




import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


__magic_name__  :  int					=       False





class     SCREAMING_SNAKE_CASE__		(unittest.TestCase			):
     def        A__      (						self	:		Optional[Any]						):



       """simple docstring"""
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()
     @property
     def        A__      (						self	:		List[str]						):



       """simple docstring"""
       return 12
     @property
     def        A__      (						self	:		Any						):



       """simple docstring"""
       return 12
     @property
     def        A__      (						self	:		str						):



       """simple docstring"""
       return 32
     @property
     def        A__      (						self	:		int						):



       """simple docstring"""
       torch.manual_seed(0						)
       lowerCAmelCase__										=      VQModel(
           block_out_channels=[32, 64]       ,						in_channels=3       ,						out_channels=3       ,						down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']       ,						up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']       ,						latent_channels=3       ,						num_vq_embeddings=self.num_embed       ,						vq_embed_dim=3       ,						)
       return model
     @property
     def        A__      (						self	:		Dict						):



       """simple docstring"""
       lowerCAmelCase__										=      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)
       return tokenizer
     @property
     def        A__      (						self	:		Any						):



       """simple docstring"""
       torch.manual_seed(0						)
       lowerCAmelCase__										=      CLIPTextConfig(
           bos_token_id=0       ,						eos_token_id=2       ,						hidden_size=self.text_embedder_hidden_size       ,						intermediate_size=37       ,						layer_norm_eps=1e-05       ,						num_attention_heads=4       ,						num_hidden_layers=5       ,						pad_token_id=1       ,						vocab_size=10_00       ,						)
       return CLIPTextModel(__lowerCamelCase						)
     @property
     def        A__      (						self	:		Dict						):



       """simple docstring"""
       torch.manual_seed(0						)

       lowerCAmelCase__										=      12
       lowerCAmelCase__										=      12

       lowerCAmelCase__										=      {
           '''attention_bias''': True,
           '''cross_attention_dim''': 32,
           '''attention_head_dim''': height * width,
           '''num_attention_heads''': 1,
           '''num_vector_embeds''': self.num_embed,
           '''num_embeds_ada_norm''': self.num_embeds_ada_norm,
           '''norm_num_groups''': 32,
           '''sample_size''': width,
           '''activation_fn''': '''geglu-approximate''',
       }

       lowerCAmelCase__										=      TransformeraDModel(**__lowerCamelCase						)
       return model
     def        A__      (						self	:		Optional[Any]						):



       """simple docstring"""
       lowerCAmelCase__										=      '''cpu'''

       lowerCAmelCase__										=      self.dummy_vqvae
       lowerCAmelCase__										=      self.dummy_text_encoder
       lowerCAmelCase__										=      self.dummy_tokenizer
       lowerCAmelCase__										=      self.dummy_transformer
       lowerCAmelCase__										=      VQDiffusionScheduler(self.num_embed						)
       lowerCAmelCase__										=      LearnedClassifierFreeSamplingEmbeddings(learnable=__lowerCamelCase						)

       lowerCAmelCase__										=      VQDiffusionPipeline(
           vqvae=__lowerCamelCase       ,						text_encoder=__lowerCamelCase       ,						tokenizer=__lowerCamelCase       ,						transformer=__lowerCamelCase       ,						scheduler=__lowerCamelCase       ,						learned_classifier_free_sampling_embeddings=__lowerCamelCase       ,						)
       lowerCAmelCase__										=      pipe.to(__lowerCamelCase						)
       pipe.set_progress_bar_config(disable=__lowerCamelCase						)

       lowerCAmelCase__										=      '''teddy bear playing in the pool'''

       lowerCAmelCase__										=      torch.Generator(device=__lowerCamelCase						).manual_seed(0						)
       lowerCAmelCase__										=      pipe([prompt]       ,						generator=__lowerCamelCase       ,						num_inference_steps=2       ,						output_type='''np'''						)
       lowerCAmelCase__										=      output.images

       lowerCAmelCase__										=      torch.Generator(device=__lowerCamelCase						).manual_seed(0						)
       lowerCAmelCase__										=      pipe(
           [prompt]       ,						generator=__lowerCamelCase       ,						output_type='''np'''       ,						return_dict=__lowerCamelCase       ,						num_inference_steps=2						)[0]

