



"""simple docstring"""






import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel






def        snake_case_  (      A_					:		BertModel,				A_					:		str,				A_					:		str						):


       '''simple docstring'''





       _lowerCamelCase						:				Optional[int]    			= ('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

       _lowerCamelCase						:				Any    			= (
           ('layer.', 'layer_'),
           ('word_embeddings.weight', 'word_embeddings'),
           ('position_embeddings.weight', 'position_embeddings'),
           ('token_type_embeddings.weight', 'token_type_embeddings'),
           ('.', '/'),
           ('LayerNorm/weight', 'LayerNorm/gamma'),
           ('LayerNorm/bias', 'LayerNorm/beta'),
           ('weight', 'kernel'),
       )

       if not os.path.isdir(UpperCAmelCase_						):
              os.makedirs(UpperCAmelCase_						)

       _lowerCamelCase						:				str    			= model.state_dict()

       def to_tf_var_name(A_					:		str						):
              for patt, repl in iter(UpperCAmelCase_						):
                     _lowerCamelCase						:				str    			= name.replace(UpperCAmelCase_,				UpperCAmelCase_						)
              return F'''bert/{name}'''

       def create_tf_var(A_					:		np.ndarray,				A_					:		str,				A_					:		tf.Session						):
              _lowerCamelCase						:				str    			= tf.dtypes.as_dtype(tensor.dtype						)
              _lowerCamelCase						:				Dict    			= tf.get_variable(dtype=UpperCAmelCase_,				shape=tensor.shape,				name=UpperCAmelCase_,				initializer=tf.zeros_initializer()						)
              session.run(tf.variables_initializer([tf_var]						)						)
              session.run(UpperCAmelCase_						)
              return tf_var

       tf.reset_default_graph()
       with tf.Session() as session:
              for var_name in state_dict:
                     _lowerCamelCase						:				List[Any]    			= to_tf_var_name(UpperCAmelCase_						)
                     _lowerCamelCase						:				List[str]    			= state_dict[var_name].numpy()
                     if any(x in var_name for x in tensors_to_transpose						):
                            _lowerCamelCase						:				List[str]    			= torch_tensor.T
                     _lowerCamelCase						:				str    			= create_tf_var(tensor=UpperCAmelCase_,				name=UpperCAmelCase_,				session=UpperCAmelCase_						)
                     tf.keras.backend.set_value(UpperCAmelCase_,				UpperCAmelCase_						)
                     _lowerCamelCase						:				int    			= session.run(UpperCAmelCase_						)
                     print(F'''Successfully created {tf_name}: {np.allclose(UpperCAmelCase_,				UpperCAmelCase_						)}'''						)

              _lowerCamelCase						:				List[str]    			= tf.train.Saver(tf.trainable_variables()						)
              saver.save(UpperCAmelCase_,				os.path.join(UpperCAmelCase_,				model_name.replace('''-''',				'''_'''						) + '''.ckpt'''						)						)






def        snake_case_  (      A_					:		List[Any]=None						):


       '''simple docstring'''





       _lowerCamelCase						:				Optional[int]    			= argparse.ArgumentParser()
       parser.add_argument('''--model_name''',				type=UpperCAmelCase_,				required=UpperCAmelCase_,				help='''model name e.g. bert-base-uncased'''						)
       parser.add_argument(
           '''--cache_dir''',				type=UpperCAmelCase_,				default=UpperCAmelCase_,				required=UpperCAmelCase_,				help='''Directory containing pytorch model'''						)
       parser.add_argument('''--pytorch_model_path''',				type=UpperCAmelCase_,				required=UpperCAmelCase_,				help='''/path/to/<pytorch-model-name>.bin'''						)
       parser.add_argument('''--tf_cache_dir''',				type=UpperCAmelCase_,				required=UpperCAmelCase_,				help='''Directory in which to save tensorflow model'''						)
       _lowerCamelCase						:				Optional[int]    			= parser.parse_args(UpperCAmelCase_						)

       _lowerCamelCase						:				int    			= BertModel.from_pretrained(
           pretrained_model_name_or_path=args.model_name,				state_dict=torch.load(args.pytorch_model_path						),				cache_dir=args.cache_dir,				)

       convert_pytorch_checkpoint_to_tf(model=UpperCAmelCase_,				ckpt_dir=args.tf_cache_dir,				model_name=args.model_name						)


if __name__ == "__main__":
   main()






'''simple docstring'''



import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
      import onnxruntime as ort





class    UpperCAmelCase_  (_UpperCAmelCase					,		unittest.TestCase  ):





