import jax
import jax.numpy as jnp
from bigbird_flax import FlaxBigBirdForNaturalQuestions
from datasets import load_from_disk

from transformers import BigBirdTokenizerFast


UpperCAmelCase							   =      {0: """null""", 1: """short""", 2: """long""", 3: """yes""", 4: """no"""}
UpperCAmelCase							   =      set("""""".join(["""‘""", """’""", """´""", """`""", """.""", """,""", """-""", """\""""]))







def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_=0						,      lowerCAmelCase_=None       )	->	int:
     return [" ".join(x.split(''' '''       )[begin:end]       ) for x in answers if len(x.split(''' '''       )       ) > 1]







def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_=False       )	->	List[Any]:
     if make_sub_answers:
          # if answers are longer than one word, make sure a predictions is correct if it coresponds to the complete 1: or :-1 sub word
          # *e.g.* if the correct answer contains a prefix such as "the", or "a"
          a  :    str				=						(
              given_answers + get_sub_answers(lowerCAmelCase_						,      begin=1       ) + get_sub_answers(lowerCAmelCase_						,      end=-1       )
          )
     a  :    List[str]				=						[]
     for answer in given_answers:
          a  :    List[str]				=						answer.replace('''_'''						,      ''' '''       ).lower()
          a  :    str				=						''''''.join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else ''' ''' for c in alias       )
          answers.append(''' '''.join(alias.split()       ).strip()       )
     return set(lowerCAmelCase_       )







def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_=1						,      lowerCAmelCase_=1_0_0       )	->	List[Any]:
     a			,							a  :    Union[str, Any]				=						jax.lax.top_k(lowerCAmelCase_						,      lowerCAmelCase_       )
     a			,							a  :    str				=						jax.lax.top_k(lowerCAmelCase_						,      lowerCAmelCase_       )

     a  :    int				=						best_end_idx[:, None] - best_start_idx[None, :]
     a  :    Dict				=						jnp.logical_or(widths < 0						,      widths > max_size       )
     a  :    List[str]				=						(best_end_scores[:, None] + best_start_scores[None, :]) - (1e8 * mask)
     a  :    Optional[Any]				=						jnp.argmax(lowerCAmelCase_       ).item()

     return best_start_idx[best_score % top_k], best_end_idx[best_score // top_k]







def 	UpperCAmelCase       (		lowerCAmelCase_       )	->	Union[str, Any]:
     a  :    Union[str, Any]				=						sample['''question''']['''text''']
     a  :    str				=						sample['''document''']['''tokens''']['''token''']
     a  :    Any				=						sample['''document''']['''tokens''']['''is_html''']
     a  :    Dict				=						sample['''annotations''']['''long_answer''']
     a  :    Optional[Any]				=						sample['''annotations''']['''short_answers''']

     a  :    List[str]				=						''' '''.join([context[i] for i in range(len(lowerCAmelCase_       )       ) if not is_html[i]]       )

     # 0 - No ; 1 - Yes
     for answer in sample["annotations"]["yes_no_answer"]:
          if answer == 0 or answer == 1:
               return {
                   "question": question,
                   "context": context_string,
                   "short": [],
                   "long": [],
                   "category": "no" if answer == 0 else "yes",
               }

     a  :    int				=						[]
     for s in short_answers:
          short_targets.extend(s['''text''']       )
     a  :    str				=						list(set(lowerCAmelCase_       )       )

     a  :    List[str]				=						[]
     for s in long_answers:
          if s["start_token"] == -1:
               continue
          a  :    int				=						context[s['''start_token'''] : s['''end_token''']]
          a  :    List[Any]				=						is_html[s['''start_token'''] : s['''end_token''']]
          a  :    Optional[int]				=						''' '''.join([answer[i] for i in range(len(lowerCAmelCase_       )       ) if not html[i]]       )
          if new_answer not in long_targets:
               long_targets.append(lowerCAmelCase_       )

     a  :    Any				=						'''long_short''' if len(short_targets + long_targets       ) > 0 else '''null'''

     return {
         "question": question,
         "context": context_string,
         "short": short_targets,
         "long": long_targets,
         "category": category,
     }