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

       assert image.shape == (1, 24, 24, 3)

       lowerCAmelCase__										=      np.array([0.6551, 0.6168, 0.5008, 0.5676, 0.5659, 0.4295, 0.6073, 0.5599, 0.4992]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1e-2



     def        A__      (						self	:		List[Any]						):



       """simple docstring"""
       lowerCAmelCase__										=      '''cpu'''

       lowerCAmelCase__										=      self.dummy_vqvae
       lowerCAmelCase__										=      self.dummy_text_encoder
       lowerCAmelCase__										=      self.dummy_tokenizer
       lowerCAmelCase__										=      self.dummy_transformer
       lowerCAmelCase__										=      VQDiffusionScheduler(self.num_embed						)
       lowerCAmelCase__										=      LearnedClassifierFreeSamplingEmbeddings(
           learnable=__lowerCamelCase       ,						hidden_size=self.text_embedder_hidden_size       ,						length=tokenizer.model_max_length						)

       lowerCAmelCase__										=      VQDiffusionPipeline(
           vqvae=__lowerCamelCase       ,						text_encoder=__lowerCamelCase       ,						tokenizer=__lowerCamelCase       ,						transformer=__lowerCamelCase       ,						scheduler=__lowerCamelCase       ,						learned_classifier_free_sampling_embeddings=__lowerCamelCase       ,						)
       lowerCAmelCase__										=      pipe.to(__lowerCamelCase						)
       pipe.set_progress_bar_config(disable=__lowerCamelCase						)

       lowerCAmelCase__										=      '''teddy bear playing in the pool'''

       lowerCAmelCase__										=      torch.Generator(device=__lowerCamelCase						).manual_seed(0						)
       lowerCAmelCase__										=      pipe([prompt]       ,						generator=__lowerCamelCase       ,						num_inference_steps=2       ,						output_type='''np'''						)
       lowerCAmelCase__										=      output.images

       lowerCAmelCase__										=      torch.Generator(device=__lowerCamelCase						).manual_seed(0						)
       lowerCAmelCase__										=      pipe(
           [prompt]       ,						generator=__lowerCamelCase       ,						output_type='''np'''       ,						return_dict=__lowerCamelCase       ,						num_inference_steps=2						)[0]

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

       assert image.shape == (1, 24, 24, 3)

       lowerCAmelCase__										=      np.array([0.6693, 0.6075, 0.4959, 0.5701, 0.5583, 0.4333, 0.6171, 0.5684, 0.4988]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 2.0
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1e-2





@slow
@require_torch_gpu
class     SCREAMING_SNAKE_CASE__		(unittest.TestCase			):
     def        A__      (						self	:		Union[str, Any]						):



       """simple docstring"""
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()



     def        A__      (						self	:		Any						):



       """simple docstring"""
       lowerCAmelCase__										=      load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy'''						)

       lowerCAmelCase__										=      VQDiffusionPipeline.from_pretrained('''microsoft/vq-diffusion-ithq'''						)
       lowerCAmelCase__										=      pipeline.to(__lowerCamelCase						)
       pipeline.set_progress_bar_config(disable=__lowerCamelCase						)

       # requires GPU generator for gumbel softmax
       # don't use GPU generator in tests though
       lowerCAmelCase__										=      torch.Generator(device=__lowerCamelCase						).manual_seed(0						)
       lowerCAmelCase__										=      pipeline(
           '''teddy bear playing in the pool'''       ,						num_images_per_prompt=1       ,						generator=__lowerCamelCase       ,						output_type='''np'''       ,						)

       lowerCAmelCase__										=      output.images[0]

       assert image.shape == (2_56, 2_56, 3)
       assert np.abs(expected_image - image						).max() < 2.0







import argparse
import json
import os
import time
import zipfile

from get_ci_error_statistics import download_artifact, get_artifacts_links

from transformers import logging


__magic_name__  :  List[Any]					=       logging.get_logger(__name__)
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase				):
  lowerCAmelCase__										=      set()
  lowerCAmelCase__										=      []

