

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


_SCREAMING_SNAKE_CASE					:  List[str]             =      logging.get_logger(__name__)


_SCREAMING_SNAKE_CASE					:  Dict             =      {
    '''Salesforce/codegen-350M-nl''': '''https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json''',
    '''Salesforce/codegen-350M-multi''': '''https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json''',
    '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json''',
    '''Salesforce/codegen-2B-nl''': '''https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json''',
    '''Salesforce/codegen-2B-multi''': '''https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json''',
    '''Salesforce/codegen-2B-mono''': '''https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json''',
    '''Salesforce/codegen-6B-nl''': '''https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json''',
    '''Salesforce/codegen-6B-multi''': '''https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json''',
    '''Salesforce/codegen-6B-mono''': '''https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json''',
    '''Salesforce/codegen-16B-nl''': '''https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json''',
    '''Salesforce/codegen-16B-multi''': '''https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json''',
    '''Salesforce/codegen-16B-mono''': '''https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json''',
}






class 	UpperCAmelCase__		(	a__	):






  """simple docstring"""


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






  def __init__(							self						:		Dict     ,       __lowerCamelCase						:		Tuple=5_0400     ,       __lowerCamelCase						:		Union[str, Any]=2048     ,       __lowerCamelCase						:		Dict=2048     ,       __lowerCamelCase						:		List[Any]=4096     ,       __lowerCamelCase						:		Union[str, Any]=28     ,       __lowerCamelCase						:		int=16     ,       __lowerCamelCase						:		str=64     ,       __lowerCamelCase						:		str=None     ,       __lowerCamelCase						:		Dict="gelu_new"     ,       __lowerCamelCase						:		Optional[int]=0.0     ,       __lowerCamelCase						:		Union[str, Any]=0.0     ,       __lowerCamelCase						:		Optional[int]=0.0     ,       __lowerCamelCase						:		Optional[int]=1e-5     ,       __lowerCamelCase						:		str=0.02     ,       __lowerCamelCase						:		Union[str, Any]=True     ,       __lowerCamelCase						:		Union[str, Any]=5_0256     ,       __lowerCamelCase						:		Union[str, Any]=5_0256     ,       __lowerCamelCase						:		Tuple=False     ,       **__lowerCamelCase						:		Optional[Any]     ,       )		->							Dict:
       SCREAMING_SNAKE_CASE__   =   vocab_size
       SCREAMING_SNAKE_CASE__   =   n_ctx
       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 	UpperCAmelCase__		(	a__	):






  """simple docstring"""


  def __init__(							self						:		str     ,       __lowerCamelCase						:		Dict     ,       __lowerCamelCase						:		List[str] = "default"     ,       __lowerCamelCase						:		Any = None     ,       __lowerCamelCase						:		Dict = False     ,       )		->							List[str]:
       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 				lowercase_		(							self						:		str )		->							Mapping[str, Mapping[int, str]]:
       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 				lowercase_		(							self						:		str )		->							int:
       return self._config.n_layer






  @property
  def 				lowercase_		(							self						:		Tuple )		->							int:
       return self._config.n_head






  def 				lowercase_		(							self						:		List[Any]     ,       __lowerCamelCase						:		Tuple     ,       __lowerCamelCase						:		Optional[int] = -1     ,       __lowerCamelCase						:		Optional[Any] = -1     ,       __lowerCamelCase						:		Union[str, Any] = False     ,       __lowerCamelCase						:		List[Any] = None     ,       )		->							Mapping[str, Any]:
       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__   =   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 				lowercase_		(							self						:		Optional[Any] )		->							int:
       return 13





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE					:  Any             =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE					:  Optional[Any]             =      {
    '''google/vivit-b-16x2-kinetics400''': (
        '''https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json'''
    ),
    # See all Vivit models at https://huggingface.co/models?filter=vivit
}






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  a									= "vivit"






  def __init__(							self						:		str     ,       __lowerCamelCase						:		List[Any]=224     ,       __lowerCamelCase						:		Optional[int]=32     ,       __lowerCamelCase						:		Tuple=[2, 16, 16]     ,       __lowerCamelCase						:		Union[str, Any]=3     ,       __lowerCamelCase						:		Optional[Any]=768     ,       __lowerCamelCase						:		Any=12     ,       __lowerCamelCase						:		Optional[Any]=12     ,       __lowerCamelCase						:		List[str]=3072     ,       __lowerCamelCase						:		Any="gelu_fast"     ,       __lowerCamelCase						:		Union[str, Any]=0.0     ,       __lowerCamelCase						:		int=0.0     ,       __lowerCamelCase						:		str=0.02     ,       __lowerCamelCase						:		Any=1e-06     ,       __lowerCamelCase						:		Dict=True     ,       **__lowerCamelCase						:		Any     ,       )		->							List[str]:
       SCREAMING_SNAKE_CASE__   =   hidden_size
       SCREAMING_SNAKE_CASE__   =   num_hidden_layers
       SCREAMING_SNAKE_CASE__   =   num_attention_heads
       SCREAMING_SNAKE_CASE__   =   intermediate_size
       SCREAMING_SNAKE_CASE__   =   hidden_act
       SCREAMING_SNAKE_CASE__   =   hidden_dropout_prob
       SCREAMING_SNAKE_CASE__   =   attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__   =   initializer_range
       SCREAMING_SNAKE_CASE__   =   layer_norm_eps

       SCREAMING_SNAKE_CASE__   =   image_size
       SCREAMING_SNAKE_CASE__   =   num_frames
       SCREAMING_SNAKE_CASE__   =   tubelet_size
       SCREAMING_SNAKE_CASE__   =   num_channels
       SCREAMING_SNAKE_CASE__   =   qkv_bias

       super().__init__(**__lowerCamelCase )


