




"""simple docstring"""




import argparse
import re

import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SamConfig,
    SamImageProcessor,
    SamModel,
    SamProcessor,
    SamVisionConfig,
)


a_        =       {
    'iou_prediction_head.layers.0': 'iou_prediction_head.proj_in',
    'iou_prediction_head.layers.1': 'iou_prediction_head.layers.0',
    'iou_prediction_head.layers.2': 'iou_prediction_head.proj_out',
    'mask_decoder.output_upscaling.0': 'mask_decoder.upscale_conv1',
    'mask_decoder.output_upscaling.1': 'mask_decoder.upscale_layer_norm',
    'mask_decoder.output_upscaling.3': 'mask_decoder.upscale_conv2',
    'mask_downscaling.0': 'mask_embed.conv1',
    'mask_downscaling.1': 'mask_embed.layer_norm1',
    'mask_downscaling.3': 'mask_embed.conv2',
    'mask_downscaling.4': 'mask_embed.layer_norm2',
    'mask_downscaling.6': 'mask_embed.conv3',
    'point_embeddings': 'point_embed',
    'pe_layer.positional_encoding_gaussian_matrix': 'shared_embedding.positional_embedding',
    'image_encoder': 'vision_encoder',
    'neck.0': 'neck.conv1',
    'neck.1': 'neck.layer_norm1',
    'neck.2': 'neck.conv2',
    'neck.3': 'neck.layer_norm2',
    'patch_embed.proj': 'patch_embed.projection',
    '.norm': '.layer_norm',
    'blocks': 'layers',
}







def      __UpperCAmelCase     ( __UpperCamelCase	):
			__lowercase			:  Optional[int]								=       {}
			state_dict.pop('''pixel_mean'''	,				__UpperCamelCase	)
			state_dict.pop('''pixel_std'''	,				__UpperCamelCase	)

			__lowercase			:  List[str]								=       R'''.*.output_hypernetworks_mlps.(\d+).layers.(\d+).*'''

			for key, value in state_dict.items():
						for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
									if key_to_modify in key:
												__lowercase			:  Any								=       key.replace(__UpperCamelCase	,				__UpperCamelCase	)

						if re.match(__UpperCamelCase	,				__UpperCamelCase	):
									__lowercase			:  Union[str, Any]								=       int(re.match(__UpperCamelCase	,				__UpperCamelCase	).group(2	)	)
									if layer_nb == 0:
												__lowercase			:  Union[str, Any]								=       key.replace('''layers.0'''	,				'''proj_in'''	)
									elif layer_nb == 1:
												__lowercase			:  List[Any]								=       key.replace('''layers.1'''	,				'''layers.0'''	)
									elif layer_nb == 2:
												__lowercase			:  int								=       key.replace('''layers.2'''	,				'''proj_out'''	)

						__lowercase			:  Tuple								=       value

			__lowercase			:  int								=       model_state_dict[
			    '''prompt_encoder.shared_embedding.positional_embedding'''
			]

			return model_state_dict







def      __UpperCAmelCase     ( __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase="ybelkada/segment-anything"	):
			__lowercase			:  int								=       hf_hub_download(__UpperCamelCase	,				f"""checkpoints/{model_name}.pth"""	)

			if "sam_vit_b" in model_name:
						__lowercase			:  int								=       SamConfig()
			elif "sam_vit_l" in model_name:
						__lowercase			:  Dict								=       SamVisionConfig(
						    hidden_size=10_24	,				num_hidden_layers=24	,				num_attention_heads=16	,				global_attn_indexes=[5, 11, 17, 23]	,				)

						__lowercase			:  List[Any]								=       SamConfig(
						    vision_config=__UpperCamelCase	,				)
			elif "sam_vit_h" in model_name:
						__lowercase			:  Dict								=       SamVisionConfig(
						    hidden_size=12_80	,				num_hidden_layers=32	,				num_attention_heads=16	,				global_attn_indexes=[7, 15, 23, 31]	,				)

						__lowercase			:  List[str]								=       SamConfig(
						    vision_config=__UpperCamelCase	,				)

			__lowercase			:  List[Any]								=       torch.load(__UpperCamelCase	,				map_location='''cpu'''	)
			__lowercase			:  List[Any]								=       replace_keys(__UpperCamelCase	)

			__lowercase			:  Optional[Any]								=       SamImageProcessor()

			__lowercase			:  Union[str, Any]								=       SamProcessor(image_processor=__UpperCamelCase	)
			__lowercase			:  List[Any]								=       SamModel(__UpperCamelCase	)

			hf_model.load_state_dict(__UpperCamelCase	)
			__lowercase			:  Dict								=       hf_model.to('''cuda'''	)

