


from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


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

__snake_case             =	'''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)["depth"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline("depth-estimation")

        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to("cuda")

        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")

        >>> img = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/cat.png"
        ... ).resize((768, 768))


        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")

        >>> prompt = "A robot, 4k photo"
        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"

        >>> generator = torch.Generator(device="cuda").manual_seed(43)

        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

        >>> images = pipe(
        ...     image=img,
        ...     strength=0.5,
        ...     image_embeds=img_emb.image_embeds,
        ...     negative_image_embeds=negative_emb.image_embeds,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save("robot_cat.png")
        ```
'''






def 		_A		(		_lowercase				,     _lowercase				,     _lowercase=8   )				->   List[Any]:


     """simple docstring"""


     __UpperCamelCase   = height // scale_factor**2
     if height % scale_factor**2 != 0:
          new_height += 1
     __UpperCamelCase   = width // scale_factor**2
     if width % scale_factor**2 != 0:
          new_width += 1
     return new_height * scale_factor, new_width * scale_factor






def 		_A		(		_lowercase				,     _lowercase=5_12				,     _lowercase=5_12   )				->   Tuple:


     """simple docstring"""


     __UpperCamelCase   = pil_image.resize((w, h)				,     resample=Image.BICUBIC				,     reducing_gap=1   )
     __UpperCamelCase   = np.array(pil_image.convert('RGB'   )   )
     __UpperCamelCase   = arr.astype(np.floataa   ) / 1_27.5 - 1
     __UpperCamelCase   = np.transpose(_lowercase				,     [2, 0, 1]   )
     __UpperCamelCase   = torch.from_numpy(_lowercase   ).unsqueeze(0   )
     return image



class   __lowerCamelCase (_a	):







       def __init__( self:   Union[str, Any],A_:   UNetaDConditionModel,A_:   DDPMScheduler,A_:   VQModel,):






            '''simple docstring'''




            super().__init__()

            self.register_modules(
                unet=A_,scheduler=A_,movq=A_,)
            __UpperCamelCase   = 2 ** (len(self.movq.config.block_out_channels				) - 1)







       def 		snake_case_  ( self:   int,A_:   List[Any],A_:   Optional[Any],A_:   Dict				):






            '''simple docstring'''




            __UpperCamelCase   = min(int(num_inference_steps * strength				),A_				)

            __UpperCamelCase   = max(num_inference_steps - init_timestep,0				)
            __UpperCamelCase   = self.scheduler.timesteps[t_start:]

            return timesteps, num_inference_steps - t_start







       def 		snake_case_  ( self:   Optional[int],A_:   Optional[int],A_:   Tuple,A_:   Optional[Any],A_:   Any,A_:   Union[str, Any],A_:   int,A_:   Optional[Any]=None				):






            '''simple docstring'''




            if not isinstance(A_,(torch.Tensor, PIL.Image.Image, list)				):
                 raise ValueError(
                     F'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(A_				)}'''				)

            __UpperCamelCase   = image.to(device=A_,dtype=A_				)

            __UpperCamelCase   = batch_size * num_images_per_prompt

            if image.shape[1] == 4:
                 __UpperCamelCase   = image

            else:
                 if isinstance(A_,A_				) and len(A_				) != batch_size:
                      raise ValueError(
                          F'''You have passed a list of generators of length {len(A_				)}, but requested an effective batch'''
                          F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''				)

                 elif isinstance(A_,A_				):
                      __UpperCamelCase   = [
                          self.movq.encode(image[i : i + 1]				).latent_dist.sample(generator[i]				) for i in range(A_				)
                      ]
                      __UpperCamelCase   = torch.cat(A_,dim=0				)
                 else:
                      __UpperCamelCase   = self.movq.encode(A_				).latent_dist.sample(A_				)

                 __UpperCamelCase   = self.movq.config.scaling_factor * init_latents

            __UpperCamelCase   = torch.cat([init_latents],dim=0				)

            __UpperCamelCase   = init_latents.shape
            __UpperCamelCase   = randn_tensor(A_,generator=A_,device=A_,dtype=A_				)

