



"""simple docstring"""







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))








"""simple docstring"""







import os
import textwrap

import pyarrow as pa
import pytest

from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv

from ..utils import require_pil

@pytest.fixture
def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[Any] ):
						"""simple docstring"""






						snake_case   :			str						    = tmp_path / "file.csv"
						snake_case   :			Tuple						    = textwrap.dedent(
						    "\\n        header1,header2\n        1,2\n        10,20\n        " )
						with open(lowerCamelCase_     ,		"w" ) as f:
												f.write(lowerCamelCase_ )
						return str(lowerCamelCase_ )

@pytest.fixture
def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							List[str] ):
						"""simple docstring"""






						snake_case   :			List[Any]						    = tmp_path / "malformed_file.csv"
						snake_case   :			Union[str, Any]						    = textwrap.dedent(
						    "\\n        header1,header2\n        1,2\n        10,20,\n        " )
						with open(lowerCamelCase_     ,		"w" ) as f:
												f.write(lowerCamelCase_ )
						return str(lowerCamelCase_ )

@pytest.fixture
def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							int     ,		lowerCamelCase_:							Optional[Any] ):
						"""simple docstring"""






						snake_case   :			Dict						    = tmp_path / "csv_with_image.csv"
						snake_case   :			List[Any]						    = textwrap.dedent(
						    f'''\
        image
        {image_file}
        ''' )
						with open(lowerCamelCase_     ,		"w" ) as f:
												f.write(lowerCamelCase_ )
						return str(lowerCamelCase_ )

@pytest.fixture
def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							List[str] ):
						"""simple docstring"""






						snake_case   :			Optional[Any]						    = tmp_path / "csv_with_label.csv"
						snake_case   :			Tuple						    = textwrap.dedent(
						    "\\n        label\n        good\n        bad\n        good\n        " )
						with open(lowerCamelCase_     ,		"w" ) as f:
												f.write(lowerCamelCase_ )
						return str(lowerCamelCase_ )

@pytest.fixture
def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							List[Any] ):
						"""simple docstring"""






						snake_case   :			Union[str, Any]						    = tmp_path / "csv_with_int_list.csv"
						snake_case   :			Optional[int]						    = textwrap.dedent(
						    "\\n        int_list\n        1 2 3\n        4 5 6\n        7 8 9\n        " )
						with open(lowerCamelCase_     ,		"w" ) as f:
												f.write(lowerCamelCase_ )
						return str(lowerCamelCase_ )

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							List[str]     ,		lowerCamelCase_:							Any     ,		lowerCamelCase_:							List[str] ):
						"""simple docstring"""






						snake_case   :			Tuple						    = Csv()
						snake_case   :			str						    = csv._generate_tables([[csv_file, malformed_csv_file]] )
						with pytest.raises(lowerCamelCase_     ,		match="Error tokenizing data" ):
												for _ in generator:
																		pass
						assert any(
						    record.levelname == "ERROR"
						    and "Failed to read file" in record.message
						    and os.path.basename(lowerCamelCase_ ) in record.message
						    for record in caplog.records )

@require_pil
def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							str ):
						"""simple docstring"""






						with open(lowerCamelCase_     ,		encoding="utf-8" ) as f:
												snake_case   :			List[Any]						    = f.read().splitlines()[1]
						snake_case   :			List[str]						    = Csv(encoding="utf-8"     ,		features=Features({"image": Image()} ) )
						snake_case   :			Optional[int]						    = csv._generate_tables([[csv_file_with_image]] )
						snake_case   :			List[Any]						    = pa.concat_tables([table for _, table in generator] )
						assert pa_table.schema.field("image" ).type == Image()()
						snake_case   :			str						    = pa_table.to_pydict()["image"]
						assert generated_content == [{"path": image_file, "bytes": None}]

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							int ):
						"""simple docstring"""






						with open(lowerCamelCase_     ,		encoding="utf-8" ) as f:
												snake_case   :			Optional[Any]						    = f.read().splitlines()[1:]
						snake_case   :			List[str]						    = Csv(encoding="utf-8"     ,		features=Features({"label": ClassLabel(names=["good", "bad"] )} ) )
						snake_case   :			int						    = csv._generate_tables([[csv_file_with_label]] )
						snake_case   :			Dict						    = pa.concat_tables([table for _, table in generator] )
						assert pa_table.schema.field("label" ).type == ClassLabel(names=["good", "bad"] )()
						snake_case   :			Dict						    = pa_table.to_pydict()["label"]
						assert generated_content == [ClassLabel(names=["good", "bad"] ).straint(lowerCamelCase_ ) for label in labels]

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Union[str, Any] ):
						"""simple docstring"""






						snake_case   :			List[Any]						    = Csv(encoding="utf-8"     ,		sep=","     ,		converters={"int_list": lambda lowerCamelCase_ : [int(lowerCamelCase_ ) for i in x.split()]} )
						snake_case   :			Tuple						    = csv._generate_tables([[csv_file_with_int_list]] )
						snake_case   :			str						    = pa.concat_tables([table for _, table in generator] )
						assert pa.types.is_list(pa_table.schema.field("int_list" ).type )
						snake_case   :			Any						    = pa_table.to_pydict()["int_list"]
						assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]



