





'''simple docstring'''



import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


_SCREAMING_SNAKE_CASE					=    logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE					=    {
    '''Salesforce/instruct-blip-flan-t5''': '''https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json''',
}






class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	a    :				str											=       "instructblip_vision_model"


	def __init__(self     ,_lowerCamelCase=1408     ,_lowerCamelCase=6144     ,_lowerCamelCase=39     ,_lowerCamelCase=16     ,_lowerCamelCase=224     ,_lowerCamelCase=14     ,_lowerCamelCase="gelu"     ,_lowerCamelCase=1E-6     ,_lowerCamelCase=0.0     ,_lowerCamelCase=1E-1_0     ,_lowerCamelCase=True     ,**_lowerCamelCase     ,)							->							Tuple:







				'''simple docstring'''





				super().__init__(**_lowerCamelCase )

				__lowercase										=							hidden_size
				__lowercase										=							intermediate_size
				__lowercase										=							num_hidden_layers
				__lowercase										=							num_attention_heads
				__lowercase										=							patch_size
				__lowercase										=							image_size
				__lowercase										=							initializer_range
				__lowercase										=							attention_dropout
				__lowercase										=							layer_norm_eps
				__lowercase										=							hidden_act
				__lowercase										=							qkv_bias


	@classmethod
	def 						_UpperCAmelCase     (cls     ,_lowerCamelCase     ,**_lowerCamelCase )							->							"PretrainedConfig":







				'''simple docstring'''





				cls._set_token_in_kwargs(_lowerCamelCase )

				__lowercase						,		__lowercase										=							cls.get_config_dict(_lowerCamelCase     ,**_lowerCamelCase )

				# get the vision config dict if we are loading from InstructBlipConfig
				if config_dict.get('''model_type''' ) == "instructblip":
							__lowercase										=							config_dict['''vision_config''']

				if "model_type" in config_dict and hasattr(cls     ,'''model_type''' ) and config_dict["model_type"] != cls.model_type:
							logger.warning(
							    f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
							    f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." )

				return cls.from_dict(_lowerCamelCase     ,**_lowerCamelCase )






class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	a    :				Dict											=       "instructblip_qformer"


	def __init__(self     ,_lowerCamelCase=30522     ,_lowerCamelCase=768     ,_lowerCamelCase=12     ,_lowerCamelCase=12     ,_lowerCamelCase=3072     ,_lowerCamelCase="gelu"     ,_lowerCamelCase=0.1     ,_lowerCamelCase=0.1     ,_lowerCamelCase=512     ,_lowerCamelCase=0.0_2     ,_lowerCamelCase=1E-1_2     ,_lowerCamelCase=0     ,_lowerCamelCase="absolute"     ,_lowerCamelCase=2     ,_lowerCamelCase=1408     ,**_lowerCamelCase     ,)							->							Union[str, Any]:







				'''simple docstring'''





				super().__init__(pad_token_id=_lowerCamelCase     ,**_lowerCamelCase )

				__lowercase										=							vocab_size
				__lowercase										=							hidden_size
				__lowercase										=							num_hidden_layers
				__lowercase										=							num_attention_heads
				__lowercase										=							hidden_act
				__lowercase										=							intermediate_size
				__lowercase										=							hidden_dropout_prob
				__lowercase										=							attention_probs_dropout_prob
				__lowercase										=							max_position_embeddings
				__lowercase										=							initializer_range
				__lowercase										=							layer_norm_eps
				__lowercase										=							position_embedding_type
				__lowercase										=							cross_attention_frequency
				__lowercase										=							encoder_hidden_size


	@classmethod
	def 						_UpperCAmelCase     (cls     ,_lowerCamelCase     ,**_lowerCamelCase )							->							"PretrainedConfig":







				'''simple docstring'''





				cls._set_token_in_kwargs(_lowerCamelCase )

				__lowercase						,		__lowercase										=							cls.get_config_dict(_lowerCamelCase     ,**_lowerCamelCase )

				# get the qformer config dict if we are loading from InstructBlipConfig
				if config_dict.get('''model_type''' ) == "instructblip":
							__lowercase										=							config_dict['''qformer_config''']

				if "model_type" in config_dict and hasattr(cls     ,'''model_type''' ) and config_dict["model_type"] != cls.model_type:
							logger.warning(
							    f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
							    f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." )

				return cls.from_dict(_lowerCamelCase     ,**_lowerCamelCase )






class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	a    :				List[str]											=       "instructblip"
	a    :				str											=       True


	def __init__(self     ,_lowerCamelCase=None     ,_lowerCamelCase=None     ,_lowerCamelCase=None     ,_lowerCamelCase=32     ,**_lowerCamelCase )							->							Optional[Any]:







