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

import torch
from transformers import CLIPTextModel, CLIPTokenizer

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


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

class __snake_case		(				lowerCAmelCase ,     lowerCAmelCase					):
    @register_to_config
    def __init__(  self		,snake_case		,snake_case = None		,snake_case = None				):



           '''simple docstring'''







           super().__init__()

           lowercase							:					Dict	     =				learnable

           if self.learnable:
                  assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
                  assert length is not None, "learnable=True requires `length` to be set"

                  lowercase							:					int	     =				torch.zeros(snake_case		,snake_case				)
           else:
                  lowercase							:					Tuple	     =				None

           lowercase							:					List[str]	     =				torch.nn.Parameter(snake_case				)






class __snake_case		(				lowerCAmelCase					):
    _a		:						List[str]=    42
    _a		:						List[Any]=    42
    _a		:						List[str]=    42
    _a		:						List[Any]=    42
    _a		:						Optional[int]=    42
    _a		:						List[Any]=    42
    def __init__(  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,):



           '''simple docstring'''







           super().__init__()

           self.register_modules(
               vqvae=snake_case		,transformer=snake_case		,text_encoder=snake_case		,tokenizer=snake_case		,scheduler=snake_case		,learned_classifier_free_sampling_embeddings=snake_case		,)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case		,snake_case				):



           '''simple docstring'''







           lowercase							:					Tuple	     =				len(snake_case				) if isinstance(snake_case		,snake_case				) else 1

           # get prompt text embeddings
           lowercase							:					Tuple	     =				self.tokenizer(
               snake_case		,padding="""max_length"""		,max_length=self.tokenizer.model_max_length		,return_tensors="""pt"""		,)
           lowercase							:					Optional[Any]	     =				text_inputs.input_ids

           if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                  lowercase							:					Tuple	     =				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}"				)
                  lowercase							:					Tuple	     =				text_input_ids[:, : self.tokenizer.model_max_length]
           lowercase							:					List[str]	     =				self.text_encoder(text_input_ids.to(self.device				)				)[0]

           # NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
           # While CLIP does normalize the pooled output of the text transformer when combining
           # the image and text embeddings, CLIP does not directly normalize the last hidden state.
           #
           # CLIP normalizing the pooled output.
           # https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
           lowercase							:					Optional[int]	     =				prompt_embeds / prompt_embeds.norm(dim=-1		,keepdim=snake_case				)

           # duplicate text embeddings for each generation per prompt
           lowercase							:					Optional[int]	     =				prompt_embeds.repeat_interleave(snake_case		,dim=0				)

           if do_classifier_free_guidance:
                  if self.learned_classifier_free_sampling_embeddings.learnable:
                         lowercase							:					Optional[Any]	     =				self.learned_classifier_free_sampling_embeddings.embeddings
                         lowercase							:					Optional[int]	     =				negative_prompt_embeds.unsqueeze(0				).repeat(snake_case		,1		,1				)
                  else:
                         lowercase							:					Optional[int]	     =				[''] * batch_size

                         lowercase							:					Optional[int]	     =				text_input_ids.shape[-1]
                         lowercase							:					List[Any]	     =				self.tokenizer(
                             snake_case		,padding="""max_length"""		,max_length=snake_case		,truncation=snake_case		,return_tensors="""pt"""		,)
                         lowercase							:					Dict	     =				self.text_encoder(uncond_input.input_ids.to(self.device				)				)[0]
                         # See comment for normalizing text embeddings
                         lowercase							:					Optional[int]	     =				negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1		,keepdim=snake_case				)

                  # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
                  lowercase							:					List[str]	     =				negative_prompt_embeds.shape[1]
                  lowercase							:					int	     =				negative_prompt_embeds.repeat(1		,snake_case		,1				)
                  lowercase							:					Any	     =				negative_prompt_embeds.view(batch_size * num_images_per_prompt		,snake_case		,-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
                  lowercase							:					str	     =				torch.cat([negative_prompt_embeds, prompt_embeds]				)

           return prompt_embeds
    @torch.no_grad()
    def __call__(  self		,snake_case		,snake_case = 100		,snake_case = 5.0		,snake_case = 1.0		,snake_case = 1		,snake_case = None		,snake_case = None		,snake_case = "pil"		,snake_case = True		,snake_case = None		,snake_case = 1		,):



