



import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


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


class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      def __init__(    self:	List[str]						,				a:	AutoencoderKL						,				a:	CLIPTextModel						,				a:	CLIPTokenizer						,				a:	UNetaDConditionModel						,				a:	Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]						,				a:	StableDiffusionSafetyChecker						,				a:	CLIPImageProcessor						,				):
       super().__init__()
       self.register_modules(
           vae=a						,				text_encoder=a						,				tokenizer=a						,				unet=a						,				scheduler=a						,				safety_checker=a						,				feature_extractor=a						,				)




      def 					_snake_case     (    self:	Any						,				a:	Optional[Union[str, int]] = "auto"    ):
       if slice_size == "auto":
        # half the attention head size is usually a good trade-off between
        # speed and memory
        __lowerCamelCase		:							List[Any]		 =						self.unet.config.attention_head_dim // 2
       self.unet.set_attention_slice(a    )




      def 					_snake_case     (    self:	List[str]    ):
       self.enable_attention_slicing(a    )




      @torch.no_grad()
      def __call__(    self:	str						,				a:	Union[str, List[str]]						,				a:	int = 512						,				a:	int = 512						,				a:	int = 50						,				a:	float = 7.5						,				a:	Optional[Union[str, List[str]]] = None						,				a:	Optional[int] = 1						,				a:	float = 0.0						,				a:	Optional[torch.Generator] = None						,				a:	Optional[torch.FloatTensor] = None						,				a:	Optional[str] = "pil"						,				a:	bool = True						,				a:	Optional[Callable[[int, int, torch.FloatTensor], None]] = None						,				a:	int = 1						,				a:	Optional[torch.FloatTensor] = None						,				**a:	int						,				):

       if isinstance(a						,				a    ):
        __lowerCamelCase		:							List[str]		 =						1
       elif isinstance(a						,				a    ):
        __lowerCamelCase		:							List[Any]		 =						len(a    )
       else:
        raise ValueError(F'`prompt` has to be of type `str` or `list` but is {type(a    )}'    )

       if height % 8 != 0 or width % 8 != 0:
        raise ValueError(F'`height` and `width` have to be divisible by 8 but are {height} and {width}.'    )

       if (callback_steps is None) or (
           callback_steps is not None and (not isinstance(a						,				a    ) or callback_steps <= 0)
       ):
        raise ValueError(
            F'`callback_steps` has to be a positive integer but is {callback_steps} of type'
            F' {type(a    )}.'    )

       # get prompt text embeddings
       __lowerCamelCase		:							Union[str, Any]		 =						self.tokenizer(
           a						,				padding='max_length'						,				max_length=self.tokenizer.model_max_length						,				return_tensors='pt'						,				)
       __lowerCamelCase		:							Any		 =						text_inputs.input_ids

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

       if text_embeddings is None:
        __lowerCamelCase		:							Optional[Any]		 =						self.text_encoder(text_input_ids.to(self.device    )    )[0]

       # duplicate text embeddings for each generation per prompt, using mps friendly method
       __lowerCamelCase		:							List[str]		 =						text_embeddings.shape
       __lowerCamelCase		:							Any		 =						text_embeddings.repeat(1						,				a						,				1    )
       __lowerCamelCase		:							Dict		 =						text_embeddings.view(bs_embed * num_images_per_prompt						,				a						,				-1    )

       # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
       # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
       # corresponds to doing no classifier free guidance.
       __lowerCamelCase		:							int		 =						guidance_scale > 1.0
       # get unconditional embeddings for classifier free guidance
       if do_classifier_free_guidance:
        __lowerCamelCase		:							List[str]
        if negative_prompt is None:
         __lowerCamelCase		:							List[str]		 =						['']
        elif type(a    ) is not type(a    ):
         raise TypeError(
             F'`negative_prompt` should be the same type to `prompt`, but got {type(a    )} !='
             F' {type(a    )}.'    )
        elif isinstance(a						,				a    ):
         __lowerCamelCase		:							List[Any]		 =						[negative_prompt]
        elif batch_size != len(a    ):
         raise ValueError(
             F'`negative_prompt`: {negative_prompt} has batch size {len(a    )}, but `prompt`:'
             F' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
             ' the batch size of `prompt`.'    )
        else:
         __lowerCamelCase		:							Optional[Any]		 =						negative_prompt

        __lowerCamelCase		:							Optional[int]		 =						text_input_ids.shape[-1]
        __lowerCamelCase		:							Any		 =						self.tokenizer(
            a						,				padding='max_length'						,				max_length=a						,				truncation=a						,				return_tensors='pt'						,				)
        __lowerCamelCase		:							Any		 =						self.text_encoder(uncond_input.input_ids.to(self.device    )    )[0]

        # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
        __lowerCamelCase		:							Union[str, Any]		 =						uncond_embeddings.shape[1]
        __lowerCamelCase		:							int		 =						uncond_embeddings.repeat(a						,				a						,				1    )
        __lowerCamelCase		:							List[Any]		 =						uncond_embeddings.view(batch_size * num_images_per_prompt						,				a						,				-1    )

        # 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		:							List[str]		 =						torch.cat([uncond_embeddings, text_embeddings]    )

       # get the initial random noise unless the user supplied it

       # Unlike in other pipelines, latents need to be generated in the target device
       # for 1-to-1 results reproducibility with the CompVis implementation.
       # However this currently doesn't work in `mps`.
       __lowerCamelCase		:							List[str]		 =						(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
       __lowerCamelCase		:							Optional[int]		 =						(batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
       __lowerCamelCase		:							int		 =						text_embeddings.dtype
       if latents is None:
        if self.device.type == "mps":
         # randn does not exist on mps
         __lowerCamelCase		:							Dict		 =						torch.randn(
             a						,				generator=a						,				device='cpu'						,				dtype=a    ).to(self.device    )
         __lowerCamelCase		:							Optional[Any]		 =						torch.randn(a						,				generator=a						,				device='cpu'						,				dtype=a    ).to(
             self.device    )
        else:
         __lowerCamelCase		:							str		 =						torch.randn(
             a						,				generator=a						,				device=self.device						,				dtype=a    )
         __lowerCamelCase		:							Dict		 =						torch.randn(a						,				generator=a						,				device=self.device						,				dtype=a    )
       else:
        if latents_reference.shape != latents_shape:
         raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {latents_shape}'    )
        __lowerCamelCase		:							Optional[int]		 =						latents_reference.to(self.device    )
        __lowerCamelCase		:							int		 =						latents.to(self.device    )

       # This is the key part of the pipeline where we
       # try to ensure that the generated images w/ the same seed
       # but different sizes actually result in similar images
       __lowerCamelCase		:							Tuple		 =						(latents_shape[3] - latents_shape_reference[3]) // 2
       __lowerCamelCase		:							Dict		 =						(latents_shape[2] - latents_shape_reference[2]) // 2
       __lowerCamelCase		:							Dict		 =						latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
       __lowerCamelCase		:							List[str]		 =						latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
       __lowerCamelCase		:							str		 =						0 if dx < 0 else dx
       __lowerCamelCase		:							Tuple		 =						0 if dy < 0 else dy
       __lowerCamelCase		:							str		 =						max(-dx						,				0    )
       __lowerCamelCase		:							Optional[int]		 =						max(-dy						,				0    )
       # import pdb
       # pdb.set_trace()
       __lowerCamelCase		:							Optional[int]		 =						latents_reference[:, :, dy : dy + h, dx : dx + w]

       # set timesteps
       self.scheduler.set_timesteps(a    )

       # Some schedulers like PNDM have timesteps as arrays
       # It's more optimized to move all timesteps to correct device beforehand
       __lowerCamelCase		:							str		 =						self.scheduler.timesteps.to(self.device    )

       # scale the initial noise by the standard deviation required by the scheduler
       __lowerCamelCase		:							List[str]		 =						latents * self.scheduler.init_noise_sigma

       # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
       # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
       # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
       # and should be between [0, 1]
       __lowerCamelCase		:							int		 =						'eta' in set(inspect.signature(self.scheduler.step    ).parameters.keys()    )
       __lowerCamelCase		:							List[str]		 =						{}
       if accepts_eta:
        __lowerCamelCase		:							List[str]		 =						eta

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

        # predict the noise residual
        __lowerCamelCase		:							Any		 =						self.unet(a						,				a						,				encoder_hidden_states=a    ).sample

        # perform guidance
        if do_classifier_free_guidance:
         __lowerCamelCase		:							str		 =						noise_pred.chunk(2    )
         __lowerCamelCase		:							List[Any]		 =						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

        # compute the previous noisy sample x_t -> x_t-1
        __lowerCamelCase		:							List[Any]		 =						self.scheduler.step(a						,				a						,				a						,				**a    ).prev_sample

        # call the callback, if provided
        if callback is not None and i % callback_steps == 0:
         callback(a						,				a						,				a    )

       __lowerCamelCase		:							Dict		 =						1 / 0.1_8_2_1_5 * latents
       __lowerCamelCase		:							Union[str, Any]		 =						self.vae.decode(a    ).sample