    """simple docstring"""






    pass







@nightly
@require_onnxruntime
@require_torch_gpu
class    UpperCAmelCase_  (unittest.TestCase  ):





    """simple docstring"""






    @property
    def       lowercase_				(							self	)				->     int:
         return (
             "CUDAExecutionProvider",
             {
                 "gpu_mem_limit": "15000000000",  # 15GB
                 "arena_extend_strategy": "kSameAsRequested",
             },
         )







    @property
    def       lowercase_				(							self	)				->     Dict:
         __lowerCamelCase   :	int								=		ort.SessionOptions()
         __lowerCamelCase   :	Optional[Any]								=		False
         return options







    def       lowercase_				(							self	)				->     Union[str, Any]:
         __lowerCamelCase   :	Any								=		load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/in_paint/overture-creations-5sI6fQgYIuo.png'	)
         __lowerCamelCase   :	Any								=		load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'	)
         __lowerCamelCase   :	Any								=		OnnxStableDiffusionInpaintPipeline.from_pretrained(
             'runwayml/stable-diffusion-inpainting'			,     revision='onnx'			,     safety_checker=SCREAMING_SNAKE_CASE_			,     feature_extractor=SCREAMING_SNAKE_CASE_			,     provider=self.gpu_provider			,     sess_options=self.gpu_options			,     )
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	Tuple								=		'A red cat sitting on a park bench'

         __lowerCamelCase   :	Union[str, Any]								=		np.random.RandomState(0	)
         __lowerCamelCase   :	Union[str, Any]								=		pipe(
             prompt=SCREAMING_SNAKE_CASE_			,     image=SCREAMING_SNAKE_CASE_			,     mask_image=SCREAMING_SNAKE_CASE_			,     guidance_scale=7.5			,     num_inference_steps=10			,     generator=SCREAMING_SNAKE_CASE_			,     output_type='np'			,     )
         __lowerCamelCase   :	Optional[Any]								=		output.images
         __lowerCamelCase   :	Tuple								=		images[0, 2_55:2_58, 2_55:2_58, -1]

         assert images.shape == (1, 5_12, 5_12, 3)
         __lowerCamelCase   :	Union[str, Any]								=		np.array([0.2_5_1_4, 0.3_0_0_7, 0.3_5_1_7, 0.1_7_9_0, 0.2_3_8_2, 0.3_1_6_7, 0.1_9_4_4, 0.2_2_7_3, 0.2_4_6_4]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-3







    def       lowercase_				(							self	)				->     Optional[Any]:
         __lowerCamelCase   :	Union[str, Any]								=		load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/in_paint/overture-creations-5sI6fQgYIuo.png'	)
         __lowerCamelCase   :	List[str]								=		load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'	)
         __lowerCamelCase   :	Dict								=		LMSDiscreteScheduler.from_pretrained(
             'runwayml/stable-diffusion-inpainting'			,     subfolder='scheduler'			,     revision='onnx'	)
         __lowerCamelCase   :	Tuple								=		OnnxStableDiffusionInpaintPipeline.from_pretrained(
             'runwayml/stable-diffusion-inpainting'			,     revision='onnx'			,     scheduler=SCREAMING_SNAKE_CASE_			,     safety_checker=SCREAMING_SNAKE_CASE_			,     feature_extractor=SCREAMING_SNAKE_CASE_			,     provider=self.gpu_provider			,     sess_options=self.gpu_options			,     )
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	str								=		'A red cat sitting on a park bench'

         __lowerCamelCase   :	Tuple								=		np.random.RandomState(0	)
         __lowerCamelCase   :	Optional[int]								=		pipe(
             prompt=SCREAMING_SNAKE_CASE_			,     image=SCREAMING_SNAKE_CASE_			,     mask_image=SCREAMING_SNAKE_CASE_			,     guidance_scale=7.5			,     num_inference_steps=20			,     generator=SCREAMING_SNAKE_CASE_			,     output_type='np'			,     )
         __lowerCamelCase   :	List[str]								=		output.images
         __lowerCamelCase   :	Dict								=		images[0, 2_55:2_58, 2_55:2_58, -1]

         assert images.shape == (1, 5_12, 5_12, 3)
         __lowerCamelCase   :	Tuple								=		np.array([0.0_0_8_6, 0.0_0_7_7, 0.0_0_8_3, 0.0_0_9_3, 0.0_1_0_7, 0.0_1_3_9, 0.0_0_9_4, 0.0_0_9_7, 0.0_1_2_5]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-3