            # get latents
            __UpperCamelCase   = self.scheduler.add_noise(A_,A_,A_				)

            __UpperCamelCase   = init_latents

            return latents







       def 		snake_case_  ( self:   List[str],A_:   Dict=0				):






            '''simple docstring'''




            if is_accelerate_available():
                 from accelerate import cpu_offload
            else:
                 raise ImportError('Please install accelerate via `pip install accelerate`'				)

            __UpperCamelCase   = torch.device(F'''cuda:{gpu_id}'''				)

            __UpperCamelCase   = [
                self.unet,
                self.movq,
            ]
            for cpu_offloaded_model in models:
                 if cpu_offloaded_model is not None:
                      cpu_offload(A_,A_				)







       def 		snake_case_  ( self:   Dict,A_:   Tuple=0				):






            '''simple docstring'''




            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.'				)

            __UpperCamelCase   = torch.device(F'''cuda:{gpu_id}'''				)

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

            __UpperCamelCase   = None
            for cpu_offloaded_model in [self.unet, self.movq]:
                 __UpperCamelCase,						__UpperCamelCase   = cpu_offload_with_hook(A_,A_,prev_module_hook=A_				)

            # We'll offload the last model manually.
            __UpperCamelCase   = hook







       @property
       # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
       def 		snake_case_  ( self:   List[Any]				):






            '''simple docstring'''




            if not hasattr(self.unet,'_hf_hook'				):
                 return self.device
            for module in self.unet.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







       @torch.no_grad()
       @replace_example_docstring(A_				)
       def __call__( self:   Any,A_:   Union[torch.FloatTensor, List[torch.FloatTensor]],A_:   Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]],A_:   Union[torch.FloatTensor, List[torch.FloatTensor]],A_:   torch.FloatTensor,A_:   int = 512,A_:   int = 512,A_:   int = 100,A_:   float = 4.0,A_:   float = 0.3,A_:   int = 1,A_:   Optional[Union[torch.Generator, List[torch.Generator]]] = None,A_:   Optional[str] = "pil",A_:   bool = True,):






            '''simple docstring'''




            __UpperCamelCase   = self._execution_device

            __UpperCamelCase   = guidance_scale > 1.0

            if isinstance(A_,A_				):
                 __UpperCamelCase   = torch.cat(A_,dim=0				)
            if isinstance(A_,A_				):
                 __UpperCamelCase   = torch.cat(A_,dim=0				)
            if isinstance(A_,A_				):
                 __UpperCamelCase   = torch.cat(A_,dim=0				)

            __UpperCamelCase   = image_embeds.shape[0]

            if do_classifier_free_guidance:
                 __UpperCamelCase   = image_embeds.repeat_interleave(A_,dim=0				)
                 __UpperCamelCase   = negative_image_embeds.repeat_interleave(A_,dim=0				)
                 __UpperCamelCase   = hint.repeat_interleave(A_,dim=0				)

            __UpperCamelCase   = torch.cat([negative_image_embeds, image_embeds],dim=0				).to(dtype=self.unet.dtype,device=A_				)
            __UpperCamelCase   = torch.cat([hint, hint],dim=0				).to(dtype=self.unet.dtype,device=A_				)

            if not isinstance(A_,A_				):
                 __UpperCamelCase   = [image]
            if not all(isinstance(A_,(PIL.Image.Image, torch.Tensor)				) for i in image				):
                 raise ValueError(
                     F'''Input is in incorrect format: {[type(A_				) for i in image]}. Currently, we only support  PIL image and pytorch tensor'''				)

            __UpperCamelCase   = torch.cat([prepare_image(A_,A_,A_				) for i in image],dim=0				)
            __UpperCamelCase   = image.to(dtype=image_embeds.dtype,device=A_				)