  def parse_line(__lowerCAmelCase				):
    for line in fp:
      if isinstance(__lowerCAmelCase   , __lowerCAmelCase				):
        lowerCAmelCase__										=      line.decode('''UTF-8'''				)
      if "warnings summary (final)" in line:
        continue
      # This means we are outside the body of a warning
      elif not line.startswith(''' '''				):
        # process a single warning and move it to `selected_warnings`.
        if len(__lowerCAmelCase				) > 0:
          lowerCAmelCase__										=      '''\n'''.join(__lowerCAmelCase				)
          # Only keep the warnings specified in `targets`
          if any(F""": {x}: """ in warning for x in targets				):
            selected_warnings.add(__lowerCAmelCase				)
          buffer.clear()
        continue
      else:
        lowerCAmelCase__										=      line.strip()
        buffer.append(__lowerCAmelCase				)

  if from_gh:
    for filename in os.listdir(__lowerCAmelCase				):
      lowerCAmelCase__										=      os.path.join(__lowerCAmelCase   , __lowerCAmelCase				)
      if not os.path.isdir(__lowerCAmelCase				):
        # read the file
        if filename != "warnings.txt":
          continue
        with open(__lowerCAmelCase				) as fp:
          parse_line(__lowerCAmelCase				)
  else:
    try:
      with zipfile.ZipFile(__lowerCAmelCase				) as z:
        for filename in z.namelist():
          if not os.path.isdir(__lowerCAmelCase				):
            # read the file
            if filename != "warnings.txt":
              continue
            with z.open(__lowerCAmelCase				) as fp:
              parse_line(__lowerCAmelCase				)
    except Exception:
      logger.warning(
          F"""{artifact_path} is either an invalid zip file or something else wrong. This file is skipped."""				)

  return selected_warnings
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase				):
  lowerCAmelCase__										=      set()

  lowerCAmelCase__										=      [os.path.join(__lowerCAmelCase   , __lowerCAmelCase				) for p in os.listdir(__lowerCAmelCase				) if (p.endswith('''.zip'''				) or from_gh)]
  for p in paths:
    selected_warnings.update(extract_warnings_from_single_artifact(__lowerCAmelCase   , __lowerCAmelCase				)				)

  return selected_warnings


if __name__ == "__main__":
      def 		a_							( __lowerCAmelCase				):
        return values.split(''','''				)

      __magic_name__  :  Optional[int]					=       argparse.ArgumentParser()
      # Required parameters
      parser.add_argument("""--workflow_run_id""", type=str, required=True, help="""A GitHub Actions workflow run id.""")
      parser.add_argument(
          """--output_dir""",
          type=str,
          required=True,
          help="""Where to store the downloaded artifacts and other result files.""",
      )
      parser.add_argument("""--token""", default=None, type=str, help="""A token that has actions:read permission.""")
      # optional parameters
      parser.add_argument(
          """--targets""",
          default="""DeprecationWarning,UserWarning,FutureWarning""",
          type=list_str,
          help="""Comma-separated list of target warning(s) which we want to extract.""",
      )
      parser.add_argument(
          """--from_gh""",
          action="""store_true""",
          help="""If running from a GitHub action workflow and collecting warnings from its artifacts.""",
      )

      __magic_name__  :  Optional[Any]					=       parser.parse_args()

      __magic_name__  :  Optional[int]					=       args.from_gh
      if from_gh:
            # The artifacts have to be downloaded using `actions/download-artifact@v3`
            pass
      else:
            os.makedirs(args.output_dir, exist_ok=True)

            # get download links
            __magic_name__  :  Optional[Any]					=       get_artifacts_links(args.workflow_run_id, token=args.token)
            with open(os.path.join(args.output_dir, """artifacts.json"""), """w""", encoding="""UTF-8""") as fp:
                  json.dump(artifacts, fp, ensure_ascii=False, indent=4)

            # download artifacts
            for idx, (name, url) in enumerate(artifacts.items()):
                  print(name)
                  print(url)
                  print("""=""" * 80)
                  download_artifact(name, url, args.output_dir, args.token)
                  # Be gentle to GitHub
                  time.sleep(1)

    # extract warnings from artifacts
      __magic_name__  :  Optional[Any]					=       extract_warnings(args.output_dir, args.targets)
      __magic_name__  :  Any					=       sorted(selected_warnings)
      with open(os.path.join(args.output_dir, """selected_warnings.json"""), """w""", encoding="""UTF-8""") as fp:
            json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)

