import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class  SCREAMING_SNAKE_CASE_ (					_snake_case   ,						unittest.TestCase			):
  '''simple docstring'''


  lowercase		:     str            =				DiTPipeline
  lowercase		:     Optional[int]            =				CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
  lowercase		:     Any            =				PipelineTesterMixin.required_optional_params - {
      """latents""",
      """num_images_per_prompt""",
      """callback""",
      """callback_steps""",
  }
  lowercase		:     Optional[int]            =				CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
  lowercase		:     Optional[int]            =				False







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[Any]     )    ->				Dict:
         torch.manual_seed(0     )
         A						:     Union[str, Any]         =TransformeraDModel(
             sample_size=16      ,	num_layers=2      ,	patch_size=4      ,	attention_head_dim=8      ,	num_attention_heads=2      ,	in_channels=4      ,	out_channels=8      ,	attention_bias=snake_case_      ,	activation_fn='gelu-approximate'      ,	num_embeds_ada_norm=10_00      ,	norm_type='ada_norm_zero'      ,	norm_elementwise_affine=snake_case_      ,	)
         A						:     int         =AutoencoderKL()
         A						:     List[str]         =DDIMScheduler()
         A						:     Dict         ={"transformer": transformer.eval(), "vae": vae.eval(), "scheduler": scheduler}
         return components







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]      ,	SCREAMING_SNAKE_CASE__    :					Any      ,	SCREAMING_SNAKE_CASE__    :					int=0     )    ->				Any:
         if str(snake_case_     ).startswith('mps'     ):
                A						:     Dict         =torch.manual_seed(snake_case_     )
         else:
                A						:     List[Any]         =torch.Generator(device=snake_case_     ).manual_seed(snake_case_     )
         A						:     List[Any]         ={
             "class_labels": [1],
             "generator": generator,
             "num_inference_steps": 2,
             "output_type": "numpy",
         }
         return inputs







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]     )    ->				Tuple:
         A						:     Any         ="cpu"

         A						:     str         =self.get_dummy_components()
         A						:     Union[str, Any]         =self.pipeline_class(**snake_case_     )
         pipe.to(snake_case_     )
         pipe.set_progress_bar_config(disable=snake_case_     )

         A						:     Tuple         =self.get_dummy_inputs(snake_case_     )
         A						:     List[str]         =pipe(**snake_case_     ).images
         A						:     Optional[int]         =image[0, -3:, -3:, -1]

         self.assertEqual(image.shape      ,	(1, 16, 16, 3)     )
         A						:     List[str]         =np.array([0.2_9_4_6, 0.6_6_0_1, 0.4_3_2_9, 0.3_2_9_6, 0.4_1_4_4, 0.5_3_1_9, 0.7_2_7_3, 0.5_0_1_3, 0.4_4_5_7]     )
         A						:     Optional[Any]         =np.abs(image_slice.flatten() - expected_slice     ).max()
         self.assertLessEqual(snake_case_      ,	1e-3     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]     )    ->				Union[str, Any]:
         self._test_inference_batch_single_identical(relax_max_difference=snake_case_      ,	expected_max_diff=1e-3     )







  @unittest.skipIf(
      torch_device != 'cuda' or not is_xformers_available()      ,	reason='XFormers attention is only available with CUDA and `xformers` installed'      ,	)
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple     )    ->				Union[str, Any]:
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3     )







@require_torch_gpu
@slow
class  SCREAMING_SNAKE_CASE_ (					unittest.TestCase			):
  '''simple docstring'''


  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]     )    ->				str:
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()







  def 		SCREAMING_SNAKE_CASE_		(				self    :					int     )    ->				str:
         A						:     List[str]         =torch.manual_seed(0     )

         A						:     Dict         =DiTPipeline.from_pretrained('facebook/DiT-XL-2-256'     )
         pipe.to('cuda'     )

         A						:     Union[str, Any]         =["vase", "umbrella", "white shark", "white wolf"]
         A						:     Optional[Any]         =pipe.get_label_ids(snake_case_     )

         A						:     Tuple         =pipe(snake_case_      ,	generator=snake_case_      ,	num_inference_steps=40      ,	output_type='np'     ).images

         for word, image in zip(snake_case_      ,	snake_case_     ):
                A						:     str         =load_numpy(
                    f'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy'     )
                assert np.abs((expected_image - image).max()     ) < 1e-2







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]     )    ->				str:
         A						:     int         =DiTPipeline.from_pretrained('facebook/DiT-XL-2-512'     )
         A						:     Optional[Any]         =DPMSolverMultistepScheduler.from_config(pipe.scheduler.config     )
         pipe.to('cuda'     )

         A						:     str         =["vase", "umbrella"]
         A						:     Any         =pipe.get_label_ids(snake_case_     )

