


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=5_1_2,
		    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					(  UpperCAmelCase   )->		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)

		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)



'''simple docstring'''






import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
   import torch







@is_pipeline_test
class lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   __lowerCAmelCase         =   MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
   __lowerCAmelCase         =   TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
   def 	__UpperCAmelCase   (      self      :			Any, UpperCamelCase__      :			str, UpperCamelCase__      :			Optional[int], UpperCamelCase__      :			Any     )  ->			List[str]:
        _A    		=  TextaTextGenerationPipeline(model=UpperCamelCase__, tokenizer=UpperCamelCase__     )
        return generator, ["Something to write", "Something else"]
   def 	__UpperCAmelCase   (      self      :			Any, UpperCamelCase__      :			Tuple, UpperCamelCase__      :			Optional[Any]     )  ->			List[str]:
        _A    		=  generator('Something there'     )
        self.assertEqual(UpperCamelCase__, [{'generated_text': ANY(UpperCamelCase__     )}]     )
        # These are encoder decoder, they don't just append to incoming string
        self.assertFalse(outputs[0]['generated_text'].startswith('Something there'     )     )

        _A    		=  generator(['This is great !', 'Something else'], num_return_sequences=2, do_sample=UpperCamelCase__     )
        self.assertEqual(
            UpperCamelCase__, [
                [{'generated_text': ANY(UpperCamelCase__     )}, {'generated_text': ANY(UpperCamelCase__     )}],
                [{'generated_text': ANY(UpperCamelCase__     )}, {'generated_text': ANY(UpperCamelCase__     )}],
            ], )

        _A    		=  generator(
            ['This is great !', 'Something else'], num_return_sequences=2, batch_size=2, do_sample=UpperCamelCase__     )
        self.assertEqual(
            UpperCamelCase__, [
                [{'generated_text': ANY(UpperCamelCase__     )}, {'generated_text': ANY(UpperCamelCase__     )}],
                [{'generated_text': ANY(UpperCamelCase__     )}, {'generated_text': ANY(UpperCamelCase__     )}],
            ], )

        with self.assertRaises(UpperCamelCase__     ):
             generator(4     )
   @require_torch
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			Optional[Any]:
        _A    		=  pipeline('text2text-generation', model='patrickvonplaten/t5-tiny-random', framework='pt'     )
        # do_sample=False necessary for reproducibility
        _A    		=  generator('Something there', do_sample=UpperCamelCase__     )
        self.assertEqual(UpperCamelCase__, [{'generated_text': ''}]     )

        _A    		=  3
        _A    		=  generator(
            'Something there', num_return_sequences=UpperCamelCase__, num_beams=UpperCamelCase__, )
        _A    		=  [
            {'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide Beide'},
            {'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide'},
            {'generated_text': ''},
        ]
        self.assertEqual(UpperCamelCase__, UpperCamelCase__     )

        _A    		=  generator('This is a test', do_sample=UpperCamelCase__, num_return_sequences=2, return_tensors=UpperCamelCase__     )
        self.assertEqual(
            UpperCamelCase__, [
                {'generated_token_ids': ANY(torch.Tensor     )},
                {'generated_token_ids': ANY(torch.Tensor     )},
            ], )
        _A    		=  generator.model.config.eos_token_id
        _A    		=  '<pad>'
        _A    		=  generator(
            ['This is a test', 'This is a second test'], do_sample=UpperCamelCase__, num_return_sequences=2, batch_size=2, return_tensors=UpperCamelCase__, )
        self.assertEqual(
            UpperCamelCase__, [
                [
                    {'generated_token_ids': ANY(torch.Tensor     )},
                    {'generated_token_ids': ANY(torch.Tensor     )},
                ],
                [
                    {'generated_token_ids': ANY(torch.Tensor     )},
                    {'generated_token_ids': ANY(torch.Tensor     )},
                ],
            ], )

   @require_tf
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			Optional[int]:
        _A    		=  pipeline('text2text-generation', model='patrickvonplaten/t5-tiny-random', framework='tf'     )
        # do_sample=False necessary for reproducibility
        _A    		=  generator('Something there', do_sample=UpperCamelCase__     )
        self.assertEqual(UpperCamelCase__, [{'generated_text': ''}]     )



