


"""simple docstring"""







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


lowerCAmelCase__      				=   logging.get_logger(__name__)

lowerCAmelCase__      				=   {
    '''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       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				Any   ="gptj"
       a  :				Any   ={
           "max_position_embeddings": "n_positions",
           "hidden_size": "n_embd",
           "num_attention_heads": "n_head",
           "num_hidden_layers": "n_layer",
       }

       def __init__(						self		,     snake_case__=50_400		,     snake_case__=2_048		,     snake_case__=4_096		,     snake_case__=28		,     snake_case__=16		,     snake_case__=64		,     snake_case__=None		,     snake_case__="gelu_new"		,     snake_case__=0.0		,     snake_case__=0.0		,     snake_case__=0.0		,     snake_case__=1e-5		,     snake_case__=0.02		,     snake_case__=True		,     snake_case__=50_256		,     snake_case__=50_256		,     snake_case__=False		,     **snake_case__		,     ):






         """simple docstring"""





         lowerCAmelCase     :     Any								=							vocab_size
         lowerCAmelCase     :     Tuple								=							n_positions
         lowerCAmelCase     :     List[Any]								=							n_embd
         lowerCAmelCase     :     Any								=							n_layer
         lowerCAmelCase     :     List[Any]								=							n_head
         lowerCAmelCase     :     Optional[int]								=							n_inner
         lowerCAmelCase     :     List[str]								=							rotary_dim
         lowerCAmelCase     :     Dict								=							activation_function
         lowerCAmelCase     :     Dict								=							resid_pdrop
         lowerCAmelCase     :     List[Any]								=							embd_pdrop
         lowerCAmelCase     :     List[str]								=							attn_pdrop
         lowerCAmelCase     :     Optional[int]								=							layer_norm_epsilon
         lowerCAmelCase     :     Optional[int]								=							initializer_range
         lowerCAmelCase     :     int								=							use_cache

         lowerCAmelCase     :     Dict								=							bos_token_id
         lowerCAmelCase     :     List[str]								=							eos_token_id

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


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       def __init__(						self		,     snake_case__		,     snake_case__ = "default"		,     snake_case__ = None		,     snake_case__ = False		,     ):






         """simple docstring"""





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

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Dict								=							OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}						)
         if self.use_past:
           self.fill_with_past_key_values_(snake_case__		,     direction="inputs"						)
           lowerCAmelCase     :     int								=							{0: "batch", 1: "past_sequence + sequence"}
         else:
           lowerCAmelCase     :     Optional[int]								=							{0: "batch", 1: "sequence"}

         return common_inputs

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         return self._config.n_layer

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         return self._config.n_head

       def        lowercase__							(						self		,     snake_case__		,     snake_case__ = -1		,     snake_case__ = -1		,     snake_case__ = False		,     snake_case__ = None		,     ):






         """simple docstring"""





         lowerCAmelCase     :     Tuple								=							super(snake_case__		,     self						).generate_dummy_inputs(
             snake_case__		,     batch_size=snake_case__		,     seq_length=snake_case__		,     is_pair=snake_case__		,     framework=snake_case__						)

         # We need to order the input in the way they appears in the forward()
         lowerCAmelCase     :     List[str]								=							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

             lowerCAmelCase						,  lowerCAmelCase     :     Dict								=							common_inputs["input_ids"].shape
             # Not using the same length for past_key_values
             lowerCAmelCase     :     Dict								=							seqlen + 2
             lowerCAmelCase     :     Dict								=							(
                 batch,
                 self.num_attention_heads,
                 past_key_values_length,
                 self._config.hidden_size // self.num_attention_heads,
             )
             lowerCAmelCase     :     Union[str, Any]								=							[
                 (torch.zeros(snake_case__						), torch.zeros(snake_case__						)) for _ in range(self.num_layers						)
             ]

         lowerCAmelCase     :     Optional[Any]								=							common_inputs["attention_mask"]
         if self.use_past:
           lowerCAmelCase     :     Optional[Any]								=							ordered_inputs["attention_mask"].dtype
           lowerCAmelCase     :     Union[str, Any]								=							torch.cat(
               [ordered_inputs["attention_mask"], torch.ones(snake_case__		,     snake_case__		,     dtype=snake_case__						)]		,     dim=1						)

         return ordered_inputs







       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         return 13









"""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


lowerCAmelCase__      				=   logging.get_logger(__name__)

lowerCAmelCase__      				=   '''T5Config'''
def 					a__			(				SCREAMING_SNAKE_CASE    :    jnp.array				,      SCREAMING_SNAKE_CASE    :    int				,      SCREAMING_SNAKE_CASE    :    int				):


  '''simple docstring'''







  lowerCAmelCase     :     List[str]								=							jnp.zeros_like(SCREAMING_SNAKE_CASE				)
  lowerCAmelCase     :     Optional[int]								=							shifted_input_ids.at[:, 1:].set(input_ids[:, :-1]				)
  lowerCAmelCase     :     List[str]								=							shifted_input_ids.at[:, 0].set(SCREAMING_SNAKE_CASE				)

  lowerCAmelCase     :     str								=							jnp.where(shifted_input_ids == -1_0_0				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				)
  return shifted_input_ids


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				List[Any]   ="mt5"
       a  :				Tuple   =MTaConfig


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				Union[str, Any]   ="mt5"
       a  :				Optional[Any]   =MTaConfig


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				str   ="mt5"
       a  :				Dict   =MTaConfig





