





import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union


if TYPE_CHECKING:
						from ...processing_utils import ProcessorMixin
						from ...utils import TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A_												=     logging.get_logger(__name__)

A_												=     {
    "google/owlvit-base-patch32": "https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json",
    "google/owlvit-base-patch16": "https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json",
    "google/owlvit-large-patch14": "https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json",
}

class SCREAMING_SNAKE_CASE_ (					__SCREAMING_SNAKE_CASE   ):





					"""simple docstring"""


					A__				 =    "owlvit_text_model"





					def __init__( self	,  _lowerCAmelCase=4_9408	,  _lowerCAmelCase=512	,  _lowerCAmelCase=2048	,  _lowerCAmelCase=12	,  _lowerCAmelCase=8	,  _lowerCAmelCase=16	,  _lowerCAmelCase="quick_gelu"	,  _lowerCAmelCase=1E-5	,  _lowerCAmelCase=0.0	,  _lowerCAmelCase=0.02	,  _lowerCAmelCase=1.0	,  _lowerCAmelCase=0	,  _lowerCAmelCase=4_9406	,  _lowerCAmelCase=4_9407	,  **_lowerCAmelCase	,  ):
										super().__init__(pad_token_id=_a	,  bos_token_id=_a	,  eos_token_id=_a	,  **_a	)

										lowerCamelCase__       =       vocab_size
										lowerCamelCase__       =       hidden_size
										lowerCamelCase__       =       intermediate_size
										lowerCamelCase__       =       num_hidden_layers
										lowerCamelCase__       =       num_attention_heads
										lowerCamelCase__       =       max_position_embeddings
										lowerCamelCase__       =       hidden_act
										lowerCamelCase__       =       layer_norm_eps
										lowerCamelCase__       =       attention_dropout
										lowerCamelCase__       =       initializer_range
										lowerCamelCase__       =       initializer_factor






					@classmethod
					def 					__magic_name__ ( cls	,  _lowerCAmelCase	,  **_lowerCAmelCase	):
										cls._set_token_in_kwargs(_a	)

										lowerCamelCase__       =       cls.get_config_dict(_a	,  **_a	)

										# get the text config dict if we are loading from OwlViTConfig
										if config_dict.get("model_type"	) == "owlvit":
															lowerCamelCase__       =       config_dict["""text_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(_a	,  **_a	)

class SCREAMING_SNAKE_CASE_ (					__SCREAMING_SNAKE_CASE   ):





					"""simple docstring"""


					A__				 =    "owlvit_vision_model"





					def __init__( self	,  _lowerCAmelCase=768	,  _lowerCAmelCase=3072	,  _lowerCAmelCase=12	,  _lowerCAmelCase=12	,  _lowerCAmelCase=3	,  _lowerCAmelCase=768	,  _lowerCAmelCase=32	,  _lowerCAmelCase="quick_gelu"	,  _lowerCAmelCase=1E-5	,  _lowerCAmelCase=0.0	,  _lowerCAmelCase=0.02	,  _lowerCAmelCase=1.0	,  **_lowerCAmelCase	,  ):
										super().__init__(**_a	)

										lowerCamelCase__       =       hidden_size
										lowerCamelCase__       =       intermediate_size
										lowerCamelCase__       =       num_hidden_layers
										lowerCamelCase__       =       num_attention_heads
										lowerCamelCase__       =       num_channels
										lowerCamelCase__       =       image_size
										lowerCamelCase__       =       patch_size
										lowerCamelCase__       =       hidden_act
										lowerCamelCase__       =       layer_norm_eps
										lowerCamelCase__       =       attention_dropout
										lowerCamelCase__       =       initializer_range
										lowerCamelCase__       =       initializer_factor






					@classmethod
					def 					__magic_name__ ( cls	,  _lowerCAmelCase	,  **_lowerCAmelCase	):
										cls._set_token_in_kwargs(_a	)

										lowerCamelCase__       =       cls.get_config_dict(_a	,  **_a	)

										# get the vision config dict if we are loading from OwlViTConfig
										if config_dict.get("model_type"	) == "owlvit":
															lowerCamelCase__       =       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(_a	,  **_a	)

class SCREAMING_SNAKE_CASE_ (					__SCREAMING_SNAKE_CASE   ):





					"""simple docstring"""


					A__				 =    "owlvit"
					A__				 =    True





					def __init__( self	,  _lowerCAmelCase=None	,  _lowerCAmelCase=None	,  _lowerCAmelCase=512	,  _lowerCAmelCase=2.6_592	,  _lowerCAmelCase=True	,  **_lowerCAmelCase	,  ):
										super().__init__(**_a	)

										if text_config is None:
															lowerCamelCase__       =       {}
															logger.info("text_config is None. Initializing the OwlViTTextConfig with default values."	)

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

										lowerCamelCase__       =       OwlViTTextConfig(**_a	)
										lowerCamelCase__       =       OwlViTVisionConfig(**_a	)

										lowerCamelCase__       =       projection_dim
										lowerCamelCase__       =       logit_scale_init_value
										lowerCamelCase__       =       return_dict
										lowerCamelCase__       =       1.0





