




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__					:  Optional[Any]      							=				logging.get_logger(__name__)

lowerCamelCase__					:  Optional[int]      							=				{
    '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 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







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



 def __init__(				self    :						str							,							_lowerCAmelCase    :						str=50_400							,							_lowerCAmelCase    :						int=2_048							,							_lowerCAmelCase    :						Union[str, Any]=4_096							,							_lowerCAmelCase    :						Tuple=28							,							_lowerCAmelCase    :						List[str]=16							,							_lowerCAmelCase    :						Dict=64							,							_lowerCAmelCase    :						Tuple=None							,							_lowerCAmelCase    :						str="gelu_new"							,							_lowerCAmelCase    :						List[str]=0.0							,							_lowerCAmelCase    :						Any=0.0							,							_lowerCAmelCase    :						Optional[Any]=0.0							,							_lowerCAmelCase    :						str=1E-5							,							_lowerCAmelCase    :						Union[str, Any]=0.02							,							_lowerCAmelCase    :						Optional[Any]=True							,							_lowerCAmelCase    :						Any=50_256							,							_lowerCAmelCase    :						Optional[int]=50_256							,							_lowerCAmelCase    :						Tuple=False							,							**_lowerCAmelCase    :						Tuple							,							):
       SCREAMING_SNAKE_CASE_      		=     vocab_size
       SCREAMING_SNAKE_CASE_      		=     n_positions
       SCREAMING_SNAKE_CASE_      		=     n_embd
       SCREAMING_SNAKE_CASE_      		=     n_layer
       SCREAMING_SNAKE_CASE_      		=     n_head
       SCREAMING_SNAKE_CASE_      		=     n_inner
       SCREAMING_SNAKE_CASE_      		=     rotary_dim
       SCREAMING_SNAKE_CASE_      		=     activation_function
       SCREAMING_SNAKE_CASE_      		=     resid_pdrop
       SCREAMING_SNAKE_CASE_      		=     embd_pdrop
       SCREAMING_SNAKE_CASE_      		=     attn_pdrop
       SCREAMING_SNAKE_CASE_      		=     layer_norm_epsilon
       SCREAMING_SNAKE_CASE_      		=     initializer_range
       SCREAMING_SNAKE_CASE_      		=     use_cache

       SCREAMING_SNAKE_CASE_      		=     bos_token_id
       SCREAMING_SNAKE_CASE_      		=     eos_token_id

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





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 def __init__(				self    :						Union[str, Any]							,							_lowerCAmelCase    :						PretrainedConfig							,							_lowerCAmelCase    :						str = "default"							,							_lowerCAmelCase    :						List[PatchingSpec] = None							,							_lowerCAmelCase    :						bool = False							,							):
       super().__init__(_lowerCAmelCase							,							task=_lowerCAmelCase							,							patching_specs=_lowerCAmelCase							,							use_past=_lowerCAmelCase		)
       if not getattr(self._config							,							'pad_token_id'							,							_lowerCAmelCase		):
             # TODO: how to do that better?
             SCREAMING_SNAKE_CASE_      		=     0



 @property
 def  lowerCAmelCase_    (				self    :						int		):
       SCREAMING_SNAKE_CASE_      		=     OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}		)
       if self.use_past:
             self.fill_with_past_key_values_(_lowerCAmelCase							,							direction='inputs'		)
             SCREAMING_SNAKE_CASE_      		=     {0: 'batch', 1: 'past_sequence + sequence'}
       else:
             SCREAMING_SNAKE_CASE_      		=     {0: 'batch', 1: 'sequence'}

       return common_inputs



 @property
 def  lowerCAmelCase_    (				self    :						Dict		):
       return self._config.n_layer



 @property
 def  lowerCAmelCase_    (				self    :						Optional[Any]		):
       return self._config.n_head



 def  lowerCAmelCase_    (				self    :						Optional[int]							,							_lowerCAmelCase    :						PreTrainedTokenizer							,							_lowerCAmelCase    :						int = -1							,							_lowerCAmelCase    :						int = -1							,							_lowerCAmelCase    :						bool = False							,							_lowerCAmelCase    :						Optional[TensorType] = None							,							):
       SCREAMING_SNAKE_CASE_      		=     super(_lowerCAmelCase							,							self		).generate_dummy_inputs(
           _lowerCAmelCase							,							batch_size=_lowerCAmelCase							,							seq_length=_lowerCAmelCase							,							is_pair=_lowerCAmelCase							,							framework=_lowerCAmelCase		)

       # We need to order the input in the way they appears in the forward()
       SCREAMING_SNAKE_CASE_      		=     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

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

       SCREAMING_SNAKE_CASE_      		=     common_inputs['attention_mask']
       if self.use_past:
             SCREAMING_SNAKE_CASE_      		=     ordered_inputs['attention_mask'].dtype
             SCREAMING_SNAKE_CASE_      		=     torch.cat(
                 [ordered_inputs['attention_mask'], torch.ones(_lowerCAmelCase							,							_lowerCAmelCase							,							dtype=_lowerCAmelCase		)]							,							dim=1		)

       return ordered_inputs




 @property
 def  lowerCAmelCase_    (				self    :						int		):
       return 13


'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_a					:			Any							=    {
    'configuration_groupvit': [
        'GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'GroupViTConfig',
        'GroupViTOnnxConfig',
        'GroupViTTextConfig',
        'GroupViTVisionConfig',
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a					:			Union[str, Any]							=    [
      'GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'GroupViTModel',
      'GroupViTPreTrainedModel',
      'GroupViTTextModel',
      'GroupViTVisionModel',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a					:			Tuple							=    [
      'TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFGroupViTModel',
      'TFGroupViTPreTrainedModel',
      'TFGroupViTTextModel',
      'TFGroupViTVisionModel',
  ]

if TYPE_CHECKING:
  from .configuration_groupvit import (
      GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      GroupViTConfig,
      GroupViTOnnxConfig,
      GroupViTTextConfig,
      GroupViTVisionConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_groupvit import (
        GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        GroupViTModel,
        GroupViTPreTrainedModel,
        GroupViTTextModel,
        GroupViTVisionModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_groupvit import (
        TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFGroupViTModel,
        TFGroupViTPreTrainedModel,
        TFGroupViTTextModel,
        TFGroupViTVisionModel,
    )

else:
  import sys

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


