
"""simple docstring"""

import gc
import unittest

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

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class   _UpperCAmelCase				(		UpperCAmelCase__  ,      unittest.TestCase				):
      __SCREAMING_SNAKE_CASE :				Optional[int]				   =						ShapEPipeline
      __SCREAMING_SNAKE_CASE :				Any				   =						["prompt"]
      __SCREAMING_SNAKE_CASE :				Dict				   =						["prompt"]
      __SCREAMING_SNAKE_CASE :				Optional[int]				   =						[
          "num_images_per_prompt",
          "num_inference_steps",
          "generator",
          "latents",
          "guidance_scale",
          "frame_size",
          "output_type",
          "return_dict",
      ]
      __SCREAMING_SNAKE_CASE :				int				   =						False




      @property
      def      a_   (				self				)    ->       List[Any]:
          return 3_2




      @property
      def      a_   (				self				)    ->       int:
          return 3_2




      @property
      def      a_   (				self				)    ->       Optional[Any]:
          return self.time_input_dim * 4




      @property
      def      a_   (				self				)    ->       str:
          return 8




      @property
      def      a_   (				self				)    ->       Optional[int]:
          UpperCAmelCase          =       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				)
          return tokenizer




      @property
      def      a_   (				self				)    ->       List[Any]:
          torch.manual_seed(0				)
          UpperCAmelCase          =       CLIPTextConfig(
              bos_token_id=0 ,							eos_token_id=2 ,							hidden_size=self.text_embedder_hidden_size ,							projection_dim=self.text_embedder_hidden_size ,							intermediate_size=3_7 ,							layer_norm_eps=1E-05 ,							num_attention_heads=4 ,							num_hidden_layers=5 ,							pad_token_id=1 ,							vocab_size=1_0_0_0 ,							)
          return CLIPTextModelWithProjection(UpperCAmelCase__				)




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

          UpperCAmelCase          =       {
              'num_attention_heads': 2,
              'attention_head_dim': 1_6,
              'embedding_dim': self.time_input_dim,
              'num_embeddings': 3_2,
              'embedding_proj_dim': self.text_embedder_hidden_size,
              'time_embed_dim': self.time_embed_dim,
              'num_layers': 1,
              'clip_embed_dim': self.time_input_dim * 2,
              'additional_embeddings': 0,
              'time_embed_act_fn': 'gelu',
              'norm_in_type': 'layer',
              'encoder_hid_proj_type': None,
              'added_emb_type': None,
          }

          UpperCAmelCase          =       PriorTransformer(**UpperCAmelCase__				)
          return model




      @property
      def      a_   (				self				)    ->       List[str]:
          torch.manual_seed(0				)

          UpperCAmelCase          =       {
              'param_shapes': (
                  (self.renderer_dim, 9_3),
                  (self.renderer_dim, 8),
                  (self.renderer_dim, 8),
                  (self.renderer_dim, 8),
              ),
              'd_latent': self.time_input_dim,
              'd_hidden': self.renderer_dim,
              'n_output': 1_2,
              'background': (
                  0.1,
                  0.1,
                  0.1,
              ),
          }
          UpperCAmelCase          =       ShapERenderer(**UpperCAmelCase__				)
          return model




      def      a_   (				self				)    ->       List[Any]:
          UpperCAmelCase          =       self.dummy_prior
          UpperCAmelCase          =       self.dummy_text_encoder
          UpperCAmelCase          =       self.dummy_tokenizer
          UpperCAmelCase          =       self.dummy_renderer

          UpperCAmelCase          =       HeunDiscreteScheduler(
              beta_schedule='exp' ,							num_train_timesteps=1_0_2_4 ,							prediction_type='sample' ,							use_karras_sigmas=UpperCAmelCase__ ,							clip_sample=UpperCAmelCase__ ,							clip_sample_range=1.0 ,							)
          UpperCAmelCase          =       {
              'prior': prior,
              'text_encoder': text_encoder,
              'tokenizer': tokenizer,
              'renderer': renderer,
              'scheduler': scheduler,
          }

          return components




      def      a_   (				self ,							lowercase_ ,							lowercase_=0				)    ->       Union[str, Any]:

          if str(UpperCAmelCase__				).startswith('mps'				):
              UpperCAmelCase          =       torch.manual_seed(UpperCAmelCase__				)
          else:
              UpperCAmelCase          =       torch.Generator(device=UpperCAmelCase__				).manual_seed(UpperCAmelCase__				)
          UpperCAmelCase          =       {
              'prompt': 'horse',
              'generator': generator,
              'num_inference_steps': 1,
              'frame_size': 3_2,
              'output_type': 'np',
          }
          return inputs




      def      a_   (				self				)    ->       Union[str, Any]:
          UpperCAmelCase          =       'cpu'

          UpperCAmelCase          =       self.get_dummy_components()

          UpperCAmelCase          =       self.pipeline_class(**UpperCAmelCase__				)
          UpperCAmelCase          =       pipe.to(UpperCAmelCase__				)

          pipe.set_progress_bar_config(disable=UpperCAmelCase__				)

