
import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__SCREAMING_SNAKE_CASE			:   Dict         =     logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE			:   Tuple         =     {
    'facebook/sam-vit-huge': 'https://huggingface.co/facebook/sam-vit-huge/resolve/main/config.json',
    'facebook/sam-vit-large': 'https://huggingface.co/facebook/sam-vit-large/resolve/main/config.json',
    'facebook/sam-vit-base': 'https://huggingface.co/facebook/sam-vit-base/resolve/main/config.json',
}


class 		__magic_name__   (						snake_case    ):





		def __init__(							self    :	Tuple						,  lowerCamelCase__    :	int=2_5_6						,  lowerCamelCase__    :	Any=1_0_2_4						,  lowerCamelCase__    :	Tuple=1_6						,  lowerCamelCase__    :	Optional[int]=1_6						,  lowerCamelCase__    :	List[str]=4						,  lowerCamelCase__    :	str="gelu"						,  lowerCamelCase__    :	Optional[int]=1E-6						,  **lowerCamelCase__    :	Any						,  ):
							super().__init__(**lowerCamelCase__				)
							lowerCAmelCase		:					str  	=     hidden_size
							lowerCAmelCase		:					Any  	=     image_size
							lowerCAmelCase		:					int  	=     patch_size
							lowerCAmelCase		:					List[str]  	=     image_size // patch_size
							lowerCAmelCase		:					Tuple  	=     mask_input_channels
							lowerCAmelCase		:					List[str]  	=     num_point_embeddings
							lowerCAmelCase		:					Dict  	=     hidden_act
							lowerCAmelCase		:					Union[str, Any]  	=     layer_norm_eps



class 		__magic_name__   (						snake_case    ):





		def __init__(							self    :	List[Any]						,  lowerCamelCase__    :	Optional[int]=2_5_6						,  lowerCamelCase__    :	Optional[int]="relu"						,  lowerCamelCase__    :	Optional[Any]=2_0_4_8						,  lowerCamelCase__    :	Tuple=2						,  lowerCamelCase__    :	Dict=8						,  lowerCamelCase__    :	str=2						,  lowerCamelCase__    :	List[Any]=3						,  lowerCamelCase__    :	int=3						,  lowerCamelCase__    :	str=2_5_6						,  lowerCamelCase__    :	Optional[Any]=1E-6						,  **lowerCamelCase__    :	Tuple						,  ):
							super().__init__(**lowerCamelCase__				)
							lowerCAmelCase		:					Any  	=     hidden_size
							lowerCAmelCase		:					Dict  	=     hidden_act
							lowerCAmelCase		:					Dict  	=     mlp_dim
							lowerCAmelCase		:					Any  	=     num_hidden_layers
							lowerCAmelCase		:					List[str]  	=     num_attention_heads
							lowerCAmelCase		:					int  	=     attention_downsample_rate
							lowerCAmelCase		:					List[Any]  	=     num_multimask_outputs
							lowerCAmelCase		:					Optional[Any]  	=     iou_head_depth
							lowerCAmelCase		:					Tuple  	=     iou_head_hidden_dim
							lowerCAmelCase		:					Dict  	=     layer_norm_eps



class 		__magic_name__   (						snake_case    ):





		def __init__(							self    :	Tuple						,  lowerCamelCase__    :	List[Any]=7_6_8						,  lowerCamelCase__    :	int=2_5_6						,  lowerCamelCase__    :	Any=1_2						,  lowerCamelCase__    :	int=1_2						,  lowerCamelCase__    :	Union[str, Any]=3						,  lowerCamelCase__    :	int=1_0_2_4						,  lowerCamelCase__    :	Optional[Any]=1_6						,  lowerCamelCase__    :	List[str]="gelu"						,  lowerCamelCase__    :	Tuple=1E-06						,  lowerCamelCase__    :	List[str]=0.0						,  lowerCamelCase__    :	List[str]=1E-10						,  lowerCamelCase__    :	Optional[int]=True						,  lowerCamelCase__    :	Tuple=4.0						,  lowerCamelCase__    :	List[Any]=True						,  lowerCamelCase__    :	Union[str, Any]=True						,  lowerCamelCase__    :	str=1_4						,  lowerCamelCase__    :	int=[2, 5, 8, 1_1]						,  lowerCamelCase__    :	List[str]=1_2_8						,  lowerCamelCase__    :	int=None						,  **lowerCamelCase__    :	Dict						,  ):
							super().__init__(**lowerCamelCase__				)

							lowerCAmelCase		:					List[str]  	=     hidden_size
							lowerCAmelCase		:					Any  	=     output_channels
							lowerCAmelCase		:					Optional[int]  	=     num_hidden_layers
							lowerCAmelCase		:					Optional[int]  	=     num_attention_heads
							lowerCAmelCase		:					Union[str, Any]  	=     num_channels
							lowerCAmelCase		:					int  	=     image_size
							lowerCAmelCase		:					Optional[Any]  	=     patch_size
							lowerCAmelCase		:					List[Any]  	=     hidden_act
							lowerCAmelCase		:					List[str]  	=     layer_norm_eps
							lowerCAmelCase		:					Optional[Any]  	=     attention_dropout
							lowerCAmelCase		:					Any  	=     initializer_range
							lowerCAmelCase		:					List[Any]  	=     qkv_bias
							lowerCAmelCase		:					Dict  	=     mlp_ratio
							lowerCAmelCase		:					Optional[Any]  	=     use_abs_pos
							lowerCAmelCase		:					List[Any]  	=     use_rel_pos
							lowerCAmelCase		:					List[str]  	=     window_size
							lowerCAmelCase		:					int  	=     global_attn_indexes
							lowerCAmelCase		:					List[Any]  	=     num_pos_feats
							lowerCAmelCase		:					List[str]  	=     int(hidden_size * mlp_ratio				) if mlp_dim is None else mlp_dim



