



'''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      __lowerCamelCase     (      lowerCAmelCase_  )			->      Tuple:
		_a     :      int        =							test_results.split(' '  )

		_a     :      Optional[Any]        =							0
		_a     :      Optional[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     :      Union[str, Any]        =							expressions[-2] if '=' in expressions[-1] else expressions[-1]

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

		return failed, success, time_spent



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

		return failures

class 	__magic_name__						:







							def __init__(							self					:       Optional[Any]	,_UpperCAmelCase					:       str	,_UpperCAmelCase					:       Dict		):
									_a     :      str        =							title

									_a     :      Tuple        =							doc_test_results['time_spent'].split(','		)[0]
									_a     :      List[Any]        =							doc_test_results['success']
									_a     :      Dict        =							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 							__lowercase			(							self					:       Tuple		):
									_a     :      Union[str, Any]        =							[self._time_spent]
									_a     :      List[str]        =							0

									for time in time_spent:
											_a     :      Optional[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(_UpperCAmelCase		) == 1:
													_a     :      Union[str, Any]        =							[0, 0, time_parts[0]]

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

									_a       ,					_a       ,					_a     :      Any        =							total_secs // 3600, (total_secs % 3600) // 60, total_secs % 60
									return F"""{int(_UpperCAmelCase		)}h{int(_UpperCAmelCase		)}m{int(_UpperCAmelCase		)}s"""







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







							@property
							def 							__lowercase			(							self					:       Optional[int]		):
									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 							__lowercase			(							self					:       Tuple		):
									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 							__lowercase			(							self					:       Optional[int]		):
									_a     :      List[str]        =							40
									_a     :      Optional[int]        =							{k: v['failed'] for k, v in doc_test_results.items() if isinstance(_UpperCAmelCase	,_UpperCAmelCase		)}

									_a     :      Optional[int]        =							''
									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 							__lowercase			(							self					:       List[Any]		):
									_a     :      List[Any]        =							[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 							__lowercase			(							):
									_a     :      Union[str, Any]        =							[
									    {
									        '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 							__lowercase			(							self					:       str		):
									print('Sending the following payload'		)
									print(json.dumps({'blocks': json.loads(self.payload		)}		)		)

									_a     :      List[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=_UpperCAmelCase	,)







							def 							__lowercase			(							self					:       List[str]	,_UpperCAmelCase					:       List[str]	,_UpperCAmelCase					:       Tuple	,_UpperCAmelCase					:       Optional[int]	,_UpperCAmelCase					:       Tuple		):
									_a     :      Union[str, Any]        =							''
									for key, value in failures.items():
											_a     :      Optional[Any]        =							value[:200] + ' [Truncated]' if len(_UpperCAmelCase		) > 250 else value
											failures_text += F"""*{key}*\n_{value}_\n\n"""

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

									if job_link is not None:
											_a     :      Optional[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 							__lowercase			(							self					:       List[Any]		):
									if self.thread_ts is None:
											raise ValueError('Can only post reply if a post has been made.'		)

									_a     :      str        =							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     :      Tuple        =							sorted(self.doc_test_results.items()	,key=lambda _UpperCAmelCase		: 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     :      List[str]        =							job_result['failures']
													_a     :      str        =							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      __lowerCamelCase     (      )			->      Optional[Any]:
		_a     :      Optional[int]        =							os.environ['GITHUB_RUN_ID']
		_a     :      Any        =							f"""https://api.github.com/repos/huggingface/transformers/actions/runs/{run_id}/jobs?per_page=100"""
		_a     :      Union[str, Any]        =							requests.get(lowerCAmelCase_  ).json()
		_a     :      List[Any]        =							{}

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

				for i in range(lowerCAmelCase_  ):
						_a     :      List[Any]        =							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.'						,			lowerCAmelCase_  )

		return {}



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      Optional[int]:
		_a     :      Optional[Any]        =							{}

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

		return _artifact



def      __lowerCamelCase     (      )			->      Optional[int]:

		class 	__magic_name__						:







									def __init__(							self					:       Tuple	,_UpperCAmelCase					:       str		):
											_a     :      str        =							name
											_a     :      Union[str, Any]        =							[]







									def __str__(							self					:       Optional[Any]		):
											return self.name







									def 							__lowercase			(							self					:       Optional[int]	,_UpperCAmelCase					:       str		):
											self.paths.append({'name': self.name, 'path': path}		)
		_a     :      Dict[str, Artifact]        =							{}

		_a     :      Any        =							filter(os.path.isdir						,			os.listdir()  )
		for directory in directories:
				_a     :      Optional[int]        =							directory
				if artifact_name not in _available_artifacts:
						_a     :      Optional[Any]        =							Artifact(lowerCAmelCase_  )

