

import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
A_			:					int    			=     logging.get_logger(__name__)

def 		UpperCamelCase   (lowercase_:					Dict       , lowercase_:					Tuple=False   )	-> Optional[int]:
	A__				:       Optional[int]	  =						[]

	# fmt: off
	# stem:
	rename_keys.append(("""cls_token""", """vit.embeddings.cls_token""")   )
	rename_keys.append(("""pos_embed""", """vit.embeddings.position_embeddings""")   )

	rename_keys.append(("""patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight""")   )
	rename_keys.append(("""patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias""")   )

	# backbone
	rename_keys.append(("""patch_embed.backbone.stem.conv.weight""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight""")   )
	rename_keys.append(("""patch_embed.backbone.stem.norm.weight""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight""")   )
	rename_keys.append(("""patch_embed.backbone.stem.norm.bias""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias""")   )

	for stage_idx in range(len(config.backbone_config.depths   )   ):
		for layer_idx in range(config.backbone_config.depths[stage_idx]   ):
			rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight""")   )
			rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight""")   )
			rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias""")   )
			rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight""")   )
			rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight""")   )
			rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias""")   )
			rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight""")   )
			rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight""")   )
			rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias""")   )

		rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight""")   )
		rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight""")   )
		rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias""")   )

	# transformer encoder
	for i in range(config.num_hidden_layers   ):
		# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
		rename_keys.append((f"""blocks.{i}.norm1.weight""", f"""vit.encoder.layer.{i}.layernorm_before.weight""")   )
		rename_keys.append((f"""blocks.{i}.norm1.bias""", f"""vit.encoder.layer.{i}.layernorm_before.bias""")   )
		rename_keys.append((f"""blocks.{i}.attn.proj.weight""", f"""vit.encoder.layer.{i}.attention.output.dense.weight""")   )
		rename_keys.append((f"""blocks.{i}.attn.proj.bias""", f"""vit.encoder.layer.{i}.attention.output.dense.bias""")   )
		rename_keys.append((f"""blocks.{i}.norm2.weight""", f"""vit.encoder.layer.{i}.layernorm_after.weight""")   )
		rename_keys.append((f"""blocks.{i}.norm2.bias""", f"""vit.encoder.layer.{i}.layernorm_after.bias""")   )
		rename_keys.append((f"""blocks.{i}.mlp.fc1.weight""", f"""vit.encoder.layer.{i}.intermediate.dense.weight""")   )
		rename_keys.append((f"""blocks.{i}.mlp.fc1.bias""", f"""vit.encoder.layer.{i}.intermediate.dense.bias""")   )
		rename_keys.append((f"""blocks.{i}.mlp.fc2.weight""", f"""vit.encoder.layer.{i}.output.dense.weight""")   )
		rename_keys.append((f"""blocks.{i}.mlp.fc2.bias""", f"""vit.encoder.layer.{i}.output.dense.bias""")   )

	if base_model:
		# layernorm + pooler
		rename_keys.extend(
		    [
		        ("""norm.weight""", """layernorm.weight"""),
		        ("""norm.bias""", """layernorm.bias"""),
		        ("""pre_logits.fc.weight""", """pooler.dense.weight"""),
		        ("""pre_logits.fc.bias""", """pooler.dense.bias"""),
		    ]   )

		# if just the base model, we should remove "vit" from all keys that start with "vit"
		A__				:       int	  =						[(pair[0], pair[1][4:]) if pair[1].startswith("""vit"""   ) else pair for pair in rename_keys]
	else:
		# layernorm + classification head
		rename_keys.extend(
		    [
		        ("""norm.weight""", """vit.layernorm.weight"""),
		        ("""norm.bias""", """vit.layernorm.bias"""),
		        ("""head.weight""", """classifier.weight"""),
		        ("""head.bias""", """classifier.bias"""),
		    ]   )
	# fmt: on

