




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


__SCREAMING_SNAKE_CASE 						=logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE 						={
    """caidas/swin2sr-classicalsr-x2-64""": (
        """https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json"""
    ),
}






class  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							Optional[Any]	      =					"swin2sr"

				SCREAMING_SNAKE_CASE__					:							str	      =					{
				    "hidden_size": "embed_dim",
				    "num_attention_heads": "num_heads",
				    "num_hidden_layers": "num_layers",
				}


				def __init__(							self:   Optional[int]		,					_lowerCamelCase:   Dict=64		,					_lowerCamelCase:   str=1		,					_lowerCamelCase:   Optional[int]=3		,					_lowerCamelCase:   List[Any]=1_80		,					_lowerCamelCase:   Optional[int]=[6, 6, 6, 6, 6, 6]		,					_lowerCamelCase:   Union[str, Any]=[6, 6, 6, 6, 6, 6]		,					_lowerCamelCase:   Tuple=8		,					_lowerCamelCase:   List[Any]=2.0		,					_lowerCamelCase:   Dict=True		,					_lowerCamelCase:   Dict=0.0		,					_lowerCamelCase:   int=0.0		,					_lowerCamelCase:   Optional[int]=0.1		,					_lowerCamelCase:   Any="gelu"		,					_lowerCamelCase:   int=False		,					_lowerCamelCase:   Tuple=0.02		,					_lowerCamelCase:   List[str]=1E-5		,					_lowerCamelCase:   List[Any]=2		,					_lowerCamelCase:   str=1.0		,					_lowerCamelCase:   List[Any]="1conv"		,					_lowerCamelCase:   str="pixelshuffle"		,					**_lowerCamelCase:   List[str]		,					):
											super().__init__(**_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    image_size
											SCREAMING_SNAKE_CASE_   	=    patch_size
											SCREAMING_SNAKE_CASE_   	=    num_channels
											SCREAMING_SNAKE_CASE_   	=    embed_dim
											SCREAMING_SNAKE_CASE_   	=    depths
											SCREAMING_SNAKE_CASE_   	=    len(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    num_heads
											SCREAMING_SNAKE_CASE_   	=    window_size
											SCREAMING_SNAKE_CASE_   	=    mlp_ratio
											SCREAMING_SNAKE_CASE_   	=    qkv_bias
											SCREAMING_SNAKE_CASE_   	=    hidden_dropout_prob
											SCREAMING_SNAKE_CASE_   	=    attention_probs_dropout_prob
											SCREAMING_SNAKE_CASE_   	=    drop_path_rate
											SCREAMING_SNAKE_CASE_   	=    hidden_act
											SCREAMING_SNAKE_CASE_   	=    use_absolute_embeddings
											SCREAMING_SNAKE_CASE_   	=    layer_norm_eps
											SCREAMING_SNAKE_CASE_   	=    initializer_range
											SCREAMING_SNAKE_CASE_   	=    upscale
											SCREAMING_SNAKE_CASE_   	=    img_range
											SCREAMING_SNAKE_CASE_   	=    resi_connection
											SCREAMING_SNAKE_CASE_   	=    upsampler








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  __magic_name__						(  unittest.TestCase):
				'''simple docstring'''







				def     _A							(							self:   str		):
											# For consistency across different places the DisjunctiveConstraint is called,
											# dc.token_ids is a list of integers. It is also initialized only by integers.

											SCREAMING_SNAKE_CASE_   	=    [[1, 2, 4], [1, 2, 3, 4]]
											SCREAMING_SNAKE_CASE_   	=    DisjunctiveConstraint(_lowerCamelCase		)
											self.assertTrue(isinstance(dc.token_ids		,					_lowerCamelCase		)		)

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

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


				def     _A							(							self:   Union[str, Any]		):
											# 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).
											SCREAMING_SNAKE_CASE_   	=    [[1, 2], [1, 2, 3, 4]]

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


				def     _A							(							self:   Dict		):
											SCREAMING_SNAKE_CASE_   	=    [[1, 2, 3], [1, 2, 4]]

											SCREAMING_SNAKE_CASE_   	=    DisjunctiveConstraint(_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    dc.update(1		)
											SCREAMING_SNAKE_CASE_   	=    stepped is True and completed is False and reset is False
											self.assertTrue(_lowerCamelCase		)
											self.assertTrue(not dc.completed		)
											self.assertTrue(dc.current_seq == [1]		)

											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    dc.update(2		)
											SCREAMING_SNAKE_CASE_   	=    stepped is True and completed is False and reset is False
											self.assertTrue(_lowerCamelCase		)
											self.assertTrue(not dc.completed		)
											self.assertTrue(dc.current_seq == [1, 2]		)

											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    dc.update(3		)
											SCREAMING_SNAKE_CASE_   	=    stepped is True and completed is True and reset is False
											self.assertTrue(_lowerCamelCase		)
											self.assertTrue(dc.completed		)  # Completed!
											self.assertTrue(dc.current_seq == [1, 2, 3]		)





				def     _A							(							self:   Optional[int]		):
											SCREAMING_SNAKE_CASE_   	=    [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

											SCREAMING_SNAKE_CASE_   	=    DisjunctiveConstraint(_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    dc.update(1		)
											self.assertTrue(not dc.completed		)
											self.assertTrue(dc.current_seq == [1]		)

											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    dc.update(2		)
											self.assertTrue(not dc.completed		)
											self.assertTrue(dc.current_seq == [1, 2]		)

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

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

											dc.reset()

											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    dc.update(1		)
											self.assertTrue(not dc.completed		)
											self.assertTrue(dc.remaining() == 3		)
											self.assertTrue(dc.current_seq == [1]		)

											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    dc.update(2		)
											self.assertTrue(not dc.completed		)
											self.assertTrue(dc.remaining() == 2		)
											self.assertTrue(dc.current_seq == [1, 2]		)

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


