

import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class    UpperCamelCase_			(  _snake_case		,     unittest.TestCase			):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__				: str										=      KandinskyPipeline
  SCREAMING_SNAKE_CASE__				: List[Any]										=      [
      """prompt""",
      """image_embeds""",
      """negative_image_embeds""",
  ]
  SCREAMING_SNAKE_CASE__				: str										=      ["""prompt""", """negative_prompt""", """image_embeds""", """negative_image_embeds"""]
  SCREAMING_SNAKE_CASE__				: Any										=      [
      """generator""",
      """height""",
      """width""",
      """latents""",
      """guidance_scale""",
      """negative_prompt""",
      """num_inference_steps""",
      """return_dict""",
      """guidance_scale""",
      """num_images_per_prompt""",
      """output_type""",
      """return_dict""",
  ]
  SCREAMING_SNAKE_CASE__				: Any										=      False



  @property
  def      _UpperCamelCase    (	self    :				Optional[Any]	)					-> Optional[int]:
      return 32



  @property
  def      _UpperCamelCase    (	self    :				int	)					-> Optional[Any]:
      return 32



  @property
  def      _UpperCamelCase    (	self    :				str	)					-> Dict:
      return self.time_input_dim



  @property
  def      _UpperCamelCase    (	self    :				Union[str, Any]	)					-> Union[str, Any]:
      return self.time_input_dim * 4



  @property
  def      _UpperCamelCase    (	self    :				str	)					-> List[Any]:
      return 100



  @property
  def      _UpperCamelCase    (	self    :				Optional[Any]	)					-> List[Any]:
      SCREAMING_SNAKE_CASE__					     =						XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"	)
      return tokenizer



  @property
  def      _UpperCamelCase    (	self    :				int	)					-> Dict:
      torch.manual_seed(0	)
      SCREAMING_SNAKE_CASE__					     =						MCLIPConfig(
          numDims=self.cross_attention_dim     ,  transformerDimensions=self.text_embedder_hidden_size     ,  hidden_size=self.text_embedder_hidden_size     ,  intermediate_size=37     ,  num_attention_heads=4     ,  num_hidden_layers=5     ,  vocab_size=1005     ,  )

      SCREAMING_SNAKE_CASE__					     =						MultilingualCLIP(A_	)
      SCREAMING_SNAKE_CASE__					     =						text_encoder.eval()

      return text_encoder



  @property
  def      _UpperCamelCase    (	self    :				Union[str, Any]	)					-> Optional[Any]:
      torch.manual_seed(0	)

      SCREAMING_SNAKE_CASE__					     =						{
          "in_channels": 4,
          # Out channels is double in channels because predicts mean and variance
          "out_channels": 8,
          "addition_embed_type": "text_image",
          "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
          "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
          "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
          "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
          "layers_per_block": 1,
          "encoder_hid_dim": self.text_embedder_hidden_size,
          "encoder_hid_dim_type": "text_image_proj",
          "cross_attention_dim": self.cross_attention_dim,
          "attention_head_dim": 4,
          "resnet_time_scale_shift": "scale_shift",
          "class_embed_type": None,
      }

      SCREAMING_SNAKE_CASE__					     =						UNetaDConditionModel(**A_	)
      return model



  @property
  def      _UpperCamelCase    (	self    :				Any	)					-> Dict:
      return {
          "block_out_channels": [32, 64],
          "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
          "in_channels": 3,
          "latent_channels": 4,
          "layers_per_block": 1,
          "norm_num_groups": 8,
          "norm_type": "spatial",
          "num_vq_embeddings": 12,
          "out_channels": 3,
          "up_block_types": [
              "AttnUpDecoderBlock2D",
              "UpDecoderBlock2D",
          ],
          "vq_embed_dim": 4,
      }



  @property
  def      _UpperCamelCase    (	self    :				Union[str, Any]	)					-> Optional[Any]:
      torch.manual_seed(0	)
      SCREAMING_SNAKE_CASE__					     =						VQModel(**self.dummy_movq_kwargs	)
      return model



  def      _UpperCamelCase    (	self    :				List[str]	)					-> List[Any]:
      SCREAMING_SNAKE_CASE__					     =						self.dummy_text_encoder
      SCREAMING_SNAKE_CASE__					     =						self.dummy_tokenizer
      SCREAMING_SNAKE_CASE__					     =						self.dummy_unet
      SCREAMING_SNAKE_CASE__					     =						self.dummy_movq

