"""simple docstring"""
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter

import requests







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE=None   )	->Optional[int]:
  a__:     int										=	None
  if token is not None:
    a__:     Tuple										=	{'Accept': 'application/vnd.github+json', 'Authorization': F'Bearer {token}'}

  a__:     Optional[Any]										=	F'https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'
  a__:     str										=	requests.get(_SCREAMING_SNAKE_CASE		,     headers=_SCREAMING_SNAKE_CASE   ).json()
  a__:     str										=	{}

  try:
    job_links.update({job['name']: job['html_url'] for job in result['jobs']}   )
    a__:     int										=	math.ceil((result['total_count'] - 100) / 100   )

    for i in range(_SCREAMING_SNAKE_CASE   ):
      a__:     Dict										=	requests.get(url + F'&page={i + 2}'		,     headers=_SCREAMING_SNAKE_CASE   ).json()
      job_links.update({job['name']: job['html_url'] for job in result['jobs']}   )

    return job_links
  except Exception:
    print(F'Unknown error, could not fetch links:\n{traceback.format_exc()}'   )

  return {}







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE=None   )	->Dict:
  a__:     Dict										=	None
  if token is not None:
    a__:     List[str]										=	{'Accept': 'application/vnd.github+json', 'Authorization': F'Bearer {token}'}

  a__:     Dict										=	F'https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100'
  a__:     Union[str, Any]										=	requests.get(_SCREAMING_SNAKE_CASE		,     headers=_SCREAMING_SNAKE_CASE   ).json()
  a__:     List[Any]										=	{}

  try:
    artifacts.update({artifact['name']: artifact['archive_download_url'] for artifact in result['artifacts']}   )
    a__:     Dict										=	math.ceil((result['total_count'] - 100) / 100   )

    for i in range(_SCREAMING_SNAKE_CASE   ):
      a__:     Optional[int]										=	requests.get(url + F'&page={i + 2}'		,     headers=_SCREAMING_SNAKE_CASE   ).json()
      artifacts.update({artifact['name']: artifact['archive_download_url'] for artifact in result['artifacts']}   )

    return artifacts
  except Exception:
    print(F'Unknown error, could not fetch links:\n{traceback.format_exc()}'   )

  return {}







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->str:
  a__:     List[Any]										=	None
  if token is not None:
    a__:     Optional[int]										=	{'Accept': 'application/vnd.github+json', 'Authorization': F'Bearer {token}'}

  a__:     Union[str, Any]										=	requests.get(_SCREAMING_SNAKE_CASE		,     headers=_SCREAMING_SNAKE_CASE		,     allow_redirects=_SCREAMING_SNAKE_CASE   )
  a__:     Optional[Any]										=	result.headers['Location']
  a__:     Optional[int]										=	requests.get(_SCREAMING_SNAKE_CASE		,     allow_redirects=_SCREAMING_SNAKE_CASE   )
  a__:     int										=	os.path.join(_SCREAMING_SNAKE_CASE		,     F'{artifact_name}.zip'   )
  with open(_SCREAMING_SNAKE_CASE		,     'wb'   ) as fp:
    fp.write(response.content   )







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE=None   )	->List[Any]:
  a__:     List[Any]										=	[]
  a__:     Optional[Any]										=	[]
  a__:     List[Any]										=	None

  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 in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
          with z.open(_SCREAMING_SNAKE_CASE   ) as f:
            for line in f:
              a__:     Optional[int]										=	line.decode('UTF-8'   ).strip()
              if filename == "failures_line.txt":
                try:
                  # `error_line` is the place where `error` occurs
                  a__:     Union[str, Any]										=	line[: line.index(': '   )]
                  a__:     Union[str, Any]										=	line[line.index(': '   ) + len(': '   ) :]
                  errors.append([error_line, error]   )
                except Exception:
                  # skip un-related lines
                  pass
              elif filename == "summary_short.txt" and line.startswith('FAILED '   ):
                # `test` is the test method that failed
                a__:     Optional[int]										=	line[len('FAILED '   ) :]
                failed_tests.append(_SCREAMING_SNAKE_CASE   )
              elif filename == "job_name.txt":
                a__:     Union[str, Any]										=	line

  if len(_SCREAMING_SNAKE_CASE   ) != len(_SCREAMING_SNAKE_CASE   ):
    raise ValueError(
        F'`errors` and `failed_tests` should have the same number of elements. Got {len(_SCREAMING_SNAKE_CASE   )} for `errors` '
        F'and {len(_SCREAMING_SNAKE_CASE   )} for `failed_tests` instead. The test reports in {artifact_zip_path} have some'
        ' problem.'   )

  a__:     Tuple										=	None
  if job_name and job_links:
    a__:     Dict										=	job_links.get(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )

