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


SCREAMING_SNAKE_CASE_:					Tuple  =logging.get_logger(__name__)


SCREAMING_SNAKE_CASE_:					Any  ={
    '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 	__A				(			UpperCamelCase__     ):
    a__	:						Any	       =  """codegen"""
    a__	:						int	       =  {
        """max_position_embeddings""": """n_positions""",
        """hidden_size""": """n_embd""",
        """num_attention_heads""": """n_head""",
        """num_hidden_layers""": """n_layer""",
    }



    def __init__(self   :   List[Any]       ,      __a   :   Any=50400       ,      __a   :   Dict=2048       ,      __a   :   Tuple=2048       ,      __a   :   List[str]=4096       ,      __a   :   Tuple=28       ,      __a   :   str=16       ,      __a   :   Dict=64       ,      __a   :   Tuple=None       ,      __a   :   int="gelu_new"       ,      __a   :   Optional[Any]=0.0       ,      __a   :   Optional[int]=0.0       ,      __a   :   Tuple=0.0       ,      __a   :   List[str]=1E-5       ,      __a   :   int=0.02       ,      __a   :   Dict=True       ,      __a   :   Optional[Any]=50256       ,      __a   :   int=50256       ,      __a   :   Dict=False       ,      **__a   :   int       ,      ):
         UpperCAmelCase_	   =       vocab_size
         UpperCAmelCase_	   =       n_ctx
         UpperCAmelCase_	   =       n_positions
         UpperCAmelCase_	   =       n_embd
         UpperCAmelCase_	   =       n_layer
         UpperCAmelCase_	   =       n_head
         UpperCAmelCase_	   =       n_inner
         UpperCAmelCase_	   =       rotary_dim
         UpperCAmelCase_	   =       activation_function
         UpperCAmelCase_	   =       resid_pdrop
         UpperCAmelCase_	   =       embd_pdrop
         UpperCAmelCase_	   =       attn_pdrop
         UpperCAmelCase_	   =       layer_norm_epsilon
         UpperCAmelCase_	   =       initializer_range
         UpperCAmelCase_	   =       use_cache

         UpperCAmelCase_	   =       bos_token_id
         UpperCAmelCase_	   =       eos_token_id

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




class 	__A				(			UpperCamelCase__     ):



    def __init__(self   :   Optional[int]       ,      __a   :   PretrainedConfig       ,      __a   :   str = "default"       ,      __a   :   List[PatchingSpec] = None       ,      __a   :   bool = False       ,      ):
         super().__init__(__a       ,      task=__a       ,      patching_specs=__a       ,      use_past=__a			)
         if not getattr(self._config       ,      "pad_token_id"       ,      __a			):
              # TODO: how to do that better?
              UpperCAmelCase_	   =       0



    @property
    def  _lowercase						(self   :   Optional[Any]			):
         UpperCAmelCase_	   =       OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}			)
         if self.use_past:
              self.fill_with_past_key_values_(__a       ,      direction="inputs"			)
              UpperCAmelCase_	   =       {0: "batch", 1: "past_sequence + sequence"}
         else:
              UpperCAmelCase_	   =       {0: "batch", 1: "sequence"}

         return common_inputs



    @property
    def  _lowercase						(self   :   Any			):
         return self._config.n_layer



    @property
    def  _lowercase						(self   :   Any			):
         return self._config.n_head



    def  _lowercase						(self   :   List[str]       ,      __a   :   PreTrainedTokenizer       ,      __a   :   int = -1       ,      __a   :   int = -1       ,      __a   :   bool = False       ,      __a   :   Optional[TensorType] = None       ,      ):
         UpperCAmelCase_	   =       super(__a       ,      self			).generate_dummy_inputs(
             __a       ,      batch_size=__a       ,      seq_length=__a       ,      is_pair=__a       ,      framework=__a			)

         # We need to order the input in the way they appears in the forward()
         UpperCAmelCase_	   =       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_	   =       common_inputs["input_ids"].shape
                   # Not using the same length for past_key_values
                   UpperCAmelCase_	   =       seqlen + 2
                   UpperCAmelCase_	   =       (
                       batch,
                       self.num_attention_heads,
                       past_key_values_length,
                       self._config.hidden_size // self.num_attention_heads,
                   )
                   UpperCAmelCase_	   =       [
                       (torch.zeros(__a			), torch.zeros(__a			)) for _ in range(self.num_layers			)
                   ]

         UpperCAmelCase_	   =       common_inputs["attention_mask"]
         if self.use_past:
              UpperCAmelCase_	   =       ordered_inputs["attention_mask"].dtype
              UpperCAmelCase_	   =       torch.cat(
                  [ordered_inputs["attention_mask"], torch.ones(__a       ,      __a       ,      dtype=__a			)]       ,      dim=1			)

         return ordered_inputs






    @property
    def  _lowercase						(self   :   Union[str, Any]			):
         return 13





'''simple docstring'''





from unittest import TestCase

from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters





def      lowerCAmelCase_    ( )   ->				str:


     '''simple docstring'''




     UpperCAmelCase_	   =       {
         "repo_name": ["test_repo1", "test_repo2", "test_repo3"],
         "path": ["test_1.py", "test_2.py", "unit_test.py"],
         "content": ["a " * 20, "a " * 30, "b " * 7],
     }
     UpperCAmelCase_	   =       Dataset.from_dict(snake_case_      )
     return dataset






class 	__A				(			UpperCamelCase__     ):



    def  _lowercase						(self   :   str			):
         UpperCAmelCase_	   =       get_dataset()
         UpperCAmelCase_	   =       make_duplicate_clusters(__a       ,      0.85			)
         self.assertEqual(len(duplicate_clusters[0]			)       ,      2			)






    def  _lowercase						(self   :   Optional[Any]			):
         UpperCAmelCase_	   =       get_dataset()
         UpperCAmelCase_   ,			UpperCAmelCase_	   =       deduplicate_dataset(__a			)
         self.assertEqual(len(__a			)       ,      2			)
         print(__a			)
         self.assertEqual(duplicate_clusters[0][0]["copies"]       ,      2			)
         self.assertEqual(duplicate_clusters[0][0]["is_extreme"]       ,      __a			)