	return rename_keys

def 		UpperCamelCase   (lowercase_:					List[str]       , lowercase_:					Dict       , lowercase_:					int=False   )	-> Union[str, Any]:
	for i in range(config.num_hidden_layers   ):
		if base_model:
			A__				:       List[str]	  =						""""""
		else:
			A__				:       Optional[Any]	  =						"""vit."""
		# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
		A__				:       List[str]	  =						state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""   )
		A__				:       Tuple	  =						state_dict.pop(f"""blocks.{i}.attn.qkv.bias"""   )
		# next, add query, keys and values (in that order) to the state dict
		A__				:       List[Any]	  =						in_proj_weight[
		    : config.hidden_size, :
		]
		A__				:       int	  =						in_proj_bias[: config.hidden_size]
		A__				:       List[str]	  =						in_proj_weight[
		    config.hidden_size : config.hidden_size * 2, :
		]
		A__				:       Optional[int]	  =						in_proj_bias[
		    config.hidden_size : config.hidden_size * 2
		]
		A__				:       str	  =						in_proj_weight[
		    -config.hidden_size :, :
		]
		A__				:       Dict	  =						in_proj_bias[-config.hidden_size :]

def 		UpperCamelCase   (lowercase_:					Optional[int]   )	-> Union[str, Any]:
	A__				:       int	  =						["""head.weight""", """head.bias"""]
	for k in ignore_keys:
		state_dict.pop(lowercase_       , lowercase_   )

def 		UpperCamelCase   (lowercase_:					Any       , lowercase_:					Optional[int]       , lowercase_:					List[Any]   )	-> List[str]:
	A__				:       Tuple	  =						dct.pop(lowercase_   )
	A__				:       List[str]	  =						val

def 		UpperCamelCase   ()	-> str:
	A__				:       List[str]	  =						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
	A__				:       Dict	  =						Image.open(requests.get(lowercase_       , stream=lowercase_   ).raw   )
	return im

@torch.no_grad()
def 		UpperCamelCase   (lowercase_:					str       , lowercase_:					Optional[Any]       , lowercase_:					Dict=False   )	-> int:
	A__				:       str	  =						BitConfig(
	    global_padding="""same"""       , layer_type="""bottleneck"""       , depths=(3, 4, 9)       , out_features=["""stage3"""]       , embedding_dynamic_padding=lowercase_       , )
	A__				:       Any	  =						ViTHybridConfig(backbone_config=lowercase_       , image_size=384       , num_labels=1000   )
	A__				:       List[Any]	  =						False

	# load original model from timm
	A__				:       Dict	  =						timm.create_model(lowercase_       , pretrained=lowercase_   )
	timm_model.eval()

	# load state_dict of original model, remove and rename some keys
	A__				:       Union[str, Any]	  =						timm_model.state_dict()
	if base_model:
		remove_classification_head_(lowercase_   )
	A__				:       str	  =						create_rename_keys(lowercase_       , lowercase_   )
	for src, dest in rename_keys:
		rename_key(lowercase_       , lowercase_       , lowercase_   )
	read_in_q_k_v(lowercase_       , lowercase_       , lowercase_   )

	A__				:       str	  =						"""huggingface/label-files"""
	A__				:       Union[str, Any]	  =						"""imagenet-1k-id2label.json"""
	A__				:       Optional[int]	  =						json.load(open(hf_hub_download(lowercase_       , lowercase_       , repo_type="""dataset"""   )       , """r"""   )   )
	A__				:       Optional[int]	  =						{int(lowercase_   ): v for k, v in idalabel.items()}
	A__				:       Optional[int]	  =						idalabel
	A__				:       Optional[int]	  =						{v: k for k, v in idalabel.items()}

	# load HuggingFace model
	if vit_name[-5:] == "in21k":
		A__				:       str	  =						ViTHybridModel(lowercase_   ).eval()
	else:
		A__				:       Any	  =						ViTHybridForImageClassification(lowercase_   ).eval()
	model.load_state_dict(lowercase_   )

	# create image processor
	A__				:       List[Any]	  =						create_transform(**resolve_data_config({}       , model=lowercase_   )   )
	A__				:       Optional[Any]	  =						transform.transforms

