


import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter

import requests


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase=None	):


			'''simple docstring'''







			__lowercase			       = None
			if token is not None:
						__lowercase			       = {'Accept': 'application/vnd.github+json', 'Authorization': F'Bearer {token}'}

			__lowercase			       = F'https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'
			__lowercase			       = requests.get(_snake_case							,      headers=_snake_case	).json()
			__lowercase			       = {}

			try:
						job_links.update({job['''name''']: job['''html_url'''] for job in result['''jobs''']}	)
						__lowercase			       = math.ceil((result['''total_count'''] - 1_00) / 1_00	)

						for i in range(_snake_case	):
									__lowercase			       = requests.get(url + F'&page={i + 2}'							,      headers=_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 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase=None	):


			'''simple docstring'''







			__lowercase			       = None
			if token is not None:
						__lowercase			       = {'Accept': 'application/vnd.github+json', 'Authorization': F'Bearer {token}'}

			__lowercase			       = F'https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100'
			__lowercase			       = requests.get(_snake_case							,      headers=_snake_case	).json()
			__lowercase			       = {}

			try:
						artifacts.update({artifact['''name''']: artifact['''archive_download_url'''] for artifact in result['''artifacts''']}	)
						__lowercase			       = math.ceil((result['''total_count'''] - 1_00) / 1_00	)

						for i in range(_snake_case	):
									__lowercase			       = requests.get(url + F'&page={i + 2}'							,      headers=_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 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = None
			if token is not None:
						__lowercase			       = {'Accept': 'application/vnd.github+json', 'Authorization': F'Bearer {token}'}

			__lowercase			       = requests.get(_snake_case							,      headers=_snake_case							,      allow_redirects=_snake_case	)
			__lowercase			       = result.headers['Location']
			__lowercase			       = requests.get(_snake_case							,      allow_redirects=_snake_case	)
			__lowercase			       = os.path.join(_snake_case							,      F'{artifact_name}.zip'	)
			with open(_snake_case							,      '''wb'''	) as fp:
						fp.write(response.content	)


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase=None	):


			'''simple docstring'''







			__lowercase			       = []
			__lowercase			       = []
			__lowercase			       = None

			with zipfile.ZipFile(_snake_case	) as z:
						for filename in z.namelist():
									if not os.path.isdir(_snake_case	):
												# read the file
												if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
															with z.open(_snake_case	) as f:
																		for line in f:
																					__lowercase			       = line.decode('''UTF-8'''	).strip()
																					if filename == "failures_line.txt":
																								try:
																											# `error_line` is the place where `error` occurs
																											__lowercase			       = line[: line.index(''': '''	)]
																											__lowercase			       = 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
																								__lowercase			       = line[len('''FAILED '''	) :]
																								failed_tests.append(_snake_case	)
																					elif filename == "job_name.txt":
																								__lowercase			       = line

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

			__lowercase			       = None
			if job_name and job_links:
						__lowercase			       = job_links.get(_snake_case							,      _snake_case	)

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

			return result


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase=None	):


			'''simple docstring'''







			__lowercase			       = []

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

			return errors


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase=None	):


			'''simple docstring'''







			__lowercase			       = Counter()
			counter.update([x[1] for x in logs]	)
			__lowercase			       = counter.most_common()
			__lowercase			       = {}
			for error, count in counts:
						if error_filter is None or error not in error_filter:
									__lowercase			       = {'count': count, 'failed_tests': [(x[2], x[0]) for x in logs if x[1] == error]}

			__lowercase			       = dict(sorted(r.items()							,      key=lambda _UpperCamelCase	: item[1]["count"]							,      reverse=_snake_case	)	)
			return r


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = test.split('''::'''	)[0]
			if test.startswith('''tests/models/'''	):
						__lowercase			       = test.split('''/'''	)[2]
			else:
						__lowercase			       = None

			return test


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase=None	):


			'''simple docstring'''







			__lowercase			       = [(x[0], x[1], get_model(x[2]	)) for x in logs]
			__lowercase			       = [x for x in logs if x[2] is not None]
			__lowercase			       = {x[2] for x in logs}

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

			__lowercase			       = dict(sorted(r.items()							,      key=lambda _UpperCamelCase	: item[1]["count"]							,      reverse=_snake_case	)	)
			return r


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = '| no. | error | status |'
			__lowercase			       = '|-:|:-|:-|'
			__lowercase			       = [header, sep]
			for error in reduced_by_error:
						__lowercase			       = reduced_by_error[error]['count']
						__lowercase			       = F'| {count} | {error[:1_00]} |  |'
						lines.append(_snake_case	)

			return "\n".join(_snake_case	)