         A						:     List[str]         =torch.manual_seed(0     )
         A						:     Tuple         =pipe(snake_case_      ,	generator=snake_case_      ,	num_inference_steps=25      ,	output_type='np'     ).images

         for word, image in zip(snake_case_      ,	snake_case_     ):
                A						:     Any         =load_numpy(
                    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                    f'/dit/{word}_512.npy'     )

                assert np.abs((expected_image - image).max()     ) < 1e-1







import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict

import requests
from slack_sdk import WebClient


_lowercase							:      Optional[Any]      							=WebClient(token=os.environ['''CI_SLACK_BOT_TOKEN'''])



def A__  (			lowercase:     Optional[int]					)				->		Optional[int]:
       A						:     str         =test_results.split(' '					)

       A						:     List[str]         =0
       A						:     Tuple         =0

       # When the output is short enough, the output is surrounded by = signs: "== OUTPUT =="
       # When it is too long, those signs are not present.
       A						:     List[str]         =expressions[-2] if '=' in expressions[-1] else expressions[-1]

       for i, expression in enumerate(lowercase					):
              if "failed" in expression:
                     failed += int(expressions[i - 1]					)
              if "passed" in expression:
                     success += int(expressions[i - 1]					)

       return failed, success, time_spent



def A__  (			lowercase:     List[Any]					)				->		str:
       A						:     Union[str, Any]         ={}
       A						:     Optional[Any]         =None
       A						:     Union[str, Any]         =False
       for line in failures_short_lines.split('\n'					):
              if re.search(r'_ \[doctest\]',		lowercase					):
                     A						:     List[Any]         =True
                     A						:     Any         =line.split(' '					)[2]
              elif in_error and not line.split(' '					)[0].isdigit():
                     A						:     Dict         =line
                     A						:     List[str]         =False

       return failures



class  SCREAMING_SNAKE_CASE_ :
  '''simple docstring'''


  def __init__(				self    :					str      ,	SCREAMING_SNAKE_CASE__    :					str      ,	SCREAMING_SNAKE_CASE__    :					Dict     )    ->				List[str]:
         A						:     Tuple         =title

         A						:     Dict         =doc_test_results['time_spent'].split(','     )[0]
         A						:     Union[str, Any]         =doc_test_results['success']
         A						:     Any         =doc_test_results['failures']
         A						:     Optional[Any]         =self.n_success + self.n_failures

         # Failures and success of the modeling tests
         A						:     Union[str, Any]         =doc_test_results







  @property
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple     )    ->				str:
         A						:     Any         =[self._time_spent]
         A						:     List[str]         =0

         for time in time_spent:
                A						:     List[Any]         =time.split(':'     )

                # Time can be formatted as xx:xx:xx, as .xx, or as x.xx if the time spent was less than a minute.
                if len(SCREAMING_SNAKE_CASE__     ) == 1:
                       A						:     List[str]         =[0, 0, time_parts[0]]

                A  ,  A  ,  A						:     Tuple         =int(time_parts[0]     ), int(time_parts[1]     ), float(time_parts[2]     )
                total_secs += hours * 36_00 + minutes * 60 + seconds

         A  ,  A  ,  A						:     str         =total_secs // 36_00, (total_secs % 36_00) // 60, total_secs % 60
         return f'{int(SCREAMING_SNAKE_CASE__     )}h{int(SCREAMING_SNAKE_CASE__     )}m{int(SCREAMING_SNAKE_CASE__     )}s'







  @property
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple     )    ->				Dict:
         return {"type": "header", "text": {"type": "plain_text", "text": self.title}}







  @property
  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				Dict:
         return {
             "type": "section",
             "text": {
                 "type": "plain_text",
                 "text": f'🌞 There were no failures: all {self.n_tests} tests passed. The suite ran in {self.time}.',
                 "emoji": True,
             },
             "accessory": {
                 "type": "button",
                 "text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
                 "url": f'https://github.com/huggingface/transformers/actions/runs/{os.environ["GITHUB_RUN_ID"]}',
             },
         }







  @property
  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				Dict:
         return {
             "type": "section",
             "text": {
                 "type": "plain_text",
                 "text": (
                     f'There were {self.n_failures} failures, out of {self.n_tests} tests.\nThe suite ran in'
                     f' {self.time}.'
                 ),
                 "emoji": True,
             },
             "accessory": {
                 "type": "button",
                 "text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
                 "url": f'https://github.com/huggingface/transformers/actions/runs/{os.environ["GITHUB_RUN_ID"]}',
             },
         }







  @property
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]     )    ->				Dict:
         A						:     Tuple         =40
         A						:     Optional[Any]         ={k: v['failed'] for k, v in doc_test_results.items() if isinstance(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )}

