





import argparse
import json
import os
import time
import zipfile

from get_ci_error_statistics import download_artifact, get_artifacts_links

from transformers import logging


__lowerCAmelCase :List[str]     =    logging.get_logger(__name__)







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					int				       =   set()
					_snake_case       :					Optional[int]				       =   []

					def parse_line(UpperCAmelCase				):
										for line in fp:
															if isinstance(UpperCAmelCase  ,  UpperCAmelCase				):
																				_snake_case       :					str				       =   line.decode("UTF-8"				)
															if "warnings summary (final)" in line:
																				continue
															# This means we are outside the body of a warning
															elif not line.startswith(" "				):
																				# process a single warning and move it to `selected_warnings`.
																				if len(UpperCAmelCase				) > 0:
																									_snake_case       :					List[str]				       =   "\n".join(UpperCAmelCase				)
																									# Only keep the warnings specified in `targets`
																									if any(F""": {x}: """ in warning for x in targets				):
																														selected_warnings.add(UpperCAmelCase				)
																									buffer.clear()
																				continue
															else:
																				_snake_case       :					str				       =   line.strip()
																				buffer.append(UpperCAmelCase				)

					if from_gh:
										for filename in os.listdir(UpperCAmelCase				):
															_snake_case       :					Union[str, Any]				       =   os.path.join(UpperCAmelCase  ,  UpperCAmelCase				)
															if not os.path.isdir(UpperCAmelCase				):
																				# read the file
																				if filename != "warnings.txt":
																									continue
																				with open(UpperCAmelCase				) as fp:
																									parse_line(UpperCAmelCase				)
					else:
										try:
															with zipfile.ZipFile(UpperCAmelCase				) as z:
																				for filename in z.namelist():
																									if not os.path.isdir(UpperCAmelCase				):
																														# read the file
																														if filename != "warnings.txt":
																																			continue
																														with z.open(UpperCAmelCase				) as fp:
																																			parse_line(UpperCAmelCase				)
										except Exception:
															logger.warning(
															    F"""{artifact_path} is either an invalid zip file or something else wrong. This file is skipped."""				)

					return selected_warnings







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					Dict				       =   set()

					_snake_case       :					Dict				       =   [os.path.join(UpperCAmelCase  ,  UpperCAmelCase				) for p in os.listdir(UpperCAmelCase				) if (p.endswith(".zip"				) or from_gh)]
					for p in paths:
										selected_warnings.update(extract_warnings_from_single_artifact(UpperCAmelCase  ,  UpperCAmelCase				)				)

					return selected_warnings


if __name__ == "__main__":







							def    A				(							UpperCAmelCase				):
												return values.split(","				)

							__lowerCAmelCase :Optional[int]     =    argparse.ArgumentParser()
							# Required parameters
							parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
							parser.add_argument(
							    '--output_dir',
							    type=str,
							    required=True,
							    help='Where to store the downloaded artifacts and other result files.',
							)
							parser.add_argument('--token', default=None, type=str, help='A token that has actions:read permission.')
							# optional parameters
							parser.add_argument(
							    '--targets',
							    default='DeprecationWarning,UserWarning,FutureWarning',
							    type=list_str,
							    help='Comma-separated list of target warning(s) which we want to extract.',
							)
							parser.add_argument(
							    '--from_gh',
							    action='store_true',
							    help='If running from a GitHub action workflow and collecting warnings from its artifacts.',
							)

							__lowerCAmelCase :int     =    parser.parse_args()

							__lowerCAmelCase :List[Any]     =    args.from_gh
							if from_gh:
														# The artifacts have to be downloaded using `actions/download-artifact@v3`
														pass
							else:
														os.makedirs(args.output_dir, exist_ok=True)

														# get download links
														__lowerCAmelCase :Optional[int]     =    get_artifacts_links(args.workflow_run_id, token=args.token)
														with open(os.path.join(args.output_dir, 'artifacts.json'), 'w', encoding='UTF-8') as fp:
																					json.dump(artifacts, fp, ensure_ascii=False, indent=4)

														# download artifacts
														for idx, (name, url) in enumerate(artifacts.items()):
																					print(name)
																					print(url)
																					print('=' * 80)
																					download_artifact(name, url, args.output_dir, args.token)
																					# Be gentle to GitHub
																					time.sleep(1)

