

import argparse
import json
import logging
import os
import sys
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


a__												=						[
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        '''text-classification''',
        '''language-modeling''',
        '''summarization''',
        '''token-classification''',
        '''question-answering''',
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
			import run_clm_flax
			import run_flax_glue
			import run_flax_ner
			import run_mlm_flax
			import run_qa
			import run_summarization_flax
			import run_ta_mlm_flax


logging.basicConfig(level=logging.DEBUG)

a__												=						logging.getLogger()



def   __UpperCAmelCase					(      )     ->	Optional[int]:

					"""simple docstring"""

					_a      :       Optional[Any]   =						argparse.ArgumentParser()
					parser.add_argument('''-f'''      )
					_a      :       List[str]   =						parser.parse_args()
					return args.f



def   __UpperCAmelCase					(      __a :     str						,__a :     Optional[int]="eval"      )     ->	int:

					"""simple docstring"""

					_a      :       Optional[int]   =						os.path.join(__a						,F"""{split}_results.json"""      )
					if os.path.exists(__a      ):
										with open(__a						,'''r'''      ) as f:
															return json.load(__a      )
					raise ValueError(F"""can't find {path}"""      )


a__												=						logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							def 							__lowercase							(     self			)       ->       Optional[int]:
												_a      :       List[str]   =						self.get_auto_remove_tmp_dir()
												_a      :       Union[str, Any]   =						F"""
            run_glue.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --eval_steps=2
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

												with patch.object(_a			,				'''argv'''			,				_a			):
																	run_flax_glue.main()
																	_a      :       List[Any]   =						get_results(_a			)
																	self.assertGreaterEqual(result['''eval_accuracy''']			,				0.75			)


							@slow
							def 							__lowercase							(     self			)       ->       int:
												_a      :       Optional[Any]   =						self.get_auto_remove_tmp_dir()
												_a      :       Union[str, Any]   =						F"""
            run_clm_flax.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --block_size 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

												with patch.object(_a			,				'''argv'''			,				_a			):
																	run_clm_flax.main()
																	_a      :       List[Any]   =						get_results(_a			)
																	self.assertLess(result['''eval_perplexity''']			,				1_0_0			)


							@slow
							def 							__lowercase							(     self			)       ->       List[Any]:
												_a      :       str   =						self.get_auto_remove_tmp_dir()
												_a      :       List[Any]   =						F"""
            run_summarization.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --test_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=8
            --do_train
            --do_eval
            --do_predict
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --predict_with_generate
        """.split()

												with patch.object(_a			,				'''argv'''			,				_a			):
																	run_summarization_flax.main()
																	_a      :       Optional[Any]   =						get_results(_a			,				split='''test'''			)
																	self.assertGreaterEqual(result['''test_rouge1''']			,				1_0			)
																	self.assertGreaterEqual(result['''test_rouge2''']			,				2			)
																	self.assertGreaterEqual(result['''test_rougeL''']			,				7			)
																	self.assertGreaterEqual(result['''test_rougeLsum''']			,				7			)


							@slow
							def 							__lowercase							(     self			)       ->       Dict:
												_a      :       str   =						self.get_auto_remove_tmp_dir()
												_a      :       str   =						F"""
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --logging_steps 2 --eval_steps 2
            --do_train
            --do_eval
            --num_train_epochs=1
        """.split()

												with patch.object(_a			,				'''argv'''			,				_a			):
																	run_mlm_flax.main()
																	_a      :       List[Any]   =						get_results(_a			)
																	self.assertLess(result['''eval_perplexity''']			,				4_2			)


							@slow
							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												_a      :       List[Any]   =						self.get_auto_remove_tmp_dir()
												_a      :       Tuple   =						F"""
            run_t5_mlm_flax.py
            --model_name_or_path t5-small
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

												with patch.object(_a			,				'''argv'''			,				_a			):
																	run_ta_mlm_flax.main()
																	_a      :       Union[str, Any]   =						get_results(_a			)
																	self.assertGreaterEqual(result['''eval_accuracy''']			,				0.42			)