				'''simple docstring'''





				super().__init__(**_lowerCamelCase )

				if vision_config is None:
							__lowercase										=							{}
							logger.info('''vision_config is None. initializing the InstructBlipVisionConfig with default values.''' )

				if qformer_config is None:
							__lowercase										=							{}
							logger.info('''qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.''' )

				if text_config is None:
							__lowercase										=							{}
							logger.info('''text_config is None. Initializing the text config with default values (`OPTConfig`).''' )

				__lowercase										=							InstructBlipVisionConfig(**_lowerCamelCase )
				__lowercase										=							InstructBlipQFormerConfig(**_lowerCamelCase )
				__lowercase										=							text_config['''model_type'''] if '''model_type''' in text_config else '''opt'''
				__lowercase										=							CONFIG_MAPPING[text_model_type](**_lowerCamelCase )

				__lowercase										=							self.text_config.tie_word_embeddings
				__lowercase										=							self.text_config.is_encoder_decoder

				__lowercase										=							num_query_tokens
				__lowercase										=							self.vision_config.hidden_size
				__lowercase										=							self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
				__lowercase										=							1.0
				__lowercase										=							0.0_2


	@classmethod
	def 						_UpperCAmelCase     (cls     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,**_lowerCamelCase     ,)							->							Optional[int]:







				'''simple docstring'''





				return cls(
				    vision_config=vision_config.to_dict()     ,qformer_config=qformer_config.to_dict()     ,text_config=text_config.to_dict()     ,**_lowerCamelCase     ,)


	def 						_UpperCAmelCase     (self )							->							Any:







				'''simple docstring'''





				__lowercase										=							copy.deepcopy(self.__dict__ )
				__lowercase										=							self.vision_config.to_dict()
				__lowercase										=							self.qformer_config.to_dict()
				__lowercase										=							self.text_config.to_dict()
				__lowercase										=							self.__class__.model_type
				return output












'''simple docstring'''



from __future__ import annotations

import time
from collections.abc import Sequence
from random import randint

from matplotlib import pyplot as plt




def    _lowerCAmelCase						(				lowerCamelCase_			:  Sequence[float]		,       lowerCamelCase_			:  int		,       lowerCamelCase_			:  int				):
			if not arr:
						return None, None, 0
			if low == high:
						return low, high, arr[low]

			__lowercase										=							(low + high) // 2
			__lowercase						,		__lowercase						,		__lowercase										=							max_subarray(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_				)
			__lowercase						,		__lowercase						,		__lowercase										=							max_subarray(lowerCamelCase_		,       mid + 1		,       lowerCamelCase_				)
			__lowercase						,		__lowercase						,		__lowercase										=							max_cross_sum(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_				)
			if left_sum >= right_sum and left_sum >= cross_sum:
						return left_low, left_high, left_sum
			elif right_sum >= left_sum and right_sum >= cross_sum:
						return right_low, right_high, right_sum
			return cross_left, cross_right, cross_sum




def    _lowerCAmelCase						(				lowerCamelCase_			:  Sequence[float]		,       lowerCamelCase_			:  int		,       lowerCamelCase_			:  int		,       lowerCamelCase_			:  int				):
			__lowercase						,		__lowercase										=							float('''-inf'''				), -1
			__lowercase						,		__lowercase										=							float('''-inf'''				), -1

			__lowercase										=							0
			for i in range(lowerCamelCase_		,       low - 1		,       -1				):
						summ += arr[i]
						if summ > left_sum:
									__lowercase										=							summ
									__lowercase										=							i

			__lowercase										=							0
			for i in range(mid + 1		,       high + 1				):
						summ += arr[i]
						if summ > right_sum:
									__lowercase										=							summ
									__lowercase										=							i

			return max_left, max_right, (left_sum + right_sum)




def    _lowerCAmelCase						(				lowerCamelCase_			:  int				):
			__lowercase										=							[randint(1		,       lowerCamelCase_				) for _ in range(lowerCamelCase_				)]
			__lowercase										=							time.time()
			max_subarray(lowerCamelCase_		,       0		,       input_size - 1				)
			__lowercase										=							time.time()
			return end - start




def    _lowerCAmelCase						(				):
			__lowercase										=							[1_0, 1_0_0, 1_0_0_0, 1_0_0_0_0, 5_0_0_0_0, 1_0_0_0_0_0, 2_0_0_0_0_0, 3_0_0_0_0_0, 4_0_0_0_0_0, 5_0_0_0_0_0]
			__lowercase										=							[time_max_subarray(lowerCamelCase_				) for input_size in input_sizes]
			print('''No of Inputs\t\tTime Taken'''				)
			for input_size, runtime in zip(lowerCamelCase_		,       lowerCamelCase_				):
						print(lowerCamelCase_		,       '''\t\t'''		,       lowerCamelCase_				)
			plt.plot(lowerCamelCase_		,       lowerCamelCase_				)
			plt.xlabel('''Number of Inputs'''				)
			plt.ylabel('''Time taken in seconds'''				)
			plt.show()


if __name__ == "__main__":
	from doctest import testmod

	testmod()





