


"""simple docstring"""






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


__snake_case		=       logging.get_logger(__name__)


__snake_case		=       {
    '''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__				):

     '''simple docstring'''
     A_       :	int    =		'codegen'
     A_       :	Union[str, Any]    =		{
         'max_position_embeddings': 'n_positions',
         'hidden_size': 'n_embd',
         'num_attention_heads': 'n_head',
         'num_hidden_layers': 'n_layer',
     }


     def __init__(      self    ,				__UpperCAmelCase=50400    ,				__UpperCAmelCase=2048    ,				__UpperCAmelCase=2048    ,				__UpperCAmelCase=4096    ,				__UpperCAmelCase=28    ,				__UpperCAmelCase=16    ,				__UpperCAmelCase=64    ,				__UpperCAmelCase=None    ,				__UpperCAmelCase="gelu_new"    ,				__UpperCAmelCase=0.0    ,				__UpperCAmelCase=0.0    ,				__UpperCAmelCase=0.0    ,				__UpperCAmelCase=1e-5    ,				__UpperCAmelCase=0.02    ,				__UpperCAmelCase=True    ,				__UpperCAmelCase=50256    ,				__UpperCAmelCase=50256    ,				__UpperCAmelCase=False    ,				**__UpperCAmelCase    ,				)       ->							str:
          _a    	=				vocab_size
          _a    	=				n_ctx
          _a    	=				n_positions
          _a    	=				n_embd
          _a    	=				n_layer
          _a    	=				n_head
          _a    	=				n_inner
          _a    	=				rotary_dim
          _a    	=				activation_function
          _a    	=				resid_pdrop
          _a    	=				embd_pdrop
          _a    	=				attn_pdrop
          _a    	=				layer_norm_epsilon
          _a    	=				initializer_range
          _a    	=				use_cache

          _a    	=				bos_token_id
          _a    	=				eos_token_id

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







class     __lowerCamelCase  (					a__				):

     '''simple docstring'''


     def __init__(      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = "default"    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = False    ,				)       ->							List[Any]:
          super().__init__(__UpperCAmelCase    ,				task=__UpperCAmelCase    ,				patching_specs=__UpperCAmelCase    ,				use_past=__UpperCAmelCase							)
          if not getattr(self._config    ,				'''pad_token_id'''    ,				__UpperCAmelCase							):
               # TODO: how to do that better?
               _a    	=				0


     @property
     def 							_UpperCAmelCase      (      self							)       ->							Mapping[str, Mapping[int, str]]:
          _a    	=				OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}							)
          if self.use_past:
               self.fill_with_past_key_values_(__UpperCAmelCase    ,				direction='''inputs'''							)
               _a    	=				{0: '''batch''', 1: '''past_sequence + sequence'''}
          else:
               _a    	=				{0: '''batch''', 1: '''sequence'''}

          return common_inputs


     @property
     def 							_UpperCAmelCase      (      self							)       ->							int:
          return self._config.n_layer


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


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = -1    ,				__UpperCAmelCase = -1    ,				__UpperCAmelCase = False    ,				__UpperCAmelCase = None    ,				)       ->							Mapping[str, Any]:
          _a    	=				super(__UpperCAmelCase    ,				self							).generate_dummy_inputs(
              __UpperCAmelCase    ,				batch_size=__UpperCAmelCase    ,				seq_length=__UpperCAmelCase    ,				is_pair=__UpperCAmelCase    ,				framework=__UpperCAmelCase							)

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

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

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

          return ordered_inputs





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



"""simple docstring"""






from pathlib import Path

import fire
def       A_   (   _lowerCAmelCase     :					str,     _lowerCAmelCase     :					str,     _lowerCAmelCase     :					int       ):






     """simple docstring"""

     _a    	=				Path(_lowerCAmelCase       )
     _a    	=				Path(_lowerCAmelCase       )
     dest_dir.mkdir(exist_ok=_lowerCAmelCase       )
     for path in src_dir.iterdir():
          _a    	=				[x.rstrip() for x in list(path.open().readlines()       )][:n]
          _a    	=				dest_dir.joinpath(path.name       )
          print(_lowerCAmelCase       )
          dest_path.open('''w'''       ).write('''\n'''.join(_lowerCAmelCase       )       )


if __name__ == "__main__":
   fire.Fire(minify)