						_available_artifacts[artifact_name].add_path(lowerCAmelCase_  )

		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()









'''simple docstring'''

import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_=1  )			->      Dict:
		if n_shave_prefix_segments >= 0:
				return ".".join(path.split('.'  )[n_shave_prefix_segments:]  )
		else:
				return ".".join(path.split('.'  )[:n_shave_prefix_segments]  )



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_=0  )			->      Tuple:
		_a     :      Any        =							[]
		for old_item in old_list:
				_a     :      Union[str, Any]        =							old_item.replace('in_layers.0'						,			'norm1'  )
				_a     :      Optional[int]        =							new_item.replace('in_layers.2'						,			'conv1'  )

				_a     :      str        =							new_item.replace('out_layers.0'						,			'norm2'  )
				_a     :      List[str]        =							new_item.replace('out_layers.3'						,			'conv2'  )

				_a     :      str        =							new_item.replace('emb_layers.1'						,			'time_emb_proj'  )
				_a     :      Tuple        =							new_item.replace('skip_connection'						,			'conv_shortcut'  )

				_a     :      Any        =							shave_segments(lowerCAmelCase_						,			n_shave_prefix_segments=lowerCAmelCase_  )

				mapping.append({'old': old_item, 'new': new_item}  )

		return mapping



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_=0  )			->      Any:
		_a     :      List[str]        =							[]
		for old_item in old_list:
				_a     :      List[Any]        =							old_item

				_a     :      Optional[int]        =							new_item.replace('norm.weight'						,			'group_norm.weight'  )
				_a     :      Optional[Any]        =							new_item.replace('norm.bias'						,			'group_norm.bias'  )

				_a     :      Any        =							new_item.replace('proj_out.weight'						,			'proj_attn.weight'  )
				_a     :      Optional[Any]        =							new_item.replace('proj_out.bias'						,			'proj_attn.bias'  )

				_a     :      Optional[int]        =							shave_segments(lowerCAmelCase_						,			n_shave_prefix_segments=lowerCAmelCase_  )

				mapping.append({'old': old_item, 'new': new_item}  )

		return mapping



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_=None						,			lowerCAmelCase_=None						,			lowerCAmelCase_=None  )			->      Any:
		assert isinstance(lowerCAmelCase_						,			lowerCAmelCase_  ), "Paths should be a list of dicts containing 'old' and 'new' keys."

		# Splits the attention layers into three variables.
		if attention_paths_to_split is not None:
				for path, path_map in attention_paths_to_split.items():
						_a     :      Optional[Any]        =							old_checkpoint[path]
						_a     :      Optional[Any]        =							old_tensor.shape[0] // 3

						_a     :      Any        =							(-1, channels) if len(old_tensor.shape  ) == 3 else (-1)

						_a     :      int        =							old_tensor.shape[0] // config['num_head_channels'] // 3

						_a     :      str        =							old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]  )
						_a       ,					_a       ,					_a     :      Tuple        =							old_tensor.split(channels // num_heads						,			dim=1  )

						_a     :      Dict        =							query.reshape(lowerCAmelCase_  )
						_a     :      str        =							key.reshape(lowerCAmelCase_  )
						_a     :      Optional[int]        =							value.reshape(lowerCAmelCase_  )

		for path in paths:
				_a     :      Dict        =							path['new']

				# These have already been assigned
				if attention_paths_to_split is not None and new_path in attention_paths_to_split:
						continue

				# Global renaming happens here
				_a     :      Any        =							new_path.replace('middle_block.0'						,			'mid_block.resnets.0'  )
				_a     :      str        =							new_path.replace('middle_block.1'						,			'mid_block.attentions.0'  )
				_a     :      Union[str, Any]        =							new_path.replace('middle_block.2'						,			'mid_block.resnets.1'  )

				if additional_replacements is not None:
						for replacement in additional_replacements:
								_a     :      int        =							new_path.replace(replacement['old']						,			replacement['new']  )

        # proj_attn.weight has to be converted from conv 1D to linear
				if "proj_attn.weight" in new_path:
						_a     :      List[str]        =							old_checkpoint[path['old']][:, :, 0]
				else:
						_a     :      Dict        =							old_checkpoint[path['old']]



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      List[Any]:
		_a     :      Optional[int]        =							{}

		_a     :      Dict        =							checkpoint['time_embed.0.weight']
		_a     :      Tuple        =							checkpoint['time_embed.0.bias']
		_a     :      Union[str, Any]        =							checkpoint['time_embed.2.weight']
		_a     :      List[str]        =							checkpoint['time_embed.2.bias']