           '''simple docstring'''







           if isinstance(snake_case		,snake_case				):
                  lowercase							:					str	     =				1
           elif isinstance(snake_case		,snake_case				):
                  lowercase							:					List[Any]	     =				len(snake_case				)
           else:
                  raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(snake_case				)}"				)

           lowercase							:					str	     =				batch_size * num_images_per_prompt

           lowercase							:					List[Any]	     =				guidance_scale > 1.0

           lowercase							:					List[str]	     =				self._encode_prompt(snake_case		,snake_case		,snake_case				)

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

           # get the initial completely masked latents unless the user supplied it

           lowercase							:					Tuple	     =				(batch_size, self.transformer.num_latent_pixels)
           if latents is None:
                  lowercase							:					Tuple	     =				self.transformer.num_vector_embeds - 1
                  lowercase							:					List[Any]	     =				torch.full(snake_case		,snake_case				).to(self.device				)
           else:
                  if latents.shape != latents_shape:
                         raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"				)
                  if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
                         raise ValueError(
                             """Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,"""
                             f" {self.transformer.num_vector_embeds - 1} (inclusive)."				)
                  lowercase							:					List[str]	     =				latents.to(self.device				)

           # set timesteps
           self.scheduler.set_timesteps(snake_case		,device=self.device				)

           lowercase							:					int	     =				self.scheduler.timesteps.to(self.device				)

           lowercase							:					Tuple	     =				latents

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

                  # predict the un-noised image
                  # model_output == `log_p_x_0`
                  lowercase							:					str	     =				self.transformer(snake_case		,encoder_hidden_states=snake_case		,timestep=snake_case				).sample

                  if do_classifier_free_guidance:
                         lowercase							:					str	     =				model_output.chunk(2				)
                         lowercase							:					List[Any]	     =				model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
                         model_output -= torch.logsumexp(snake_case		,dim=1		,keepdim=snake_case				)

                  lowercase							:					Dict	     =				self.truncate(snake_case		,snake_case				)

                  # remove `log(0)`'s (`-inf`s)
                  lowercase							:					Optional[int]	     =				model_output.clamp(-70				)

                  # compute the previous noisy sample x_t -> x_t-1
                  lowercase							:					Any	     =				self.scheduler.step(snake_case		,timestep=snake_case		,sample=snake_case		,generator=snake_case				).prev_sample

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

           lowercase							:					Optional[int]	     =				self.vqvae.config.vq_embed_dim
           lowercase							:					Dict	     =				(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
           lowercase							:					Optional[int]	     =				self.vqvae.quantize.get_codebook_entry(snake_case		,shape=snake_case				)
           lowercase							:					int	     =				self.vqvae.decode(snake_case		,force_not_quantize=snake_case				).sample

           lowercase							:					Any	     =				(image / 2 + 0.5).clamp(0		,1				)
           lowercase							:					Union[str, Any]	     =				image.cpu().permute(0		,2		,3		,1				).numpy()

           if output_type == "pil":
                  lowercase							:					Union[str, Any]	     =				self.numpy_to_pil(snake_case				)

           if not return_dict:
                  return (image,)

           return ImagePipelineOutput(images=snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case				):



           '''simple docstring'''







           lowercase							:					int	     =				torch.sort(snake_case		,1		,descending=snake_case				)
           lowercase							:					int	     =				torch.exp(snake_case				)
           lowercase							:					int	     =				sorted_p_x_0.cumsum(dim=1				) < truncation_rate

           # Ensure that at least the largest probability is not zeroed out
           lowercase							:					List[str]	     =				torch.full_like(keep_mask[:, 0:1, :]		,snake_case				)
           lowercase							:					Dict	     =				torch.cat((all_true, keep_mask)		,dim=1				)
           lowercase							:					List[Any]	     =				keep_mask[:, :-1, :]

           lowercase							:					str	     =				keep_mask.gather(1		,indices.argsort(1				)				)

           lowercase							:					List[str]	     =				log_p_x_0.clone()

           lowercase							:					List[str]	     =				-torch.inf  # -inf = log(0)

           return rv


import argparse
import json
import os
from collections import OrderedDict

import torch

from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken







@torch.no_grad()
def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   int:
       # Load configuration defined in the metadata file
       with open(SCREAMING_SNAKE_CASE__    ) as metadata_file:
              lowercase							:					Union[str, Any]	     =				json.load(SCREAMING_SNAKE_CASE__    )
       lowercase							:					Tuple	     =				LukeConfig(use_entity_aware_attention=SCREAMING_SNAKE_CASE__ ,     **metadata["""model_config"""]    )