         A						:     Any         =''
         for category, failures in category_failures.items():
                if len(SCREAMING_SNAKE_CASE__     ) == 0:
                       continue

                if report != "":
                       report += "\n\n"

                report += f'*{category} failures*:'.ljust(line_length // 2     ).rjust(line_length // 2     ) + "\n"
                report += "`"
                report += "`\n`".join(SCREAMING_SNAKE_CASE__     )
                report += "`"

         return {
             "type": "section",
             "text": {
                 "type": "mrkdwn",
                 "text": f'The following examples had failures:\n\n\n{report}\n',
             },
         }







  @property
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[Any]     )    ->				str:
         A						:     Optional[int]         =[self.header]

         if self.n_failures > 0:
                blocks.append(self.failures     )

         if self.n_failures > 0:
                blocks.extend([self.category_failures]     )

         if self.n_failures == 0:
                blocks.append(self.no_failures     )

         return json.dumps(SCREAMING_SNAKE_CASE__     )







  @staticmethod
  def 		SCREAMING_SNAKE_CASE_		(				)    ->				Optional[Any]:
         A						:     Tuple         =[
             {
                 'type': 'section',
                 'text': {
                     'type': 'plain_text',
                     'text': 'There was an issue running the tests.',
                 },
                 'accessory': {
                     'type': 'button',
                     'text': {'type': 'plain_text', 'text': 'Check Action results', 'emoji': True},
                     'url': f'https://github.com/huggingface/transformers/actions/runs/{os.environ["GITHUB_RUN_ID"]}',
                 },
             }
         ]

         print('Sending the following payload'     )
         print(json.dumps({'blocks': json.loads(SCREAMING_SNAKE_CASE__     )}     )     )

         client.chat_postMessage(
             channel=os.environ['CI_SLACK_CHANNEL_ID_DAILY']      ,	text='There was an issue running the tests.'      ,	blocks=SCREAMING_SNAKE_CASE__      ,	)







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict     )    ->				Optional[int]:
         print('Sending the following payload'     )
         print(json.dumps({'blocks': json.loads(self.payload     )}     )     )

         A						:     Any         =f'{self.n_failures} failures out of {self.n_tests} tests,' if self.n_failures else 'All tests passed.'

         A						:     Dict         =client.chat_postMessage(
             channel=os.environ['CI_SLACK_CHANNEL_ID_DAILY']      ,	blocks=self.payload      ,	text=SCREAMING_SNAKE_CASE__      ,	)







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict      ,	SCREAMING_SNAKE_CASE__    :					List[str]      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					int      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]     )    ->				Optional[int]:
         A						:     List[str]         =''
         for key, value in failures.items():
                A						:     Any         =value[:2_00] + ' [Truncated]' if len(SCREAMING_SNAKE_CASE__     ) > 2_50 else value
                failures_text += f'*{key}*\n_{value}_\n\n'

         A						:     Union[str, Any]         =job_name
         A						:     Any         ={'type': 'section', 'text': {'type': 'mrkdwn', 'text': text}}

         if job_link is not None:
                A						:     int         ={
                    'type': 'button',
                    'text': {'type': 'plain_text', 'text': 'GitHub Action job', 'emoji': True},
                    'url': job_link,
                }

         return [
             {"type": "header", "text": {"type": "plain_text", "text": title.upper(), "emoji": True}},
             content,
             {"type": "section", "text": {"type": "mrkdwn", "text": failures_text}},
         ]







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple     )    ->				List[Any]:
         if self.thread_ts is None:
                raise ValueError('Can only post reply if a post has been made.'     )

         A						:     Union[str, Any]         =self.doc_test_results.pop('job_link'     )
         self.doc_test_results.pop('failures'     )
         self.doc_test_results.pop('success'     )
         self.doc_test_results.pop('time_spent'     )

         A						:     Union[str, Any]         =sorted(self.doc_test_results.items()      ,	key=lambda SCREAMING_SNAKE_CASE__     : t[0]     )
         for job, job_result in sorted_dict:
                if len(job_result['failures']     ):
                       A						:     Any         =f'*Num failures* :{len(job_result["failed"]     )} \n'
                       A						:     List[Any]         =job_result['failures']
                       A						:     Any         =self.get_reply_blocks(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	text=SCREAMING_SNAKE_CASE__     )

                       print('Sending the following reply'     )
                       print(json.dumps({'blocks': blocks}     )     )

                       client.chat_postMessage(
                           channel=os.environ['CI_SLACK_CHANNEL_ID_DAILY']      ,	text=f'Results for {job}'      ,	blocks=SCREAMING_SNAKE_CASE__      ,	thread_ts=self.thread_ts['ts']      ,	)

                       time.sleep(1     )







def A__  (			)				->		Union[str, Any]:
       A						:     Any         =os.environ['GITHUB_RUN_ID']
       A						:     List[Any]         =F'https://api.github.com/repos/huggingface/transformers/actions/runs/{run_id}/jobs?per_page=100'
       A						:     Union[str, Any]         =requests.get(lowercase					).json()
       A						:     List[Any]         ={}

