


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


lowercase_			       =      logging.get_logger(__name__)

lowercase_			       =      {
    '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 			A_   (			snake_case_       ):




      '''simple docstring'''


      __snake_case   			=  '''gptj'''
      __snake_case   			=  {
          '''max_position_embeddings''': '''n_positions''',
          '''hidden_size''': '''n_embd''',
          '''num_attention_heads''': '''n_head''',
          '''num_hidden_layers''': '''n_layer''',
      }




      def __init__(							self:			str		, a:			int=5_0400		, a:			Tuple=2048		, a:			List[str]=4096		, a:			List[str]=28		, a:			Optional[Any]=16		, a:			Optional[int]=64		, a:			List[str]=None		, a:			Any="gelu_new"		, a:			List[str]=0.0		, a:			Optional[int]=0.0		, a:			List[str]=0.0		, a:			List[Any]=1e-5		, a:			Tuple=0.0_2		, a:			List[str]=True		, a:			Any=5_0256		, a:			Optional[Any]=5_0256		, a:			Optional[int]=False		, **a:			Tuple		, ):
       __lowerCamelCase						:				List[Any]						   =		vocab_size
       __lowerCamelCase						:				Tuple						   =		n_positions
       __lowerCamelCase						:				str						   =		n_embd
       __lowerCamelCase						:				Optional[Any]						   =		n_layer
       __lowerCamelCase						:				List[str]						   =		n_head
       __lowerCamelCase						:				List[str]						   =		n_inner
       __lowerCamelCase						:				str						   =		rotary_dim
       __lowerCamelCase						:				Optional[int]						   =		activation_function
       __lowerCamelCase						:				Optional[Any]						   =		resid_pdrop
       __lowerCamelCase						:				Dict						   =		embd_pdrop
       __lowerCamelCase						:				str						   =		attn_pdrop
       __lowerCamelCase						:				Union[str, Any]						   =		layer_norm_epsilon
       __lowerCamelCase						:				Optional[int]						   =		initializer_range
       __lowerCamelCase						:				List[str]						   =		use_cache

       __lowerCamelCase						:				Tuple						   =		bos_token_id
       __lowerCamelCase						:				Union[str, Any]						   =		eos_token_id

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



class 			A_   (			snake_case_       ):




      '''simple docstring'''


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




      @property
      def 	_snake_case      (							self:			Tuple							):
       __lowerCamelCase						:				Optional[Any]						   =		OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}							)
       if self.use_past:
        self.fill_with_past_key_values_(lowercase__		, direction='inputs'							)
        __lowerCamelCase						:				List[str]						   =		{0: """batch""", 1: """past_sequence + sequence"""}
       else:
        __lowerCamelCase						:				List[str]						   =		{0: """batch""", 1: """sequence"""}

       return common_inputs




      @property
      def 	_snake_case      (							self:			List[str]							):
       return self._config.n_layer




      @property
      def 	_snake_case      (							self:			str							):
       return self._config.n_head




      def 	_snake_case      (							self:			int		, a:			PreTrainedTokenizer		, a:			int = -1		, a:			int = -1		, a:			bool = False		, a:			Optional[TensorType] = None		, ):
       __lowerCamelCase						:				Any						   =		super(lowercase__		, self							).generate_dummy_inputs(
           lowercase__		, batch_size=lowercase__		, seq_length=lowercase__		, is_pair=lowercase__		, framework=lowercase__							)

       # We need to order the input in the way they appears in the forward()
       __lowerCamelCase						:				Union[str, Any]						   =		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						:				List[str]						   =		common_inputs["""input_ids"""].shape
         # Not using the same length for past_key_values
         __lowerCamelCase						:				Any						   =		seqlen + 2
         __lowerCamelCase						:				Tuple						   =		(
             batch,
             self.num_attention_heads,
             past_key_values_length,
             self._config.hidden_size // self.num_attention_heads,
         )
         __lowerCamelCase						:				Optional[Any]						   =		[
             (torch.zeros(lowercase__							), torch.zeros(lowercase__							)) for _ in range(self.num_layers							)
         ]

       __lowerCamelCase						:				List[Any]						   =		common_inputs["""attention_mask"""]
       if self.use_past:
        __lowerCamelCase						:				List[Any]						   =		ordered_inputs["""attention_mask"""].dtype
        __lowerCamelCase						:				List[str]						   =		torch.cat(
            [ordered_inputs['attention_mask'], torch.ones(lowercase__		, lowercase__		, dtype=lowercase__							)]		, dim=1							)

       return ordered_inputs




      @property
      def 	_snake_case      (							self:			List[Any]							):
       return 13







'''simple docstring'''


from typing import TYPE_CHECKING

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


lowerCAmelCase_      :  Optional[Any]				     =  {
    'configuration_mobilevit': ['MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MobileViTConfig', 'MobileViTOnnxConfig'],
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_      :  int				     =  ['MobileViTFeatureExtractor']
 lowerCAmelCase_      :  int				     =  ['MobileViTImageProcessor']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_      :  str				     =  [
     'MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'MobileViTForImageClassification',
     'MobileViTForSemanticSegmentation',
     'MobileViTModel',
     'MobileViTPreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_      :  Optional[Any]				     =  [
     'TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFMobileViTForImageClassification',
     'TFMobileViTForSemanticSegmentation',
     'TFMobileViTModel',
     'TFMobileViTPreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_mobilevit import MobileViTFeatureExtractor
  from .image_processing_mobilevit import MobileViTImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mobilevit import (
      MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      MobileViTForImageClassification,
      MobileViTForSemanticSegmentation,
      MobileViTModel,
      MobileViTPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_mobilevit import (
      TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFMobileViTForImageClassification,
      TFMobileViTForSemanticSegmentation,
      TFMobileViTModel,
      TFMobileViTPreTrainedModel,
  )


else:
 import sys

 lowerCAmelCase_      :  List[Any]				     =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