       # Load in the weights from the checkpoint_path
       lowercase							:					Tuple	     =				torch.load(SCREAMING_SNAKE_CASE__ ,     map_location="""cpu"""    )["""module"""]

       # Load the entity vocab file
       lowercase							:					str	     =				load_original_entity_vocab(SCREAMING_SNAKE_CASE__    )
       # add an entry for [MASK2]
       lowercase							:					Any	     =				max(entity_vocab.values()    ) + 1
       config.entity_vocab_size += 1

       lowercase							:					Dict	     =				XLMRobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""]    )

       # Add special tokens to the token vocabulary for downstream tasks
       lowercase							:					List[Any]	     =				AddedToken("""<ent>""" ,     lstrip=SCREAMING_SNAKE_CASE__ ,     rstrip=SCREAMING_SNAKE_CASE__    )
       lowercase							:					int	     =				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__ ,     """tokenizer_config.json"""    ) ,     """r"""    ) as f:
              lowercase							:					List[str]	     =				json.load(SCREAMING_SNAKE_CASE__    )
       lowercase							:					Optional[Any]	     =				"""MLukeTokenizer"""
       with open(os.path.join(SCREAMING_SNAKE_CASE__ ,     """tokenizer_config.json"""    ) ,     """w"""    ) as f:
              json.dump(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )

       with open(os.path.join(SCREAMING_SNAKE_CASE__ ,     MLukeTokenizer.vocab_files_names["""entity_vocab_file"""]    ) ,     """w"""    ) as f:
              json.dump(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )

       lowercase							:					int	     =				MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__    )

       # Initialize the embeddings of the special tokens
       lowercase							:					Dict	     =				tokenizer.convert_tokens_to_ids(["""@"""]    )[0]
       lowercase							:					Dict	     =				tokenizer.convert_tokens_to_ids(["""#"""]    )[0]

       lowercase							:					int	     =				state_dict["""embeddings.word_embeddings.weight"""]
       lowercase							:					Union[str, Any]	     =				word_emb[ent_init_index].unsqueeze(0    )
       lowercase							:					List[str]	     =				word_emb[enta_init_index].unsqueeze(0    )
       lowercase							:					str	     =				torch.cat([word_emb, ent_emb, enta_emb]    )
       # add special tokens for 'entity_predictions.bias'
       for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
              lowercase							:					List[Any]	     =				state_dict[bias_name]
              lowercase							:					Any	     =				decoder_bias[ent_init_index].unsqueeze(0    )
              lowercase							:					Tuple	     =				decoder_bias[enta_init_index].unsqueeze(0    )
              lowercase							:					int	     =				torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias]    )

       # 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"]:
                     lowercase							:					Union[str, Any]	     =				f"encoder.layer.{layer_index}.attention.self."
                     lowercase							:					List[str]	     =				state_dict[prefix + matrix_name]
                     lowercase							:					Any	     =				state_dict[prefix + matrix_name]
                     lowercase							:					str	     =				state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
       lowercase							:					Any	     =				state_dict["""entity_embeddings.entity_embeddings.weight"""]
       lowercase							:					Tuple	     =				entity_emb[entity_vocab["""[MASK]"""]].unsqueeze(0    )
       lowercase							:					Optional[Any]	     =				torch.cat([entity_emb, entity_mask_emb]    )
       # add [MASK2] for 'entity_predictions.bias'
       lowercase							:					Optional[Any]	     =				state_dict["""entity_predictions.bias"""]
       lowercase							:					str	     =				entity_prediction_bias[entity_vocab["""[MASK]"""]].unsqueeze(0    )
       lowercase							:					List[str]	     =				torch.cat([entity_prediction_bias, entity_mask_bias]    )

       lowercase							:					List[str]	     =				LukeForMaskedLM(config=SCREAMING_SNAKE_CASE__    ).eval()

       state_dict.pop("""entity_predictions.decoder.weight"""    )
       state_dict.pop("""lm_head.decoder.weight"""    )
       state_dict.pop("""lm_head.decoder.bias"""    )
       lowercase							:					List[str]	     =				OrderedDict()
       for key, value in state_dict.items():
              if not (key.startswith("""lm_head"""    ) or key.startswith("""entity_predictions"""    )):
                     lowercase							:					List[Any]	     =				state_dict[key]
              else:
                     lowercase							:					Union[str, Any]	     =				state_dict[key]

