


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

_snake_case             =     logging.get_logger(__name__)

_snake_case             =     {
    '''openai/imagegpt-small''': '''''',
    '''openai/imagegpt-medium''': '''''',
    '''openai/imagegpt-large''': '''''',
}
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Optional[int]     		=		"imagegpt"
   __A :      List[Any]     		=		["past_key_values"]
   __A :      Optional[int]     		=		{
       "hidden_size": "n_embd",
       "max_position_embeddings": "n_positions",
       "num_attention_heads": "n_head",
       "num_hidden_layers": "n_layer",
   }






   def __init__(		self						,				__A=512 + 1						,				__A=32 * 32						,				__A=512						,				__A=24						,				__A=8						,				__A=None						,				__A="quick_gelu"						,				__A=0.1						,				__A=0.1						,				__A=0.1						,				__A=1e-5						,				__A=0.02						,				__A=True						,				__A=True						,				__A=False						,				__A=False						,				__A=False						,				**__A						,				):






     """simple docstring"""





     lowerCamelCase :					Any    =				vocab_size
     lowerCamelCase :					Any    =				n_positions
     lowerCamelCase :					Any    =				n_embd
     lowerCamelCase :					List[Any]    =				n_layer
     lowerCamelCase :					str    =				n_head
     lowerCamelCase :					List[str]    =				n_inner
     lowerCamelCase :					List[Any]    =				activation_function
     lowerCamelCase :					List[Any]    =				resid_pdrop
     lowerCamelCase :					List[Any]    =				embd_pdrop
     lowerCamelCase :					List[str]    =				attn_pdrop
     lowerCamelCase :					int    =				layer_norm_epsilon
     lowerCamelCase :					Any    =				initializer_range
     lowerCamelCase :					Optional[Any]    =				scale_attn_weights
     lowerCamelCase :					Optional[Any]    =				use_cache
     lowerCamelCase :					str    =				scale_attn_by_inverse_layer_idx
     lowerCamelCase :					Optional[Any]    =				reorder_and_upcast_attn
     lowerCamelCase :					List[Any]    =				tie_word_embeddings

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



class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   @property
   def _snake_case    (		self ):






     """simple docstring"""





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






   def _snake_case    (		self						,				__A						,				__A = 1						,				__A = -1						,				__A = False						,				__A = None						,				__A = 3						,				__A = 32						,				__A = 32						,				):






     """simple docstring"""





     lowerCamelCase :					Optional[int]    =				self._generate_dummy_images(__A						,				__A						,				__A						,				__A )
     lowerCamelCase :					List[Any]    =				dict(preprocessor(images=__A						,				return_tensors=__A ) )

     return inputs







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             =     logging.get_logger(__name__)

_snake_case             =     {
    '''google/mobilenet_v2_1.4_224''': '''https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json''',
    '''google/mobilenet_v2_1.0_224''': '''https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json''',
    '''google/mobilenet_v2_0.75_160''': '''https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json''',
    '''google/mobilenet_v2_0.35_96''': '''https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json''',
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Dict     		=		"mobilenet_v2"






   def __init__(		self						,				__A=3						,				__A=224						,				__A=1.0						,				__A=8						,				__A=8						,				__A=6						,				__A=32						,				__A=True						,				__A=True						,				__A="relu6"						,				__A=True						,				__A=0.8						,				__A=0.02						,				__A=0.001						,				__A=255						,				**__A						,				):






     """simple docstring"""





     super().__init__(**__A )

     if depth_multiplier <= 0:
       raise ValueError("depth_multiplier must be greater than zero." )

     lowerCamelCase :					str    =				num_channels
     lowerCamelCase :					Any    =				image_size
     lowerCamelCase :					Union[str, Any]    =				depth_multiplier
     lowerCamelCase :					Tuple    =				depth_divisible_by
     lowerCamelCase :					Dict    =				min_depth
     lowerCamelCase :					Dict    =				expand_ratio
     lowerCamelCase :					Optional[Any]    =				output_stride
     lowerCamelCase :					int    =				first_layer_is_expansion
     lowerCamelCase :					Union[str, Any]    =				finegrained_output
     lowerCamelCase :					Optional[Any]    =				hidden_act
     lowerCamelCase :					Optional[Any]    =				tf_padding
     lowerCamelCase :					Optional[Any]    =				classifier_dropout_prob
     lowerCamelCase :					Dict    =				initializer_range
     lowerCamelCase :					str    =				layer_norm_eps
     lowerCamelCase :					Optional[Any]    =				semantic_loss_ignore_index



class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Union[str, Any]     		=		version.parse("1.11"    )






   @property
   def _snake_case    (		self ):






     """simple docstring"""





     return OrderedDict([("pixel_values", {0: "batch"})] )






   @property
   def _snake_case    (		self ):






     """simple docstring"""





     if self.task == "image-classification":
       return OrderedDict([("logits", {0: "batch"})] )
     else:
       return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})] )






   @property
   def _snake_case    (		self ):






     """simple docstring"""





     return 1e-4



