"""simple docstring"""




from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
				import PIL

__lowerCamelCase							 =     logging.get_logger(__name__)






def      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





							if isinstance(UpperCamelCase__				,			(list, tuple) ) and isinstance(videos[0]				,			(list, tuple) ) and is_valid_image(videos[0][0] ):
														return videos

							elif isinstance(UpperCamelCase__				,			(list, tuple) ) and is_valid_image(videos[0] ):
														return [videos]

							elif is_valid_image(UpperCamelCase__ ):
														return [[videos]]

							raise ValueError(F'''Could not make batched video from {videos}''' )


class 			UpperCamelCase__(	__A       ):
						lowerCAmelCase__     :  List[Any]  							=						['pixel_values']





						def __init__(				self						,__UpperCAmelCase = True						,__UpperCAmelCase = None						,__UpperCAmelCase = PILImageResampling.BILINEAR						,__UpperCAmelCase = True						,__UpperCAmelCase = None						,__UpperCAmelCase = True						,__UpperCAmelCase = 1 / 2_55						,__UpperCAmelCase = True						,__UpperCAmelCase = True						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> None:
													super().__init__(**__UpperCAmelCase   )
													A__		   =							size if size is not None else {'shortest_edge': 2_56}
													A__		   =							get_size_dict(__UpperCAmelCase						,default_to_square=__UpperCAmelCase   )
													A__		   =							crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
													A__		   =							get_size_dict(__UpperCAmelCase						,param_name='crop_size'   )

													A__		   =							do_resize
													A__		   =							size
													A__		   =							do_center_crop
													A__		   =							crop_size
													A__		   =							resample
													A__		   =							do_rescale
													A__		   =							rescale_factor
													A__		   =							offset
													A__		   =							do_normalize
													A__		   =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
													A__		   =							image_std if image_std is not None else IMAGENET_STANDARD_STD





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = PILImageResampling.BILINEAR						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> np.ndarray:
													A__		   =							get_size_dict(__UpperCAmelCase						,default_to_square=__UpperCAmelCase   )
													if "shortest_edge" in size:
																				A__		   =							get_resize_output_image_size(__UpperCAmelCase						,size['shortest_edge']						,default_to_square=__UpperCAmelCase   )
													elif "height" in size and "width" in size:
																				A__		   =							(size['height'], size['width'])
													else:
																				raise ValueError(f'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''   )
													return resize(__UpperCAmelCase						,size=__UpperCAmelCase						,resample=__UpperCAmelCase						,data_format=__UpperCAmelCase						,**__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> np.ndarray:
													A__		   =							get_size_dict(__UpperCAmelCase   )
													if "height" not in size or "width" not in size:
																				raise ValueError(f'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}'''   )
													return center_crop(__UpperCAmelCase						,size=(size['height'], size['width'])						,data_format=__UpperCAmelCase						,**__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = True						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> Optional[Any]:
													A__		   =							image.astype(np.floataa   )
													if offset:
																				A__		   =							image - (scale / 2)
													return rescale(__UpperCAmelCase						,scale=__UpperCAmelCase						,data_format=__UpperCAmelCase						,**__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> np.ndarray:
													return normalize(__UpperCAmelCase						,mean=__UpperCAmelCase						,std=__UpperCAmelCase						,data_format=__UpperCAmelCase						,**__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = ChannelDimension.FIRST						,)    -> np.ndarray:
													if do_resize and size is None or resample is None:
																				raise ValueError('Size and resample must be specified if do_resize is True.'   )

													if do_center_crop and crop_size is None:
																				raise ValueError('Crop size must be specified if do_center_crop is True.'   )

													if do_rescale and rescale_factor is None:
																				raise ValueError('Rescale factor must be specified if do_rescale is True.'   )

													if do_normalize and (image_mean is None or image_std is None):
																				raise ValueError('Image mean and std must be specified if do_normalize is True.'   )

													if offset and not do_rescale:
																				raise ValueError('For offset, do_rescale must also be set to True.'   )