       lowercase     ,      lowercase							:					int	     =				model.load_state_dict(SCREAMING_SNAKE_CASE__ ,     strict=SCREAMING_SNAKE_CASE__    )

       if set(SCREAMING_SNAKE_CASE__    ) != {"luke.embeddings.position_ids"}:
              raise ValueError(f"Unexpected unexpected_keys: {unexpected_keys}"    )
       if set(SCREAMING_SNAKE_CASE__    ) != {
           "lm_head.decoder.weight",
           "lm_head.decoder.bias",
           "entity_predictions.decoder.weight",
       }:
              raise ValueError(f"Unexpected missing_keys: {missing_keys}"    )

       model.tie_weights()
       assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
       assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()

       # Check outputs
       lowercase							:					str	     =				MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__ ,     task="""entity_classification"""    )

       lowercase							:					str	     =				"""ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan)."""
       lowercase							:					str	     =				(0, 9)
       lowercase							:					Dict	     =				tokenizer(SCREAMING_SNAKE_CASE__ ,     entity_spans=[span] ,     return_tensors="""pt"""    )

       lowercase							:					Any	     =				model(**SCREAMING_SNAKE_CASE__    )

       # Verify word hidden states
       if model_size == "large":
              raise NotImplementedError
       else:  # base
              lowercase							:					List[Any]	     =				torch.Size((1, 33, 768)    )
              lowercase							:					str	     =				torch.tensor([[0.0892, 0.0596, -0.2819], [0.0134, 0.1199, 0.0573], [-0.0169, 0.0927, 0.0644]]    )

       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":
              raise NotImplementedError
       else:  # base
              lowercase							:					Optional[int]	     =				torch.Size((1, 1, 768)    )
              lowercase							:					List[Any]	     =				torch.tensor([[-0.1482, 0.0609, 0.0322]]    )

       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

       # Verify masked word/entity prediction
       lowercase							:					Any	     =				MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__    )
       lowercase							:					List[Any]	     =				"""Tokyo is the capital of <mask>."""
       lowercase							:					List[Any]	     =				(24, 30)
       lowercase							:					int	     =				tokenizer(SCREAMING_SNAKE_CASE__ ,     entity_spans=[span] ,     return_tensors="""pt"""    )

       lowercase							:					Dict	     =				model(**SCREAMING_SNAKE_CASE__    )

       lowercase							:					Union[str, Any]	     =				encoding["""input_ids"""][0].tolist()
       lowercase							:					List[Any]	     =				input_ids.index(tokenizer.convert_tokens_to_ids("""<mask>"""    )    )
       lowercase							:					Dict	     =				outputs.logits[0][mask_position_id].argmax(dim=-1    )
       assert "Japan" == tokenizer.decode(SCREAMING_SNAKE_CASE__    )

       lowercase							:					Union[str, Any]	     =				outputs.entity_logits[0][0].argmax().item()
       lowercase							:					int	     =				[
           entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
       ]
       assert [e for e in multilingual_predicted_entities if e.startswith("""en:"""    )][0] == "en:Japan"

       # Finally, save our PyTorch model and tokenizer
       print("""Saving PyTorch model to {}""".format(SCREAMING_SNAKE_CASE__    )    )
       model.save_pretrained(SCREAMING_SNAKE_CASE__    )







def 						_snake_case(  SCREAMING_SNAKE_CASE__    )			->   Dict:
       lowercase							:					Optional[int]	     =				["""[MASK]""", """[PAD]""", """[UNK]"""]

       lowercase							:					List[str]	     =				[json.loads(SCREAMING_SNAKE_CASE__    ) for line in open(SCREAMING_SNAKE_CASE__    )]

       lowercase							:					int	     =				{}
       for entry in data:
              lowercase							:					Optional[Any]	     =				entry["""id"""]
              for entity_name, language in entry["entities"]:
                     if entity_name in SPECIAL_TOKENS:
                            lowercase							:					Optional[Any]	     =				entity_id
                            break
                     lowercase							:					List[Any]	     =				f"{language}:{entity_name}"
                     lowercase							:					Union[str, Any]	     =				entity_id
       return new_mapping


if __name__ == "__main__":
  lowercase  : Any						   =							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  : str						   =							parser.parse_args()
  convert_luke_checkpoint(
      args.checkpoint_path,
      args.metadata_path,
      args.entity_vocab_path,
      args.pytorch_dump_folder_path,
      args.model_size,
  )