def 	UpperCAmelCase       (		)	->	Tuple:
     a  :    Any				=						load_from_disk('''natural-questions-validation'''       )
     a  :    Dict				=						dataset.map(lowerCAmelCase_       ).remove_columns(['''annotations''', '''document''', '''id''']       )
     print(lowerCAmelCase_       )

     a  :    Dict				=						dataset.filter(lambda lowerCAmelCase_       : (len(x['''question''']       ) + len(x['''context''']       )) < 4 * 4_0_9_6       )
     a  :    Optional[Any]				=						short_validation_dataset.filter(lambda lowerCAmelCase_       : x["category"] != "null"       )
     short_validation_dataset

     a  :    Any				=						'''vasudevgupta/flax-bigbird-natural-questions'''
     a  :    Optional[Any]				=						FlaxBigBirdForNaturalQuestions.from_pretrained(lowerCAmelCase_       )
     a  :    List[str]				=						BigBirdTokenizerFast.from_pretrained(lowerCAmelCase_       )

     @jax.jit
     def forward(*lowerCAmelCase_						,      **lowerCAmelCase_       ):
          a			,							a			,							a  :    Any				=						model(*lowerCAmelCase_						,      **lowerCAmelCase_       )
          return start_logits, end_logits, jnp.argmax(lowerCAmelCase_						,      axis=-1       )

     def evaluate(lowerCAmelCase_       ):
          # encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
          a  :    Union[str, Any]				=						tokenizer(
              example['''question''']						,      example['''context''']						,      return_tensors='''np'''						,      max_length=4_0_9_6						,      padding='''max_length'''						,      truncation=lowerCAmelCase_						,      )

          a			,							a			,							a  :    Optional[int]				=						forward(**lowerCAmelCase_       )

          a  :    List[str]				=						CATEGORY_MAPPING[category.item()]

          a  :    Union[str, Any]				=						example['''long'''] + example['''short''']
          if example["category"] in ["yes", "no", "null"]:
               a  :    Optional[Any]				=						[example['''category''']]
          a  :    int				=						example['''category'''] != '''null'''
          # Now target can be: "yes", "no", "null", "list of long & short answers"

          if predicted_category in ["yes", "no", "null"]:
               a  :    Any				=						[predicted_category]
               a  :    Tuple				=						example['''output'''] == example['''targets''']
               a  :    Any				=						predicted_category != '''null'''
               return example

          a  :    Dict				=						3_8 if predicted_category == '''short''' else 1_0_2_4
          a			,							a  :    Optional[int]				=						get_best_valid_start_end_idx(
              start_scores[0]						,      end_scores[0]						,      top_k=8						,      max_size=lowerCAmelCase_       )

          a  :    Any				=						inputs['''input_ids'''][0].tolist()
          a  :    Any				=						[tokenizer.decode(input_ids[start_score : end_score + 1]       )]

          a  :    List[Any]				=						expand_to_aliases(example['''targets''']						,      make_sub_answers=lowerCAmelCase_       )
          a  :    List[Any]				=						expand_to_aliases(example['''output''']       )

          # some preprocessing to both prediction and answer
          a  :    List[str]				=						{''''''.join(a.split()       ) for a in answers}
          a  :    Union[str, Any]				=						{''''''.join(p.split()       ) for p in predictions}
          a  :    Tuple				=						{s for s in predictions if s not in ['''``''', '''\'\'''', '''`''', '''\'''']}

          # if there is a common element, it's a exact match
          a  :    Tuple				=						len(list(answers & predictions       )       ) > 0
          a  :    Any				=						predicted_category != '''null''' and len(lowerCAmelCase_       ) > 0

          return example

     a  :    Union[str, Any]				=						short_validation_dataset.map(lowerCAmelCase_       )

     a  :    str				=						len(lowerCAmelCase_       )
     a  :    int				=						len(short_validation_dataset.filter(lambda lowerCAmelCase_       : x["match"] == 1       )       )
     print('''EM score:'''						,      (matched / total) * 1_0_0						,      '''%'''       )


if __name__ == "__main__":
  main()
import math
from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


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

UpperCAmelCase							   =      """
    Examples:
        ```py
        >>> import torch
        >>> from diffusers import DiffusionPipeline
        >>> from diffusers.utils import export_to_gif

        >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")

        >>> repo = \"openai/shap-e\"
        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
        >>> pipe = pipe.to(device)

