



"""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

lowerCAmelCase_     =	logging.get_logger(__name__)

lowerCAmelCase_     =	{
    '''openai/imagegpt-small''': '''''',
    '''openai/imagegpt-medium''': '''''',
    '''openai/imagegpt-large''': '''''',
}




class  _snake_case		(    __A ):







				"""simple docstring"""






				a			=						"""imagegpt"""
				a			=						["""past_key_values"""]
				a			=						{
				    """hidden_size""": """n_embd""",
				    """max_position_embeddings""": """n_positions""",
				    """num_attention_heads""": """n_head""",
				    """num_hidden_layers""": """n_layer""",
				}





				def __init__(      self							:							Any      ,    _A							:							int=5_1_2 + 1      ,    _A							:							Any=3_2 * 3_2      ,    _A							:							str=5_1_2      ,    _A							:							Dict=2_4      ,    _A							:							List[str]=8      ,    _A							:							Optional[int]=None      ,    _A							:							Any="quick_gelu"      ,    _A							:							List[str]=0.1      ,    _A							:							Tuple=0.1      ,    _A							:							List[str]=0.1      ,    _A							:							Dict=1e-5      ,    _A							:							Dict=0.02      ,    _A							:							str=True      ,    _A							:							str=True      ,    _A							:							int=False      ,    _A							:							Optional[int]=False      ,    _A							:							Optional[Any]=False      ,    **_A							:							Union[str, Any]      ,    ):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    vocab_size
											_SCREAMING_SNAKE_CASE		:						str			  =    n_positions
											_SCREAMING_SNAKE_CASE		:						Dict			  =    n_embd
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    n_layer
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    n_head
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    n_inner
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    activation_function
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    resid_pdrop
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    embd_pdrop
											_SCREAMING_SNAKE_CASE		:						Any			  =    attn_pdrop
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    layer_norm_epsilon
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    initializer_range
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    scale_attn_weights
											_SCREAMING_SNAKE_CASE		:						Dict			  =    use_cache
											_SCREAMING_SNAKE_CASE		:						Dict			  =    scale_attn_by_inverse_layer_idx
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    reorder_and_upcast_attn
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    tie_word_embeddings

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




class  _snake_case		(    __A ):







				"""simple docstring"""






				@property
				def 		_lowerCAmelCase		(      self							:							Union[str, Any]):



											"""simple docstring"""







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





				def 		_lowerCAmelCase		(      self							:							Dict      ,    _A							:							Optional[int]      ,    _A							:							Optional[Any] = 1      ,    _A							:							int = -1      ,    _A							:							Optional[int] = False      ,    _A							:							str = None      ,    _A							:							Dict = 3      ,    _A							:							List[Any] = 3_2      ,    _A							:							Tuple = 3_2      ,    ):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Dict			  =    self._generate_dummy_images(UpperCamelCase__      ,    UpperCamelCase__      ,    UpperCamelCase__      ,    UpperCamelCase__)
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    dict(preprocessor(images=UpperCamelCase__      ,    return_tensors=UpperCamelCase__))

											return inputs



"""simple docstring"""





import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
			import torch

			from transformers import AutoModelForImageClassification

if is_vision_available():
			from transformers import AutoImageProcessor




@require_torch
@require_vision
class  _snake_case		(    unittest.TestCase ):







				"""simple docstring"""






				@slow
				def 		_lowerCAmelCase		(      self							:							str):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Any			  =    AutoImageProcessor.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip""")
											_SCREAMING_SNAKE_CASE		:						str			  =    AutoModelForImageClassification.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip""")
											model.to(_A)

											from datasets import load_dataset

											_SCREAMING_SNAKE_CASE		:						Any			  =    load_dataset("""nielsr/rvlcdip-demo""")

											_SCREAMING_SNAKE_CASE		:						Any			  =    dataset["""train"""][0]["""image"""].convert("""RGB""")

											_SCREAMING_SNAKE_CASE		:						str			  =    image_processor(_A      ,    return_tensors="""pt""").to(_A)

											# forward pass
											with torch.no_grad():
																		_SCREAMING_SNAKE_CASE		:						Any			  =    model(**_A)
																		_SCREAMING_SNAKE_CASE		:						List[Any]			  =    outputs.logits

											_SCREAMING_SNAKE_CASE		:						List[str]			  =    torch.Size((1, 1_6))
											self.assertEqual(logits.shape      ,    _A)

											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    torch.tensor(
											    [-0.4_158, -0.4_092, -0.4_347]      ,    device=_A      ,    dtype=torch.float      ,    )
											self.assertTrue(torch.allclose(logits[0, :3]      ,    _A      ,    atol=1e-4))


