

import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


A__						=							False
A__						=							True
A__						=							False


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

						parser.add_argument(
						    "--repo_path",
						    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.")

						A__						=							parser.parse_args()

						A__						=							{
						    "image_size": "sample_size",
						    "num_res_blocks": "layers_per_block",
						    "block_channels": "block_out_channels",
						    "down_blocks": "down_block_types",
						    "up_blocks": "up_block_types",
						    "downscale_freq_shift": "freq_shift",
						    "resnet_num_groups": "norm_num_groups",
						    "resnet_act_fn": "act_fn",
						    "resnet_eps": "norm_eps",
						    "num_head_channels": "attention_head_dim",
						}

						A__						=							{
						    "time_steps": "time_proj",
						    "mid": "mid_block",
						    "downsample_blocks": "down_blocks",
						    "upsample_blocks": "up_blocks",
						}

						A__						=							"" if has_file(args.repo_path, "config.json") else "unet"

						with open(os.path.join(args.repo_path, subfolder, "config.json"), "r", encoding="utf-8") as reader:
												A__						=							reader.read()
												A__						=							json.loads(text)

						if do_only_config:
												for key in config_parameters_to_change.keys():
																		config.pop(key, None)

						if has_file(args.repo_path, "config.json"):
												A__						=							UNetaDModel(**config)
						else:
												A__						=							UNetaDConditionModel if "ldm-text2im-large-256" in args.repo_path else UNetaDModel
												A__						=							class_name(**config)

						if do_only_config:
												model.save_config(os.path.join(args.repo_path, subfolder))

						A__						=							dict(model.config)

						if do_only_renaming:
												for key, value in config_parameters_to_change.items():
																		if key in config:
																								A__						=							config[key]
																								del config[key]

												A__						=							[k.replace("UNetRes", "") for k in config["down_block_types"]]
												A__						=							[k.replace("UNetRes", "") for k in config["up_block_types"]]

						if do_only_weights:
												A__						=							torch.load(os.path.join(args.repo_path, subfolder, "diffusion_pytorch_model.bin"))

												A__						=							{}
												for param_key, param_value in state_dict.items():
																		if param_key.endswith(".op.bias") or param_key.endswith(".op.weight"):
																								continue
																		A__						=							False
																		for key, new_key in key_parameters_to_change.items():
																								if not has_changed and param_key.split(".")[0] == key:
																														A__						=							param_value
																														A__						=							True
																		if not has_changed:
																								A__						=							param_value

												model.load_state_dict(new_state_dict)
												model.save_pretrained(os.path.join(args.repo_path, subfolder))







import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE  ,						unittest.TestCase			):
	_lowercase  :   Tuple       				=  LayoutLMTokenizer
	_lowercase  :   List[str]       				=  LayoutLMTokenizerFast
	_lowercase  :   List[Any]       				=  True
	_lowercase  :   Optional[int]       				=  True







	def 	_SCREAMING_SNAKE_CASE							(   self: Any      ):

						'''simple docstring'''





						super().setUp()

						__magic_name__							=    [
						    '[UNK]',
						    '[CLS]',
						    '[SEP]',
						    'want',
						    '##want',
						    '##ed',
						    'wa',
						    'un',
						    'runn',
						    '##ing',
						    ',',
						    'low',
						    'lowest',
						]
						__magic_name__							=    os.path.join(self.tmpdirname  , VOCAB_FILES_NAMES['vocab_file']      )
						with open(self.vocab_file  , 'w'  , encoding='utf-8'      ) as vocab_writer:
											vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]      )      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]  , **__UpperCamelCase: Union[str, Any]      ):

						'''simple docstring'''





						return LayoutLMTokenizer.from_pretrained(self.tmpdirname  , **__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple  , __UpperCamelCase: List[str]      ):

						'''simple docstring'''





						__magic_name__							=    'UNwant\u00E9d,running'
						__magic_name__							=    'unwanted, running'
						return input_text, output_text







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





						__magic_name__							=    self.tokenizer_class(self.vocab_file      )

						__magic_name__							=    tokenizer.tokenize('UNwant\u00E9d,running'      )
						self.assertListEqual(__UpperCamelCase  , ['un', '##want', '##ed', ',', 'runn', '##ing']      )
						self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCamelCase      )  , [7, 4, 5, 10, 8, 9]      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						pass




