
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__          ={
    '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__  (	__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							Optional[Any]     	=				"codegen"
   _SCREAMING_SNAKE_CASE				:							List[str]     	=				{
       "max_position_embeddings": "n_positions",
       "hidden_size": "n_embd",
       "num_attention_heads": "n_head",
       "num_hidden_layers": "n_layer",
   }
   def __init__(self			: Union[str, Any] ,    snake_case_			: List[Any]=5_0_4_0_0 ,    snake_case_			: Tuple=2_0_4_8 ,    snake_case_			: Dict=2_0_4_8 ,    snake_case_			: Tuple=4_0_9_6 ,    snake_case_			: Any=2_8 ,    snake_case_			: Optional[int]=1_6 ,    snake_case_			: int=6_4 ,    snake_case_			: List[Any]=None ,    snake_case_			: List[str]="gelu_new" ,    snake_case_			: str=0.0 ,    snake_case_			: List[str]=0.0 ,    snake_case_			: Any=0.0 ,    snake_case_			: Optional[int]=1E-5 ,    snake_case_			: Any=0.02 ,    snake_case_			: int=True ,    snake_case_			: str=5_0_2_5_6 ,    snake_case_			: Any=5_0_2_5_6 ,    snake_case_			: Union[str, Any]=False ,    **snake_case_			: Dict ,    ):
          __a			:  Optional[int]		    =  vocab_size
          __a			:  Optional[int]		    =  n_ctx
          __a			:  Any		    =  n_positions
          __a			:  int		    =  n_embd
          __a			:  List[str]		    =  n_layer
          __a			:  Any		    =  n_head
          __a			:  Optional[Any]		    =  n_inner
          __a			:  Tuple		    =  rotary_dim
          __a			:  Any		    =  activation_function
          __a			:  Union[str, Any]		    =  resid_pdrop
          __a			:  Dict		    =  embd_pdrop
          __a			:  Optional[int]		    =  attn_pdrop
          __a			:  List[str]		    =  layer_norm_epsilon
          __a			:  List[str]		    =  initializer_range
          __a			:  List[Any]		    =  use_cache

          __a			:  Tuple		    =  bos_token_id
          __a			:  List[str]		    =  eos_token_id

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





class 		UpperCamelCase__  (	__lowercase   ):
   def __init__(self			: Tuple ,    snake_case_			: PretrainedConfig ,    snake_case_			: str = "default" ,    snake_case_			: List[PatchingSpec] = None ,    snake_case_			: bool = False ,    ):
          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?
                 __a			:  int		    =  0
   @property
   def 				lowerCAmelCase      (self			: Tuple       ):
          __a			:  Dict		    =  OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}       )
          if self.use_past:
                 self.fill_with_past_key_values_(_lowerCAmelCase ,    direction='''inputs'''       )
                 __a			:  Any		    =  {0: '''batch''', 1: '''past_sequence + sequence'''}
          else:
                 __a			:  List[Any]		    =  {0: '''batch''', 1: '''sequence'''}

          return common_inputs
   @property
   def 				lowerCAmelCase      (self			: Dict       ):
          return self._config.n_layer
   @property
   def 				lowerCAmelCase      (self			: List[str]       ):
          return self._config.n_head
   def 				lowerCAmelCase      (self			: Optional[Any] ,    snake_case_			: PreTrainedTokenizer ,    snake_case_			: int = -1 ,    snake_case_			: int = -1 ,    snake_case_			: bool = False ,    snake_case_			: Optional[TensorType] = None ,    ):
          __a			:  Optional[int]		    =  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()
          __a			:  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

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

          __a			:  Any		    =  common_inputs['''attention_mask''']
          if self.use_past:
                 __a			:  Any		    =  ordered_inputs['''attention_mask'''].dtype
                 __a			:  int		    =  torch.cat(
                     [ordered_inputs['''attention_mask'''], torch.ones(_lowerCAmelCase ,    _lowerCAmelCase ,    dtype=_lowerCAmelCase       )] ,    dim=1       )

          return ordered_inputs







   @property
   def 				lowerCAmelCase      (self			: Optional[Any]       ):
          return 1_3


# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowercase__          ={
    'configuration_efficientnet': [
        'EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EfficientNetConfig',
        'EfficientNetOnnxConfig',
    ]
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__          =['EfficientNetImageProcessor']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__          =[
          'EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST',
          'EfficientNetForImageClassification',
          'EfficientNetModel',
          'EfficientNetPreTrainedModel',
      ]

if TYPE_CHECKING:
      from .configuration_efficientnet import (
          EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
          EfficientNetConfig,
          EfficientNetOnnxConfig,
      )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .image_processing_efficientnet import EfficientNetImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_efficientnet import (
                EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                EfficientNetForImageClassification,
                EfficientNetModel,
                EfficientNetPreTrainedModel,
            )

else:
      import sys

      lowercase__          =_LazyModule(__name__, globals()['__file__'], _import_structure)
