
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


lowerCamelCase  =WebClient(token=os.environ["CI_SLACK_BOT_TOKEN"])


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__		):
      UpperCamelCase__     : Any											=					test_results.split(''' '''		)

      UpperCamelCase__     : Dict											=					0
      UpperCamelCase__     : List[str]											=					0

      # When the output is short enough, the output is surrounded by = signs: "== OUTPUT =="
      # When it is too long, those signs are not present.
      UpperCamelCase__     : Union[str, Any]											=					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 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__		):
      UpperCamelCase__     : Dict											=					{}
      UpperCamelCase__     : int											=					None
      UpperCamelCase__     : Any											=					False
      for line in failures_short_lines.split('''\n'''		):
            if re.search(R'''_ \[doctest\]'''			,		lowercase_		):
                  UpperCamelCase__     : Optional[int]											=					True
                  UpperCamelCase__     : Dict											=					line.split(''' '''		)[2]
            elif in_error and not line.split(''' '''		)[0].isdigit():
                  UpperCamelCase__     : Dict											=					line
                  UpperCamelCase__     : Any											=					False

      return failures




class 					_lowerCamelCase			:





     """simple docstring"""







     def __init__(				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)	-> Optional[int]:




           """simple docstring"""







           UpperCamelCase__     : Union[str, Any]											=					title

           UpperCamelCase__     : Tuple											=					doc_test_results['''time_spent'''].split(''','''	)[0]
           UpperCamelCase__     : int											=					doc_test_results['''success''']
           UpperCamelCase__     : List[str]											=					doc_test_results['''failures''']
           UpperCamelCase__     : Dict											=					self.n_success + self.n_failures

           # Failures and success of the modeling tests
           UpperCamelCase__     : int											=					doc_test_results






     @property
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> str:




           """simple docstring"""







           UpperCamelCase__     : Dict											=					[self._time_spent]
           UpperCamelCase__     : List[Any]											=					0

           for time in time_spent:
                 UpperCamelCase__     : Optional[int]											=					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(a__	) == 1:
                       UpperCamelCase__     : Any											=					[0, 0, time_parts[0]]

                 UpperCamelCase__     : Union[str, Any]											=					int(time_parts[0]	), int(time_parts[1]	), float(time_parts[2]	)
                 total_secs += hours * 3_6_0_0 + minutes * 6_0 + seconds

           UpperCamelCase__     : Any											=					total_secs // 3_6_0_0, (total_secs % 3_6_0_0) // 6_0, total_secs % 6_0
           return F'''{int(a__	)}h{int(a__	)}m{int(a__	)}s'''






     @property
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Any:




           """simple docstring"""







           return {"type": "header", "text": {"type": "plain_text", "text": self.title}}






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




           """simple docstring"""







           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]:




           """simple docstring"""







           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	)	-> List[str]:




           """simple docstring"""







           UpperCamelCase__     : List[Any]											=					4_0
           UpperCamelCase__     : Union[str, Any]											=					{k: v['''failed'''] for k, v in doc_test_results.items() if isinstance(a__    ,    a__	)}

           UpperCamelCase__     : Dict											=					''''''
           for category, failures in category_failures.items():
                 if len(a__	) == 0:
                       continue

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

                 report += F'''*{category} failures*:'''.ljust(line_length // 2	).rjust(line_length // 2	) + "\n"
                 report += "`"
                 report += "`\n`".join(a__	)
                 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	)	-> int:




           """simple docstring"""







           UpperCamelCase__     : List[str]											=					[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(a__	)






     @staticmethod
     def  __SCREAMING_SNAKE_CASE      (				)	-> Optional[int]:




           """simple docstring"""







           UpperCamelCase__     : Dict											=					[
               {
                   '''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(a__	)}	)	)

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






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Union[str, Any]:




           """simple docstring"""







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

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

           UpperCamelCase__     : List[Any]											=					client.chat_postMessage(
               channel=os.environ['''CI_SLACK_CHANNEL_ID_DAILY''']    ,    blocks=self.payload    ,    text=a__    ,    )






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)	-> Dict:




           """simple docstring"""







           UpperCamelCase__     : Any											=					''''''
           for key, value in failures.items():
                 UpperCamelCase__     : int											=					value[:2_0_0] + ''' [Truncated]''' if len(a__	) > 2_5_0 else value
                 failures_text += F'''*{key}*\n_{value}_\n\n'''