      SCREAMING_SNAKE_CASE__					     =						DDIMScheduler(
          num_train_timesteps=1000     ,  beta_schedule="linear"     ,  beta_start=0.00_085     ,  beta_end=0.012     ,  clip_sample=A_     ,  set_alpha_to_one=A_     ,  steps_offset=1     ,  prediction_type="epsilon"     ,  thresholding=A_     ,  )

      SCREAMING_SNAKE_CASE__					     =						{
          "text_encoder": text_encoder,
          "tokenizer": tokenizer,
          "unet": unet,
          "scheduler": scheduler,
          "movq": movq,
      }
      return components



  def      _UpperCamelCase    (	self    :				str     ,  A_    :				Optional[int]     ,  A_    :				List[Any]=0	)					-> Any:
      SCREAMING_SNAKE_CASE__					     =						floats_tensor((1, self.cross_attention_dim)     ,  rng=random.Random(A_	)	).to(A_	)
      SCREAMING_SNAKE_CASE__					     =						floats_tensor((1, self.cross_attention_dim)     ,  rng=random.Random(seed + 1	)	).to(A_	)
      if str(A_	).startswith("mps"	):
          SCREAMING_SNAKE_CASE__					     =						torch.manual_seed(A_	)
      else:
          SCREAMING_SNAKE_CASE__					     =						torch.Generator(device=A_	).manual_seed(A_	)
      SCREAMING_SNAKE_CASE__					     =						{
          "prompt": "horse",
          "image_embeds": image_embeds,
          "negative_image_embeds": negative_image_embeds,
          "generator": generator,
          "height": 64,
          "width": 64,
          "guidance_scale": 4.0,
          "num_inference_steps": 2,
          "output_type": "np",
      }
      return inputs







  def      _UpperCamelCase    (	self    :				Dict	)					-> int:
      SCREAMING_SNAKE_CASE__					     =						"cpu"

      SCREAMING_SNAKE_CASE__					     =						self.get_dummy_components()

      SCREAMING_SNAKE_CASE__					     =						self.pipeline_class(**A_	)
      SCREAMING_SNAKE_CASE__					     =						pipe.to(A_	)

      pipe.set_progress_bar_config(disable=A_	)

      SCREAMING_SNAKE_CASE__					     =						pipe(**self.get_dummy_inputs(A_	)	)
      SCREAMING_SNAKE_CASE__					     =						output.images

      SCREAMING_SNAKE_CASE__					     =						pipe(
          **self.get_dummy_inputs(A_	)     ,  return_dict=A_     ,  )[0]

      SCREAMING_SNAKE_CASE__					     =						image[0, -3:, -3:, -1]
      SCREAMING_SNAKE_CASE__					     =						image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)

      SCREAMING_SNAKE_CASE__					     =						np.array(
          [0.328_663, 1.0, 0.23_216_873, 1.0, 0.92_717_564, 0.4_639_046, 0.96_894_777, 0.31_713_378, 0.6_293_953]	)

      assert (
          np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2
      ), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
      assert (
          np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1E-2
      ), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"







@slow
@require_torch_gpu
class    UpperCamelCase_			(  unittest.TestCase			):


  """simple docstring"""



  def      _UpperCamelCase    (	self    :				str	)					-> Any:
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()







  def      _UpperCamelCase    (	self    :				Optional[int]	)					-> Optional[int]:
      SCREAMING_SNAKE_CASE__					     =						load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/kandinsky/kandinsky_text2img_cat_fp16.npy"	)

      SCREAMING_SNAKE_CASE__					     =						KandinskyPriorPipeline.from_pretrained(
          "kandinsky-community/kandinsky-2-1-prior"     ,  torch_dtype=torch.floataa	)
      pipe_prior.to(A_	)

      SCREAMING_SNAKE_CASE__					     =						KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1"     ,  torch_dtype=torch.floataa	)
      SCREAMING_SNAKE_CASE__					     =						pipeline.to(A_	)
      pipeline.set_progress_bar_config(disable=A_	)

      SCREAMING_SNAKE_CASE__					     =						"red cat, 4k photo"

      SCREAMING_SNAKE_CASE__					     =						torch.Generator(device="cuda"	).manual_seed(0	)
      SCREAMING_SNAKE_CASE__     ,		SCREAMING_SNAKE_CASE__					     =						pipe_prior(
          A_     ,  generator=A_     ,  num_inference_steps=5     ,  negative_prompt=""     ,  ).to_tuple()