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

              for i in range(lowercase					):
                     A						:     List[str]         =requests.get(url + F'&page={i + 2}'					).json()
                     jobs.update({job['name']: job['html_url'] for job in result['jobs']}					)

              return jobs
       except Exception as e:
              print('Unknown error, could not fetch links.',		lowercase					)

       return {}



def A__  (			lowercase:     str					)				->		Optional[Any]:
       A						:     Any         ={}

       if os.path.exists(lowercase					):
              A						:     List[Any]         =os.listdir(lowercase					)
              for file in files:
                     try:
                            with open(os.path.join(lowercase,		lowercase					),		encoding='utf-8'					) as f:
                                   A						:     Optional[int]         =f.read()
                     except UnicodeDecodeError as e:
                            raise ValueError(F'Could not open {os.path.join(lowercase,		lowercase					)}.'					) from e

       return _artifact



def A__  (			)				->		int:



       class  SCREAMING_SNAKE_CASE_ :
         '''simple docstring'''


         def __init__(				self    :					Optional[int]      ,	SCREAMING_SNAKE_CASE__    :					str     )    ->				List[str]:
                A						:     Dict         =name
                A						:     Dict         =[]







         def __str__(				self    :					Optional[Any]     )    ->				List[str]:
                return self.name







         def 		SCREAMING_SNAKE_CASE_		(				self    :					int      ,	SCREAMING_SNAKE_CASE__    :					str     )    ->				List[Any]:
                self.paths.append({'name': self.name, 'path': path}     )







       A						:     Dict[str, Artifact]         ={}

       A						:     str         =filter(os.path.isdir,		os.listdir()					)
       for directory in directories:
              A						:     Tuple         =directory
              if artifact_name not in _available_artifacts:
                     A						:     int         =Artifact(lowercase					)

                     _available_artifacts[artifact_name].add_path(lowercase					)

       return _available_artifacts


if __name__ == "__main__":
    _lowercase							:      Optional[int]      							=get_job_links()
    _lowercase							:      str      							=retrieve_available_artifacts()

    _lowercase							:      List[Any]      							=collections.OrderedDict(
        [
            ('''*.py''', '''API Examples'''),
            ('''*.md''', '''MD Examples'''),
        ]
    )

    # This dict will contain all the information relative to each doc test category:
    # - failed: list of failed tests
    # - failures: dict in the format 'test': 'error_message'
    _lowercase							:      Optional[Any]      							={
        v: {
            '''failed''': [],
            '''failures''': {},
        }
        for v in docs.values()
    }

    # Link to the GitHub Action job
    _lowercase							:      List[Any]      							=github_actions_job_links.get('''run_doctests''')

    _lowercase							:      int      							=available_artifacts['''doc_tests_gpu_test_reports'''].paths[0]
    _lowercase							:      Dict      							=retrieve_artifact(artifact_path['''name'''])
    if "stats" in artifact:
        _lowercase							,	_lowercase							,	_lowercase							:      List[Any]      							=handle_test_results(artifact['''stats'''])
        _lowercase							:      Any      							=failed
        _lowercase							:      Union[str, Any]      							=success
        _lowercase							:      str      							=time_spent[1:-1] + ''', '''

        _lowercase							:      Any      							=extract_first_line_failure(artifact['''failures_short'''])
        for line in artifact["summary_short"].split('''\n'''):
            if re.search('''FAILED''', line):
                _lowercase							:      Tuple      							=line.replace('''FAILED ''', '''''')
                _lowercase							:      int      							=line.split()[0].replace('''\n''', '''''')

                if "::" in line:
                    _lowercase							,	_lowercase							:      str      							=line.split('''::''')
                else:
                    _lowercase							,	_lowercase							:      Union[str, Any]      							=line, line

                for file_regex in docs.keys():
                    if fnmatch(file_path, file_regex):
                        _lowercase							:      Any      							=docs[file_regex]
                        doc_test_results[category]["failed"].append(test)

                        _lowercase							:      Any      							=all_failures[test] if test in all_failures else '''N/A'''
                        _lowercase							:      Tuple      							=failure
                        break

    _lowercase							:      Optional[int]      							=Message('''🤗 Results of the doc tests.''', doc_test_results)
    message.post()
    message.post_reply()