           UpperCamelCase__     : Optional[Any]											=					job_name
           UpperCamelCase__     : Any											=					{'''type''': '''section''', '''text''': {'''type''': '''mrkdwn''', '''text''': text}}

           if job_link is not None:
                 UpperCamelCase__     : Dict											=					{
                     '''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	)	-> str:




           """simple docstring"""







           if self.thread_ts is None:
                 raise ValueError('''Can only post reply if a post has been made.'''	)

           UpperCamelCase__     : Optional[int]											=					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'''	)

           UpperCamelCase__     : Tuple											=					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''']	):
                       UpperCamelCase__     : Optional[int]											=					F'''*Num failures* :{len(job_result["failed"]	)} \n'''
                       UpperCamelCase__     : Optional[int]											=					job_result['''failures''']
                       UpperCamelCase__     : Tuple											=					self.get_reply_blocks(a__    ,    a__    ,    a__    ,    text=a__	)

                       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=a__    ,    thread_ts=self.thread_ts['''ts''']    ,    )

                       time.sleep(1	)


def 				SCREAMING_SNAKE_CASE_   (      ):
      UpperCamelCase__     : Any											=					os.environ['''GITHUB_RUN_ID''']
      UpperCamelCase__     : Any											=					f'''https://api.github.com/repos/huggingface/transformers/actions/runs/{run_id}/jobs?per_page=100'''
      UpperCamelCase__     : int											=					requests.get(lowercase_		).json()
      UpperCamelCase__     : Optional[Any]											=					{}

      try:
            jobs.update({job['''name''']: job['''html_url'''] for job in result['''jobs''']}		)
            UpperCamelCase__     : int											=					math.ceil((result['''total_count'''] - 1_0_0) / 1_0_0		)

            for i in range(lowercase_		):
                  UpperCamelCase__     : 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 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__		):
      UpperCamelCase__     : int											=					{}

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

      return _artifact


def 				SCREAMING_SNAKE_CASE_   (      ):




      class 					_lowerCamelCase			:





           """simple docstring"""







           def __init__(				self    ,    __SCREAMING_SNAKE_CASE	)	-> str:




                 """simple docstring"""







                 UpperCamelCase__     : Union[str, Any]											=					name
                 UpperCamelCase__     : str											=					[]






           def __str__(				self	)	-> Any:




                 """simple docstring"""







                 return self.name






           def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE	)	-> Union[str, Any]:




                 """simple docstring"""







                 self.paths.append({'''name''': self.name, '''path''': path}	)
      UpperCamelCase__     : Dict[str, Artifact]											=					{}

      UpperCamelCase__     : Tuple											=					filter(os.path.isdir			,		os.listdir()		)
      for directory in directories:
            UpperCamelCase__     : List[str]											=					directory
            if artifact_name not in _available_artifacts:
                  UpperCamelCase__     : str											=					Artifact(lowercase_		)

                  _available_artifacts[artifact_name].add_path(lowercase_		)

      return _available_artifacts


if __name__ == "__main__":
    lowerCamelCase  =get_job_links()
    lowerCamelCase  =retrieve_available_artifacts()

    lowerCamelCase  =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'
    lowerCamelCase  ={
        v: {
            "failed": [],
            "failures": {},
        }
        for v in docs.values()
    }

    # Link to the GitHub Action job
    lowerCamelCase  =github_actions_job_links.get("run_doctests")

    lowerCamelCase  =available_artifacts["doc_tests_gpu_test_reports"].paths[0]
    lowerCamelCase  =retrieve_artifact(artifact_path["name"])
    if "stats" in artifact:
        lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase  =handle_test_results(artifact["stats"])
        lowerCamelCase  =failed
        lowerCamelCase  =success
        lowerCamelCase  =time_spent[1:-1] + ", "

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

                if "::" in line:
                    lowerCamelCase ,  lowerCamelCase  =line.split("::")
                else:
                    lowerCamelCase ,  lowerCamelCase  =line, line

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

                        lowerCamelCase  =all_failures[test] if test in all_failures else "N/A"
                        lowerCamelCase  =failure
                        break

    lowerCamelCase  =Message("🤗 Results of the doc tests.", doc_test_results)
    message.post()
    message.post_reply()









from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


_lowerCamelCase      							=   transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)




def  __UpperCAmelCase(   lowercase_   ):
       if isinstance(lowercase_			,     torch.Tensor   ):
              return image
       elif isinstance(lowercase_			,     PIL.Image.Image   ):
              _lowerCamelCase				:  List[str]						=		[image]

