
"""simple docstring"""





from __future__ import annotations

__magic_name__       =			[-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
__magic_name__       =			[-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = []
		__SCREAMING_SNAKE_CASE            = len(UpperCamelCase_				)

		for i in range(UpperCamelCase_				):
				__SCREAMING_SNAKE_CASE            = -1
				for j in range(i + 1  ,				UpperCamelCase_				):
						if arr[i] < arr[j]:
								__SCREAMING_SNAKE_CASE            = arr[j]
								break
				result.append(UpperCamelCase_				)
		return result



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = []
		for i, outer in enumerate(UpperCamelCase_				):
				__SCREAMING_SNAKE_CASE            = -1
				for inner in arr[i + 1 :]:
						if outer < inner:
								__SCREAMING_SNAKE_CASE            = inner
								break
				result.append(UpperCamelCase_				)
		return result



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = len(UpperCamelCase_				)
		__SCREAMING_SNAKE_CASE            = []
		__SCREAMING_SNAKE_CASE            = [-1] * arr_size

		for index in reversed(range(UpperCamelCase_				)				):
				if stack:
						while stack[-1] <= arr[index]:
								stack.pop()
								if not stack:
										break
				if stack:
						__SCREAMING_SNAKE_CASE            = stack[-1]
				stack.append(arr[index]				)
		return result


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

							testmod()
							print(next_greatest_element_slow(arr))
							print(next_greatest_element_fast(arr))
							print(next_greatest_element(arr))

							__magic_name__       =			(
							    "from __main__ import arr, next_greatest_element_slow, "
							    "next_greatest_element_fast, next_greatest_element"
							)
							print(
							    "next_greatest_element_slow():",
							    timeit("next_greatest_element_slow(arr)", setup=setup),
							)
							print(
							    "next_greatest_element_fast():",
							    timeit("next_greatest_element_fast(arr)", setup=setup),
							)
							print(
							    "     next_greatest_element():",
							    timeit("next_greatest_element(arr)", setup=setup),
							)






"""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


__magic_name__       =			logging.get_logger(__name__)

__magic_name__       =			{
    "salesforce/blip2-opt-2.7b": "https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json",
}
class 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		__lowercase							:  Optional[Any]						   =		'''blip_2_vision_model'''




		def __init__(     self							,  lowerCAmelCase__=1_4_0_8							,  lowerCAmelCase__=6_1_4_4							,  lowerCAmelCase__=3_9							,  lowerCAmelCase__=1_6							,  lowerCAmelCase__=2_2_4							,  lowerCAmelCase__=1_4							,  lowerCAmelCase__="gelu"							,  lowerCAmelCase__=0.0_00_01							,  lowerCAmelCase__=0.0							,  lowerCAmelCase__=1E-10							,  lowerCAmelCase__=True							,  **lowerCAmelCase__							,  ):
				super().__init__(**lowerCAmelCase__)

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




		@classmethod
		def 	snake_case_     (     cls							,  lowerCAmelCase__							,  **lowerCAmelCase__):
				cls._set_token_in_kwargs(lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = cls.get_config_dict(lowerCAmelCase__							,  **lowerCAmelCase__)

				# get the vision config dict if we are loading from Blip2Config
				if config_dict.get("""model_type""") == "blip-2":
						__SCREAMING_SNAKE_CASE            = 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 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		__lowercase							:  Tuple						   =		'''blip_2_qformer'''




		def __init__(     self							,  lowerCAmelCase__=3_0_5_2_2							,  lowerCAmelCase__=7_6_8							,  lowerCAmelCase__=1_2							,  lowerCAmelCase__=1_2							,  lowerCAmelCase__=3_0_7_2							,  lowerCAmelCase__="gelu"							,  lowerCAmelCase__=0.1							,  lowerCAmelCase__=0.1							,  lowerCAmelCase__=5_1_2							,  lowerCAmelCase__=0.02							,  lowerCAmelCase__=1E-12							,  lowerCAmelCase__=0							,  lowerCAmelCase__="absolute"							,  lowerCAmelCase__=2							,  lowerCAmelCase__=1_4_0_8							,  **lowerCAmelCase__							,  ):
				super().__init__(pad_token_id=lowerCAmelCase__							,  **lowerCAmelCase__)

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




		@classmethod
		def 	snake_case_     (     cls							,  lowerCAmelCase__							,  **lowerCAmelCase__):
				cls._set_token_in_kwargs(lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = cls.get_config_dict(lowerCAmelCase__							,  **lowerCAmelCase__)

				# get the qformer config dict if we are loading from Blip2Config
				if config_dict.get("""model_type""") == "blip-2":
						__SCREAMING_SNAKE_CASE            = 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 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		__lowercase							:  Optional[Any]						   =		'''blip-2'''
		__lowercase							:  Any						   =		True




		def __init__(     self							,  lowerCAmelCase__=None							,  lowerCAmelCase__=None							,  lowerCAmelCase__=None							,  lowerCAmelCase__=3_2							,  **lowerCAmelCase__):
				super().__init__(**lowerCAmelCase__)

				if vision_config is None:
						__SCREAMING_SNAKE_CASE            = {}
						logger.info("""vision_config is None. initializing the Blip2VisionConfig with default values.""")

				if qformer_config is None:
						__SCREAMING_SNAKE_CASE            = {}
						logger.info("""qformer_config is None. Initializing the Blip2QFormerConfig with default values.""")

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

				__SCREAMING_SNAKE_CASE            = BlipaVisionConfig(**lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = BlipaQFormerConfig(**lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = text_config["""model_type"""] if """model_type""" in text_config else """opt"""
				__SCREAMING_SNAKE_CASE            = CONFIG_MAPPING[text_model_type](**lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = self.text_config.tie_word_embeddings
				__SCREAMING_SNAKE_CASE            = self.text_config.is_encoder_decoder

				__SCREAMING_SNAKE_CASE            = num_query_tokens
				__SCREAMING_SNAKE_CASE            = self.vision_config.hidden_size
				__SCREAMING_SNAKE_CASE            = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
				__SCREAMING_SNAKE_CASE            = 1.0
				__SCREAMING_SNAKE_CASE            = 0.02




		@classmethod
		def 	snake_case_     (     cls							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  **lowerCAmelCase__							,  ):
				return cls(
				    vision_config=vision_config.to_dict()							,  qformer_config=qformer_config.to_dict()							,  text_config=text_config.to_dict()							,  **lowerCAmelCase__							,  )




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = copy.deepcopy(self.__dict__)
				__SCREAMING_SNAKE_CASE            = self.vision_config.to_dict()
				__SCREAMING_SNAKE_CASE            = self.qformer_config.to_dict()
				__SCREAMING_SNAKE_CASE            = self.text_config.to_dict()
				__SCREAMING_SNAKE_CASE            = self.__class__.model_type
				return output