  # A list with elements of the form (line of error, error, failed test)
  a__:     int										=	[x + [y] + [job_link] for x, y in zip(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )]

  return result







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE=None   )	->str:
  a__:     int										=	[]

  a__:     Optional[int]										=	[os.path.join(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   ) for p in os.listdir(_SCREAMING_SNAKE_CASE   ) if p.endswith('.zip'   )]
  for p in paths:
    errors.extend(get_errors_from_single_artifact(_SCREAMING_SNAKE_CASE		,     job_links=_SCREAMING_SNAKE_CASE   )   )

  return errors







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE=None   )	->Any:
  a__:     str										=	Counter()
  counter.update([x[1] for x in logs]   )
  a__:     int										=	counter.most_common()
  a__:     Any										=	{}
  for error, count in counts:
    if error_filter is None or error not in error_filter:
      a__:     List[str]										=	{'count': count, 'failed_tests': [(x[2], x[0]) for x in logs if x[1] == error]}

  a__:     Optional[Any]										=	dict(sorted(r.items()		,     key=lambda _SCREAMING_SNAKE_CASE   : item[1]["count"]		,     reverse=_SCREAMING_SNAKE_CASE   )   )
  return r







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->Union[str, Any]:
  a__:     List[str]										=	test.split('::'   )[0]
  if test.startswith('tests/models/'   ):
    a__:     Dict										=	test.split('/'   )[2]
  else:
    a__:     Any										=	None

  return test







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE=None   )	->List[str]:
  a__:     int										=	[(x[0], x[1], get_model(x[2]   )) for x in logs]
  a__:     List[Any]										=	[x for x in logs if x[2] is not None]
  a__:     Optional[Any]										=	{x[2] for x in logs}

  a__:     Dict										=	{}
  for test in tests:
    a__:     Union[str, Any]										=	Counter()
    # count by errors in `test`
    counter.update([x[1] for x in logs if x[2] == test]   )
    a__:     Union[str, Any]										=	counter.most_common()
    a__:     List[str]										=	{error: count for error, count in counts if (error_filter is None or error not in error_filter)}
    a__:     List[Any]										=	sum(error_counts.values()   )
    if n_errors > 0:
      a__:     Any										=	{'count': n_errors, 'errors': error_counts}

  a__:     Optional[int]										=	dict(sorted(r.items()		,     key=lambda _SCREAMING_SNAKE_CASE   : item[1]["count"]		,     reverse=_SCREAMING_SNAKE_CASE   )   )
  return r







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->Union[str, Any]:
  a__:     Any										=	'| no. | error | status |'
  a__:     Any										=	'|-:|:-|:-|'
  a__:     str										=	[header, sep]
  for error in reduced_by_error:
    a__:     int										=	reduced_by_error[error]['count']
    a__:     Tuple										=	F'| {count} | {error[:100]} |  |'
    lines.append(_SCREAMING_SNAKE_CASE   )

  return "\n".join(_SCREAMING_SNAKE_CASE   )







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->str:
  a__:     List[str]										=	'| model | no. of errors | major error | count |'
  a__:     str										=	'|-:|-:|-:|-:|'
  a__:     int										=	[header, sep]
  for model in reduced_by_model:
    a__:     Tuple										=	reduced_by_model[model]['count']
    a__							,    a__:     Dict										=	list(reduced_by_model[model]['errors'].items()   )[0]
    a__:     Dict										=	F'| {model} | {count} | {error[:60]} | {_count} |'
    lines.append(_SCREAMING_SNAKE_CASE   )

  return "\n".join(_SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
 lowercase__					       =	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.')
 lowercase__					       =	parser.parse_args()

