


import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()







class lowerCAmelCase_		(				lowerCamelCase__      ,		lowerCamelCase__      ,		lowerCamelCase__      ,		unittest.TestCase ):

 '''simple docstring'''

 __snake_case				=						StableUnCLIPPipeline
 __snake_case				=						TEXT_TO_IMAGE_PARAMS
 __snake_case				=						TEXT_TO_IMAGE_BATCH_PARAMS
 __snake_case				=						TEXT_TO_IMAGE_IMAGE_PARAMS
 __snake_case				=						TEXT_TO_IMAGE_IMAGE_PARAMS

 # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
 __snake_case				=						False



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   32
       snake_case_ =   embedder_hidden_size

       # prior components

       torch.manual_seed(0      )
       snake_case_ =   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )

       torch.manual_seed(0      )
       snake_case_ =   CLIPTextModelWithProjection(
           CLIPTextConfig(
               bos_token_id=0	,   eos_token_id=2	,   hidden_size=_UpperCAmelCase	,   projection_dim=_UpperCAmelCase	,   intermediate_size=37	,   layer_norm_eps=1E-05	,   num_attention_heads=4	,   num_hidden_layers=5	,   pad_token_id=1	,   vocab_size=10_00	,   )      )

       torch.manual_seed(0      )
       snake_case_ =   PriorTransformer(
           num_attention_heads=2	,   attention_head_dim=12	,   embedding_dim=_UpperCAmelCase	,   num_layers=1	,   )

       torch.manual_seed(0      )
       snake_case_ =   DDPMScheduler(
           variance_type='''fixed_small_log'''	,   prediction_type='''sample'''	,   num_train_timesteps=10_00	,   clip_sample=_UpperCAmelCase	,   clip_sample_range=5.0	,   beta_schedule='''squaredcos_cap_v2'''	,   )

       # regular denoising components

       torch.manual_seed(0      )
       snake_case_ =   StableUnCLIPImageNormalizer(embedding_dim=_UpperCAmelCase      )
       snake_case_ =   DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''      )

       torch.manual_seed(0      )
       snake_case_ =   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )

       torch.manual_seed(0      )
       snake_case_ =   CLIPTextModel(
           CLIPTextConfig(
               bos_token_id=0	,   eos_token_id=2	,   hidden_size=_UpperCAmelCase	,   projection_dim=32	,   intermediate_size=37	,   layer_norm_eps=1E-05	,   num_attention_heads=4	,   num_hidden_layers=5	,   pad_token_id=1	,   vocab_size=10_00	,   )      )

       torch.manual_seed(0      )
       snake_case_ =   UNetaDConditionModel(
           sample_size=32	,   in_channels=4	,   out_channels=4	,   down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''')	,   up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''')	,   block_out_channels=(32, 64)	,   attention_head_dim=(2, 4)	,   class_embed_type='''projection'''	,   projection_class_embeddings_input_dim=embedder_projection_dim * 2	,   cross_attention_dim=_UpperCAmelCase	,   layers_per_block=1	,   upcast_attention=_UpperCAmelCase	,   use_linear_projection=_UpperCAmelCase	,   )

       torch.manual_seed(0      )
       snake_case_ =   DDIMScheduler(
           beta_schedule='''scaled_linear'''	,   beta_start=0.00_085	,   beta_end=0.012	,   prediction_type='''v_prediction'''	,   set_alpha_to_one=_UpperCAmelCase	,   steps_offset=1	,   )

       torch.manual_seed(0      )
       snake_case_ =   AutoencoderKL()

       snake_case_ =   {
           # prior components
           '''prior_tokenizer''': prior_tokenizer,
           '''prior_text_encoder''': prior_text_encoder,
           '''prior''': prior,
           '''prior_scheduler''': prior_scheduler,
           # image noising components
           '''image_normalizer''': image_normalizer,
           '''image_noising_scheduler''': image_noising_scheduler,
           # regular denoising components
           '''tokenizer''': tokenizer,
           '''text_encoder''': text_encoder,
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''vae''': vae,
       }

       return components



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase=0      ):
       if str(_UpperCAmelCase      ).startswith('''mps'''      ):
             snake_case_ =   torch.manual_seed(_UpperCAmelCase      )
       else:
             snake_case_ =   torch.Generator(device=_UpperCAmelCase      ).manual_seed(_UpperCAmelCase      )
       snake_case_ =   {
           '''prompt''': '''A painting of a squirrel eating a burger''',
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''prior_num_inference_steps''': 2,
           '''output_type''': '''numpy''',
       }
       return inputs



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   torch_device == '''cpu'''

       self._test_attention_slicing_forward_pass(test_max_difference=_UpperCAmelCase      )





 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   torch_device in ['''cpu''', '''mps''']

       self._test_inference_batch_single_identical(test_max_difference=_UpperCAmelCase      )







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

 '''simple docstring'''

 def 		UpperCamelCase__  (			self      ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy'''      )

       snake_case_ =   StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l'''	,   torch_dtype=torch.floataa      )
       pipe.to(_UpperCAmelCase      )
       pipe.set_progress_bar_config(disable=_UpperCAmelCase      )
       # stable unclip will oom when integration tests are run on a V100,
       # so turn on memory savings
       pipe.enable_attention_slicing()
       pipe.enable_sequential_cpu_offload()

       snake_case_ =   torch.Generator(device='''cpu'''      ).manual_seed(0      )
       snake_case_ =   pipe('''anime turle'''	,   generator=_UpperCAmelCase	,   output_type='''np'''      )

       snake_case_ =   output.images[0]

       assert image.shape == (7_68, 7_68, 3)

       assert_mean_pixel_difference(_UpperCAmelCase	,   _UpperCAmelCase      )





 def 		UpperCamelCase__  (			self      ):
       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()

       snake_case_ =   StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l'''	,   torch_dtype=torch.floataa      )
       snake_case_ =   pipe.to(_UpperCAmelCase      )
       pipe.set_progress_bar_config(disable=_UpperCAmelCase      )
       pipe.enable_attention_slicing()
       pipe.enable_sequential_cpu_offload()

       snake_case_ =   pipe(
           '''anime turtle'''	,   prior_num_inference_steps=2	,   num_inference_steps=2	,   output_type='''np'''	,   )

       snake_case_ =   torch.cuda.max_memory_allocated()
       # make sure that less than 7 GB is allocated
       assert mem_bytes < 7 * 10**9



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


UpperCAmelCase    =					logging.get_logger(__name__)






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						List[str]:




      """simple docstring"""

      snake_case_ =   set()
      snake_case_ =   []

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

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




      """simple docstring"""

      snake_case_ =   set()

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

      return selected_warnings


if __name__ == "__main__":






   def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						List[str]:




         """simple docstring"""

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

   UpperCAmelCase    =					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.""",
   )

   UpperCAmelCase    =					parser.parse_args()

   UpperCAmelCase    =					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
      UpperCAmelCase    =					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
   UpperCAmelCase    =					extract_warnings(args.output_dir, args.targets)
   UpperCAmelCase    =					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)