            __UpperCamelCase   = self.movq.encode(A_				)['latents']
            __UpperCamelCase   = latents.repeat_interleave(A_,dim=0				)
            self.scheduler.set_timesteps(A_,device=A_				)
            __UpperCamelCase,						__UpperCamelCase   = self.get_timesteps(A_,A_,A_				)
            __UpperCamelCase   = timesteps[:1].repeat(batch_size * num_images_per_prompt				)
            __UpperCamelCase,						__UpperCamelCase   = downscale_height_and_width(A_,A_,self.movq_scale_factor				)
            __UpperCamelCase   = self.prepare_latents(
                A_,A_,A_,A_,image_embeds.dtype,A_,A_				)
            for i, t in enumerate(self.progress_bar(A_				)				):
                 # expand the latents if we are doing classifier free guidance
                 __UpperCamelCase   = torch.cat([latents] * 2				) if do_classifier_free_guidance else latents

                 __UpperCamelCase   = {'image_embeds': image_embeds, 'hint': hint}
                 __UpperCamelCase   = self.unet(
                     sample=A_,timestep=A_,encoder_hidden_states=A_,added_cond_kwargs=A_,return_dict=A_,)[0]

                 if do_classifier_free_guidance:
                      __UpperCamelCase,						__UpperCamelCase   = noise_pred.split(latents.shape[1],dim=1				)
                      __UpperCamelCase,						__UpperCamelCase   = noise_pred.chunk(2				)
                      __UpperCamelCase,						__UpperCamelCase   = variance_pred.chunk(2				)
                      __UpperCamelCase   = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                      __UpperCamelCase   = torch.cat([noise_pred, variance_pred_text],dim=1				)

                 if not (
                     hasattr(self.scheduler.config,'variance_type'				)
                     and self.scheduler.config.variance_type in ["learned", "learned_range"]
                 ):
                      __UpperCamelCase,						__UpperCamelCase   = noise_pred.split(latents.shape[1],dim=1				)

                 # compute the previous noisy sample x_t -> x_t-1

                 __UpperCamelCase   = self.scheduler.step(
                     A_,A_,A_,generator=A_,)[0]

            # post-processing
            __UpperCamelCase   = self.movq.decode(A_,force_not_quantize=A_				)['sample']

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

            if output_type in ["np", "pil"]:
                 __UpperCamelCase   = image * 0.5 + 0.5
                 __UpperCamelCase   = image.clamp(0,1				)
                 __UpperCamelCase   = image.cpu().permute(0,2,3,1				).float().numpy()

            if output_type == "pil":
                 __UpperCamelCase   = self.numpy_to_pil(A_				)

            if not return_dict:
                 return (image,)

            return ImagePipelineOutput(images=A_				)






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

from transformers import BigBirdTokenizerFast


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






def 		_A		(		_lowercase				,     _lowercase=0				,     _lowercase=None   )				->   Union[str, Any]:


     """simple docstring"""


     return [" ".join(x.split(' '   )[begin:end]   ) for x in answers if len(x.split(' '   )   ) > 1]






def 		_A		(		_lowercase				,     _lowercase=False   )				->   Tuple:


     """simple docstring"""


     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"
          __UpperCamelCase   = (
              given_answers + get_sub_answers(_lowercase				,     begin=1   ) + get_sub_answers(_lowercase				,     end=-1   )
          )
     __UpperCamelCase   = []
     for answer in given_answers:
          __UpperCamelCase   = answer.replace('_'				,     ' '   ).lower()
          __UpperCamelCase   = ''.join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else ' ' for c in alias   )
          answers.append(' '.join(alias.split()   ).strip()   )
     return set(_lowercase   )






def 		_A		(		_lowercase				,     _lowercase				,     _lowercase=1				,     _lowercase=1_00   )				->   Any:


     """simple docstring"""


     __UpperCamelCase,						__UpperCamelCase   = jax.lax.top_k(_lowercase				,     _lowercase   )
     __UpperCamelCase,						__UpperCamelCase   = jax.lax.top_k(_lowercase				,     _lowercase   )