							@slow
							def 							__lowercase							(     self			)       ->       Tuple:
												# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
												_a      :       Optional[int]   =						7 if get_gpu_count() > 1 else 2

												_a      :       str   =						self.get_auto_remove_tmp_dir()
												_a      :       int   =						F"""
            run_flax_ner.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --warmup_steps=2
            --learning_rate=2e-4
            --logging_steps 2 --eval_steps 2
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
        """.split()

												with patch.object(_a			,				'''argv'''			,				_a			):
																	run_flax_ner.main()
																	_a      :       List[str]   =						get_results(_a			)
																	self.assertGreaterEqual(result['''eval_accuracy''']			,				0.75			)
																	self.assertGreaterEqual(result['''eval_f1''']			,				0.3			)




							@slow
							def 							__lowercase							(     self			)       ->       Optional[Any]:
												_a      :       Tuple   =						self.get_auto_remove_tmp_dir()
												_a      :       Tuple   =						F"""
            run_qa.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=2
            --do_train
            --do_eval
            --logging_steps 2 --eval_steps 2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

												with patch.object(_a			,				'''argv'''			,				_a			):
																	run_qa.main()
																	_a      :       Any   =						get_results(_a			)
																	self.assertGreaterEqual(result['''eval_f1''']			,				3_0			)
																	self.assertGreaterEqual(result['''eval_exact''']			,				3_0			)






import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


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

			parser.add_argument(
			    '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
			)
			parser.add_argument(
			    '''--original_config_file''',
			    type=str,
			    required=True,
			    help='''The YAML config file corresponding to the original architecture.''',
			)
			parser.add_argument(
			    '''--num_in_channels''',
			    default=None,
			    type=int,
			    help='''The number of input channels. If `None` number of input channels will be automatically inferred.''',
			)
			parser.add_argument(
			    '''--image_size''',
			    default=512,
			    type=int,
			    help=(
			        '''The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'''
			        ''' Base. Use 768 for Stable Diffusion v2.'''
			    ),
			)
			parser.add_argument(
			    '''--extract_ema''',
			    action='''store_true''',
			    help=(
			        '''Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'''
			        ''' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'''
			        ''' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'''
			    ),
			)
			parser.add_argument(
			    '''--upcast_attention''',
			    action='''store_true''',
			    help=(
			        '''Whether the attention computation should always be upcasted. This is necessary when running stable'''
			        ''' diffusion 2.1.'''
			    ),
			)
			parser.add_argument(
			    '''--from_safetensors''',
			    action='''store_true''',
			    help='''If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.''',
			)
			parser.add_argument(
			    '''--to_safetensors''',
			    action='''store_true''',
			    help='''Whether to store pipeline in safetensors format or not.''',
			)
			parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
			parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')



			def   __UpperCAmelCase					(      __a :     Any      )     ->	List[Any]:

								"""simple docstring"""

								if string == "True":
													return True
								elif string == "False":
													return False
								else:
													raise ValueError(F"""could not parse string as bool {string}"""      )

			parser.add_argument(
			    '''--use_linear_projection''', help='''Override for use linear projection''', required=False, type=parse_bool
			)

			parser.add_argument('''--cross_attention_dim''', help='''Override for cross attention_dim''', required=False, type=int)

			a__												=						parser.parse_args()

			a__												=						download_controlnet_from_original_ckpt(
			    checkpoint_path=args.checkpoint_path,
			    original_config_file=args.original_config_file,
			    image_size=args.image_size,
			    extract_ema=args.extract_ema,
			    num_in_channels=args.num_in_channels,
			    upcast_attention=args.upcast_attention,
			    from_safetensors=args.from_safetensors,
			    device=args.device,
			    use_linear_projection=args.use_linear_projection,
			    cross_attention_dim=args.cross_attention_dim,
			)







			controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)