 os.makedirs(args.output_dir, exist_ok=True)

 lowercase__					       =	get_job_links(args.workflow_run_id, token=args.token)
 lowercase__					       =	{}
 # To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.
 # For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.
 if _job_links:
  for k, v in _job_links.items():
   # This is how GitHub actions combine job names.
   if " / " in k:
    lowercase__					       =	k.find(' / ')
    lowercase__					       =	k[index + len(' / ') :]
   lowercase__					       =	v
 with open(os.path.join(args.output_dir, 'job_links.json'), 'w', encoding='UTF-8') as fp:
  json.dump(job_links, fp, ensure_ascii=False, indent=4)

 lowercase__					       =	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)

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

 lowercase__					       =	get_all_errors(args.output_dir, job_links=job_links)

 # `e[1]` is the error
 lowercase__					       =	Counter()
 counter.update([e[1] for e in errors])

 # print the top 30 most common test errors
 lowercase__					       =	counter.most_common(30)
 for item in most_common:
  print(item)

 with open(os.path.join(args.output_dir, 'errors.json'), 'w', encoding='UTF-8') as fp:
  json.dump(errors, fp, ensure_ascii=False, indent=4)

 lowercase__					       =	reduce_by_error(errors)
 lowercase__					       =	reduce_by_model(errors)

 lowercase__					       =	make_github_table(reduced_by_error)
 lowercase__					       =	make_github_table_per_model(reduced_by_model)

 with open(os.path.join(args.output_dir, 'reduced_by_error.txt'), 'w', encoding='UTF-8') as fp:
  fp.write(sa)
 with open(os.path.join(args.output_dir, 'reduced_by_model.txt'), 'w', encoding='UTF-8') as fp:
  fp.write(sa)



"""simple docstring"""
import gc
import random
import unittest

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

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, 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    __snake_case       (     __lowerCAmelCase       ,      unittest.TestCase				):
      a__        =		KandinskyInpaintPipeline
      a__        =		["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
      a__        =		[
          """prompt""",
          """negative_prompt""",
          """image_embeds""",
          """negative_image_embeds""",
          """image""",
          """mask_image""",
      ]
      a__        =		[
          """generator""",
          """height""",
          """width""",
          """latents""",
          """guidance_scale""",
          """negative_prompt""",
          """num_inference_steps""",
          """return_dict""",
          """guidance_scale""",
          """num_images_per_prompt""",
          """output_type""",
          """return_dict""",
      ]
      a__        =		False



      @property
      def   lowerCamelCase_   (						self)	->			Optional[int]:





        '''simple docstring'''





        return 32



      @property
      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        return 32



      @property
      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        return self.time_input_dim



      @property
      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        return self.time_input_dim * 4



      @property
      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        return 1_00



      @property
      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        a__:     Optional[int]										=	XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base')
        return tokenizer



      @property
      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        torch.manual_seed(0)
        a__:     Dict										=	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=10_05    ,			)

        a__:     Optional[Any]										=	MultilingualCLIP(lowercase)
        a__:     int										=	text_encoder.eval()

        return text_encoder



      @property
      def   lowerCamelCase_   (						self)	->			List[str]:





        '''simple docstring'''





        torch.manual_seed(0)

        a__:     Any										=	{
            'in_channels': 9,
            # 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,
        }

        a__:     str										=	UNetaDConditionModel(**lowercase)
        return model



      @property
      def   lowerCamelCase_   (						self)	->			Union[str, Any]:





        '''simple docstring'''





        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   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        torch.manual_seed(0)
        a__:     Any										=	VQModel(**self.dummy_movq_kwargs)
        return model



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        a__:     Dict										=	self.dummy_text_encoder
        a__:     int										=	self.dummy_tokenizer
        a__:     str										=	self.dummy_unet
        a__:     Any										=	self.dummy_movq

        a__:     Tuple										=	DDIMScheduler(
            num_train_timesteps=10_00    ,			beta_schedule='linear'    ,			beta_start=0.00085    ,			beta_end=0.012    ,			clip_sample=lowercase    ,			set_alpha_to_one=lowercase    ,			steps_offset=1    ,			prediction_type='epsilon'    ,			thresholding=lowercase    ,			)

        a__:     Tuple										=	{
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'unet': unet,
            'scheduler': scheduler,
            'movq': movq,
        }

        return components



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase=0)	->			Any:





