
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


__lowerCamelCase				=			logging.get_logger(__name__)

__lowerCamelCase				=			{
    """EleutherAI/gpt-j-6B""": """https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json""",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}






class     UpperCAmelCase						(		A_					):
   A__   :						Optional[Any]			=	"gptj"
   A__   :						List[Any]			=	{
       "max_position_embeddings": "n_positions",
       "hidden_size": "n_embd",
       "num_attention_heads": "n_head",
       "num_hidden_layers": "n_layer",
   }



   def __init__(self						:   Tuple       ,		snake_case__						:   Optional[Any]=5_04_00       ,		snake_case__						:   str=20_48       ,		snake_case__						:   str=40_96       ,		snake_case__						:   Optional[int]=28       ,		snake_case__						:   Optional[int]=16       ,		snake_case__						:   Optional[int]=64       ,		snake_case__						:   List[str]=None       ,		snake_case__						:   Dict="gelu_new"       ,		snake_case__						:   List[Any]=0.0       ,		snake_case__						:   Dict=0.0       ,		snake_case__						:   Union[str, Any]=0.0       ,		snake_case__						:   Tuple=1e-5       ,		snake_case__						:   List[str]=0.02       ,		snake_case__						:   Optional[Any]=True       ,		snake_case__						:   Optional[Any]=5_02_56       ,		snake_case__						:   List[str]=5_02_56       ,		snake_case__						:   Optional[Any]=False       ,		**snake_case__						:   int       ,		)						->      List[str]:


          '''simple docstring'''


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

          snake_case			:  str     							= bos_token_id
          snake_case			:  Any     							= eos_token_id

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






class     UpperCAmelCase						(		A_					):



   def __init__(self						:   List[str]       ,		snake_case__						:   PretrainedConfig       ,		snake_case__						:   str = "default"       ,		snake_case__						:   List[PatchingSpec] = None       ,		snake_case__						:   bool = False       ,		)						->      Optional[Any]:


          '''simple docstring'''


          super().__init__(snake_case__       ,		task=snake_case__       ,		patching_specs=snake_case__       ,		use_past=snake_case__					)
          if not getattr(self._config       ,		"pad_token_id"       ,		snake_case__					):
                 # TODO: how to do that better?
                 snake_case			:  Dict     							= 0



   @property
   def 							_SCREAMING_SNAKE_CASE  (self						:   str					)						->      Mapping[str, Mapping[int, str]]:


          '''simple docstring'''


          snake_case			:  Dict     							= OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}					)
          if self.use_past:
                 self.fill_with_past_key_values_(snake_case__       ,		direction="inputs"					)
                 snake_case			:  Any     							= {0: "batch", 1: "past_sequence + sequence"}
          else:
                 snake_case			:  Union[str, Any]     							= {0: "batch", 1: "sequence"}

          return common_inputs



   @property
   def 							_SCREAMING_SNAKE_CASE  (self						:   List[str]					)						->      int:


          '''simple docstring'''


          return self._config.n_layer



   @property
   def 							_SCREAMING_SNAKE_CASE  (self						:   str					)						->      int:


          '''simple docstring'''


          return self._config.n_head



   def 							_SCREAMING_SNAKE_CASE  (self						:   Dict       ,		snake_case__						:   PreTrainedTokenizer       ,		snake_case__						:   int = -1       ,		snake_case__						:   int = -1       ,		snake_case__						:   bool = False       ,		snake_case__						:   Optional[TensorType] = None       ,		)						->      Mapping[str, Any]:


          '''simple docstring'''


          snake_case			:  Tuple     							= super(snake_case__       ,		self					).generate_dummy_inputs(
              snake_case__       ,		batch_size=snake_case__       ,		seq_length=snake_case__       ,		is_pair=snake_case__       ,		framework=snake_case__					)

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

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

          snake_case			:  Tuple     							= common_inputs["attention_mask"]
          if self.use_past:
                 snake_case			:  int     							= ordered_inputs["attention_mask"].dtype
                 snake_case			:  Any     							= torch.cat(
                     [ordered_inputs["attention_mask"], torch.ones(snake_case__       ,		snake_case__       ,		dtype=snake_case__					)]       ,		dim=1					)

          return ordered_inputs



   @property
   def 							_SCREAMING_SNAKE_CASE  (self						:   Union[str, Any]					)						->      int:


          '''simple docstring'''


          return 13







def      UpperCamelCase   (       __lowerCamelCase	:					str = "The quick brown fox jumps over the lazy dog"							,   ):
       snake_case			:  Dict     							= set()

       # Replace all the whitespace in our sentence
       snake_case			:  List[Any]     							= input_str.replace(" "							,   ""       )
       for alpha in input_str:
              if "a" <= alpha.lower() <= "z":
                     frequency.add(alpha.lower()       )
       return len(__lowerCamelCase       ) == 26


def      UpperCamelCase   (       __lowerCamelCase	:					str = "The quick brown fox jumps over the lazy dog"							,   ):
       snake_case			:  Optional[int]     							= [False] * 26
       for char in input_str:
              if char.islower():
                     snake_case			:  Dict     							= True
              elif char.isupper():
                     snake_case			:  Optional[Any]     							= True
       return all(__lowerCamelCase       )


def      UpperCamelCase   (       __lowerCamelCase	:					str = "The quick brown fox jumps over the lazy dog"							,   ):
       return len({char for char in input_str.lower() if char.isalpha()}       ) == 26


def      UpperCamelCase   (       ):
       from timeit import timeit

       snake_case			:  Tuple     							= "from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"
       print(timeit("is_pangram()"							,   setup=__lowerCamelCase       )       )
       print(timeit("is_pangram_faster()"							,   setup=__lowerCamelCase       )       )
       print(timeit("is_pangram_fastest()"							,   setup=__lowerCamelCase       )       )
       # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
       # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   benchmark()





