





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 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       "imagegpt"
							lowerCamelCase_							     =       ["past_key_values"]
							lowerCamelCase_							     =       {
							    "hidden_size": "n_embd",
							    "max_position_embeddings": "n_positions",
							    "num_attention_heads": "n_head",
							    "num_hidden_layers": "n_layer",
							}


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



									"""simple docstring"""



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

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




class 	UpperCamelCase_							(  UpperCamelCase__     ):


							@property
							def   _snake_case	(				self     :str		) -> Mapping[str, Mapping[int, str]]:



									"""simple docstring"""



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


							def   _snake_case	(				self     :Dict				,					__A     :"FeatureExtractionMixin"				,					__A     :int = 1				,					__A     :int = -1				,					__A     :bool = False				,					__A     :Optional["TensorType"] = None				,					__A     :int = 3				,					__A     :int = 32				,					__A     :int = 32				,					) -> Mapping[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self._generate_dummy_images(__A				,					__A				,					__A				,					__A		)
									SCREAMING_SNAKE_CASE__								=   dict(preprocessor(images=__A				,					return_tensors=__A		)		)

									return inputs

"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE_					     =	{
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE_					     =	[
			    '''RagModel''',
			    '''RagPreTrainedModel''',
			    '''RagSequenceForGeneration''',
			    '''RagTokenForGeneration''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE_					     =	[
			    '''TFRagModel''',
			    '''TFRagPreTrainedModel''',
			    '''TFRagSequenceForGeneration''',
			    '''TFRagTokenForGeneration''',
			]


if TYPE_CHECKING:
			from .configuration_rag import RagConfig
			from .retrieval_rag import RagRetriever
			from .tokenization_rag import RagTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_rag import (
						    TFRagModel,
						    TFRagPreTrainedModel,
						    TFRagSequenceForGeneration,
						    TFRagTokenForGeneration,
						)

else:
			import sys

			SCREAMING_SNAKE_CASE_					     =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
