


from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


_UpperCAmelCase        =				logging.get_logger(__name__)

_UpperCAmelCase        =				{
    "EleutherAI/gpt-j-6B": "https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}



class 							_UpperCamelCase					(	_UpperCAmelCase     ):
       _UpperCamelCase				:					int         =   '''gptj'''
       _UpperCamelCase				:					int         =   {
           '''max_position_embeddings''': '''n_positions''',
           '''hidden_size''': '''n_embd''',
           '''num_attention_heads''': '''n_head''',
           '''num_hidden_layers''': '''n_layer''',
       }

       def __init__(		self: Any					,   _SCREAMING_SNAKE_CASE: Optional[int]=50400					,   _SCREAMING_SNAKE_CASE: Tuple=2048					,   _SCREAMING_SNAKE_CASE: Dict=4096					,   _SCREAMING_SNAKE_CASE: Dict=28					,   _SCREAMING_SNAKE_CASE: Tuple=16					,   _SCREAMING_SNAKE_CASE: int=64					,   _SCREAMING_SNAKE_CASE: Any=None					,   _SCREAMING_SNAKE_CASE: Union[str, Any]="gelu_new"					,   _SCREAMING_SNAKE_CASE: List[Any]=0.0					,   _SCREAMING_SNAKE_CASE: Dict=0.0					,   _SCREAMING_SNAKE_CASE: int=0.0					,   _SCREAMING_SNAKE_CASE: str=1e-5					,   _SCREAMING_SNAKE_CASE: List[str]=0.02					,   _SCREAMING_SNAKE_CASE: Any=True					,   _SCREAMING_SNAKE_CASE: List[str]=50256					,   _SCREAMING_SNAKE_CASE: Union[str, Any]=50256					,   _SCREAMING_SNAKE_CASE: Any=False					,   **_SCREAMING_SNAKE_CASE: List[str]					,   )       -> Dict:




              """simple docstring"""


              UpperCamelCase_          =  vocab_size
              UpperCamelCase_          =  n_positions
              UpperCamelCase_          =  n_embd
              UpperCamelCase_          =  n_layer
              UpperCamelCase_          =  n_head
              UpperCamelCase_          =  n_inner
              UpperCamelCase_          =  rotary_dim
              UpperCamelCase_          =  activation_function
              UpperCamelCase_          =  resid_pdrop
              UpperCamelCase_          =  embd_pdrop
              UpperCamelCase_          =  attn_pdrop
              UpperCamelCase_          =  layer_norm_epsilon
              UpperCamelCase_          =  initializer_range
              UpperCamelCase_          =  use_cache

              UpperCamelCase_          =  bos_token_id
              UpperCamelCase_          =  eos_token_id

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



class 							_UpperCamelCase					(	_UpperCAmelCase     ):

       def __init__(		self: Any					,   _SCREAMING_SNAKE_CASE: PretrainedConfig					,   _SCREAMING_SNAKE_CASE: str = "default"					,   _SCREAMING_SNAKE_CASE: List[PatchingSpec] = None					,   _SCREAMING_SNAKE_CASE: bool = False					,   )       -> str:




              """simple docstring"""


              super().__init__(A_					,   task=A_					,   patching_specs=A_					,   use_past=A_		)
              if not getattr(self._config					,   "pad_token_id"					,   A_		):
                     # TODO: how to do that better?
                     UpperCamelCase_          =  0

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




              """simple docstring"""


              UpperCamelCase_          =  OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}		)
              if self.use_past:
                     self.fill_with_past_key_values_(A_					,   direction="inputs"		)
                     UpperCamelCase_          =  {0: "batch", 1: "past_sequence + sequence"}
              else:
                     UpperCamelCase_          =  {0: "batch", 1: "sequence"}

              return common_inputs

       @property
       def lowercase (		self: Dict		)       -> int:




              """simple docstring"""


              return self._config.n_layer

       @property
       def lowercase (		self: Any		)       -> int:




              """simple docstring"""


              return self._config.n_head

       def lowercase (		self: Any					,   _SCREAMING_SNAKE_CASE: PreTrainedTokenizer					,   _SCREAMING_SNAKE_CASE: int = -1					,   _SCREAMING_SNAKE_CASE: int = -1					,   _SCREAMING_SNAKE_CASE: bool = False					,   _SCREAMING_SNAKE_CASE: Optional[TensorType] = None					,   )       -> Mapping[str, Any]:




              """simple docstring"""


              UpperCamelCase_          =  super(A_					,   self		).generate_dummy_inputs(
                  A_					,   batch_size=A_					,   seq_length=A_					,   is_pair=A_					,   framework=A_		)

              # We need to order the input in the way they appears in the forward()
              UpperCamelCase_          =  OrderedDict({"input_ids": common_inputs["input_ids"]}		)

              # Need to add the past_keys
              if self.use_past:
                     if not is_torch_available():
                            raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."		)
                     else:
                            import torch

                            UpperCamelCase_       , UpperCamelCase_          =  common_inputs["input_ids"].shape
                            # Not using the same length for past_key_values
                            UpperCamelCase_          =  seqlen + 2
                            UpperCamelCase_          =  (
                                batch,
                                self.num_attention_heads,
                                past_key_values_length,
                                self._config.hidden_size // self.num_attention_heads,
                            )
                            UpperCamelCase_          =  [
                                (torch.zeros(A_		), torch.zeros(A_		)) for _ in range(self.num_layers		)
                            ]

              UpperCamelCase_          =  common_inputs["attention_mask"]
              if self.use_past:
                     UpperCamelCase_          =  ordered_inputs["attention_mask"].dtype
                     UpperCamelCase_          =  torch.cat(
                         [ordered_inputs["attention_mask"], torch.ones(A_					,   A_					,   dtype=A_		)]					,   dim=1		)

              return ordered_inputs



       @property
       def lowercase (		self: List[Any]		)       -> int:




              """simple docstring"""


              return 13








import jax.numpy as jnp

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


_UpperCAmelCase        =				logging.get_logger(__name__)

_UpperCAmelCase        =				'T5Config'




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )     ->		jnp.ndarray:
       UpperCamelCase_          =  jnp.zeros_like(UpperCamelCase_  )
       UpperCamelCase_          =  shifted_input_ids.at[:, 1:].set(input_ids[:, :-1]  )
       UpperCamelCase_          =  shifted_input_ids.at[:, 0].set(UpperCamelCase_  )

       UpperCamelCase_          =  jnp.where(shifted_input_ids == -100					,		UpperCamelCase_					,		UpperCamelCase_  )
       return shifted_input_ids



class 							_UpperCamelCase					(	lowerCAmelCase_     ):
       _UpperCamelCase				:					Dict         =   '''mt5'''
       _UpperCamelCase				:					Union[str, Any]         =   MTaConfig



class 							_UpperCamelCase					(	lowerCAmelCase_     ):
       _UpperCamelCase				:					Dict         =   '''mt5'''
       _UpperCamelCase				:					Optional[int]         =   MTaConfig



class 							_UpperCamelCase					(	lowerCAmelCase_     ):
       _UpperCamelCase				:					Union[str, Any]         =   '''mt5'''
       _UpperCamelCase				:					List[Any]         =   MTaConfig