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = '| model | no. of errors | major error | count |'
			__lowercase			       = '|-:|-:|-:|-:|'
			__lowercase			       = [header, sep]
			for model in reduced_by_model:
						__lowercase			       = reduced_by_model[model]['count']
						__lowercase			       = list(reduced_by_model[model]['''errors'''].items()	)[0]
						__lowercase			       = F'| {model} | {count} | {error[:60]} | {_count} |'
						lines.append(_snake_case	)

			return "\n".join(_snake_case	)


if __name__ == "__main__":
	a			:			str												=					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.''')
	a			:			List[Any]												=					parser.parse_args()

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

	a			:			int												=					get_job_links(args.workflow_run_id, token=args.token)
	a			:			Optional[Any]												=					{}
	# 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:
				a			:			List[Any]												=					k.find(''' / ''')
				a			:			int												=					k[index + len(''' / ''') :]
			a			:			Any												=					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)

	a			:			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)

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

	a			:			List[Any]												=					get_all_errors(args.output_dir, job_links=job_links)

	# `e[1]` is the error
	a			:			List[str]												=					Counter()
	counter.update([e[1] for e in errors])

	# print the top 30 most common test errors
	a			:			Optional[int]												=					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)

	a			:			Any												=					reduce_by_error(errors)
	a			:			Optional[int]												=					reduce_by_model(errors)

	a			:			Union[str, Any]												=					make_github_table(reduced_by_error)
	a			:			Optional[Any]												=					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, KandinskyImgaImgPipeline, 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 				a			(					lowercase						,		unittest.TestCase      ):
      UpperCamelCase							:   Any           =     KandinskyImgaImgPipeline
      UpperCamelCase							:   int           =     ["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
      UpperCamelCase							:   Optional[Any]           =     [
          """prompt""",
          """negative_prompt""",
          """image_embeds""",
          """negative_image_embeds""",
          """image""",
      ]
      UpperCamelCase							:   Union[str, Any]           =     [
          """generator""",
          """height""",
          """width""",
          """strength""",
          """guidance_scale""",
          """negative_prompt""",
          """num_inference_steps""",
          """return_dict""",
          """guidance_scale""",
          """num_images_per_prompt""",
          """output_type""",
          """return_dict""",
      ]
      UpperCamelCase							:   List[str]           =     False



      @property
      def  __snake_case							(   self      ):
          return 32



      @property
      def  __snake_case							(   self      ):
          return 32



      @property
      def  __snake_case							(   self      ):
          return self.time_input_dim



      @property
      def  __snake_case							(   self      ):
          return self.time_input_dim * 4



      @property
      def  __snake_case							(   self      ):
          return 100



      @property
      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      str     							=					XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'      )
          return tokenizer



      @property
      def  __snake_case							(   self      ):
          torch.manual_seed(0      )
          UpperCAmelCase__	:      Optional[int]     							=					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=1_005     ,	)

          UpperCAmelCase__	:      Tuple     							=					MultilingualCLIP(UpperCamelCase_      )
          UpperCAmelCase__	:      List[Any]     							=					text_encoder.eval()

          return text_encoder



      @property
      def  __snake_case							(   self      ):
          torch.manual_seed(0      )

          UpperCAmelCase__	:      Optional[Any]     							=					{
              '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,
          }

          UpperCAmelCase__	:      Optional[Any]     							=					UNetaDConditionModel(**UpperCamelCase_      )
          return model



      @property
      def  __snake_case							(   self      ):
          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  __snake_case							(   self      ):
          torch.manual_seed(0      )
          UpperCAmelCase__	:      Optional[Any]     							=					VQModel(**self.dummy_movq_kwargs      )
          return model



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Union[str, Any]     							=					self.dummy_text_encoder
          UpperCAmelCase__	:      int     							=					self.dummy_tokenizer
          UpperCAmelCase__	:      int     							=					self.dummy_unet
          UpperCAmelCase__	:      Optional[Any]     							=					self.dummy_movq

          UpperCAmelCase__	:      Any     							=					{
              'num_train_timesteps': 1_000,
              'beta_schedule': 'linear',
              'beta_start': 0.00085,
              'beta_end': 0.012,
              'clip_sample': False,
              'set_alpha_to_one': False,
              'steps_offset': 0,
              'prediction_type': 'epsilon',
              'thresholding': False,
          }

          UpperCAmelCase__	:      List[Any]     							=					DDIMScheduler(**UpperCamelCase_      )