       __lowerCamelCase		:							Any		 =						(image / 2 + 0.5).clamp(0						,				1    )

       # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
       __lowerCamelCase		:							Optional[int]		 =						image.cpu().permute(0						,				2						,				3						,				1    ).float().numpy()

       if self.safety_checker is not None:
        __lowerCamelCase		:							str		 =						self.feature_extractor(self.numpy_to_pil(a    )						,				return_tensors='pt'    ).to(
            self.device    )
        __lowerCamelCase		:							Optional[int]		 =						self.safety_checker(
            images=a						,				clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype    )    )
       else:
        __lowerCamelCase		:							Tuple		 =						None

       if output_type == "pil":
        __lowerCamelCase		:							Tuple		 =						self.numpy_to_pil(a    )

       if not return_dict:
        return (image, has_nsfw_concept)

       return StableDiffusionPipelineOutput(images=a						,				nsfw_content_detected=a    )





import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 # Load configuration defined in the metadata file
 with open(SCREAMING_SNAKE_CASE__						) as metadata_file:
  __lowerCamelCase		:							List[str]		 =						json.load(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							int		 =						LukeConfig(use_entity_aware_attention=SCREAMING_SNAKE_CASE__      ,	**metadata['model_config']						)

 # Load in the weights from the checkpoint_path
 __lowerCamelCase		:							Union[str, Any]		 =						torch.load(SCREAMING_SNAKE_CASE__      ,	map_location='cpu'						)

 # Load the entity vocab file
 __lowerCamelCase		:							List[Any]		 =						load_entity_vocab(SCREAMING_SNAKE_CASE__						)

 __lowerCamelCase		:							Optional[int]		 =						RobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name']						)

 # Add special tokens to the token vocabulary for downstream tasks
 __lowerCamelCase		:							str		 =						AddedToken('<ent>'      ,	lstrip=SCREAMING_SNAKE_CASE__      ,	rstrip=SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							Dict		 =						AddedToken('<ent2>'      ,	lstrip=SCREAMING_SNAKE_CASE__      ,	rstrip=SCREAMING_SNAKE_CASE__						)
 tokenizer.add_special_tokens({'additional_special_tokens': [entity_token_a, entity_token_a]}						)
 config.vocab_size += 2

 print(f'Saving tokenizer to {pytorch_dump_folder_path}'						)
 tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__						)
 with open(os.path.join(SCREAMING_SNAKE_CASE__      ,	LukeTokenizer.vocab_files_names['entity_vocab_file']						)      ,	'w'						) as f:
  json.dump(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)

 __lowerCamelCase		:							str		 =						LukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__						)

 # Initialize the embeddings of the special tokens
 __lowerCamelCase		:							Union[str, Any]		 =						state_dict['embeddings.word_embeddings.weight']
 __lowerCamelCase		:							Tuple		 =						word_emb[tokenizer.convert_tokens_to_ids(['@']						)[0]].unsqueeze(0						)
 __lowerCamelCase		:							Any		 =						word_emb[tokenizer.convert_tokens_to_ids(['#']						)[0]].unsqueeze(0						)
 __lowerCamelCase		:							List[str]		 =						torch.cat([word_emb, ent_emb, enta_emb]						)

 # Initialize the query layers of the entity-aware self-attention mechanism
 for layer_index in range(config.num_hidden_layers						):
  for matrix_name in ["query.weight", "query.bias"]:
   __lowerCamelCase		:							Optional[int]		 =						f'encoder.layer.{layer_index}.attention.self.'
   __lowerCamelCase		:							Dict		 =						state_dict[prefix + matrix_name]
   __lowerCamelCase		:							List[Any]		 =						state_dict[prefix + matrix_name]
   __lowerCamelCase		:							Union[str, Any]		 =						state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
 __lowerCamelCase		:							Optional[int]		 =						state_dict['entity_embeddings.entity_embeddings.weight']
 __lowerCamelCase		:							Union[str, Any]		 =						entity_emb[entity_vocab['[MASK]']]

 __lowerCamelCase		:							Optional[Any]		 =						LukeModel(config=SCREAMING_SNAKE_CASE__						).eval()

 __lowerCamelCase ,					__lowerCamelCase		:							List[Any]		 =						model.load_state_dict(SCREAMING_SNAKE_CASE__      ,	strict=SCREAMING_SNAKE_CASE__						)
 if not (len(SCREAMING_SNAKE_CASE__						) == 1 and missing_keys[0] == "embeddings.position_ids"):
  raise ValueError(f'Missing keys {", ".join(SCREAMING_SNAKE_CASE__						)}. Expected only missing embeddings.position_ids'						)
 if not (all(key.startswith('entity_predictions'						) or key.startswith('lm_head'						) for key in unexpected_keys						)):
  raise ValueError(
      'Unexpected keys'
      f' {", ".join([key for key in unexpected_keys if not (key.startswith("entity_predictions"						) or key.startswith("lm_head"						))]						)}'						)

