



from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__UpperCAmelCase          =       logging.get_logger(__name__)

__UpperCAmelCase          =       {
    '''shi-labs/dinat-mini-in1k-224''': '''https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json''',
    # See all Dinat models at https://huggingface.co/models?filter=dinat
}



class 				lowerCAmelCase_		(  a__       ,    a__							):
       UpperCAmelCase__				:					Dict        =  "dinat"

       UpperCAmelCase__				:					Union[str, Any]        =  {
           "num_attention_heads": "num_heads",
           "num_hidden_layers": "num_layers",
       }
       def __init__(			self,			SCREAMING_SNAKE_CASE_=4,			SCREAMING_SNAKE_CASE_=3,			SCREAMING_SNAKE_CASE_=64,			SCREAMING_SNAKE_CASE_=[3, 4, 6, 5],			SCREAMING_SNAKE_CASE_=[2, 4, 8, 16],			SCREAMING_SNAKE_CASE_=7,			SCREAMING_SNAKE_CASE_=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]],			SCREAMING_SNAKE_CASE_=3.0,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_=0.0,			SCREAMING_SNAKE_CASE_=0.0,			SCREAMING_SNAKE_CASE_=0.1,			SCREAMING_SNAKE_CASE_="gelu",			SCREAMING_SNAKE_CASE_=0.02,			SCREAMING_SNAKE_CASE_=1e-5,			SCREAMING_SNAKE_CASE_=0.0,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=None,			**SCREAMING_SNAKE_CASE_,			)     ->		Optional[int]:
          super().__init__(**SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Any							=	patch_size
          UpperCamelCase    :  Tuple							=	num_channels
          UpperCamelCase    :  Union[str, Any]							=	embed_dim
          UpperCamelCase    :  Union[str, Any]							=	depths
          UpperCamelCase    :  int							=	len(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Dict							=	num_heads
          UpperCamelCase    :  List[Any]							=	kernel_size
          UpperCamelCase    :  Tuple							=	dilations
          UpperCamelCase    :  Dict							=	mlp_ratio
          UpperCamelCase    :  List[Any]							=	qkv_bias
          UpperCamelCase    :  Any							=	hidden_dropout_prob
          UpperCamelCase    :  List[str]							=	attention_probs_dropout_prob
          UpperCamelCase    :  Optional[Any]							=	drop_path_rate
          UpperCamelCase    :  Tuple							=	hidden_act
          UpperCamelCase    :  Union[str, Any]							=	layer_norm_eps
          UpperCamelCase    :  List[Any]							=	initializer_range
          # we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
          # this indicates the channel dimension after the last stage of the model
          UpperCamelCase    :  Tuple							=	int(embed_dim * 2 ** (len(SCREAMING_SNAKE_CASE_       ) - 1)       )
          UpperCamelCase    :  List[str]							=	layer_scale_init_value
          UpperCamelCase    :  List[str]							=	['stem'] + [F"""stage{idx}""" for idx in range(1,			len(SCREAMING_SNAKE_CASE_       ) + 1       )]
          UpperCamelCase	,     UpperCamelCase    :  Optional[Any]							=	get_aligned_output_features_output_indices(
              out_features=SCREAMING_SNAKE_CASE_,			out_indices=SCREAMING_SNAKE_CASE_,			stage_names=self.stage_names       )








