



'''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


SCREAMING_SNAKE_CASE__      					=     logging.get_logger(__name__)


SCREAMING_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 				snake_case (UpperCamelCase   ):
       lowerCAmelCase__  :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     ,UpperCAmelCase_=50_400     ,UpperCAmelCase_=2_048     ,UpperCAmelCase_=2_048     ,UpperCAmelCase_=4_096     ,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_=50_256     ,UpperCAmelCase_=50_256     ,UpperCAmelCase_=False     ,**UpperCAmelCase_     ,)  ->							Any:
           lowercase__		       =						vocab_size
           lowercase__		       =						n_ctx
           lowercase__		       =						n_positions
           lowercase__		       =						n_embd
           lowercase__		       =						n_layer
           lowercase__		       =						n_head
           lowercase__		       =						n_inner
           lowercase__		       =						rotary_dim
           lowercase__		       =						activation_function
           lowercase__		       =						resid_pdrop
           lowercase__		       =						embd_pdrop
           lowercase__		       =						attn_pdrop
           lowercase__		       =						layer_norm_epsilon
           lowercase__		       =						initializer_range
           lowercase__		       =						use_cache

           lowercase__		       =						bos_token_id
           lowercase__		       =						eos_token_id

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





class 				snake_case (UpperCamelCase   ):



       def __init__(				self     ,UpperCAmelCase_     ,UpperCAmelCase_ = "default"     ,UpperCAmelCase_ = None     ,UpperCAmelCase_ = False     ,)  ->							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?
               lowercase__		       =						0



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

           return common_inputs



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



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



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_ = -1     ,UpperCAmelCase_ = -1     ,UpperCAmelCase_ = False     ,UpperCAmelCase_ = None     ,)  ->							Mapping[str, Any]:
           lowercase__		       =						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()
           lowercase__		       =						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

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

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

           return ordered_inputs







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









'''simple docstring'''





def        lowerCamelCase  (				_snake_case  :						List[str]       ):  # noqa: E741





    '''simple docstring'''


    lowercase__		       =						len(_snake_case       )
    lowercase__		       =						0
    lowercase__		       =						[0] * n
    lowercase__		       =						[False] * n
    lowercase__		       =						[False] * n

    def dfs(_snake_case  :						Tuple     ,_snake_case  :						int     ,_snake_case  :						List[str]     ,_snake_case  :						str       ):
        if parent == root:
            out_edge_count += 1
        lowercase__		       =						True
        lowercase__		       =						at

        for to in l[at]:
            if to == parent:
                pass
            elif not visited[to]:
                lowercase__		       =						dfs(_snake_case     ,_snake_case     ,_snake_case     ,_snake_case       )
                lowercase__		       =						min(low[at]     ,low[to]       )

                # AP found via bridge
                if at < low[to]:
                    lowercase__		       =						True
                # AP found via cycle
                if at == low[to]:
                    lowercase__		       =						True
            else:
                lowercase__		       =						min(low[at]     ,_snake_case       )
        return out_edge_count

    for i in range(_snake_case       ):
        if not visited[i]:
            lowercase__		       =						0
            lowercase__		       =						dfs(_snake_case     ,_snake_case     ,-1     ,_snake_case       )
            lowercase__		       =						out_edge_count > 1

    for x in range(len(_snake_case       )       ):
        if is_art[x] is True:
            print(_snake_case       )


# Adjacency list of graph
SCREAMING_SNAKE_CASE__      					=     {
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}



compute_ap(data)