          UpperCAmelCase          =       pipe(**self.get_dummy_inputs(UpperCAmelCase__				)				)
          UpperCAmelCase          =       output.images[0]
          UpperCAmelCase          =       image[0, -3:, -3:, -1]

          assert image.shape == (2_0, 3_2, 3_2, 3)

          UpperCAmelCase          =       np.array(
              [
                  0.0_0_0_3_9_2_1_6,
                  0.0_0_0_3_9_2_1_6,
                  0.0_0_0_3_9_2_1_6,
                  0.0_0_0_3_9_2_1_6,
                  0.0_0_0_3_9_2_1_6,
                  0.0_0_0_3_9_2_1_6,
                  0.0_0_0_3_9_2_1_6,
                  0.0_0_0_3_9_2_1_6,
                  0.0_0_0_3_9_2_1_6,
              ]				)

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




      def      a_   (				self				)    ->       int:
          self._test_inference_batch_consistent(batch_sizes=[1, 2]				)




      def      a_   (				self				)    ->       List[str]:
          UpperCAmelCase          =       torch_device == 'cpu'
          UpperCAmelCase          =       True

          self._test_inference_batch_single_identical(
              batch_size=2 ,							test_max_difference=UpperCAmelCase__ ,							relax_max_difference=UpperCAmelCase__ ,							)




      def      a_   (				self				)    ->       Union[str, Any]:
          UpperCAmelCase          =       self.get_dummy_components()
          UpperCAmelCase          =       self.pipeline_class(**UpperCAmelCase__				)
          UpperCAmelCase          =       pipe.to(UpperCAmelCase__				)
          pipe.set_progress_bar_config(disable=UpperCAmelCase__				)

          UpperCAmelCase          =       1
          UpperCAmelCase          =       2

          UpperCAmelCase          =       self.get_dummy_inputs(UpperCAmelCase__				)

          for key in inputs.keys():
              if key in self.batch_params:
                  UpperCAmelCase          =       batch_size * [inputs[key]]

          UpperCAmelCase          =       pipe(**UpperCAmelCase__ ,							num_images_per_prompt=UpperCAmelCase__				)[0]

          assert images.shape[0] == batch_size * num_images_per_prompt

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




      def      a_   (				self				)    ->       List[Any]:
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()




      def      a_   (				self				)    ->       Optional[int]:
          UpperCAmelCase          =       load_numpy(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/shap_e/test_shap_e_np_out.npy'				)
          UpperCAmelCase          =       ShapEPipeline.from_pretrained('openai/shap-e'				)
          UpperCAmelCase          =       pipe.to(UpperCAmelCase__				)
          pipe.set_progress_bar_config(disable=UpperCAmelCase__				)

          UpperCAmelCase          =       torch.Generator(device=UpperCAmelCase__				).manual_seed(0				)

          UpperCAmelCase          =       pipe(
              'a shark' ,							generator=UpperCAmelCase__ ,							guidance_scale=1_5.0 ,							num_inference_steps=6_4 ,							frame_size=6_4 ,							output_type='np' ,							).images[0]

          assert images.shape == (2_0, 6_4, 6_4, 3)

          assert_mean_pixel_difference(UpperCAmelCase__ ,							UpperCAmelCase__				)









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


_lowerCamelCase      :						int							=  logging.get_logger(__name__)







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_		)  ->			Dict:



       """simple docstring"""

       A__ 					=       set()
       A__ 					=       []

       def parse_line(lowercase_		):
              for line in fp:
                     if isinstance(lowercase_					,							lowercase_		):
                            A__ 					=       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(lowercase_		) > 0:
                                   A__ 					=       '''\n'''.join(lowercase_		)
                                   # Only keep the warnings specified in `targets`
                                   if any(f""": {x}: """ in warning for x in targets		):
                                          selected_warnings.add(lowercase_		)
                                   buffer.clear()
                            continue
                     else:
                            A__ 					=       line.strip()
                            buffer.append(lowercase_		)

       if from_gh:
              for filename in os.listdir(lowercase_		):
                     A__ 					=       os.path.join(lowercase_					,							lowercase_		)
                     if not os.path.isdir(lowercase_		):
                            # read the file
                            if filename != "warnings.txt":
                                   continue
                            with open(lowercase_		) as fp:
                                   parse_line(lowercase_		)
       else:
              try:
                     with zipfile.ZipFile(lowercase_		) as z:
                            for filename in z.namelist():
                                   if not os.path.isdir(lowercase_		):
                                          # read the file
                                          if filename != "warnings.txt":
                                                 continue
                                          with z.open(lowercase_		) as fp:
                                                 parse_line(lowercase_		)
              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 SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_		)  ->			str:



       """simple docstring"""

       A__ 					=       set()

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

       return selected_warnings


if __name__ == "__main__":







  def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			int:



         """simple docstring"""

         return values.split(''','''		)

  _lowerCamelCase      :						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.""",
  )

  _lowerCamelCase      :						List[Any]							=  parser.parse_args()

  _lowerCamelCase      :						List[str]							=  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
    _lowerCamelCase      :						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
  _lowerCamelCase      :						Any							=  extract_warnings(args.output_dir, args.targets)
  _lowerCamelCase      :						Optional[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)


