





from collections import OrderedDict
from typing import Mapping

from packaging import version

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


snake_case_     :       Optional[Any]						=  logging.get_logger(__name__)

snake_case_     :       Tuple						=  {
    "facebook/levit-128S": "https://huggingface.co/facebook/levit-128S/resolve/main/config.json",
    # See all LeViT models at https://huggingface.co/models?filter=levit
}


class 					__a      (lowerCamelCase     ):
							__a					:   Tuple									=       "levit"

							def __init__(							self							:     List[str]       ,     __magic_name__							:     Optional[Any]=2_24       ,     __magic_name__							:     Tuple=3       ,     __magic_name__							:     List[str]=3       ,     __magic_name__							:     Optional[int]=2       ,     __magic_name__							:     Optional[Any]=1       ,     __magic_name__							:     Optional[Any]=16       ,     __magic_name__							:     str=[1_28, 2_56, 3_84]       ,     __magic_name__							:     Union[str, Any]=[4, 8, 12]       ,     __magic_name__							:     int=[4, 4, 4]       ,     __magic_name__							:     List[Any]=[16, 16, 16]       ,     __magic_name__							:     Dict=0       ,     __magic_name__							:     Union[str, Any]=[2, 2, 2]       ,     __magic_name__							:     int=[2, 2, 2]       ,     __magic_name__							:     Any=0.0_2       ,     **__magic_name__							:     Union[str, Any]       ,     )					->       Dict:







													"""simple docstring"""
													super().__init__(**__magic_name__   )
													UpperCAmelCase_			:      Tuple     						=  image_size
													UpperCAmelCase_			:      str     						=  num_channels
													UpperCAmelCase_			:      Tuple     						=  kernel_size
													UpperCAmelCase_			:      int     						=  stride
													UpperCAmelCase_			:      List[Any]     						=  padding
													UpperCAmelCase_			:      int     						=  hidden_sizes
													UpperCAmelCase_			:      int     						=  num_attention_heads
													UpperCAmelCase_			:      Union[str, Any]     						=  depths
													UpperCAmelCase_			:      List[Any]     						=  key_dim
													UpperCAmelCase_			:      Tuple     						=  drop_path_rate
													UpperCAmelCase_			:      Optional[Any]     						=  patch_size
													UpperCAmelCase_			:      Tuple     						=  attention_ratio
													UpperCAmelCase_			:      Union[str, Any]     						=  mlp_ratio
													UpperCAmelCase_			:      Dict     						=  initializer_range
													UpperCAmelCase_			:      Tuple     						=  [
													    ['''Subsample''', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
													    ['''Subsample''', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
													]





class 					__a      (lowerCamelCase     ):
							__a					:   Optional[Any]									=       version.parse("1.11"     )

							@property
							def     UpperCAmelCase__	(							self							:     Optional[int]   )					->       Mapping[str, Mapping[int, str]]:







													"""simple docstring"""
													return OrderedDict(
													    [
													        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
													    ]   )



							@property
							def     UpperCAmelCase__	(							self							:     Any   )					->       float:







													"""simple docstring"""
													return 1E-4












'''simple docstring'''







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
					import torch

					from transformers.generation import DisjunctiveConstraint


@require_torch
class 					__a      (unittest.TestCase     ):

							def     UpperCAmelCase__	(							self							:     Optional[Any]   )					->       List[Any]:







													"""simple docstring"""
													# For consistency across different places the DisjunctiveConstraint is called,
													# dc.token_ids is a list of integers. It is also initialized only by integers.

													UpperCAmelCase_			:      List[str]     						=  [[1, 2, 4], [1, 2, 3, 4]]
													UpperCAmelCase_			:      List[str]     						=  DisjunctiveConstraint(__magic_name__   )
													self.assertTrue(isinstance(dc.token_ids       ,     __magic_name__   )   )

													with self.assertRaises(__magic_name__   ):
																			DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]   )   )

													with self.assertRaises(__magic_name__   ):
																			DisjunctiveConstraint([torch.LongTensor([1, 2, 4]   ), torch.LongTensor([1, 2, 3, 4, 5]   )]   )

							def     UpperCAmelCase__	(							self							:     List[str]   )					->       Dict:







													"""simple docstring"""
													# We can't have constraints that are complete subsets of another. This leads to a preverse
													# interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
													# It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
													# fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
													# will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
													UpperCAmelCase_			:      Tuple     						=  [[1, 2], [1, 2, 3, 4]]

													with self.assertRaises(__magic_name__   ):
																			DisjunctiveConstraint(__magic_name__   )  # fails here

							def     UpperCAmelCase__	(							self							:     Optional[int]   )					->       Union[str, Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[int]     						=  [[1, 2, 3], [1, 2, 4]]

													UpperCAmelCase_			:      List[str]     						=  DisjunctiveConstraint(__magic_name__   )

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      List[Any]     						=  dc.update(1   )
													UpperCAmelCase_			:      Dict     						=  stepped is True and completed is False and reset is False
													self.assertTrue(__magic_name__   )
													self.assertTrue(not dc.completed   )
													self.assertTrue(dc.current_seq == [1]   )

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      str     						=  dc.update(2   )
													UpperCAmelCase_			:      Optional[Any]     						=  stepped is True and completed is False and reset is False
													self.assertTrue(__magic_name__   )
													self.assertTrue(not dc.completed   )
													self.assertTrue(dc.current_seq == [1, 2]   )

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      List[Any]     						=  dc.update(3   )
													UpperCAmelCase_			:      Dict     						=  stepped is True and completed is True and reset is False
													self.assertTrue(__magic_name__   )
													self.assertTrue(dc.completed   )  # Completed!
													self.assertTrue(dc.current_seq == [1, 2, 3]   )



							def     UpperCAmelCase__	(							self							:     int   )					->       Dict:







													"""simple docstring"""
													UpperCAmelCase_			:      Any     						=  [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

													UpperCAmelCase_			:      Tuple     						=  DisjunctiveConstraint(__magic_name__   )

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      List[Any]     						=  dc.update(1   )
													self.assertTrue(not dc.completed   )
													self.assertTrue(dc.current_seq == [1]   )

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      Optional[int]     						=  dc.update(2   )
													self.assertTrue(not dc.completed   )
													self.assertTrue(dc.current_seq == [1, 2]   )

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      int     						=  dc.update(4   )
													self.assertTrue(not dc.completed   )
													self.assertTrue(dc.current_seq == [1, 2, 4]   )

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      int     						=  dc.update(5   )
													self.assertTrue(dc.completed   )  # Completed!
													self.assertTrue(dc.current_seq == [1, 2, 4, 5]   )

													dc.reset()

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      List[Any]     						=  dc.update(1   )
													self.assertTrue(not dc.completed   )
													self.assertTrue(dc.remaining() == 3   )
													self.assertTrue(dc.current_seq == [1]   )

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      Optional[Any]     						=  dc.update(2   )
													self.assertTrue(not dc.completed   )
													self.assertTrue(dc.remaining() == 2   )
													self.assertTrue(dc.current_seq == [1, 2]   )

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      Optional[int]     						=  dc.update(5   )
													self.assertTrue(dc.completed   )  # Completed!
													self.assertTrue(dc.remaining() == 0   )
													self.assertTrue(dc.current_seq == [1, 2, 5]   )