        '''simple docstring'''





        a__:     List[Any]										=	floats_tensor((1, self.cross_attention_dim)    ,			rng=random.Random(lowercase)).to(lowercase)
        a__:     int										=	floats_tensor((1, self.cross_attention_dim)    ,			rng=random.Random(seed + 1)).to(lowercase)
        # create init_image
        a__:     Optional[int]										=	floats_tensor((1, 3, 64, 64)    ,			rng=random.Random(lowercase)).to(lowercase)
        a__:     int										=	image.cpu().permute(0    ,			2    ,			3    ,			1)[0]
        a__:     Optional[int]										=	Image.fromarray(np.uinta(lowercase)).convert('RGB').resize((2_56, 2_56))
        # create mask
        a__:     Tuple										=	np.ones((64, 64)    ,			dtype=np.floataa)
        a__:     Optional[Any]										=	0

        if str(lowercase).startswith('mps'):
          a__:     str										=	torch.manual_seed(lowercase)
        else:
          a__:     Dict										=	torch.Generator(device=lowercase).manual_seed(lowercase)
        a__:     Optional[int]										=	{
            'prompt': 'horse',
            'image': init_image,
            'mask_image': mask,
            'image_embeds': image_embeds,
            'negative_image_embeds': negative_image_embeds,
            'generator': generator,
            'height': 64,
            'width': 64,
            'num_inference_steps': 2,
            'guidance_scale': 4.0,
            'output_type': 'np',
        }
        return inputs



      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        a__:     Optional[Any]										=	'cpu'

        a__:     List[Any]										=	self.get_dummy_components()

        a__:     Optional[Any]										=	self.pipeline_class(**lowercase)
        a__:     str										=	pipe.to(lowercase)

        pipe.set_progress_bar_config(disable=lowercase)

        a__:     Optional[int]										=	pipe(**self.get_dummy_inputs(lowercase))
        a__:     List[str]										=	output.images

        a__:     int										=	pipe(
            **self.get_dummy_inputs(lowercase)    ,			return_dict=lowercase    ,			)[0]

        a__:     Optional[Any]										=	image[0, -3:, -3:, -1]
        a__:     List[Any]										=	image_from_tuple[0, -3:, -3:, -1]

        print(f'image.shape {image.shape}')

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

        a__:     str										=	np.array(
            [0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786])

        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()}'



      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        super().test_inference_batch_single_identical(expected_max_diff=3e-3)







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



      def   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()



      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        a__:     List[Any]										=	load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy')

        a__:     int										=	load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png')
        a__:     Union[str, Any]										=	np.ones((7_68, 7_68)    ,			dtype=np.floataa)
        a__:     int										=	0

        a__:     Optional[int]										=	'a hat'

        a__:     int										=	KandinskyPriorPipeline.from_pretrained(
            'kandinsky-community/kandinsky-2-1-prior'    ,			torch_dtype=torch.floataa)
        pipe_prior.to(lowercase)

        a__:     Any										=	KandinskyInpaintPipeline.from_pretrained(
            'kandinsky-community/kandinsky-2-1-inpaint'    ,			torch_dtype=torch.floataa)
        a__:     Optional[Any]										=	pipeline.to(lowercase)
        pipeline.set_progress_bar_config(disable=lowercase)

        a__:     Dict										=	torch.Generator(device='cpu').manual_seed(0)
        a__							,    a__:     Optional[Any]										=	pipe_prior(
            lowercase    ,			generator=lowercase    ,			num_inference_steps=5    ,			negative_prompt=''    ,			).to_tuple()

        a__:     List[str]										=	pipeline(
            lowercase    ,			image=lowercase    ,			mask_image=lowercase    ,			image_embeds=lowercase    ,			negative_image_embeds=lowercase    ,			generator=lowercase    ,			num_inference_steps=1_00    ,			height=7_68    ,			width=7_68    ,			output_type='np'    ,			)

        a__:     str										=	output.images[0]

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

        assert_mean_pixel_difference(lowercase    ,			lowercase)