					@classmethod
					def 					__magic_name__ ( cls	,  _lowerCAmelCase	,  **_lowerCAmelCase	):
										cls._set_token_in_kwargs(_a	)

										lowerCamelCase__       =       cls.get_config_dict(_a	,  **_a	)

										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(_a	,  **_a	)





					@classmethod
					def 					__magic_name__ ( cls	,  _lowerCAmelCase	,  _lowerCAmelCase	,  **_lowerCAmelCase	):
										lowerCamelCase__       =       {}
										lowerCamelCase__       =       text_config
										lowerCamelCase__       =       vision_config

										return cls.from_dict(_a	,  **_a	)






					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       copy.deepcopy(self.__dict__	)
										lowerCamelCase__       =       self.text_config.to_dict()
										lowerCamelCase__       =       self.vision_config.to_dict()
										lowerCamelCase__       =       self.__class__.model_type
										return output

class SCREAMING_SNAKE_CASE_ (					__SCREAMING_SNAKE_CASE   ):





					"""simple docstring"""


					@property
					def 					__magic_name__ ( self	):
										return OrderedDict(
										    [
										        ("input_ids", {0: "batch", 1: "sequence"}),
										        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
										        ("attention_mask", {0: "batch", 1: "sequence"}),
										    ]	)





					@property
					def 					__magic_name__ ( self	):
										return OrderedDict(
										    [
										        ("logits_per_image", {0: "batch"}),
										        ("logits_per_text", {0: "batch"}),
										        ("text_embeds", {0: "batch"}),
										        ("image_embeds", {0: "batch"}),
										    ]	)





					@property
					def 					__magic_name__ ( self	):
										return 1E-4





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase = -1	,  _lowerCAmelCase = -1	,  _lowerCAmelCase = None	,  ):
										lowerCamelCase__       =       super().generate_dummy_inputs(
										    processor.tokenizer	,  batch_size=_a	,  seq_length=_a	,  framework=_a	)
										lowerCamelCase__       =       super().generate_dummy_inputs(
										    processor.image_processor	,  batch_size=_a	,  framework=_a	)
										return {**text_input_dict, **image_input_dict}






					@property
					def 					__magic_name__ ( self	):
										return 14













from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
A_												=     Lock()





def    __UpperCamelCase      (    a,     a,     a,     a,     a,     a,     a) ->Optional[int]:
					global process_lock

					# we perform n swaps since after n swaps we know we are sorted
					# we *could* stop early if we are sorted already, but it takes as long to
					# find out we are sorted as it does to sort the list with this algorithm
					for i in range(0,     10):
										if (i + position) % 2 == 0 and r_send is not None:
															# send your value to your right neighbor
															process_lock.acquire()
															r_send[1].send(a)
															process_lock.release()

															# receive your right neighbor's value
															process_lock.acquire()
															lowerCamelCase__       =       rr_cv[0].recv()
															process_lock.release()

															# take the lower value since you are on the left
															lowerCamelCase__       =       min(a,     a)
										elif (i + position) % 2 != 0 and l_send is not None:
															# send your value to your left neighbor
															process_lock.acquire()
															l_send[1].send(a)
															process_lock.release()

															# receive your left neighbor's value
															process_lock.acquire()
															lowerCamelCase__       =       lr_cv[0].recv()
															process_lock.release()

															# take the higher value since you are on the right
															lowerCamelCase__       =       max(a,     a)
    # after all swaps are performed, send the values back to main
					result_pipe[1].send(a)





def    __UpperCamelCase      (    a) ->Optional[Any]:
					lowerCamelCase__       =       []
					lowerCamelCase__       =       []
					# initialize the list of pipes where the values will be retrieved
					for _ in arr:
										result_pipe.append(Pipe())
					# creates the processes
					# the first and last process only have one neighbor so they are made outside
					# of the loop
					lowerCamelCase__       =       Pipe()
					lowerCamelCase__       =       Pipe()
					process_array_.append(
					    Process(
					        target=a,     args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0]),     ))
					lowerCamelCase__       =       temp_rs
					lowerCamelCase__       =       temp_rr

					for i in range(1,     len(a) - 1):
										lowerCamelCase__       =       Pipe()
										lowerCamelCase__       =       Pipe()
										process_array_.append(
										    Process(
										        target=a,     args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i]),     ))
										lowerCamelCase__       =       temp_rs
										lowerCamelCase__       =       temp_rr

					process_array_.append(
					    Process(
					        target=a,     args=(
					            len(a) - 1,
					            arr[len(a) - 1],
					            temp_ls,
					            None,
					            temp_lr,
					            None,
					            result_pipe[len(a) - 1],
					        ),     ))

					# start the processes
					for p in process_array_:
										p.start()

					# wait for the processes to end and write their values to the list
					for p in range(0,     len(a)):
										lowerCamelCase__       =       result_pipe[p][0].recv()
										process_array_[p].join()
					return arr





def    __UpperCamelCase      (    ) ->List[Any]:
					lowerCamelCase__       =       list(range(10,     0,     -1))
					print("Initial List")
					print(*a)
					lowerCamelCase__       =       odd_even_transposition(a)
					print("Sorted List\n")
					print(*a)


if __name__ == "__main__":
						main()