													# All transformations expect numpy arrays.
													A__		   =							to_numpy_array(__UpperCAmelCase   )

													if do_resize:
																				A__		   =							self.resize(image=__UpperCAmelCase						,size=__UpperCAmelCase						,resample=__UpperCAmelCase   )

													if do_center_crop:
																				A__		   =							self.center_crop(__UpperCAmelCase						,size=__UpperCAmelCase   )

													if do_rescale:
																				A__		   =							self.rescale(image=__UpperCAmelCase						,scale=__UpperCAmelCase						,offset=__UpperCAmelCase   )

													if do_normalize:
																				A__		   =							self.normalize(image=__UpperCAmelCase						,mean=__UpperCAmelCase						,std=__UpperCAmelCase   )

													A__		   =							to_channel_dimension_format(__UpperCAmelCase						,__UpperCAmelCase   )
													return image





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = ChannelDimension.FIRST						,**__UpperCAmelCase						,)    -> PIL.Image.Image:
													A__		   =							do_resize if do_resize is not None else self.do_resize
													A__		   =							resample if resample is not None else self.resample
													A__		   =							do_center_crop if do_center_crop is not None else self.do_center_crop
													A__		   =							do_rescale if do_rescale is not None else self.do_rescale
													A__		   =							rescale_factor if rescale_factor is not None else self.rescale_factor
													A__		   =							offset if offset is not None else self.offset
													A__		   =							do_normalize if do_normalize is not None else self.do_normalize
													A__		   =							image_mean if image_mean is not None else self.image_mean
													A__		   =							image_std if image_std is not None else self.image_std

													A__		   =							size if size is not None else self.size
													A__		   =							get_size_dict(__UpperCAmelCase						,default_to_square=__UpperCAmelCase   )
													A__		   =							crop_size if crop_size is not None else self.crop_size
													A__		   =							get_size_dict(__UpperCAmelCase						,param_name='crop_size'   )

													if not valid_images(__UpperCAmelCase   ):
																				raise ValueError(
																				    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
																				    'torch.Tensor, tf.Tensor or jax.ndarray.'   )

													A__		   =							make_batched(__UpperCAmelCase   )

													A__		   =							[
													    [
													        self._preprocess_image(
													            image=__UpperCAmelCase						,do_resize=__UpperCAmelCase						,size=__UpperCAmelCase						,resample=__UpperCAmelCase						,do_center_crop=__UpperCAmelCase						,crop_size=__UpperCAmelCase						,do_rescale=__UpperCAmelCase						,rescale_factor=__UpperCAmelCase						,offset=__UpperCAmelCase						,do_normalize=__UpperCAmelCase						,image_mean=__UpperCAmelCase						,image_std=__UpperCAmelCase						,data_format=__UpperCAmelCase						,)
													        for img in video
													    ]
													    for video in videos
													]

													A__		   =							{'pixel_values': videos}
													return BatchFeature(data=__UpperCAmelCase						,tensor_type=__UpperCAmelCase   )






"""simple docstring"""




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      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							test_results.split(' ' )

							A__		   =							0
							A__		   =							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__		   =							expressions[-2] if '=' in expressions[-1] else expressions[-1]

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

							return failed, success, time_spent






def      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							{}
							A__		   =							None
							A__		   =							False
							for line in failures_short_lines.split('\n' ):
														if re.search(r'_ \[doctest\]'				,			UpperCamelCase__ ):
																					A__		   =							True
																					A__		   =							line.split(' ' )[2]
														elif in_error and not line.split(' ' )[0].isdigit():
																					A__		   =							line
																					A__		   =							False

							return failures
class 			UpperCamelCase__:





						def __init__(				self						,__UpperCAmelCase						,__UpperCAmelCase   )    -> Dict:
													A__		   =							title