	A__				:       Dict	  =						{
	    """bilinear""": PILImageResampling.BILINEAR,
	    """bicubic""": PILImageResampling.BICUBIC,
	    """nearest""": PILImageResampling.NEAREST,
	}

	A__				:       Optional[Any]	  =						ViTHybridImageProcessor(
	    do_resize=lowercase_       , size={"""shortest_edge""": timm_transforms[0].size}       , resample=pillow_resamplings[timm_transforms[0].interpolation.value]       , do_center_crop=lowercase_       , crop_size={"""height""": timm_transforms[1].size[0], """width""": timm_transforms[1].size[1]}       , do_normalize=lowercase_       , image_mean=timm_transforms[-1].mean.tolist()       , image_std=timm_transforms[-1].std.tolist()       , )

	A__				:       Optional[int]	  =						prepare_img()
	A__				:       int	  =						transform(lowercase_   ).unsqueeze(0   )
	A__				:       Optional[Any]	  =						processor(lowercase_       , return_tensors="""pt"""   ).pixel_values

	# verify pixel values
	assert torch.allclose(lowercase_       , lowercase_   )

	# verify logits
	with torch.no_grad():
		A__				:       Dict	  =						model(lowercase_   )
		A__				:       Tuple	  =						outputs.logits

	print("""Predicted class:"""       , logits.argmax(-1   ).item()   )
	if base_model:
		A__				:       int	  =						timm_model.forward_features(lowercase_   )
		assert timm_pooled_output.shape == outputs.pooler_output.shape
		assert torch.allclose(lowercase_       , outputs.pooler_output       , atol=1E-3   )
	else:
		A__				:       Dict	  =						timm_model(lowercase_   )
		assert timm_logits.shape == outputs.logits.shape
		assert torch.allclose(lowercase_       , outputs.logits       , atol=1E-3   )
	print("""Looks ok!"""   )

	if pytorch_dump_folder_path is not None:
		Path(lowercase_   ).mkdir(exist_ok=lowercase_   )
		print(f"""Saving model {vit_name} to {pytorch_dump_folder_path}"""   )
		model.save_pretrained(lowercase_   )
		print(f"""Saving processor to {pytorch_dump_folder_path}"""   )
		processor.save_pretrained(lowercase_   )

	if push_to_hub:
		print(f"""Pushing model and processor to the hub {vit_name}"""   )
		model.push_to_hub(f"""ybelkada/{vit_name}"""   )
		processor.push_to_hub(f"""ybelkada/{vit_name}"""   )


if __name__ == "__main__":
	A_			:					Dict    			=     argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--vit_name',
	    default='vit_base_r50_s16_384',
	    type=str,
	    help='Name of the hybrid ViT timm model you\'d like to convert.',
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
	)
	parser.add_argument(
	    '--push_to_hub', action='store_true', help='Whether to upload the model to the HuggingFace hub.'
	)

	A_			:					Optional[Any]    			=     parser.parse_args()
	convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)






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


A_			:					str    			=     WebClient(token=os.environ['CI_SLACK_BOT_TOKEN'])

def 		UpperCamelCase   (lowercase_:					Union[str, Any]   )	-> Dict:
	A__				:       Union[str, Any]	  =						test_results.split(""" """   )

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

	return failures



class        _a		:


					'''simple docstring'''






					def __init__(					self       ,			A__       ,			A__     ):
						A__				:       Optional[Any]	  =						title

						A__				:       Tuple	  =						doc_test_results["""time_spent"""].split(""","""     )[0]
						A__				:       str	  =						doc_test_results["""success"""]
						A__				:       Optional[int]	  =						doc_test_results["""failures"""]
						A__				:       int	  =						self.n_success + self.n_failures

						# Failures and success of the modeling tests
						A__				:       Optional[int]	  =						doc_test_results






					@property
					def     __A     (					self     ):
						A__				:       Tuple	  =						[self._time_spent]
						A__				:       Tuple	  =						0

						for time in time_spent:
							A__				:       Dict	  =						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:
								A__				:       Dict	  =						[0, 0, time_parts[0]]

