'''simple docstring'''




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

_a   :	List[str] 							=   logging.get_logger(__name__)

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






class 		__A       (__magic_name__   ):
       snake_case       :List[str]				    =					"imagegpt"
       snake_case       :str				    =					["past_key_values"]
       snake_case       :Tuple				    =					{
           "hidden_size": "n_embd",
           "max_position_embeddings": "n_positions",
           "num_attention_heads": "n_head",
           "num_hidden_layers": "n_layer",
       }






       def __init__(      self				,     UpperCamelCase_=5_12 + 1				,     UpperCamelCase_=32 * 32				,     UpperCamelCase_=5_12				,     UpperCamelCase_=24				,     UpperCamelCase_=8				,     UpperCamelCase_=None				,     UpperCamelCase_="quick_gelu"				,     UpperCamelCase_=0.1				,     UpperCamelCase_=0.1				,     UpperCamelCase_=0.1				,     UpperCamelCase_=1E-5				,     UpperCamelCase_=0.0_2				,     UpperCamelCase_=True				,     UpperCamelCase_=True				,     UpperCamelCase_=False				,     UpperCamelCase_=False				,     UpperCamelCase_=False				,     **UpperCamelCase_				,     ):
              __UpperCAmelCase  :				Dict       				=		vocab_size
              __UpperCAmelCase  :				Dict       				=		n_positions
              __UpperCAmelCase  :				Tuple       				=		n_embd
              __UpperCAmelCase  :				List[str]       				=		n_layer
              __UpperCAmelCase  :				Any       				=		n_head
              __UpperCAmelCase  :				Optional[Any]       				=		n_inner
              __UpperCAmelCase  :				Dict       				=		activation_function
              __UpperCAmelCase  :				Optional[Any]       				=		resid_pdrop
              __UpperCAmelCase  :				List[str]       				=		embd_pdrop
              __UpperCAmelCase  :				str       				=		attn_pdrop
              __UpperCAmelCase  :				int       				=		layer_norm_epsilon
              __UpperCAmelCase  :				Tuple       				=		initializer_range
              __UpperCAmelCase  :				List[str]       				=		scale_attn_weights
              __UpperCAmelCase  :				int       				=		use_cache
              __UpperCAmelCase  :				Tuple       				=		scale_attn_by_inverse_layer_idx
              __UpperCAmelCase  :				Optional[int]       				=		reorder_and_upcast_attn
              __UpperCAmelCase  :				List[str]       				=		tie_word_embeddings

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






class 		__A       (__magic_name__   ):






       @property
       def 			_snake_case						(      self      ):
              return OrderedDict(
                  [
                      ("input_ids", {0: "batch", 1: "sequence"}),
                  ]      )







       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_ = 1				,     UpperCamelCase_ = -1				,     UpperCamelCase_ = False				,     UpperCamelCase_ = None				,     UpperCamelCase_ = 3				,     UpperCamelCase_ = 32				,     UpperCamelCase_ = 32				,     ):
              __UpperCAmelCase  :				Tuple       				=		self._generate_dummy_images(UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_      )
              __UpperCAmelCase  :				Optional[int]       				=		dict(preprocessor(images=UpperCamelCase_				,     return_tensors=UpperCamelCase_      )      )

              return inputs







'''simple docstring'''




import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
   import torch

   from transformers.generation import (
       MaxLengthCriteria,
       MaxNewTokensCriteria,
       MaxTimeCriteria,
       StoppingCriteriaList,
       validate_stopping_criteria,
   )






@require_torch
class 		__A       (unittest.TestCase   ):






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				List[str]       				=		3
              __UpperCAmelCase  :				Tuple       				=		2_50

              __UpperCAmelCase  :				str       				=		ids_tensor((batch_size, length)				,     UpperCamelCase_      )
              __UpperCAmelCase  :				Any       				=		torch.ones((batch_size, length)				,     device=UpperCamelCase_				,     dtype=torch.float      ) / length
              return input_ids, scores






       def 			_snake_case						(      self      ):
              __UpperCAmelCase				, __UpperCAmelCase  :				Tuple       				=		self._get_tensors(5      )

              __UpperCAmelCase  :				Tuple       				=		StoppingCriteriaList(
                  [
                      MaxLengthCriteria(max_length=10      ),
                      MaxTimeCriteria(max_time=0.1      ),
                  ]      )

              self.assertFalse(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )

              __UpperCAmelCase				, __UpperCAmelCase  :				int       				=		self._get_tensors(9      )
              self.assertFalse(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )

              __UpperCAmelCase				, __UpperCAmelCase  :				Optional[int]       				=		self._get_tensors(10      )
              self.assertTrue(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				int       				=		MaxLengthCriteria(max_length=10      )

              __UpperCAmelCase				, __UpperCAmelCase  :				Tuple       				=		self._get_tensors(5      )
              self.assertFalse(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )

              __UpperCAmelCase				, __UpperCAmelCase  :				Dict       				=		self._get_tensors(9      )
              self.assertFalse(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )

              __UpperCAmelCase				, __UpperCAmelCase  :				Optional[int]       				=		self._get_tensors(10      )
              self.assertTrue(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Optional[Any]       				=		MaxNewTokensCriteria(start_length=5				,     max_new_tokens=5      )

              __UpperCAmelCase				, __UpperCAmelCase  :				List[str]       				=		self._get_tensors(5      )
              self.assertFalse(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )

              __UpperCAmelCase				, __UpperCAmelCase  :				Dict       				=		self._get_tensors(9      )
              self.assertFalse(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )

              __UpperCAmelCase				, __UpperCAmelCase  :				Optional[Any]       				=		self._get_tensors(10      )
              self.assertTrue(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )

              __UpperCAmelCase  :				Union[str, Any]       				=		StoppingCriteriaList([criteria]      )
              self.assertEqual(criteria_list.max_length				,     10      )






       def 			_snake_case						(      self      ):
              __UpperCAmelCase				, __UpperCAmelCase  :				Optional[Any]       				=		self._get_tensors(5      )

              __UpperCAmelCase  :				str       				=		MaxTimeCriteria(max_time=0.1      )
              self.assertFalse(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )

              __UpperCAmelCase  :				str       				=		MaxTimeCriteria(max_time=0.1				,     initial_timestamp=time.time() - 0.2      )
              self.assertTrue(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )







       def 			_snake_case						(      self      ):
              validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10      )]      )				,     10      )

              with self.assertWarns(UpperCamelCase_      ):
                     validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10      )]      )				,     11      )

              __UpperCAmelCase  :				Optional[int]       				=		validate_stopping_criteria(StoppingCriteriaList()				,     11      )

              self.assertEqual(len(UpperCamelCase_      )				,     1      )