        >>> guidance_scale = 15.0
        >>> prompt = \"a shark\"

        >>> images = pipe(
        ...     prompt,
        ...     guidance_scale=guidance_scale,
        ...     num_inference_steps=64,
        ...     frame_size=256,
        ... ).images

        >>> gif_path = export_to_gif(images[0], \"shark_3d.gif\")
        ```
"""






@dataclass
class 							_SCREAMING_SNAKE_CASE(   snake_case__	):
   UpperCamelCase_				:						Union[List[List[PIL.Image.Image]], List[List[np.ndarray]]]






class 							_SCREAMING_SNAKE_CASE(   snake_case__	):
   def __init__(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     )	->int:
        super().__init__()

        self.register_modules(
            prior=lowerCAmelCase_							,     text_encoder=lowerCAmelCase_							,     tokenizer=lowerCAmelCase_							,     scheduler=lowerCAmelCase_							,     renderer=lowerCAmelCase_							,     )
   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_    )	->str:
        if latents is None:
             a  :    List[str]				=						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}"""    )
             a  :    str				=						latents.to(lowerCAmelCase_    )

        a  :    Tuple				=						latents * scheduler.init_noise_sigma
        return latents
   def _lowercase(    self							,     lowerCAmelCase_=0    )	->List[Any]:
        if is_accelerate_available():
             from accelerate import cpu_offload
        else:
             raise ImportError('''Please install accelerate via `pip install accelerate`'''    )

        a  :    Dict				=						torch.device(F"""cuda:{gpu_id}"""    )

        a  :    Union[str, Any]				=						[self.text_encoder, self.prior]
        for cpu_offloaded_model in models:
             if cpu_offloaded_model is not None:
                  cpu_offload(lowerCAmelCase_							,     lowerCAmelCase_    )
   def _lowercase(    self							,     lowerCAmelCase_=0    )	->str:
        if is_accelerate_available() and is_accelerate_version('''>='''							,     '''0.17.0.dev0'''    ):
             from accelerate import cpu_offload_with_hook
        else:
             raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''    )

        a  :    Union[str, Any]				=						torch.device(F"""cuda:{gpu_id}"""    )

        if self.device.type != "cpu":
             self.to('''cpu'''							,     silence_dtype_warnings=lowerCAmelCase_    )
             torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

        a  :    Dict				=						None
        for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
             a			,							a  :    str				=						cpu_offload_with_hook(lowerCAmelCase_							,     lowerCAmelCase_							,     prev_module_hook=lowerCAmelCase_    )

        if self.safety_checker is not None:
             a			,							a  :    Optional[Any]				=						cpu_offload_with_hook(self.safety_checker							,     lowerCAmelCase_							,     prev_module_hook=lowerCAmelCase_    )

        # We'll offload the last model manually.
        a  :    Any				=						hook
   @property
   def _lowercase(    self    )	->Any:
        if self.device != torch.device('''meta'''    ) or not hasattr(self.text_encoder							,     '''_hf_hook'''    ):
             return self.device
        for module in self.text_encoder.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_							,     )	->Optional[Any]:
        len(lowerCAmelCase_    ) if isinstance(lowerCAmelCase_							,     lowerCAmelCase_    ) else 1

        # YiYi Notes: set pad_token_id to be 0, not sure why I can't set in the config file
        a  :    Dict				=						0
        # get prompt text embeddings
        a  :    Any				=						self.tokenizer(
            lowerCAmelCase_							,     padding='''max_length'''							,     max_length=self.tokenizer.model_max_length							,     truncation=lowerCAmelCase_							,     return_tensors='''pt'''							,     )
        a  :    List[str]				=						text_inputs.input_ids
        a  :    Union[str, Any]				=						self.tokenizer(lowerCAmelCase_							,     padding='''longest'''							,     return_tensors='''pt'''    ).input_ids

        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(lowerCAmelCase_							,     lowerCAmelCase_    ):
             a  :    Union[str, Any]				=						self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]    )
             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  :    Optional[int]				=						self.text_encoder(text_input_ids.to(lowerCAmelCase_    )    )
        a  :    Optional[Any]				=						text_encoder_output.text_embeds

        a  :    Any				=						prompt_embeds.repeat_interleave(lowerCAmelCase_							,     dim=0    )
        # in Shap-E it normalize the prompt_embeds and then later rescale it
        a  :    List[str]				=						prompt_embeds / torch.linalg.norm(lowerCAmelCase_							,     dim=-1							,     keepdim=lowerCAmelCase_    )

        if do_classifier_free_guidance:
             a  :    Optional[int]				=						torch.zeros_like(lowerCAmelCase_    )