		_a     :      List[str]        =							checkpoint['input_blocks.0.0.weight']
		_a     :      Union[str, Any]        =							checkpoint['input_blocks.0.0.bias']

		_a     :      Optional[int]        =							checkpoint['out.0.weight']
		_a     :      int        =							checkpoint['out.0.bias']
		_a     :      List[str]        =							checkpoint['out.2.weight']
		_a     :      Optional[int]        =							checkpoint['out.2.bias']

		# Retrieves the keys for the input blocks only
		_a     :      Optional[int]        =							len({'.'.join(layer.split('.'  )[:2]  ) for layer in checkpoint if 'input_blocks' in layer}  )
		_a     :      Dict        =							{
		    layer_id: [key for key in checkpoint if f"""input_blocks.{layer_id}""" in key]
		    for layer_id in range(lowerCAmelCase_  )
		}

		# Retrieves the keys for the middle blocks only
		_a     :      List[Any]        =							len({'.'.join(layer.split('.'  )[:2]  ) for layer in checkpoint if 'middle_block' in layer}  )
		_a     :      Union[str, Any]        =							{
		    layer_id: [key for key in checkpoint if f"""middle_block.{layer_id}""" in key]
		    for layer_id in range(lowerCAmelCase_  )
		}

		# Retrieves the keys for the output blocks only
		_a     :      Optional[int]        =							len({'.'.join(layer.split('.'  )[:2]  ) for layer in checkpoint if 'output_blocks' in layer}  )
		_a     :      str        =							{
		    layer_id: [key for key in checkpoint if f"""output_blocks.{layer_id}""" in key]
		    for layer_id in range(lowerCAmelCase_  )
		}

		for i in range(1						,			lowerCAmelCase_  ):
				_a     :      List[Any]        =							(i - 1) // (config['num_res_blocks'] + 1)
				_a     :      Optional[int]        =							(i - 1) % (config['num_res_blocks'] + 1)

				_a     :      Optional[int]        =							[key for key in input_blocks[i] if f"""input_blocks.{i}.0""" in key]
				_a     :      Optional[Any]        =							[key for key in input_blocks[i] if f"""input_blocks.{i}.1""" in key]

				if f"""input_blocks.{i}.0.op.weight""" in checkpoint:
						_a     :      List[Any]        =							checkpoint[
						    f"""input_blocks.{i}.0.op.weight"""
						]
						_a     :      Union[str, Any]        =							checkpoint[
						    f"""input_blocks.{i}.0.op.bias"""
						]
						continue

				_a     :      Any        =							renew_resnet_paths(lowerCAmelCase_  )
				_a     :      List[str]        =							{'old': f"""input_blocks.{i}.0""", 'new': f"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
				_a     :      Optional[Any]        =							{'old': 'resnets.2.op', 'new': 'downsamplers.0.op'}
				assign_to_checkpoint(
				    lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			additional_replacements=[meta_path, resnet_op]						,			config=lowerCAmelCase_  )

				if len(lowerCAmelCase_  ):
						_a     :      List[str]        =							renew_attention_paths(lowerCAmelCase_  )
						_a     :      List[Any]        =							{
						    'old': f"""input_blocks.{i}.1""",
						    'new': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
						}
						_a     :      Optional[Any]        =							{
						    f"""input_blocks.{i}.1.qkv.bias""": {
						        'key': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
						        'query': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
						        'value': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
						    },
						    f"""input_blocks.{i}.1.qkv.weight""": {
						        'key': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
						        'query': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
						        'value': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
						    },
						}
						assign_to_checkpoint(
						    lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			additional_replacements=[meta_path]						,			attention_paths_to_split=lowerCAmelCase_						,			config=lowerCAmelCase_						,			)

		_a     :      str        =							middle_blocks[0]
		_a     :      Tuple        =							middle_blocks[1]
		_a     :      Any        =							middle_blocks[2]

		_a     :      List[Any]        =							renew_resnet_paths(lowerCAmelCase_  )
		assign_to_checkpoint(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			config=lowerCAmelCase_  )

		_a     :      Any        =							renew_resnet_paths(lowerCAmelCase_  )
		assign_to_checkpoint(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			config=lowerCAmelCase_  )

