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 			__magic_name__	(		__a     ):





   """simple docstring"""




   lowerCAmelCase   :     List[Any]	      = '''codegen'''
   lowerCAmelCase   :     List[Any]	      = {
       '''max_position_embeddings''': '''n_positions''',
       '''hidden_size''': '''n_embd''',
       '''num_attention_heads''': '''n_head''',
       '''num_hidden_layers''': '''n_layer''',
   }



   def __init__(						self       :	Any		,	_lowercase       :	Dict=50_400		,	_lowercase       :	Optional[int]=2_048		,	_lowercase       :	Optional[Any]=2_048		,	_lowercase       :	Optional[Any]=4_096		,	_lowercase       :	Tuple=28		,	_lowercase       :	List[Any]=16		,	_lowercase       :	int=64		,	_lowercase       :	int=None		,	_lowercase       :	Dict="gelu_new"		,	_lowercase       :	Optional[int]=0.0		,	_lowercase       :	Union[str, Any]=0.0		,	_lowercase       :	Dict=0.0		,	_lowercase       :	Dict=1E-5		,	_lowercase       :	Dict=0.02		,	_lowercase       :	Optional[int]=True		,	_lowercase       :	List[Any]=50_256		,	_lowercase       :	Optional[Any]=50_256		,	_lowercase       :	Union[str, Any]=False		,	**_lowercase       :	str		,	):



    """simple docstring"""



    _UpperCamelCase:  Union[str, Any]    						=			vocab_size
    _UpperCamelCase:  int    						=			n_ctx
    _UpperCamelCase:  Optional[int]    						=			n_positions
    _UpperCamelCase:  Optional[int]    						=			n_embd
    _UpperCamelCase:  List[str]    						=			n_layer
    _UpperCamelCase:  Tuple    						=			n_head
    _UpperCamelCase:  Any    						=			n_inner
    _UpperCamelCase:  Optional[Any]    						=			rotary_dim
    _UpperCamelCase:  Optional[Any]    						=			activation_function
    _UpperCamelCase:  int    						=			resid_pdrop
    _UpperCamelCase:  Optional[Any]    						=			embd_pdrop
    _UpperCamelCase:  List[Any]    						=			attn_pdrop
    _UpperCamelCase:  int    						=			layer_norm_epsilon
    _UpperCamelCase:  List[Any]    						=			initializer_range
    _UpperCamelCase:  Union[str, Any]    						=			use_cache

    _UpperCamelCase:  Optional[Any]    						=			bos_token_id
    _UpperCamelCase:  List[str]    						=			eos_token_id

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




class 			__magic_name__	(		__a     ):





   """simple docstring"""




   def __init__(						self       :	int		,	_lowercase       :	PretrainedConfig		,	_lowercase       :	str = "default"		,	_lowercase       :	List[PatchingSpec] = None		,	_lowercase       :	bool = False		,	):



    """simple docstring"""



    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?
     _UpperCamelCase:  Union[str, Any]    						=			0



   @property
   def 	lowerCAmelCase    (						self       :	Any				):



    """simple docstring"""



    _UpperCamelCase:  Optional[int]    						=			OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}				)
    if self.use_past:
     self.fill_with_past_key_values_(_lowercase		,	direction='''inputs'''				)
     _UpperCamelCase:  Any    						=			{0: '''batch''', 1: '''past_sequence + sequence'''}
    else:
     _UpperCamelCase:  List[Any]    						=			{0: '''batch''', 1: '''sequence'''}

    return common_inputs



   @property
   def 	lowerCAmelCase    (						self       :	Tuple				):



    """simple docstring"""



    return self._config.n_layer



   @property
   def 	lowerCAmelCase    (						self       :	List[Any]				):



    """simple docstring"""



    return self._config.n_head



   def 	lowerCAmelCase    (						self       :	Optional[Any]		,	_lowercase       :	PreTrainedTokenizer		,	_lowercase       :	int = -1		,	_lowercase       :	int = -1		,	_lowercase       :	bool = False		,	_lowercase       :	Optional[TensorType] = None		,	):



    """simple docstring"""



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

    _UpperCamelCase:  str    						=			common_inputs['''attention_mask''']
    if self.use_past:
     _UpperCamelCase:  Union[str, Any]    						=			ordered_inputs['''attention_mask'''].dtype
     _UpperCamelCase:  int    						=			torch.cat(
         [ordered_inputs['''attention_mask'''], torch.ones(_lowercase		,	_lowercase		,	dtype=_lowercase				)]		,	dim=1				)

    return ordered_inputs



   @property
   def 	lowerCAmelCase    (						self       :	Any				):



    """simple docstring"""



    return 13
UpperCAmelCase_           =				'''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

UpperCAmelCase_           =				[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
UpperCAmelCase_           =				{
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}