     __UpperCamelCase   = best_end_idx[:, None] - best_start_idx[None, :]
     __UpperCamelCase   = jnp.logical_or(widths < 0				,     widths > max_size   )
     __UpperCamelCase   = (best_end_scores[:, None] + best_start_scores[None, :]) - (1e8 * mask)
     __UpperCamelCase   = jnp.argmax(_lowercase   ).item()

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






def 		_A		(		_lowercase   )				->   List[str]:


     """simple docstring"""


     __UpperCamelCase   = sample['question']['text']
     __UpperCamelCase   = sample['document']['tokens']['token']
     __UpperCamelCase   = sample['document']['tokens']['is_html']
     __UpperCamelCase   = sample['annotations']['long_answer']
     __UpperCamelCase   = sample['annotations']['short_answers']

     __UpperCamelCase   = ' '.join([context[i] for i in range(len(_lowercase   )   ) 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",
               }

     __UpperCamelCase   = []
     for s in short_answers:
          short_targets.extend(s['text']   )
     __UpperCamelCase   = list(set(_lowercase   )   )

     __UpperCamelCase   = []
     for s in long_answers:
          if s["start_token"] == -1:
               continue
          __UpperCamelCase   = context[s['start_token'] : s['end_token']]
          __UpperCamelCase   = is_html[s['start_token'] : s['end_token']]
          __UpperCamelCase   = ' '.join([answer[i] for i in range(len(_lowercase   )   ) if not html[i]]   )
          if new_answer not in long_targets:
               long_targets.append(_lowercase   )

     __UpperCamelCase   = '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 		_A		(		)				->   Optional[int]:


     """simple docstring"""


     __UpperCamelCase   = load_from_disk('natural-questions-validation'   )
     __UpperCamelCase   = dataset.map(_lowercase   ).remove_columns(['annotations', 'document', 'id']   )
     print(_lowercase   )

     __UpperCamelCase   = dataset.filter(lambda _lowercase   : (len(x['question']   ) + len(x['context']   )) < 4 * 40_96   )
     __UpperCamelCase   = short_validation_dataset.filter(lambda _lowercase   : x["category"] != "null"   )
     short_validation_dataset

     __UpperCamelCase   = 'vasudevgupta/flax-bigbird-natural-questions'
     __UpperCamelCase   = FlaxBigBirdForNaturalQuestions.from_pretrained(_lowercase   )
     __UpperCamelCase   = BigBirdTokenizerFast.from_pretrained(_lowercase   )

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

     def evaluate(_lowercase   ):
          # encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
          __UpperCamelCase   = tokenizer(
              example['question']				,     example['context']				,     return_tensors='np'				,     max_length=40_96				,     padding='max_length'				,     truncation=_lowercase				,     )

          __UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase   = forward(**_lowercase   )

          __UpperCamelCase   = CATEGORY_MAPPING[category.item()]

          __UpperCamelCase   = example['long'] + example['short']
          if example["category"] in ["yes", "no", "null"]:
               __UpperCamelCase   = [example['category']]
          __UpperCamelCase   = example['category'] != 'null'
          # Now target can be: "yes", "no", "null", "list of long & short answers"

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

          __UpperCamelCase   = 38 if predicted_category == 'short' else 10_24
          __UpperCamelCase,						__UpperCamelCase   = get_best_valid_start_end_idx(
              start_scores[0]				,     end_scores[0]				,     top_k=8				,     max_size=_lowercase   )

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

          __UpperCamelCase   = expand_to_aliases(example['targets']				,     make_sub_answers=_lowercase   )
          __UpperCamelCase   = expand_to_aliases(example['output']   )

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

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

          return example

     __UpperCamelCase   = short_validation_dataset.map(_lowercase   )

     __UpperCamelCase   = len(_lowercase   )
     __UpperCamelCase   = len(short_validation_dataset.filter(lambda _lowercase   : x["match"] == 1   )   )
     print('EM score:'				,     (matched / total) * 1_00				,     '%'   )


if __name__ == "__main__":
    main()