			__lowercase			:  List[str]								=       '''https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png'''
			__lowercase			:  List[str]								=       Image.open(requests.get(__UpperCamelCase	,				stream=__UpperCamelCase	).raw	).convert('''RGB'''	)

			__lowercase			:  Union[str, Any]								=       [[[4_00, 6_50]]]
			__lowercase			:  List[str]								=       [[1]]

			__lowercase			:  Optional[int]								=       processor(images=np.array(__UpperCamelCase	)	,				return_tensors='''pt'''	).to('''cuda'''	)

			with torch.no_grad():
						__lowercase			:  Dict								=       hf_model(**__UpperCamelCase	)
			__lowercase			:  List[str]								=       output.iou_scores.squeeze()

			if model_name == "sam_vit_h_4b8939":
						assert scores[-1].item() == 0.579_890_251_159_668

						__lowercase			:  Optional[int]								=       processor(
						    images=np.array(__UpperCamelCase	)	,				input_points=__UpperCamelCase	,				input_labels=__UpperCamelCase	,				return_tensors='''pt'''	).to('''cuda'''	)

						with torch.no_grad():
									__lowercase			:  str								=       hf_model(**__UpperCamelCase	)
						__lowercase			:  Optional[Any]								=       output.iou_scores.squeeze()

						assert scores[-1].item() == 0.9_712_603_092_193_604

						__lowercase			:  List[str]								=       ((75, 2_75, 17_25, 8_50),)

						__lowercase			:  int								=       processor(images=np.array(__UpperCamelCase	)	,				input_boxes=__UpperCamelCase	,				return_tensors='''pt'''	).to('''cuda'''	)

						with torch.no_grad():
									__lowercase			:  str								=       hf_model(**__UpperCamelCase	)
						__lowercase			:  Optional[Any]								=       output.iou_scores.squeeze()

						assert scores[-1].item() == 0.8_686_015_605_926_514

						# Test with 2 points and 1 image.
						__lowercase			:  Optional[Any]								=       [[[4_00, 6_50], [8_00, 6_50]]]
						__lowercase			:  Union[str, Any]								=       [[1, 1]]

						__lowercase			:  str								=       processor(
						    images=np.array(__UpperCamelCase	)	,				input_points=__UpperCamelCase	,				input_labels=__UpperCamelCase	,				return_tensors='''pt'''	).to('''cuda'''	)

						with torch.no_grad():
									__lowercase			:  Optional[int]								=       hf_model(**__UpperCamelCase	)
						__lowercase			:  Any								=       output.iou_scores.squeeze()

						assert scores[-1].item() == 0.9_936_047_792_434_692


if __name__ == "__main__":
			a_        =       argparse.ArgumentParser()
			a_        =       ['sam_vit_b_01ec64', 'sam_vit_h_4b8939', 'sam_vit_l_0b3195']
			parser.add_argument(
			    '--model_name',
			    default='sam_vit_h_4b8939',
			    choices=choices,
			    type=str,
			    help='Path to hf config.json of model to convert',
			)
			parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
			parser.add_argument(
			    '--push_to_hub',
			    action='store_true',
			    help='Whether to push the model and processor to the hub after converting',
			)
			parser.add_argument(
			    '--model_hub_id',
			    default='ybelkada/segment-anything',
			    choices=choices,
			    type=str,
			    help='Path to hf config.json of model to convert',
			)

			a_        =       parser.parse_args()

			convert_sam_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub, args.model_hub_id)







import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
				from transformers.models.roberta.modeling_flax_roberta import (
				    FlaxRobertaForCausalLM,
				    FlaxRobertaForMaskedLM,
				    FlaxRobertaForMultipleChoice,
				    FlaxRobertaForQuestionAnswering,
				    FlaxRobertaForSequenceClassification,
				    FlaxRobertaForTokenClassification,
				    FlaxRobertaModel,
				)







class 	__lowercase    (unittest.TestCase  ):





