
"""simple docstring"""


import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel






def    _snake_case					(		snake_case__ :  BertModel						,   snake_case__ :  str						,   snake_case__ :  str  ):
	A     = ('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

	A     = (
	    ('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(snake_case__  ):
		os.makedirs(snake_case__  )

	A     = model.state_dict()

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

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

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

		A     = tf.train.Saver(tf.trainable_variables()  )
		saver.save(snake_case__						,   os.path.join(snake_case__						,   model_name.replace('-'						,   '_'  ) + '.ckpt'  )  )






def    _snake_case					(		snake_case__ :  Tuple=None  ):
	A     = argparse.ArgumentParser()
	parser.add_argument('--model_name'						,   type=snake_case__						,   required=snake_case__						,   help='model name e.g. bert-base-uncased'  )
	parser.add_argument(
	    '--cache_dir'						,   type=snake_case__						,   default=snake_case__						,   required=snake_case__						,   help='Directory containing pytorch model'  )
	parser.add_argument('--pytorch_model_path'						,   type=snake_case__						,   required=snake_case__						,   help='/path/to/<pytorch-model-name>.bin'  )
	parser.add_argument('--tf_cache_dir'						,   type=snake_case__						,   required=snake_case__						,   help='Directory in which to save tensorflow model'  )
	A     = parser.parse_args(snake_case__  )

	A     = 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=snake_case__						,   ckpt_dir=args.tf_cache_dir						,   model_name=args.model_name  )


if __name__ == "__main__":
		main()


'''simple docstring'''

import unittest

from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
							from PIL import Image
else:






							class    snake_case     :






											"""simple docstring"""



											@staticmethod
											def   a__					(    *_lowercase,     **_lowercase       )     ->							Tuple:
																		pass






@is_pipeline_test
@require_vision
@require_torch
class    snake_case     (      unittest.TestCase		):






				"""simple docstring"""
				_a    	=     MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING



				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase       )     ->							Tuple:
											SCREAMING_SNAKE_CASE_				=				pipeline(
											    'zero-shot-object-detection',     model='hf-internal-testing/tiny-random-owlvit-object-detection'       )

											SCREAMING_SNAKE_CASE_				=				[
											    {
											        'image': './tests/fixtures/tests_samples/COCO/000000039769.png',
											        'candidate_labels': ['cat', 'remote', 'couch'],
											    }
											]
											return object_detector, examples



				def   a__					(    self,     _lowercase,     _lowercase       )     ->							str:
											SCREAMING_SNAKE_CASE_				=				object_detector(examples[0],     threshold=0.0       )

											SCREAMING_SNAKE_CASE_				=				len(_lowercase       )
											self.assertGreater(_lowercase,     0       )
											self.assertEqual(
											    _lowercase,     [
											        {
											            'score': ANY(_lowercase       ),
											            'label': ANY(_lowercase       ),
											            'box': {'xmin': ANY(_lowercase       ), 'ymin': ANY(_lowercase       ), 'xmax': ANY(_lowercase       ), 'ymax': ANY(_lowercase       )},
											        }
											        for i in range(_lowercase       )
											    ],     )



				@require_tf
				@unittest.skip('Zero Shot Object Detection not implemented in TF'       )
				def   a__					(    self       )     ->							Union[str, Any]:
											pass



				@require_torch
				def   a__					(    self       )     ->							str:
											SCREAMING_SNAKE_CASE_				=				pipeline(
											    'zero-shot-object-detection',     model='hf-internal-testing/tiny-random-owlvit-object-detection'       )

											SCREAMING_SNAKE_CASE_				=				object_detector(
											    './tests/fixtures/tests_samples/COCO/000000039769.png',     candidate_labels=['cat', 'remote', 'couch'],     threshold=0.64,     )

											self.assertEqual(
											    nested_simplify(_lowercase,     decimals=4       ),     [
											        {'score': 0.7_235, 'label': 'cat', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
											        {'score': 0.7_218, 'label': 'remote', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
											        {'score': 0.7_184, 'label': 'couch', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
											        {'score': 0.6_748, 'label': 'remote', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
											        {'score': 0.6_656, 'label': 'cat', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
											        {'score': 0.6_614, 'label': 'couch', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
											        {'score': 0.6_456, 'label': 'remote', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
											        {'score': 0.642, 'label': 'remote', 'box': {'xmin': 67, 'ymin': 274, 'xmax': 93, 'ymax': 297}},
											        {'score': 0.6_419, 'label': 'cat', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
											    ],     )