		_a     :      int        =							renew_attention_paths(lowerCAmelCase_  )
		_a     :      int        =							{
		    'middle_block.1.qkv.bias': {
		        'key': 'mid_block.attentions.0.key.bias',
		        'query': 'mid_block.attentions.0.query.bias',
		        'value': 'mid_block.attentions.0.value.bias',
		    },
		    'middle_block.1.qkv.weight': {
		        'key': 'mid_block.attentions.0.key.weight',
		        'query': 'mid_block.attentions.0.query.weight',
		        'value': 'mid_block.attentions.0.value.weight',
		    },
		}
		assign_to_checkpoint(
		    lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			attention_paths_to_split=lowerCAmelCase_						,			config=lowerCAmelCase_  )

		for i in range(lowerCAmelCase_  ):
				_a     :      List[str]        =							i // (config['num_res_blocks'] + 1)
				_a     :      Any        =							i % (config['num_res_blocks'] + 1)
				_a     :      Union[str, Any]        =							[shave_segments(lowerCAmelCase_						,			2  ) for name in output_blocks[i]]
				_a     :      Optional[Any]        =							{}

				for layer in output_block_layers:
						_a       ,					_a     :      str        =							layer.split('.'  )[0], shave_segments(lowerCAmelCase_						,			1  )
						if layer_id in output_block_list:
								output_block_list[layer_id].append(lowerCAmelCase_  )
						else:
								_a     :      str        =							[layer_name]

				if len(lowerCAmelCase_  ) > 1:
						_a     :      str        =							[key for key in output_blocks[i] if f"""output_blocks.{i}.0""" in key]
						_a     :      Optional[Any]        =							[key for key in output_blocks[i] if f"""output_blocks.{i}.1""" in key]

						_a     :      Dict        =							renew_resnet_paths(lowerCAmelCase_  )
						_a     :      str        =							renew_resnet_paths(lowerCAmelCase_  )

						_a     :      Optional[int]        =							{'old': f"""output_blocks.{i}.0""", 'new': f"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
						assign_to_checkpoint(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			additional_replacements=[meta_path]						,			config=lowerCAmelCase_  )

						if ["conv.weight", "conv.bias"] in output_block_list.values():
								_a     :      List[Any]        =							list(output_block_list.values()  ).index(['conv.weight', 'conv.bias']  )
								_a     :      Tuple        =							checkpoint[
								    f"""output_blocks.{i}.{index}.conv.weight"""
								]
								_a     :      List[str]        =							checkpoint[
								    f"""output_blocks.{i}.{index}.conv.bias"""
								]

								# Clear attentions as they have been attributed above.
								if len(lowerCAmelCase_  ) == 2:
										_a     :      Union[str, Any]        =							[]

						if len(lowerCAmelCase_  ):
								_a     :      Tuple        =							renew_attention_paths(lowerCAmelCase_  )
								_a     :      str        =							{
								    'old': f"""output_blocks.{i}.1""",
								    'new': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
								}
								_a     :      List[Any]        =							{
								    f"""output_blocks.{i}.1.qkv.bias""": {
								        'key': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
								        'query': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
								        'value': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
								    },
								    f"""output_blocks.{i}.1.qkv.weight""": {
								        'key': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
								        'query': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
								        'value': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
								    },
								}
								assign_to_checkpoint(
								    lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			additional_replacements=[meta_path]						,			attention_paths_to_split=to_split if any('qkv' in key for key in attentions  ) else None						,			config=lowerCAmelCase_						,			)
				else:
						_a     :      List[Any]        =							renew_resnet_paths(lowerCAmelCase_						,			n_shave_prefix_segments=1  )
						for path in resnet_0_paths:
								_a     :      int        =							'.'.join(['output_blocks', str(lowerCAmelCase_  ), path['old']]  )
								_a     :      Union[str, Any]        =							'.'.join(['up_blocks', str(lowerCAmelCase_  ), 'resnets', str(lowerCAmelCase_  ), path['new']]  )

								_a     :      Union[str, Any]        =							checkpoint[old_path]

		return new_checkpoint


if __name__ == "__main__":
							__lowerCAmelCase									=   argparse.ArgumentParser()

							parser.add_argument(
							    '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
							)

							parser.add_argument(
							    '''--config_file''',
							    default=None,
							    type=str,
							    required=True,
							    help='''The config json file corresponding to the architecture.''',
							)

							parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

							__lowerCAmelCase									=   parser.parse_args()

							__lowerCAmelCase									=   torch.load(args.checkpoint_path)

							with open(args.config_file) as f:
														__lowerCAmelCase									=   json.loads(f.read())

							__lowerCAmelCase									=   convert_ldm_checkpoint(checkpoint, config)

							if "ldm" in config:
														del config["ldm"]

							__lowerCAmelCase									=   UNetaDModel(**config)
							model.load_state_dict(converted_checkpoint)

							try:
														__lowerCAmelCase									=   DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
														__lowerCAmelCase									=   VQModel.from_pretrained('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

														__lowerCAmelCase									=   LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
														pipe.save_pretrained(args.dump_path)
							except:  # noqa: E722
														model.save_pretrained(args.dump_path)