      SCREAMING_SNAKE_CASE__					     =						torch.Generator(device="cuda"	).manual_seed(0	)
      SCREAMING_SNAKE_CASE__					     =						pipeline(
          A_     ,  image_embeds=A_     ,  negative_image_embeds=A_     ,  generator=A_     ,  num_inference_steps=100     ,  output_type="np"     ,  )

      SCREAMING_SNAKE_CASE__					     =						output.images[0]

      assert image.shape == (512, 512, 3)

      assert_mean_pixel_difference(A_     ,  A_	)





import argparse
import json
import re
import string
import sys
from collections import Counter





def 							__a       (					__A	):

    def remove_articles(__A	):
        return re.sub(r"\b(a|an|the)\b" ,       " " ,       __A	)

    def white_space_fix(__A	):
        return " ".join(text.split()	)

    def remove_punc(__A	):
        SCREAMING_SNAKE_CASE__					     =						set(string.punctuation	)
        return "".join(ch for ch in text if ch not in exclude	)

    def lower(__A	):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(__A	)	)	)	)





def 							__a       (					__A ,       __A	):
    SCREAMING_SNAKE_CASE__					     =						normalize_answer(__A	).split()
    SCREAMING_SNAKE_CASE__					     =						normalize_answer(__A	).split()
    SCREAMING_SNAKE_CASE__					     =						Counter(__A	) & Counter(__A	)
    SCREAMING_SNAKE_CASE__					     =						sum(common.values()	)
    if num_same == 0:
        return 0
    SCREAMING_SNAKE_CASE__					     =						1.0 * num_same / len(__A	)
    SCREAMING_SNAKE_CASE__					     =						1.0 * num_same / len(__A	)
    SCREAMING_SNAKE_CASE__					     =						(2 * precision * recall) / (precision + recall)
    return fa





def 							__a       (					__A ,       __A	):
    return normalize_answer(__A	) == normalize_answer(__A	)





def 							__a       (					__A ,       __A ,       __A	):
    SCREAMING_SNAKE_CASE__					     =						[]
    for ground_truth in ground_truths:
        SCREAMING_SNAKE_CASE__					     =						metric_fn(__A ,       __A	)
        scores_for_ground_truths.append(__A	)
    return max(__A	)





def 							__a       (					__A ,       __A	):
    SCREAMING_SNAKE_CASE__					     =						SCREAMING_SNAKE_CASE__					     =						SCREAMING_SNAKE_CASE__					     =						0
    SCREAMING_SNAKE_CASE__					     =						[]
    for passage in dataset:
        for qa in passage["qas"]:
            total += 1
            if qa["id"] not in predictions:
                SCREAMING_SNAKE_CASE__					     =						f"Unanswered question {qa['id']} will receive score 0."
                print(__A ,       file=sys.stderr	)
                continue

            SCREAMING_SNAKE_CASE__					     =						[x["text"] for x in qa["answers"]]
            SCREAMING_SNAKE_CASE__					     =						predictions[qa["id"]]

            SCREAMING_SNAKE_CASE__					     =						metric_max_over_ground_truths(__A ,       __A ,       __A	)
            if int(_exact_match	) == 1:
                correct_ids.append(qa["id"]	)
            exact_match += _exact_match

            fa += metric_max_over_ground_truths(__A ,       __A ,       __A	)

    SCREAMING_SNAKE_CASE__					     =						exact_match / total
    SCREAMING_SNAKE_CASE__					     =						fa / total

    return {"exact_match": exact_match, "f1": fa}, correct_ids


if __name__ == "__main__":
  A__        =			"1.0"
  A__        =			argparse.ArgumentParser("Official evaluation script for ReCoRD v1.0.")
  parser.add_argument("data_file", help="The dataset file in JSON format.")
  parser.add_argument("pred_file", help="The model prediction file in JSON format.")
  parser.add_argument("--output_correct_ids", action="store_true", help="Output the correctly answered query IDs.")
  A__        =			parser.parse_args()

  with open(args.data_file) as data_file:
    A__        =			json.load(data_file)
    if dataset_json["version"] != expected_version:
      print(
          f'''Evaluation expects v-{expected_version}, but got dataset with v-{dataset_json['version']}''',
          file=sys.stderr,
      )
    A__        =			dataset_json["data"]

  with open(args.pred_file) as pred_file:
    A__        =			json.load(pred_file)

  A__       ,    A__        =			evaluate(dataset, predictions)

  if args.output_correct_ids:
    print(f'''Output {len(correct_ids)} correctly answered question IDs.''')
    with open("correct_ids.json", "w") as f:
      json.dump(correct_ids, f)