class 		__magic_name__   (						snake_case    ):
		_lowerCAmelCase		=	"sam"
		_lowerCAmelCase		=	True





		def __init__(							self    :	Any						,  lowerCamelCase__    :	Any=None						,  lowerCamelCase__    :	Dict=None						,  lowerCamelCase__    :	int=None						,  lowerCamelCase__    :	Optional[int]=0.0_2						,  **lowerCamelCase__    :	Optional[int]						,  ):
							super().__init__(**lowerCamelCase__				)
							lowerCAmelCase		:					int  	=     vision_config if vision_config is not None else {}
							lowerCAmelCase		:					int  	=     prompt_encoder_config if prompt_encoder_config is not None else {}
							lowerCAmelCase		:					Dict  	=     mask_decoder_config if mask_decoder_config is not None else {}

							if isinstance(lowerCamelCase__						,  lowerCamelCase__				):
												lowerCAmelCase		:					Optional[int]  	=     vision_config.to_dict()
							if isinstance(lowerCamelCase__						,  lowerCamelCase__				):
												lowerCAmelCase		:					Dict  	=     prompt_encoder_config.to_dict()
							if isinstance(lowerCamelCase__						,  lowerCamelCase__				):
												lowerCAmelCase		:					Optional[int]  	=     mask_decoder_config.to_dict()

							lowerCAmelCase		:					Union[str, Any]  	=     SamVisionConfig(**lowerCamelCase__				)
							lowerCAmelCase		:					Dict  	=     SamPromptEncoderConfig(**lowerCamelCase__				)
							lowerCAmelCase		:					Any  	=     SamMaskDecoderConfig(**lowerCamelCase__				)
							lowerCAmelCase		:					Union[str, Any]  	=     initializer_range





		def       _A							(							self    :	Any				):
							lowerCAmelCase		:					Optional[Any]  	=     copy.deepcopy(self.__dict__				)
							lowerCAmelCase		:					List[str]  	=     self.vision_config.to_dict()
							lowerCAmelCase		:					Optional[int]  	=     self.prompt_encoder_config.to_dict()
							lowerCAmelCase		:					int  	=     self.mask_decoder_config.to_dict()
							lowerCAmelCase		:					List[Any]  	=     self.__class__.model_type
							return output


import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


__SCREAMING_SNAKE_CASE			:   List[Any]         =     10_00_00_00_00_00
__SCREAMING_SNAKE_CASE			:   Optional[Any]         =     1_00_00

__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE			:   Any         =     os.path.split(__file__)
__SCREAMING_SNAKE_CASE			:   List[str]         =     os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
def     UpperCAmelCase__							(       __magic_name__						:     int							,  __magic_name__						:     int  ):


					'''simple docstring'''

					lowerCAmelCase		:					Optional[int]  	=     pa.Table.from_pydict({'''col''': [0] * chunk_size}  )
					lowerCAmelCase		:					int  	=     pa.concat_tables([table] * (num_examples // chunk_size)  )
					return datasets.Dataset(__magic_name__							,  fingerprint='''table_100B'''  )


@dataclass
class 		__magic_name__   :
		_lowerCAmelCase		=	42
		_lowerCAmelCase		=	42
		_lowerCAmelCase		=	42
		_lowerCAmelCase		=	42





		def       _A							(							self    :	Optional[int]				):
							lowerCAmelCase		:					Union[str, Any]  	=     np.random.default_rng(self.seed				)
							lowerCAmelCase		:					Tuple  	=     rng.integers(low=self.low						,  high=self.high						,  size=self.size				).tolist()





		def __iter__(							self    :	Dict				):
							return iter(self._sampled_values				)





		def __len__(							self    :	Tuple				):
							return self.size



@get_duration
def     UpperCAmelCase__							(       __magic_name__						:     datasets.Dataset  ):


					'''simple docstring'''

					lowerCAmelCase		:					Union[str, Any]  	=     dataset[0]
@get_duration
def     UpperCAmelCase__							(       __magic_name__						:     datasets.Dataset  ):


					'''simple docstring'''

					lowerCAmelCase		:					Any  	=     dataset[-1]
@get_duration
def     UpperCAmelCase__							(       __magic_name__						:     datasets.Dataset  ):


					'''simple docstring'''

					lowerCAmelCase		:					int  	=     dataset[range(len(__magic_name__  ) // 2							,  len(__magic_name__  ) // 2 + 10_24  )]
@get_duration
def     UpperCAmelCase__							(       __magic_name__						:     datasets.Dataset  ):


					'''simple docstring'''

					lowerCAmelCase		:					List[Any]  	=     dataset[RandIter(0							,  len(__magic_name__  )							,  10_24							,  seed=42  )]
def     UpperCAmelCase__							(       ):


					'''simple docstring'''

					lowerCAmelCase		:					Dict  	=     {'''num examples''': SPEED_TEST_N_EXAMPLES}
					lowerCAmelCase		:					Tuple  	=     (get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
					print('''generating dataset'''  )
					lowerCAmelCase		:					Dict  	=     generate_100B_dataset(num_examples=__magic_name__							,  chunk_size=__magic_name__  )
					print('''Functions'''  )
					for func in functions:
										print(func.__name__  )
										lowerCAmelCase		:					List[Any]  	=     func(__magic_name__  )

					with open(__magic_name__							,  '''wb'''  ) as f:
										f.write(json.dumps(__magic_name__  ).encode('''utf-8'''  )  )


if __name__ == "__main__":  # useful to run the profiler
		benchmark_table_100B()
