
'''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					:    Tuple           = logging.get_logger(__name__)

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


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				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						,				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_						,				)->		str:







											'''simple docstring'''






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

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


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):







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







											'''simple docstring'''






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







				def 						UpperCAmelCase_    (       self						,				A_						,				A_ = 1						,				A_ = -1						,				A_ = False						,				A_ = None						,				A_ = 3						,				A_ = 32						,				A_ = 32						,				)->		Mapping[str, Any]:







											'''simple docstring'''






											UpperCamelCase   			=     self._generate_dummy_images(A_						,				A_						,				A_						,				A_     )
											UpperCamelCase   			=     dict(preprocessor(images=A_						,				return_tensors=A_     )     )

											return inputs






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

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


if is_sentencepiece_available():
						from ..ta.tokenization_ta import TaTokenizer
else:
						from ...utils.dummy_sentencepiece_objects import TaTokenizer

lowerCAmelCase					:    Tuple           = TaTokenizer

if is_tokenizers_available():
						from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
						from ...utils.dummy_tokenizers_objects import TaTokenizerFast

lowerCAmelCase					:    Optional[int]           = TaTokenizerFast

lowerCAmelCase					:    Any           = {'configuration_mt5': ['MT5Config', 'MT5OnnxConfig']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase					:    Optional[int]           = [
						    'MT5EncoderModel',
						    'MT5ForConditionalGeneration',
						    'MT5ForQuestionAnswering',
						    'MT5Model',
						    'MT5PreTrainedModel',
						    'MT5Stack',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase					:    Dict           = ['TFMT5EncoderModel', 'TFMT5ForConditionalGeneration', 'TFMT5Model']

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase					:    Optional[Any]           = ['FlaxMT5EncoderModel', 'FlaxMT5ForConditionalGeneration', 'FlaxMT5Model']


if TYPE_CHECKING:
						from .configuration_mta import MTaConfig, MTaOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mta import (
												    MTaEncoderModel,
												    MTaForConditionalGeneration,
												    MTaForQuestionAnswering,
												    MTaModel,
												    MTaPreTrainedModel,
												    MTaStack,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
						import sys

						lowerCAmelCase					:    Tuple           = _LazyModule(
						    __name__,
						    globals()['__file__'],
						    _import_structure,
						    extra_objects={'MT5Tokenizer': MTaTokenizer, 'MT5TokenizerFast': MTaTokenizerFast},
						    module_spec=__spec__,
						)




