


"""simple docstring"""







# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


lowerCAmelCase__				    =       Path(__file__).resolve().parents[3] / '''src'''
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

lowerCAmelCase__				    =       {'''base''': '''patrickvonplaten/wav2vec2_tiny_random''', '''robust''': '''patrickvonplaten/wav2vec2_tiny_random_robust'''}

lowerCAmelCase__				    =       '''zero2'''
lowerCAmelCase__				    =       '''zero3'''
lowerCAmelCase__				    =       [ZEROa, ZEROa]
def  a__      (    SCREAMING_SNAKE_CASE   :   Optional[Any]    ,				SCREAMING_SNAKE_CASE   :   str    ,				SCREAMING_SNAKE_CASE   :   List[Any]    ):


  '''simple docstring'''







  lowerCAmelCase					:					Any								=							parameterized.to_safe_name("_".join(str(SCREAMING_SNAKE_CASE    ) for x in param.args    )    )
  return f"""{func.__name__}_{param_based_name}"""


# Cartesian-product of zero stages with models to test
lowerCAmelCase__				    =       list(itertools.product(stages, models.keys()))





@slow
@require_deepspeed
@require_torch_gpu
class 				SCREAMING_SNAKE_CASE__    (		lowercase    ):
       """simple docstring"""


       @parameterized.expand(snake_case__		,					name_func=snake_case__						)
       def 	lowercase__							(						self		,					snake_case__		,					snake_case__						):






         """simple docstring"""





         self.run_and_check(
             stage=snake_case__		,					model=snake_case__		,					distributed=snake_case__		,					fpaa=snake_case__		,					)

       @require_torch_multi_gpu
       @parameterized.expand(snake_case__		,					name_func=snake_case__						)
       def 	lowercase__							(						self		,					snake_case__		,					snake_case__						):






         """simple docstring"""





         self.run_and_check(
             stage=snake_case__		,					model=snake_case__		,					distributed=snake_case__		,					fpaa=snake_case__		,					)

       @parameterized.expand(snake_case__		,					name_func=snake_case__						)
       def 	lowercase__							(						self		,					snake_case__		,					snake_case__						):






         """simple docstring"""





         self.run_and_check(
             stage=snake_case__		,					model=snake_case__		,					distributed=snake_case__		,					fpaa=snake_case__		,					)

       @require_torch_multi_gpu
       @parameterized.expand(snake_case__		,					name_func=snake_case__						)
       def 	lowercase__							(						self		,					snake_case__		,					snake_case__						):






         """simple docstring"""





         self.run_and_check(
             stage=snake_case__		,					model=snake_case__		,					distributed=snake_case__		,					fpaa=snake_case__		,					)

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         pass

       def 	lowercase__							(						self		,					snake_case__		,					snake_case__		,					snake_case__ = 10		,					snake_case__ = True		,					snake_case__ = True		,					snake_case__ = True		,					):






         """simple docstring"""





         lowerCAmelCase					:					str								=							models[model]

         lowerCAmelCase					:					Dict								=							self.run_trainer(
             stage=snake_case__		,					model_name=snake_case__		,					eval_steps=snake_case__		,					num_train_epochs=1		,					distributed=snake_case__		,					fpaa=snake_case__		,					)

         self.do_checks(snake_case__						)

         return output_dir

       def 	lowercase__							(						self		,					snake_case__		,					snake_case__		,					snake_case__ = 10		,					snake_case__ = 1		,					snake_case__ = True		,					snake_case__ = True		,					):






         """simple docstring"""





         lowerCAmelCase					:					List[str]								=							self.get_auto_remove_tmp_dir("./xxx"		,					after=snake_case__						)
         lowerCAmelCase					:					Tuple								=							f"""
            --model_name_or_path {model_name}
            --dataset_name hf-internal-testing/librispeech_asr_dummy
            --dataset_config_name clean
            --train_split_name validation
            --validation_split_name validation
            --output_dir {output_dir}
            --num_train_epochs {str(snake_case__						)}
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 2
            --evaluation_strategy steps
            --learning_rate 5e-4
            --warmup_steps 8
            --orthography timit
            --preprocessing_num_workers 1
            --group_by_length
            --freeze_feature_extractor
            --report_to none
            --save_steps 0
            --eval_steps {eval_steps}
            --report_to none
        """.split()

         if fpaa:
           args.extend(["--fp16"]						)

         # currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
         # hence the separate config files
         lowerCAmelCase					:					Optional[Any]								=							f"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
         lowerCAmelCase					:					Tuple								=							[f"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
         lowerCAmelCase					:					Tuple								=							self.get_launcher(snake_case__						)

         lowerCAmelCase					:					Optional[Any]								=							launcher + script + args + ds_args
         # keep for quick debug
         # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
         execute_subprocess_async(snake_case__		,					env=self.get_env()						)

         return output_dir







       def 	lowercase__							(						self		,					snake_case__=False						):






         """simple docstring"""





         lowerCAmelCase					:					Union[str, Any]								=							min(2		,					get_gpu_count()						) if distributed else 1
         return f"""deepspeed --num_nodes 1 --num_gpus {num_gpus}""".split()









"""simple docstring"""







import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


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(
         '''--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  a__      (    SCREAMING_SNAKE_CASE   :   Tuple    ):


       '''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)

     lowerCAmelCase__				    =       parser.parse_args()

     lowerCAmelCase__				    =       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)