 # Check outputs
 __lowerCamelCase		:							Optional[Any]		 =						LukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__      ,	task='entity_classification'						)

 __lowerCamelCase		:							Dict		 =						(
     'Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the'
     ' new world number one avoid a humiliating second- round exit at Wimbledon .'
 )
 __lowerCamelCase		:							Union[str, Any]		 =						(39, 42)
 __lowerCamelCase		:							Optional[Any]		 =						tokenizer(SCREAMING_SNAKE_CASE__      ,	entity_spans=[span]      ,	add_prefix_space=SCREAMING_SNAKE_CASE__      ,	return_tensors='pt'						)

 __lowerCamelCase		:							List[str]		 =						model(**SCREAMING_SNAKE_CASE__						)

 # Verify word hidden states
 if model_size == "large":
  __lowerCamelCase		:							Dict		 =						torch.Size((1, 42, 1_024)						)
  __lowerCamelCase		:							int		 =						torch.tensor(
      [[0.0_133, 0.0_865, 0.0_095], [0.3_093, -0.2_576, -0.7_418], [-0.1_720, -0.2_117, -0.2_869]]						)
 else:  # base
  __lowerCamelCase		:							Union[str, Any]		 =						torch.Size((1, 42, 768)						)
  __lowerCamelCase		:							Tuple		 =						torch.tensor([[0.0_037, 0.1_368, -0.0_091], [0.1_099, 0.3_329, -0.1_095], [0.0_765, 0.5_335, 0.1_179]]						)

 if not (outputs.last_hidden_state.shape == expected_shape):
  raise ValueError(
      f'Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}'						)
 if not torch.allclose(outputs.last_hidden_state[0, :3, :3]      ,	SCREAMING_SNAKE_CASE__      ,	atol=1e-4						):
  raise ValueError

 # Verify entity hidden states
 if model_size == "large":
  __lowerCamelCase		:							Union[str, Any]		 =						torch.Size((1, 1, 1_024)						)
  __lowerCamelCase		:							Dict		 =						torch.tensor([[0.0_466, -0.0_106, -0.0_179]]						)
 else:  # base
  __lowerCamelCase		:							int		 =						torch.Size((1, 1, 768)						)
  __lowerCamelCase		:							Dict		 =						torch.tensor([[0.1_457, 0.1_044, 0.0_174]]						)

 if not (outputs.entity_last_hidden_state.shape != expected_shape):
  raise ValueError(
      f'Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'
      f' {expected_shape}'						)
 if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3]      ,	SCREAMING_SNAKE_CASE__      ,	atol=1e-4						):
  raise ValueError

 # Finally, save our PyTorch model and tokenizer
 print('Saving PyTorch model to {}'.format(SCREAMING_SNAKE_CASE__						)						)
 model.save_pretrained(SCREAMING_SNAKE_CASE__						)
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Tuple		 =						{}
 with open(SCREAMING_SNAKE_CASE__      ,	'r'      ,	encoding='utf-8'						) as f:
  for index, line in enumerate(SCREAMING_SNAKE_CASE__						):
   __lowerCamelCase ,					__lowerCamelCase		:							List[Any]		 =						line.rstrip().split('\t'						)
   __lowerCamelCase		:							Any		 =						index

 return entity_vocab


if __name__ == "__main__":
       lowercase_	   =     argparse.ArgumentParser()
       # Required parameters
       parser.add_argument('--checkpoint_path', type=str, help='Path to a pytorch_model.bin file.')
       parser.add_argument(
           '--metadata_path', default=None, type=str, help='Path to a metadata.json file, defining the configuration.'
       )
       parser.add_argument(
           '--entity_vocab_path',
           default=None,
           type=str,
           help='Path to an entity_vocab.tsv file, containing the entity vocabulary.',
       )
       parser.add_argument(
           '--pytorch_dump_folder_path', default=None, type=str, help='Path to where to dump the output PyTorch model.'
       )
       parser.add_argument(
           '--model_size', default='base', type=str, choices=['base', 'large'], help='Size of the model to be converted.'
       )
       lowercase_	   =     parser.parse_args()
       convert_luke_checkpoint(
           args.checkpoint_path,
           args.metadata_path,
           args.entity_vocab_path,
           args.pytorch_dump_folder_path,
           args.model_size,
       )

