
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

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


if TYPE_CHECKING:
					from ... import FeatureExtractionMixin, TensorType

UpperCamelCase_   	=	logging.get_logger(__name__)

UpperCamelCase_   	=	{
    "openai/imagegpt-small": "",
    "openai/imagegpt-medium": "",
    "openai/imagegpt-large": "",
}

class       a		(   __UpperCAmelCase					):
							lowercase_		:						Union[str, Any]   							=  'imagegpt'
							lowercase_		:						Optional[int]   							=  ['past_key_values']
							lowercase_		:						Optional[int]   							=  {
							    'hidden_size': 'n_embd',
							    'max_position_embeddings': 'n_positions',
							    'num_attention_heads': 'n_head',
							    'num_hidden_layers': 'n_layer',
							}







							def __init__(						self  :  int      ,				snake_case__  :  Dict=512 + 1      ,				snake_case__  :  Tuple=32 * 32      ,				snake_case__  :  List[Any]=512      ,				snake_case__  :  Any=24      ,				snake_case__  :  List[str]=8      ,				snake_case__  :  List[str]=None      ,				snake_case__  :  Optional[Any]="quick_gelu"      ,				snake_case__  :  Dict=0.1      ,				snake_case__  :  Union[str, Any]=0.1      ,				snake_case__  :  Any=0.1      ,				snake_case__  :  Tuple=1E-5      ,				snake_case__  :  List[str]=0.0_2      ,				snake_case__  :  Union[str, Any]=True      ,				snake_case__  :  Dict=True      ,				snake_case__  :  Dict=False      ,				snake_case__  :  Dict=False      ,				snake_case__  :  int=False      ,				**snake_case__  :  Union[str, Any]      ,				):
								"""simple docstring"""







								__lowerCAmelCase         =							vocab_size
								__lowerCAmelCase         =							n_positions
								__lowerCAmelCase         =							n_embd
								__lowerCAmelCase         =							n_layer
								__lowerCAmelCase         =							n_head
								__lowerCAmelCase         =							n_inner
								__lowerCAmelCase         =							activation_function
								__lowerCAmelCase         =							resid_pdrop
								__lowerCAmelCase         =							embd_pdrop
								__lowerCAmelCase         =							attn_pdrop
								__lowerCAmelCase         =							layer_norm_epsilon
								__lowerCAmelCase         =							initializer_range
								__lowerCAmelCase         =							scale_attn_weights
								__lowerCAmelCase         =							use_cache
								__lowerCAmelCase         =							scale_attn_by_inverse_layer_idx
								__lowerCAmelCase         =							reorder_and_upcast_attn
								__lowerCAmelCase         =							tie_word_embeddings

								super().__init__(tie_word_embeddings=snake_case__      ,				**snake_case__ )

class       a		(   __UpperCAmelCase					):







							@property
							def       UpperCAmelCase__					(						self  :  Tuple ):
								"""simple docstring"""







								return OrderedDict(
								    [
								        ("input_ids", {0: "batch", 1: "sequence"}),
								    ] )







							def       UpperCAmelCase__					(						self  :  str      ,				snake_case__  :  "FeatureExtractionMixin"      ,				snake_case__  :  int = 1      ,				snake_case__  :  int = -1      ,				snake_case__  :  bool = False      ,				snake_case__  :  Optional["TensorType"] = None      ,				snake_case__  :  int = 3      ,				snake_case__  :  int = 32      ,				snake_case__  :  int = 32      ,				):
								"""simple docstring"""







								__lowerCAmelCase         =							self._generate_dummy_images(snake_case__      ,				snake_case__      ,				snake_case__      ,				snake_case__ )
								__lowerCAmelCase         =							dict(preprocessor(images=snake_case__      ,				return_tensors=snake_case__ ) )

								return inputs








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[int] ):
								"""simple docstring"""







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

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

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







							def       UpperCAmelCase__					(						self  :  Tuple ):
								"""simple docstring"""







								__lowerCAmelCase         =							[[1, 2], [1, 2, 3, 4]]

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







							def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							[[1, 2, 3], [1, 2, 4]]

								__lowerCAmelCase         =							DisjunctiveConstraint(snake_case__ )

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

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

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







							def       UpperCAmelCase__					(						self  :  Optional[Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							[[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

								__lowerCAmelCase         =							DisjunctiveConstraint(snake_case__ )

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

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

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

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

								dc.reset()

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

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

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






