().to(_A)
																	A__						:				Dict         =			self.text_encoder(uncond_input.input_ids.to(_A))

																	A__						:				Union[str, Any]         =			uncond_embeddings_text_encoder_output.text_embeds
																	A__						:				Any         =			uncond_embeddings_text_encoder_output.last_hidden_state

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

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

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

																	A__						:				Optional[int]         =			torch.cat([uncond_text_mask, text_mask])

											return text_embeddings, text_encoder_hidden_states, text_mask







					@property
					def 			_lowercase      (							self   :  Optional[int]):
											if self.device != torch.device("meta") or not hasattr(self.prior      ,		"_hf_hook"):
																	return self.device
											for module in self.prior.modules():
																	if (
																	    hasattr(_A      ,		"_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   :  List[str]      ,		_A   :  Optional[Any]      ,		_A   :  Union[str, Any]      ,		_A   :  Any      ,		_A   :  Optional[Any]      ,		_A   :  Tuple      ,		_A   :  Dict):
											if latents is None:
																	A__						:				Tuple         =			randn_tensor(_A      ,		generator=_A      ,		device=_A      ,		dtype=_A)
											else:
																	if latents.shape != shape:
																							raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {shape}')
																	A__						:				Any         =			latents.to(_A)

											A__						:				Dict         =			latents * scheduler.init_noise_sigma
											return latents







					def 			_lowercase      (							self   :  List[Any]      ,		_A   :  Optional[Union[str, torch.device]] = None):
											self.decoder_pipe.to(_A)
											super().to(_A)







					@torch.no_grad()
					def __call__(							self   :  str      ,		_A   :  Optional[Union[str, List[str]]] = None      ,		_A   :  Optional[int] = None      ,		_A   :  Optional[int] = None      ,		_A   :  int = 1      ,		_A   :  int = 25      ,		_A   :  Optional[torch.Generator] = None      ,		_A   :  Optional[torch.FloatTensor] = None      ,		_A   :  Optional[Union[CLIPTextModelOutput, Tuple]] = None      ,		_A   :  Optional[torch.Tensor] = None      ,		_A   :  float = 4.0      ,		_A   :  float = 8.0      ,		_A   :  int = 50      ,		_A   :  Optional[int] = 1      ,		_A   :  float = 0.0      ,		_A   :  Optional[str] = "pil"      ,		_A   :  bool = True      ,		):
											if prompt is not None:
																	if isinstance(_A      ,		_A):
																							A__						:				Dict         =			1
																	elif isinstance(_A      ,		_A):
																							A__						:				Tuple         =			len(_A)
																	else:
																							raise ValueError(F'`prompt` has to be of type `str` or `list` but is {type(_A)}')
											else:
																	A__						:				Union[str, Any]         =			text_model_output[0].shape[0]

											A__						:				List[str]         =			self._execution_device

											A__						:				Tuple         =			batch_size * num_images_per_prompt

											A__						:				Union[str, Any]         =			prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

											A__     ,							A__     ,							A__						:				Union[str, Any]         =			self._encode_prompt(
											    _A      ,		_A      ,		_A      ,		_A      ,		_A      ,		_A)

											# prior

											self.prior_scheduler.set_timesteps(_A      ,		device=_A)
											A__						:				str         =			self.prior_scheduler.timesteps

											A__						:				int         =			self.prior.config.embedding_dim

											A__						:				int         =			self.prepare_latents(
											    (batch_size, embedding_dim)      ,		text_embeddings.dtype      ,		_A      ,		_A      ,		_A      ,		self.prior_scheduler      ,		)

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

																	A__						:				Dict         =			self.prior(
																	    _A      ,		timestep=_A      ,		proj_embedding=_A      ,		encoder_hidden_states=_A      ,		attention_mask=_A      ,		).predicted_image_embedding

																	if do_classifier_free_guidance:
																							A__     ,							A__						:				int         =			predicted_image_embedding.chunk(2)
																							A__						:				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]:
																							A__						:				Dict         =			None
																	else:
																							A__						:				List[str]         =			prior_timesteps_tensor[i + 1]

																	A__						:				Any         =			self.prior_scheduler.step(
																	    _A      ,		timestep=_A      ,		sample=_A      ,		generator=_A      ,		prev_timestep=_A      ,		).prev_sample

											A__						:				Dict         =			self.prior.post_process_latents(_A)

											A__						:				Any         =			prior_latents

											A__						:				Optional[Any]         =			self.decoder_pipe(
											    image=_A      ,		height=_A      ,		width=_A      ,		num_inference_steps=_A      ,		guidance_scale=_A      ,		generator=_A      ,		output_type=_A      ,		return_dict=_A      ,		num_images_per_prompt=_A      ,		eta=_A      ,		)
											return output




import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()
def 						snake_case__							(       __lowercase				,  __lowercase				,  __lowercase				,  __lowercase				,  __lowercase							)		->		Dict:







						"""simple docstring"""

						A__						:				str         =			BertConfig.from_pretrained(
						    "bert-large-cased"				,  vocab_size=__lowercase				,  max_position_embeddings=5_1_2				,  is_decoder=__lowercase				,  add_cross_attention=__lowercase				,  )
						A__						:				List[str]         =			bert_config.to_dict()
						del bert_config_dict["type_vocab_size"]
						A__						:				Union[str, Any]         =			BertGenerationConfig(**__lowercase							)
						if is_encoder:
												A__						:				Optional[int]         =			BertGenerationEncoder(__lowercase							)
						else:
												A__						:				List[Any]         =			BertGenerationDecoder(__lowercase							)
						print(F'Building PyTorch model from configuration: {config}'							)

						# Load weights from tf checkpoint
						load_tf_weights_in_bert_generation(
						    __lowercase				,  __lowercase				,  model_class="bert"				,  is_encoder_named_decoder=__lowercase				,  is_encoder=__lowercase				,  )

						# Save pytorch-model
						print(F'Save PyTorch model and config to {pytorch_dump_path}'							)
						model.save_pretrained(__lowercase							)


if __name__ == "__main__":
		snake_case     :    List[Any]						=    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--tf_hub_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		parser.add_argument(
		    '--is_encoder_named_decoder',
		    action='store_true',
		    help='If decoder has to be renamed to encoder in PyTorch model.',
		)
		parser.add_argument('--is_encoder', action='store_true', help='If model is an encoder.')
		parser.add_argument('--vocab_size', default=5_0_3_5_8, type=int, help='Vocab size of model')
		snake_case     :    List[Any]						=    parser.parse_args()
		convert_tf_checkpoint_to_pytorch(
		    args.tf_hub_path,
		    args.pytorch_dump_path,
		    args.is_encoder_named_decoder,
		    args.vocab_size,
		    is_encoder=args.is_encoder,
		)