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

__lowercase									=   logging.get_logger(__name__)

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


class       lowerCamelCase_	(  snake_case_					):






    '''simple docstring'''






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







    def __init__(      self					,					__lowercase=512 + 1					,					__lowercase=32 * 32					,					__lowercase=512					,					__lowercase=24					,					__lowercase=8					,					__lowercase=None					,					__lowercase="quick_gelu"					,					__lowercase=0.1					,					__lowercase=0.1					,					__lowercase=0.1					,					__lowercase=1E-5					,					__lowercase=0.02					,					__lowercase=True					,					__lowercase=True					,					__lowercase=False					,					__lowercase=False					,					__lowercase=False					,					**__lowercase					,					)			->      str:
        __UpperCamelCase	:List[str]         =						vocab_size
        __UpperCamelCase	:Dict         =						n_positions
        __UpperCamelCase	:Any         =						n_embd
        __UpperCamelCase	:Tuple         =						n_layer
        __UpperCamelCase	:Union[str, Any]         =						n_head
        __UpperCamelCase	:List[str]         =						n_inner
        __UpperCamelCase	:List[str]         =						activation_function
        __UpperCamelCase	:Dict         =						resid_pdrop
        __UpperCamelCase	:str         =						embd_pdrop
        __UpperCamelCase	:Tuple         =						attn_pdrop
        __UpperCamelCase	:Union[str, Any]         =						layer_norm_epsilon
        __UpperCamelCase	:Union[str, Any]         =						initializer_range
        __UpperCamelCase	:Any         =						scale_attn_weights
        __UpperCamelCase	:Tuple         =						use_cache
        __UpperCamelCase	:Any         =						scale_attn_by_inverse_layer_idx
        __UpperCamelCase	:Tuple         =						reorder_and_upcast_attn
        __UpperCamelCase	:Tuple         =						tie_word_embeddings

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


class       lowerCamelCase_	(  snake_case_					):






    '''simple docstring'''






    @property
    def      UpperCamelCase__  (      self)			->      Mapping[str, Mapping[int, str]]:
        return OrderedDict(
            [
                ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
            ])







    def      UpperCamelCase__  (      self					,					__lowercase					,					__lowercase = 1					,					__lowercase = -1					,					__lowercase = False					,					__lowercase = None					,					__lowercase = 3					,					__lowercase = 32					,					__lowercase = 32					,					)			->      Mapping[str, Any]:
        __UpperCamelCase	:Dict         =						self._generate_dummy_images(A_					,					A_					,					A_					,					A_)
        __UpperCamelCase	:Union[str, Any]         =						dict(preprocessor(images=A_					,					return_tensors=A_))

        return inputs







'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase					:    Union[str, Any]           = {
    'configuration_git': ['GIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GitConfig', 'GitVisionConfig'],
    'processing_git': ['GitProcessor'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase					:    List[Any]           = [
						    'GIT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'GitForCausalLM',
						    'GitModel',
						    'GitPreTrainedModel',
						    'GitVisionModel',
						]

if TYPE_CHECKING:
						from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
						from .processing_git import GitProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_git import (
												    GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    GitForCausalLM,
												    GitModel,
												    GitPreTrainedModel,
												    GitVisionModel,
												)

else:
						import sys

						lowerCAmelCase					:    Optional[Any]           = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