							A__  ,  A__  ,  A__				:       Optional[Any]	  =						int(time_parts[0]     ), int(time_parts[1]     ), float(time_parts[2]     )
							total_secs += hours * 3600 + minutes * 60 + seconds

						A__  ,  A__  ,  A__				:       List[str]	  =						total_secs // 3600, (total_secs % 3600) // 60, total_secs % 60
						return F"""{int(A__     )}h{int(A__     )}m{int(A__     )}s"""






					@property
					def     __A     (					self     ):
						return {"type": "header", "text": {"type": "plain_text", "text": self.title}}






					@property
					def     __A     (					self     ):
						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     __A     (					self     ):
						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     __A     (					self     ):
						A__				:       Tuple	  =						40
						A__				:       Dict	  =						{k: v["""failed"""] for k, v in doc_test_results.items() if isinstance(A__       ,			A__     )}

						A__				:       str	  =						""""""
						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     __A     (					self     ):
						A__				:       Tuple	  =						[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     __A     (					):
						A__				:       List[str]	  =						[
						    {
						        """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     __A     (					self     ):
						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__				:       Any	  =						client.chat_postMessage(
						    channel=os.environ["""CI_SLACK_CHANNEL_ID_DAILY"""]       ,			blocks=self.payload       ,			text=A__       ,			)






					def     __A     (					self       ,			A__       ,			A__       ,			A__       ,			A__     ):
						A__				:       Tuple	  =						""""""
						for key, value in failures.items():
							A__				:       Any	  =						value[:200] + """ [Truncated]""" if len(A__     ) > 250 else value
							failures_text += F"""*{key}*\n_{value}_\n\n"""

						A__				:       Optional[Any]	  =						job_name
						A__				:       Union[str, Any]	  =						{"""type""": """section""", """text""": {"""type""": """mrkdwn""", """text""": text}}

						if job_link is not None:
							A__				:       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     __A     (					self     ):
						if self.thread_ts is None:
							raise ValueError("""Can only post reply if a post has been made."""     )

						A__				:       List[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__				:       List[Any]	  =						sorted(self.doc_test_results.items()       ,			key=lambda A__     : t[0]     )
						for job, job_result in sorted_dict:
							if len(job_result["""failures"""]     ):
								A__				:       Optional[int]	  =						F"""*Num failures* :{len(job_result['failed']     )} \n"""
								A__				:       Any	  =						job_result["""failures"""]
								A__				:       List[str]	  =						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 		UpperCamelCase   ()	-> Dict:
	A__				:       int	  =						os.environ["""GITHUB_RUN_ID"""]
	A__				:       Union[str, Any]	  =						f"""https://api.github.com/repos/huggingface/transformers/actions/runs/{run_id}/jobs?per_page=100"""
	A__				:       Optional[int]	  =						requests.get(lowercase_   ).json()
	A__				:       List[str]	  =						{}

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

		for i in range(lowercase_   ):
			A__				:       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 		UpperCamelCase   (lowercase_:					str   )	-> Any:
	A__				:       List[Any]	  =						{}

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

	return _artifact

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



	class        _a		:


						'''simple docstring'''






						def __init__(					self       ,			A__     ):
							A__				:       str	  =						name
							A__				:       Optional[int]	  =						[]






						def __str__(					self     ):
							return self.name






						def     __A     (					self       ,			A__     ):
							self.paths.append({"""name""": self.name, """path""": path}     )



	A__				:       Dict[str, Artifact]	  =						{}

	A__				:       int	  =						filter(os.path.isdir       , os.listdir()   )
	for directory in directories:
		A__				:       Dict	  =						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__":
	A_			:					str    			=     get_job_links()
	A_			:					Dict    			=     retrieve_available_artifacts()

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

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

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

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

				if "::" in line:
					A_			,     A_			:					Dict    			=     line.split('::')
				else:
					A_			,     A_			:					Dict    			=     line, line

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

						A_			:					Optional[int]    			=     all_failures[test] if test in all_failures else 'N/A'
						A_			:					List[str]    			=     failure
						break

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