    # extract warnings from artifacts
							__lowerCAmelCase :Union[str, Any]     =    extract_warnings(args.output_dir, args.targets)
							__lowerCAmelCase :Dict     =    sorted(selected_warnings)
							with open(os.path.join(args.output_dir, 'selected_warnings.json'), 'w', encoding='UTF-8') as fp:
														json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)






import json
import os

import torch

from diffusers import UNetaDModel


os.makedirs('hub/hopper-medium-v2/unet/hor32', exist_ok=True)
os.makedirs('hub/hopper-medium-v2/unet/hor128', exist_ok=True)

os.makedirs('hub/hopper-medium-v2/value_function', exist_ok=True)







def    A				(							UpperCAmelCase				):
					if hor == 128:
										_snake_case       :					int				       =   ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
										_snake_case       :					Tuple				       =   (32, 128, 256)
										_snake_case       :					str				       =   ("UpResnetBlock1D", "UpResnetBlock1D")

					elif hor == 32:
										_snake_case       :					List[Any]				       =   ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
										_snake_case       :					Optional[int]				       =   (32, 64, 128, 256)
										_snake_case       :					int				       =   ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
					_snake_case       :					Dict				       =   torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""				)
					_snake_case       :					Dict				       =   model.state_dict()
					_snake_case       :					Tuple				       =   {
					    "down_block_types": down_block_types,
					    "block_out_channels": block_out_channels,
					    "up_block_types": up_block_types,
					    "layers_per_block": 1,
					    "use_timestep_embedding": True,
					    "out_block_type": "OutConv1DBlock",
					    "norm_num_groups": 8,
					    "downsample_each_block": False,
					    "in_channels": 14,
					    "out_channels": 14,
					    "extra_in_channels": 0,
					    "time_embedding_type": "positional",
					    "flip_sin_to_cos": False,
					    "freq_shift": 1,
					    "sample_size": 65_536,
					    "mid_block_type": "MidResTemporalBlock1D",
					    "act_fn": "mish",
					}
					_snake_case       :					Any				       =   UNetaDModel(**UpperCAmelCase				)
					print(F"""length of state dict: {len(state_dict.keys()				)}"""				)
					print(F"""length of value function dict: {len(hf_value_function.state_dict().keys()				)}"""				)
					_snake_case       :					List[Any]				       =   dict(zip(model.state_dict().keys()  ,  hf_value_function.state_dict().keys()				)				)
					for k, v in mapping.items():
										_snake_case       :					Optional[int]				       =   state_dict.pop(UpperCAmelCase				)
					hf_value_function.load_state_dict(UpperCAmelCase				)

					torch.save(hf_value_function.state_dict()  ,  F"""hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin"""				)
					with open(F"""hub/hopper-medium-v2/unet/hor{hor}/config.json"""  ,  "w"				) as f:
										json.dump(UpperCAmelCase  ,  UpperCAmelCase				)







def    A				(							):
					_snake_case       :					Any				       =   {
					    "in_channels": 14,
					    "down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
					    "up_block_types": (),
					    "out_block_type": "ValueFunction",
					    "mid_block_type": "ValueFunctionMidBlock1D",
					    "block_out_channels": (32, 64, 128, 256),
					    "layers_per_block": 1,
					    "downsample_each_block": True,
					    "sample_size": 65_536,
					    "out_channels": 14,
					    "extra_in_channels": 0,
					    "time_embedding_type": "positional",
					    "use_timestep_embedding": True,
					    "flip_sin_to_cos": False,
					    "freq_shift": 1,
					    "norm_num_groups": 8,
					    "act_fn": "mish",
					}

					_snake_case       :					Dict				       =   torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"				)
					_snake_case       :					Optional[int]				       =   model
					_snake_case       :					List[str]				       =   UNetaDModel(**UpperCAmelCase				)
					print(F"""length of state dict: {len(state_dict.keys()				)}"""				)
					print(F"""length of value function dict: {len(hf_value_function.state_dict().keys()				)}"""				)

					_snake_case       :					Optional[int]				       =   dict(zip(state_dict.keys()  ,  hf_value_function.state_dict().keys()				)				)
					for k, v in mapping.items():
										_snake_case       :					Any				       =   state_dict.pop(UpperCAmelCase				)

					hf_value_function.load_state_dict(UpperCAmelCase				)

					torch.save(hf_value_function.state_dict()  ,  "hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin"				)
					with open("hub/hopper-medium-v2/value_function/config.json"  ,  "w"				) as f:
										json.dump(UpperCAmelCase  ,  UpperCAmelCase				)


if __name__ == "__main__":
							unet(32)
							# unet(128)
							value_function()