											SCREAMING_SNAKE_CASE_				=				object_detector(
											    [
											        {
											            'image': './tests/fixtures/tests_samples/COCO/000000039769.png',
											            'candidate_labels': ['cat', 'remote', 'couch'],
											        }
											    ],     threshold=0.64,     )

											self.assertEqual(
											    nested_simplify(_lowercase,     decimals=4       ),     [
											        [
											            {'score': 0.7_235, 'label': 'cat', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
											            {'score': 0.7_218, 'label': 'remote', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
											            {'score': 0.7_184, 'label': 'couch', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
											            {'score': 0.6_748, 'label': 'remote', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
											            {'score': 0.6_656, 'label': 'cat', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
											            {'score': 0.6_614, 'label': 'couch', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
											            {'score': 0.6_456, 'label': 'remote', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
											            {'score': 0.642, 'label': 'remote', 'box': {'xmin': 67, 'ymin': 274, 'xmax': 93, 'ymax': 297}},
											            {'score': 0.6_419, 'label': 'cat', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
											        ]
											    ],     )



				@require_torch
				@slow
				def   a__					(    self       )     ->							Optional[int]:
											SCREAMING_SNAKE_CASE_				=				pipeline('zero-shot-object-detection'       )

											SCREAMING_SNAKE_CASE_				=				object_detector(
											    'http://images.cocodataset.org/val2017/000000039769.jpg',     candidate_labels=['cat', 'remote', 'couch'],     )
											self.assertEqual(
											    nested_simplify(_lowercase,     decimals=4       ),     [
											        {'score': 0.2_868, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
											        {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
											        {'score': 0.2_537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
											        {'score': 0.1_474, 'label': 'remote', 'box': {'xmin': 335, 'ymin': 74, 'xmax': 371, 'ymax': 187}},
											        {'score': 0.1_208, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}},
											    ],     )

											SCREAMING_SNAKE_CASE_				=				object_detector(
											    [
											        {
											            'image': 'http://images.cocodataset.org/val2017/000000039769.jpg',
											            'candidate_labels': ['cat', 'remote', 'couch'],
											        },
											        {
											            'image': 'http://images.cocodataset.org/val2017/000000039769.jpg',
											            'candidate_labels': ['cat', 'remote', 'couch'],
											        },
											    ],     )
											self.assertEqual(
											    nested_simplify(_lowercase,     decimals=4       ),     [
											        [
											            {'score': 0.2_868, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
											            {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
											            {'score': 0.2_537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
											            {'score': 0.1_474, 'label': 'remote', 'box': {'xmin': 335, 'ymin': 74, 'xmax': 371, 'ymax': 187}},
											            {'score': 0.1_208, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}},
											        ],
											        [
											            {'score': 0.2_868, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
											            {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
											            {'score': 0.2_537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
											            {'score': 0.1_474, 'label': 'remote', 'box': {'xmin': 335, 'ymin': 74, 'xmax': 371, 'ymax': 187}},
											            {'score': 0.1_208, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}},
											        ],
											    ],     )



				@require_tf
				@unittest.skip('Zero Shot Object Detection not implemented in TF'       )
				def   a__					(    self       )     ->							Optional[Any]:
											pass



				@require_torch
				@slow
				def   a__					(    self       )     ->							Any:
											SCREAMING_SNAKE_CASE_				=				0.2
											SCREAMING_SNAKE_CASE_				=				pipeline('zero-shot-object-detection'       )

											SCREAMING_SNAKE_CASE_				=				object_detector(
											    'http://images.cocodataset.org/val2017/000000039769.jpg',     candidate_labels=['cat', 'remote', 'couch'],     threshold=_lowercase,     )
											self.assertEqual(
											    nested_simplify(_lowercase,     decimals=4       ),     [
											        {'score': 0.2_868, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
											        {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
											        {'score': 0.2_537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
											    ],     )





				@require_torch
				@slow
				def   a__					(    self       )     ->							int:
											SCREAMING_SNAKE_CASE_				=				2
											SCREAMING_SNAKE_CASE_				=				pipeline('zero-shot-object-detection'       )

											SCREAMING_SNAKE_CASE_				=				object_detector(
											    'http://images.cocodataset.org/val2017/000000039769.jpg',     candidate_labels=['cat', 'remote', 'couch'],     top_k=_lowercase,     )
											self.assertEqual(
											    nested_simplify(_lowercase,     decimals=4       ),     [
											        {'score': 0.2_868, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
											        {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
											    ],     )