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

          return components



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_=0      ):
          UpperCAmelCase__	:      int     							=					floats_tensor((1, self.cross_attention_dim)     ,	rng=random.Random(UpperCamelCase_      )      ).to(UpperCamelCase_      )
          UpperCAmelCase__	:      Optional[Any]     							=					floats_tensor((1, self.cross_attention_dim)     ,	rng=random.Random(seed + 1      )      ).to(UpperCamelCase_      )
          # create init_image
          UpperCAmelCase__	:      Optional[int]     							=					floats_tensor((1, 3, 64, 64)     ,	rng=random.Random(UpperCamelCase_      )      ).to(UpperCamelCase_      )
          UpperCAmelCase__	:      int     							=					image.cpu().permute(0     ,	2     ,	3     ,	1      )[0]
          UpperCAmelCase__	:      Union[str, Any]     							=					Image.fromarray(np.uinta(UpperCamelCase_      )      ).convert('RGB'      ).resize((256, 256)      )

          if str(UpperCamelCase_      ).startswith('mps'      ):
              UpperCAmelCase__	:      Dict     							=					torch.manual_seed(UpperCamelCase_      )
          else:
              UpperCAmelCase__	:      Optional[int]     							=					torch.Generator(device=UpperCamelCase_      ).manual_seed(UpperCamelCase_      )
          UpperCAmelCase__	:      Tuple     							=					{
              'prompt': 'horse',
              'image': init_image,
              'image_embeds': image_embeds,
              'negative_image_embeds': negative_image_embeds,
              'generator': generator,
              'height': 64,
              'width': 64,
              'num_inference_steps': 10,
              'guidance_scale': 7.0,
              'strength': 0.2,
              'output_type': 'np',
          }
          return inputs



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Optional[int]     							=					'cpu'

          UpperCAmelCase__	:      Any     							=					self.get_dummy_components()

          UpperCAmelCase__	:      Any     							=					self.pipeline_class(**UpperCamelCase_      )
          UpperCAmelCase__	:      Tuple     							=					pipe.to(UpperCamelCase_      )

          pipe.set_progress_bar_config(disable=UpperCamelCase_      )

          UpperCAmelCase__	:      List[Any]     							=					pipe(**self.get_dummy_inputs(UpperCamelCase_      )      )
          UpperCAmelCase__	:      Optional[int]     							=					output.images

          UpperCAmelCase__	:      Dict     							=					pipe(
              **self.get_dummy_inputs(UpperCamelCase_      )     ,	return_dict=UpperCamelCase_     ,	)[0]

          UpperCAmelCase__	:      Dict     							=					image[0, -3:, -3:, -1]
          UpperCAmelCase__	:      Any     							=					image_from_tuple[0, -3:, -3:, -1]

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

          UpperCAmelCase__	:      Any     							=					np.array(
              [0.61474943, 0.6073539, 0.43308544, 0.5928269, 0.47493595, 0.46755973, 0.4613838, 0.45368797, 0.50119233]      )
          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 				a			(					unittest.TestCase      ):



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



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      str     							=					load_numpy(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/kandinsky/kandinsky_img2img_frog.npy'      )

          UpperCAmelCase__	:      List[Any]     							=					load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'      )
          UpperCAmelCase__	:      Any     							=					'A red cartoon frog, 4k'

          UpperCAmelCase__	:      Union[str, Any]     							=					KandinskyPriorPipeline.from_pretrained(
              'kandinsky-community/kandinsky-2-1-prior'     ,	torch_dtype=torch.floataa      )
          pipe_prior.to(UpperCamelCase_      )

          UpperCAmelCase__	:      Any     							=					KandinskyImgaImgPipeline.from_pretrained(
              'kandinsky-community/kandinsky-2-1'     ,	torch_dtype=torch.floataa      )
          UpperCAmelCase__	:      Optional[Any]     							=					pipeline.to(UpperCamelCase_      )

          pipeline.set_progress_bar_config(disable=UpperCamelCase_      )

          UpperCAmelCase__	:      List[Any]     							=					torch.Generator(device='cpu'      ).manual_seed(0      )
          UpperCAmelCase__							,							UpperCAmelCase__	:      Optional[int]     							=					pipe_prior(
              UpperCamelCase_     ,	generator=UpperCamelCase_     ,	num_inference_steps=5     ,	negative_prompt=''     ,	).to_tuple()

          UpperCAmelCase__	:      Optional[Any]     							=					pipeline(
              UpperCamelCase_     ,	image=UpperCamelCase_     ,	image_embeds=UpperCamelCase_     ,	negative_image_embeds=UpperCamelCase_     ,	generator=UpperCamelCase_     ,	num_inference_steps=100     ,	height=768     ,	width=768     ,	strength=0.2     ,	output_type='np'     ,	)

          UpperCAmelCase__	:      Union[str, Any]     							=					output.images[0]

          assert image.shape == (768, 768, 3)

          assert_mean_pixel_difference(UpperCamelCase_     ,	UpperCamelCase_      )

