






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

a						:			List[Any]     	=					logging.get_logger(__name__)

a						:			str     	=					{
    "openai/imagegpt-small": "",
    "openai/imagegpt-medium": "",
    "openai/imagegpt-large": "",
}
class        a       (					lowercase__  ):



      """simple docstring"""


      a    :					Any							=      'imagegpt'
      a    :					Optional[Any]							=      ['past_key_values']
      a    :					Tuple							=      {
          'hidden_size': 'n_embd',
          'max_position_embeddings': 'n_positions',
          'num_attention_heads': 'n_head',
          'num_hidden_layers': 'n_layer',
      }
      def __init__( self   :     Union[str, Any]						, __lowercase   :     Dict=512 + 1						, __lowercase   :     int=32 * 32						, __lowercase   :     List[str]=512						, __lowercase   :     Dict=24						, __lowercase   :     Optional[int]=8						, __lowercase   :     Tuple=None						, __lowercase   :     Tuple="quick_gelu"						, __lowercase   :     int=0.1						, __lowercase   :     Union[str, Any]=0.1						, __lowercase   :     Optional[int]=0.1						, __lowercase   :     List[Any]=1e-5						, __lowercase   :     Optional[Any]=0.02						, __lowercase   :     Union[str, Any]=True						, __lowercase   :     Optional[int]=True						, __lowercase   :     Optional[int]=False						, __lowercase   :     Union[str, Any]=False						, __lowercase   :     Any=False						, **__lowercase   :     Optional[Any]						, )				-> List[Any]:
        __UpperCAmelCase				:      Tuple	 =    vocab_size
        __UpperCAmelCase				:      Union[str, Any]	 =    n_positions
        __UpperCAmelCase				:      int	 =    n_embd
        __UpperCAmelCase				:      int	 =    n_layer
        __UpperCAmelCase				:      Tuple	 =    n_head
        __UpperCAmelCase				:      Tuple	 =    n_inner
        __UpperCAmelCase				:      Optional[Any]	 =    activation_function
        __UpperCAmelCase				:      Tuple	 =    resid_pdrop
        __UpperCAmelCase				:      Optional[int]	 =    embd_pdrop
        __UpperCAmelCase				:      str	 =    attn_pdrop
        __UpperCAmelCase				:      int	 =    layer_norm_epsilon
        __UpperCAmelCase				:      List[str]	 =    initializer_range
        __UpperCAmelCase				:      List[str]	 =    scale_attn_weights
        __UpperCAmelCase				:      int	 =    use_cache
        __UpperCAmelCase				:      Optional[int]	 =    scale_attn_by_inverse_layer_idx
        __UpperCAmelCase				:      Optional[int]	 =    reorder_and_upcast_attn
        __UpperCAmelCase				:      Optional[int]	 =    tie_word_embeddings

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


class        a       (					lowercase__  ):



      """simple docstring"""


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




      def       UpperCAmelCase ( self   :     Union[str, Any]						, __lowercase   :     "FeatureExtractionMixin"						, __lowercase   :     int = 1						, __lowercase   :     int = -1						, __lowercase   :     bool = False						, __lowercase   :     Optional["TensorType"] = None						, __lowercase   :     int = 3						, __lowercase   :     int = 32						, __lowercase   :     int = 32						, )				-> Mapping[str, Any]:
        __UpperCAmelCase				:      str	 =    self._generate_dummy_images(__lowercase						, __lowercase						, __lowercase						, __lowercase   )
        __UpperCAmelCase				:      Any	 =    dict(preprocessor(images=__lowercase						, return_tensors=__lowercase   )   )

        return inputs









'''simple docstring'''

import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


lowercase__		:     int			=							logging.get_logger(__name__)

lowercase__		:     Any			=							'T5Config'







def    a__							(							lowercase   :	jnp.array,   lowercase   :	int,   lowercase   :	int )						->						jnp.ndarray:





     """simple docstring"""

     _UpperCamelCase							       =			jnp.zeros_like(lowercase )
     _UpperCamelCase							       =			shifted_input_ids.at[:, 1:].set(input_ids[:, :-1] )
     _UpperCamelCase							       =			shifted_input_ids.at[:, 0].set(lowercase )

     _UpperCamelCase							       =			jnp.where(shifted_input_ids == -100,   lowercase,   lowercase )
     return shifted_input_ids
class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""
       _snake_case      :	Optional[Any]									=       'mt5'
       _snake_case      :	Union[str, Any]									=       MTaConfig

class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""
       _snake_case      :	Tuple									=       'mt5'
       _snake_case      :	int									=       MTaConfig

class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""
       _snake_case      :	Optional[int]									=       'mt5'
       _snake_case      :	Optional[Any]									=       MTaConfig