             # 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  :    Dict				=						torch.cat([negative_prompt_embeds, prompt_embeds]    )

        # Rescale the features to have unit variance
        a  :    Union[str, Any]				=						math.sqrt(prompt_embeds.shape[1]    ) * prompt_embeds

        return prompt_embeds


   @torch.no_grad()
   @replace_example_docstring(lowerCAmelCase_    )
   def __call__(    self							,     lowerCAmelCase_							,     lowerCAmelCase_ = 1							,     lowerCAmelCase_ = 25							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = 4.0							,     lowerCAmelCase_ = 64							,     lowerCAmelCase_ = "pil"							,     lowerCAmelCase_ = True							,     )	->Dict:

        if isinstance(lowerCAmelCase_							,     lowerCAmelCase_    ):
             a  :    Tuple				=						1
        elif isinstance(lowerCAmelCase_							,     lowerCAmelCase_    ):
             a  :    Dict				=						len(lowerCAmelCase_    )
        else:
             raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase_    )}"""    )

        a  :    Union[str, Any]				=						self._execution_device

        a  :    List[str]				=						batch_size * num_images_per_prompt

        a  :    Optional[int]				=						guidance_scale > 1.0
        a  :    Union[str, Any]				=						self._encode_prompt(lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_    )

        # prior

        self.scheduler.set_timesteps(lowerCAmelCase_							,     device=lowerCAmelCase_    )
        a  :    str				=						self.scheduler.timesteps

        a  :    Any				=						self.prior.config.num_embeddings
        a  :    Dict				=						self.prior.config.embedding_dim

        a  :    Any				=						self.prepare_latents(
            (batch_size, num_embeddings * embedding_dim)							,     prompt_embeds.dtype							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     self.scheduler							,     )

        # YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
        a  :    Any				=						latents.reshape(latents.shape[0]							,     lowerCAmelCase_							,     lowerCAmelCase_    )

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

             a  :    Union[str, Any]				=						self.prior(
                 lowerCAmelCase_							,     timestep=lowerCAmelCase_							,     proj_embedding=lowerCAmelCase_							,     ).predicted_image_embedding

             # remove the variance
             a			,							a  :    str				=						noise_pred.split(
                 scaled_model_input.shape[2]							,     dim=2    )  # batch_size, num_embeddings, embedding_dim

             if do_classifier_free_guidance is not None:
                  a			,							a  :    Tuple				=						noise_pred.chunk(2    )
                  a  :    Tuple				=						noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

             a  :    List[Any]				=						self.scheduler.step(
                 lowerCAmelCase_							,     timestep=lowerCAmelCase_							,     sample=lowerCAmelCase_							,     ).prev_sample

        if output_type == "latent":
             return ShapEPipelineOutput(images=lowerCAmelCase_    )

        a  :    int				=						[]
        for i, latent in enumerate(lowerCAmelCase_    ):
             a  :    Dict				=						self.renderer.decode(
                 latent[None, :]							,     lowerCAmelCase_							,     size=lowerCAmelCase_							,     ray_batch_size=40_96							,     n_coarse_samples=64							,     n_fine_samples=1_28							,     )
             images.append(lowerCAmelCase_    )

        a  :    Optional[Any]				=						torch.stack(lowerCAmelCase_    )

        if output_type not in ["np", "pil"]:
             raise ValueError(F"""Only the output types `pil` and `np` are supported not output_type={output_type}"""    )

        a  :    Any				=						images.cpu().numpy()

        if output_type == "pil":
             a  :    Dict				=						[self.numpy_to_pil(lowerCAmelCase_    ) for image in images]

        # Offload last model to CPU
        if hasattr(self							,     '''final_offload_hook'''    ) and self.final_offload_hook is not None:
             self.final_offload_hook.offload()

        if not return_dict:
             return (images,)

        return ShapEPipelineOutput(images=lowerCAmelCase_    )