							"""simple docstring"""







							def __init__(      self			,						lowerCAmelCase__			,						lowerCAmelCase__=1_3			,						lowerCAmelCase__=7			,						lowerCAmelCase__=True			,						lowerCAmelCase__=True			,						lowerCAmelCase__=True			,						lowerCAmelCase__=True			,						lowerCAmelCase__=9_9			,						lowerCAmelCase__=3_2			,						lowerCAmelCase__=5			,						lowerCAmelCase__=4			,						lowerCAmelCase__=3_7			,						lowerCAmelCase__="gelu"			,						lowerCAmelCase__=0.1			,						lowerCAmelCase__=0.1			,						lowerCAmelCase__=5_1_2			,						lowerCAmelCase__=1_6			,						lowerCAmelCase__=2			,						lowerCAmelCase__=0.02			,						lowerCAmelCase__=4			,						):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Dict					=					parent
										SCREAMING_SNAKE_CASE_		:     List[str]					=					batch_size
										SCREAMING_SNAKE_CASE_		:     Dict					=					seq_length
										SCREAMING_SNAKE_CASE_		:     Any					=					is_training
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					use_attention_mask
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					use_token_type_ids
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					use_labels
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					vocab_size
										SCREAMING_SNAKE_CASE_		:     Dict					=					hidden_size
										SCREAMING_SNAKE_CASE_		:     Any					=					num_hidden_layers
										SCREAMING_SNAKE_CASE_		:     List[str]					=					num_attention_heads
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					intermediate_size
										SCREAMING_SNAKE_CASE_		:     int					=					hidden_act
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					hidden_dropout_prob
										SCREAMING_SNAKE_CASE_		:     str					=					attention_probs_dropout_prob
										SCREAMING_SNAKE_CASE_		:     Tuple					=					max_position_embeddings
										SCREAMING_SNAKE_CASE_		:     Any					=					type_vocab_size
										SCREAMING_SNAKE_CASE_		:     str					=					type_sequence_label_size
										SCREAMING_SNAKE_CASE_		:     str					=					initializer_range
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					num_choices






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					ids_tensor([self.batch_size, self.seq_length]			,						self.vocab_size       )

										SCREAMING_SNAKE_CASE_		:     str					=					None
										if self.use_attention_mask:
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					random_attention_mask([self.batch_size, self.seq_length]       )

										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					None
										if self.use_token_type_ids:
													SCREAMING_SNAKE_CASE_		:     List[Any]					=					ids_tensor([self.batch_size, self.seq_length]			,						self.type_vocab_size       )

										SCREAMING_SNAKE_CASE_		:     Any					=					RobertaConfig(
										    vocab_size=self.vocab_size			,						hidden_size=self.hidden_size			,						num_hidden_layers=self.num_hidden_layers			,						num_attention_heads=self.num_attention_heads			,						intermediate_size=self.intermediate_size			,						hidden_act=self.hidden_act			,						hidden_dropout_prob=self.hidden_dropout_prob			,						attention_probs_dropout_prob=self.attention_probs_dropout_prob			,						max_position_embeddings=self.max_position_embeddings			,						type_vocab_size=self.type_vocab_size			,						is_decoder=lowerCAmelCase__			,						initializer_range=self.initializer_range			,						)

										return config, input_ids, token_type_ids, attention_mask






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     str					=					self.prepare_config_and_inputs()
										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     str					=					config_and_inputs
										SCREAMING_SNAKE_CASE_		:     List[str]					=					{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
										return config, inputs_dict






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[str]					=					self.prepare_config_and_inputs()
										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					config_and_inputs

										SCREAMING_SNAKE_CASE_		:     str					=					True
										SCREAMING_SNAKE_CASE_		:     Dict					=					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
										SCREAMING_SNAKE_CASE_		:     int					=					ids_tensor([self.batch_size, self.seq_length]			,						vocab_size=2       )

										return (
										    config,
										    input_ids,
										    token_type_ids,
										    encoder_hidden_states,
										    encoder_attention_mask,
										)







@require_flax
class 	__lowercase    (__SCREAMING_SNAKE_CASE	,       unittest.TestCase  ):





							"""simple docstring"""







							_UpperCAmelCase      =						True

							_UpperCAmelCase      =						(
							    (
							        FlaxRobertaModel,
							        FlaxRobertaForCausalLM,
							        FlaxRobertaForMaskedLM,
							        FlaxRobertaForSequenceClassification,
							        FlaxRobertaForTokenClassification,
							        FlaxRobertaForMultipleChoice,
							        FlaxRobertaForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Any					=					FlaxRobertaModelTester(self       )






							@slow
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										for model_class_name in self.all_model_classes:
													SCREAMING_SNAKE_CASE_		:     List[str]					=					model_class_name.from_pretrained('roberta-base'			,						from_pt=lowerCAmelCase__       )
													SCREAMING_SNAKE_CASE_		:     str					=					model(np.ones((1, 1)       )       )
													self.assertIsNotNone(lowerCAmelCase__       )