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


__UpperCAmelCase          =       logging.get_logger(__name__)


__UpperCAmelCase          =       {
    '''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 				lowerCAmelCase_		(  a__							):
       UpperCAmelCase__				:					List[str]        =  "codegen"
       UpperCAmelCase__				:					str        =  {
           "max_position_embeddings": "n_positions",
           "hidden_size": "n_embd",
           "num_attention_heads": "n_head",
           "num_hidden_layers": "n_layer",
       }
       def __init__(			self,			SCREAMING_SNAKE_CASE_=5_0400,			SCREAMING_SNAKE_CASE_=2048,			SCREAMING_SNAKE_CASE_=2048,			SCREAMING_SNAKE_CASE_=4096,			SCREAMING_SNAKE_CASE_=28,			SCREAMING_SNAKE_CASE_=16,			SCREAMING_SNAKE_CASE_=64,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_="gelu_new",			SCREAMING_SNAKE_CASE_=0.0,			SCREAMING_SNAKE_CASE_=0.0,			SCREAMING_SNAKE_CASE_=0.0,			SCREAMING_SNAKE_CASE_=1e-5,			SCREAMING_SNAKE_CASE_=0.02,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_=5_0256,			SCREAMING_SNAKE_CASE_=5_0256,			SCREAMING_SNAKE_CASE_=False,			**SCREAMING_SNAKE_CASE_,			)     ->		Tuple:
          UpperCamelCase    :  Tuple							=	vocab_size
          UpperCamelCase    :  Optional[int]							=	n_ctx
          UpperCamelCase    :  Optional[int]							=	n_positions
          UpperCamelCase    :  List[str]							=	n_embd
          UpperCamelCase    :  Dict							=	n_layer
          UpperCamelCase    :  int							=	n_head
          UpperCamelCase    :  Union[str, Any]							=	n_inner
          UpperCamelCase    :  int							=	rotary_dim
          UpperCamelCase    :  Optional[Any]							=	activation_function
          UpperCamelCase    :  Optional[int]							=	resid_pdrop
          UpperCamelCase    :  Union[str, Any]							=	embd_pdrop
          UpperCamelCase    :  Optional[Any]							=	attn_pdrop
          UpperCamelCase    :  List[str]							=	layer_norm_epsilon
          UpperCamelCase    :  Union[str, Any]							=	initializer_range
          UpperCamelCase    :  str							=	use_cache

          UpperCamelCase    :  Dict							=	bos_token_id
          UpperCamelCase    :  Union[str, Any]							=	eos_token_id

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






class 				lowerCAmelCase_		(  a__							):
       def __init__(			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_ = "default",			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = False,			)     ->		List[str]:
          super().__init__(SCREAMING_SNAKE_CASE_,			task=SCREAMING_SNAKE_CASE_,			patching_specs=SCREAMING_SNAKE_CASE_,			use_past=SCREAMING_SNAKE_CASE_       )
          if not getattr(self._config,			'pad_token_id',			SCREAMING_SNAKE_CASE_       ):
             # TODO: how to do that better?
             UpperCamelCase    :  str							=	0
       @property
       def 					snake_case_       (			self       )     ->		Mapping[str, Mapping[int, str]]:
          UpperCamelCase    :  Tuple							=	OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}       )
          if self.use_past:
             self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_,			direction='inputs'       )
             UpperCamelCase    :  List[Any]							=	{0: 'batch', 1: 'past_sequence + sequence'}
          else:
             UpperCamelCase    :  Optional[int]							=	{0: 'batch', 1: 'sequence'}

          return common_inputs
       @property
       def 					snake_case_       (			self       )     ->		int:
          return self._config.n_layer
       @property
       def 					snake_case_       (			self       )     ->		int:
          return self._config.n_head
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_ = -1,			SCREAMING_SNAKE_CASE_ = -1,			SCREAMING_SNAKE_CASE_ = False,			SCREAMING_SNAKE_CASE_ = None,			)     ->		Mapping[str, Any]:
          UpperCamelCase    :  Tuple							=	super(SCREAMING_SNAKE_CASE_,			self       ).generate_dummy_inputs(
              SCREAMING_SNAKE_CASE_,			batch_size=SCREAMING_SNAKE_CASE_,			seq_length=SCREAMING_SNAKE_CASE_,			is_pair=SCREAMING_SNAKE_CASE_,			framework=SCREAMING_SNAKE_CASE_       )

          # We need to order the input in the way they appears in the forward()
          UpperCamelCase    :  Optional[int]							=	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

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

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

          return ordered_inputs






       @property
       def 					snake_case_       (			self       )     ->		int:
          return 13



