
from typing import TYPE_CHECKING

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


_snake_case			:       Optional[Any]      =						{"configuration_gptj": ["GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTJConfig", "GPTJOnnxConfig"]}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case			:       Optional[int]      =						[
          "GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST",
          "GPTJForCausalLM",
          "GPTJForQuestionAnswering",
          "GPTJForSequenceClassification",
          "GPTJModel",
          "GPTJPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case			:       Any      =						[
          "TFGPTJForCausalLM",
          "TFGPTJForQuestionAnswering",
          "TFGPTJForSequenceClassification",
          "TFGPTJModel",
          "TFGPTJPreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case			:       str      =						[
          "FlaxGPTJForCausalLM",
          "FlaxGPTJModel",
          "FlaxGPTJPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_gptj import (
                GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
                GPTJForCausalLM,
                GPTJForQuestionAnswering,
                GPTJForSequenceClassification,
                GPTJModel,
                GPTJPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_gptj import (
                TFGPTJForCausalLM,
                TFGPTJForQuestionAnswering,
                TFGPTJForSequenceClassification,
                TFGPTJModel,
                TFGPTJPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
      import sys

      _snake_case			:       Union[str, Any]      =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types

def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__	):







				'''simple docstring'''


				if not inplace:
								lowercase						    =			ma + ma
				else:
								ma += ma

				return ma

def    __snake_case       (  __magic_name__		,						__magic_name__	):







				'''simple docstring'''


				lowercase						    =			-1 * len(__magic_name__	)
				lowercase						    =			list(range(len(tensor.shape[:zero_index]	)	)	)
				return tensor.permute(first_inds + [zero_index + i for i in inds]	)

def    __snake_case       (  __magic_name__		,						__magic_name__	):







				'''simple docstring'''


				return t.reshape(t.shape[:-no_dims] + (-1,)	)

def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__		,						__magic_name__ = 1e-4	):







				'''simple docstring'''


				lowercase						    =			mask.expand(*value.shape	)
				return torch.sum(mask * value		,						dim=__magic_name__	) / (eps + torch.sum(__magic_name__		,						dim=__magic_name__	))

def    __snake_case       (  __magic_name__		,						__magic_name__ = 2.3_1_2_5		,						__magic_name__ = 2_1.6_8_7_5		,						__magic_name__ = 64	):







				'''simple docstring'''


				lowercase						    =			torch.linspace(__magic_name__		,						__magic_name__		,						no_bins - 1		,						device=pts.device	)
				lowercase						    =			torch.sqrt(torch.sum((pts.unsqueeze(-2	) - pts.unsqueeze(-3	)) ** 2		,						dim=-1	)	)
				return torch.bucketize(__magic_name__		,						__magic_name__	)

def    __snake_case       (  __magic_name__		,						__magic_name__	):







				'''simple docstring'''


				lowercase						    =			dicts[0]
				lowercase						    =			{}
				for k, v in first.items():
								lowercase						    =			[d[k] for d in dicts]
								if isinstance(__magic_name__		,						__magic_name__	):
												lowercase						    =			dict_multimap(__magic_name__		,						__magic_name__	)
								else:
												lowercase						    =			fn(__magic_name__	)

				return new_dict

def    __snake_case       (  __magic_name__		,						__magic_name__	):







				'''simple docstring'''


				lowercase						    =			v_bins.view(((1,) * len(x.shape	)) + (len(__magic_name__	),)	)
				lowercase						    =			x[..., None] - reshaped_bins
				lowercase						    =			torch.argmin(torch.abs(__magic_name__	)		,						dim=-1	)
				return nn.functional.one_hot(__magic_name__		,						num_classes=len(__magic_name__	)	).float()

def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__ = 0		,						__magic_name__ = 0	):







				'''simple docstring'''


				lowercase						    =			[]
				for i, s in enumerate(data.shape[:no_batch_dims]	):
								lowercase						    =			torch.arange(__magic_name__	)
								lowercase						    =			r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape	) - i - 1)))	)
								ranges.append(__magic_name__	)

				lowercase						    =			[slice(__magic_name__	) for _ in range(len(data.shape	) - no_batch_dims	)]
				lowercase						    =			inds
				ranges.extend(__magic_name__	)
				# Matt note: Editing this to get around the behaviour of using a list as an array index changing
				# in recent Numpy versions
				return data[tuple(__magic_name__	)]


_snake_case			:       List[Any]      =						TypeVar("T")

def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__	):







				'''simple docstring'''


				lowercase						    =			{}
				for k, v in dic.items():
								if isinstance(__magic_name__		,						__magic_name__	):
												lowercase						    =			dict_map(__magic_name__		,						__magic_name__		,						__magic_name__	)
								else:
												lowercase						    =			tree_map(__magic_name__		,						__magic_name__		,						__magic_name__	)

				return new_dict

@overload
def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__	):







				'''simple docstring'''


				...

@overload
def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__	):







				'''simple docstring'''


				...

@overload
def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__	):







				'''simple docstring'''


				...

@overload
def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__	):







				'''simple docstring'''


				...

def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__	):







				'''simple docstring'''


				if isinstance(__magic_name__		,						__magic_name__	):
								return dict_map(__magic_name__		,						__magic_name__		,						__magic_name__	)
				elif isinstance(__magic_name__		,						__magic_name__	):
								return [tree_map(__magic_name__		,						__magic_name__		,						__magic_name__	) for x in tree]
				elif isinstance(__magic_name__		,						__magic_name__	):
								return tuple(tree_map(__magic_name__		,						__magic_name__		,						__magic_name__	) for x in tree	)
				elif isinstance(__magic_name__		,						__magic_name__	):
								return fn(__magic_name__	)
				else:
								print(type(__magic_name__	)	)
								raise ValueError("Not supported"	)




_snake_case			:       Union[str, Any]      =						partial(tree_map, leaf_type=torch.Tensor)