													A__		   =							doc_test_results['time_spent'].split(','   )[0]
													A__		   =							doc_test_results['success']
													A__		   =							doc_test_results['failures']
													A__		   =							self.n_success + self.n_failures

													# Failures and success of the modeling tests
													A__		   =							doc_test_results





						@property
						def  snake_case__		(				self   )    -> str:
													A__		   =							[self._time_spent]
													A__		   =							0

													for time in time_spent:
																				A__		   =							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(__UpperCAmelCase   ) == 1:
																											A__		   =							[0, 0, time_parts[0]]

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

													A__   ,     A__   ,     A__		   =							total_secs // 36_00, (total_secs % 36_00) // 60, total_secs % 60
													return f'''{int(__UpperCAmelCase   )}h{int(__UpperCAmelCase   )}m{int(__UpperCAmelCase   )}s'''





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





						@property
						def  snake_case__		(				self   )    -> 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  snake_case__		(				self   )    -> 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  snake_case__		(				self   )    -> Dict:
													A__		   =							40
													A__		   =							{k: v['failed'] for k, v in doc_test_results.items() if isinstance(__UpperCAmelCase						,__UpperCAmelCase   )}

													A__		   =							''
													for category, failures in category_failures.items():
																				if len(__UpperCAmelCase   ) == 0:
																											continue

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

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

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





						@property
						def  snake_case__		(				self   )    -> str:
													A__		   =							[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(__UpperCAmelCase   )





						@staticmethod
						def  snake_case__		(				)    -> Any:
													A__		   =							[
													    {
													        '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(__UpperCAmelCase   )}   )   )

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





						def  snake_case__		(				self   )    -> int:
													print('Sending the following payload'   )
													print(json.dumps({'blocks': json.loads(self.payload   )}   )   )

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

													A__		   =							client.chat_postMessage(
													    channel=os.environ['CI_SLACK_CHANNEL_ID_DAILY']						,blocks=self.payload						,text=__UpperCAmelCase						,)





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> int:
													A__		   =							''
													for key, value in failures.items():
																				A__		   =							value[:2_00] + ' [Truncated]' if len(__UpperCAmelCase   ) > 2_50 else value
																				failures_text += f'''*{key}*\n_{value}_\n\n'''

													A__		   =							job_name
													A__		   =							{'type': 'section', 'text': {'type': 'mrkdwn', 'text': text}}

													if job_link is not None:
																				A__		   =							{
																				    '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  snake_case__		(				self   )    -> Dict:
													if self.thread_ts is None:
																				raise ValueError('Can only post reply if a post has been made.'   )

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

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

																											time.sleep(1   )






def      UpperCAmelCase    (		):






							"""simple docstring"""





							A__		   =							os.environ['GITHUB_RUN_ID']
							A__		   =							F'''https://api.github.com/repos/huggingface/transformers/actions/runs/{run_id}/jobs?per_page=100'''
							A__		   =							requests.get(UpperCamelCase__ ).json()
							A__		   =							{}

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

														for i in range(UpperCamelCase__ ):
																					A__		   =							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.'				,			UpperCamelCase__ )

							return {}






def      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							{}

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

							return _artifact






def      UpperCAmelCase    (		):






							"""simple docstring"""





							class 			UpperCamelCase__:





													def __init__(				self						,__UpperCAmelCase   )    -> Optional[Any]:
																				A__		   =							name
																				A__		   =							[]





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





													def  snake_case__		(				self						,__UpperCAmelCase   )    -> int:
																				self.paths.append({'name': self.name, 'path': path}   )






							A__		   =							{}

							A__		   =							filter(os.path.isdir				,			os.listdir() )
							for directory in directories:
														A__		   =							directory
														if artifact_name not in _available_artifacts:
																					A__		   =							Artifact(UpperCamelCase__ )

																					_available_artifacts[artifact_name].add_path(UpperCamelCase__ )

							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()