       _lowerCamelCase				:  Optional[int]						=		[trans(img.convert('''RGB'''   )   ) for img in image]
       _lowerCamelCase				:  Dict						=		torch.stack(lowercase_   )
       return image







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      def __init__(		self   ,				a__   ,				a__):



             """simple docstring"""





             super().__init__()

             # make sure scheduler can always be converted to DDIM
             _lowerCamelCase				:  Optional[Any]						=		DDIMScheduler.from_config(scheduler.config)

             self.register_modules(unet=a__   ,				scheduler=a__)


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             if strength < 0 or strength > 1:
                    raise ValueError(F"""The value of strength should in [0.0, 1.0] but is {strength}""")


      def 					__snake_case      (		self   ,				a__   ,				a__   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  List[str]						=		min(int(num_inference_steps * strength)   ,				a__)

             _lowerCamelCase				:  Tuple						=		max(num_inference_steps - init_timestep   ,				0)
             _lowerCamelCase				:  int						=		self.scheduler.timesteps[t_start:]

             return timesteps, num_inference_steps - t_start


      def 					__snake_case      (		self   ,				a__   ,				a__   ,				a__   ,				a__   ,				a__   ,				a__=None):



             """simple docstring"""





             if not isinstance(a__   ,				(torch.Tensor, PIL.Image.Image, list)):
                    raise ValueError(
                        F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(a__)}""")

             _lowerCamelCase				:  Any						=		image.to(device=a__   ,				dtype=a__)

             if isinstance(a__   ,				a__) and len(a__) != batch_size:
                    raise ValueError(
                        F"""You have passed a list of generators of length {len(a__)}, but requested an effective batch"""
                        F""" size of {batch_size}. Make sure the batch size matches the length of the generators.""")

             _lowerCamelCase				:  Any						=		init_latents.shape
             _lowerCamelCase				:  Tuple						=		randn_tensor(a__   ,				generator=a__   ,				device=a__   ,				dtype=a__)

             # get latents
             print('''add noise to latents at timestep'''   ,				a__)
             _lowerCamelCase				:  List[Any]						=		self.scheduler.add_noise(a__   ,				a__   ,				a__)
             _lowerCamelCase				:  List[Any]						=		init_latents

             return latents



      @torch.no_grad()
      def __call__(		self   ,				a__ = None   ,				a__ = 0.8   ,				a__ = 1   ,				a__ = None   ,				a__ = 0.0   ,				a__ = 50   ,				a__ = None   ,				a__ = "pil"   ,				a__ = True   ,				):



             """simple docstring"""





             self.check_inputs(a__)

             # 2. Preprocess image
             _lowerCamelCase				:  Union[str, Any]						=		preprocess(a__)

             # 3. set timesteps
             self.scheduler.set_timesteps(a__   ,				device=self.device)
             _lowerCamelCase,	_lowerCamelCase				:  Any						=		self.get_timesteps(a__   ,				a__   ,				self.device)
             _lowerCamelCase				:  Union[str, Any]						=		timesteps[:1].repeat(a__)

             # 4. Prepare latent variables
             _lowerCamelCase				:  int						=		self.prepare_latents(a__   ,				a__   ,				a__   ,				self.unet.dtype   ,				self.device   ,				a__)
             _lowerCamelCase				:  int						=		latents

             # 5. Denoising loop
             for t in self.progress_bar(a__):
                    # 1. predict noise model_output
                    _lowerCamelCase				:  Dict						=		self.unet(a__   ,				a__).sample

                    # 2. predict previous mean of image x_t-1 and add variance depending on eta
                    # eta corresponds to η in paper and should be between [0, 1]
                    # do x_t -> x_t-1
                    _lowerCamelCase				:  Tuple						=		self.scheduler.step(
                        a__   ,				a__   ,				a__   ,				eta=a__   ,				use_clipped_model_output=a__   ,				generator=a__   ,				).prev_sample

             _lowerCamelCase				:  str						=		(image / 2 + 0.5).clamp(0   ,				1)
             _lowerCamelCase				:  Dict						=		image.cpu().permute(0   ,				2   ,				3   ,				1).numpy()
             if output_type == "pil":
                    _lowerCamelCase				:  Any						=		self.numpy_to_pil(a__)

             if not return_dict:
                    return (image, latent_timestep.item())

             return ImagePipelineOutput(images=a__)

