'''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


_a   :	Dict 							=   logging.get_logger(__name__)


_a   :	Tuple 							=   {
    "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       (__magic_name__   ):
       snake_case       :Union[str, Any]				    =					"codegen"
       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				,     UpperCamelCase_=5_04_00				,     UpperCamelCase_=20_48				,     UpperCamelCase_=20_48				,     UpperCamelCase_=40_96				,     UpperCamelCase_=28				,     UpperCamelCase_=16				,     UpperCamelCase_=64				,     UpperCamelCase_=None				,     UpperCamelCase_="gelu_new"				,     UpperCamelCase_=0.0				,     UpperCamelCase_=0.0				,     UpperCamelCase_=0.0				,     UpperCamelCase_=1E-5				,     UpperCamelCase_=0.0_2				,     UpperCamelCase_=True				,     UpperCamelCase_=5_02_56				,     UpperCamelCase_=5_02_56				,     UpperCamelCase_=False				,     **UpperCamelCase_				,     ):
              __UpperCAmelCase  :				Optional[Any]       				=		vocab_size
              __UpperCAmelCase  :				str       				=		n_ctx
              __UpperCAmelCase  :				Optional[int]       				=		n_positions
              __UpperCAmelCase  :				Dict       				=		n_embd
              __UpperCAmelCase  :				Tuple       				=		n_layer
              __UpperCAmelCase  :				Tuple       				=		n_head
              __UpperCAmelCase  :				List[str]       				=		n_inner
              __UpperCAmelCase  :				Union[str, Any]       				=		rotary_dim
              __UpperCAmelCase  :				Union[str, Any]       				=		activation_function
              __UpperCAmelCase  :				List[Any]       				=		resid_pdrop
              __UpperCAmelCase  :				str       				=		embd_pdrop
              __UpperCAmelCase  :				Optional[int]       				=		attn_pdrop
              __UpperCAmelCase  :				Optional[int]       				=		layer_norm_epsilon
              __UpperCAmelCase  :				str       				=		initializer_range
              __UpperCAmelCase  :				str       				=		use_cache

              __UpperCAmelCase  :				Dict       				=		bos_token_id
              __UpperCAmelCase  :				Any       				=		eos_token_id

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






class 		__A       (__magic_name__   ):






       def __init__(      self				,     UpperCamelCase_				,     UpperCamelCase_ = "default"				,     UpperCamelCase_ = None				,     UpperCamelCase_ = False				,     ):
              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?
                     __UpperCAmelCase  :				Tuple       				=		0






       @property
       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				str       				=		OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}      )
              if self.use_past:
                     self.fill_with_past_key_values_(UpperCamelCase_				,     direction="inputs"      )
                     __UpperCAmelCase  :				List[str]       				=		{0: "batch", 1: "past_sequence + sequence"}
              else:
                     __UpperCAmelCase  :				Optional[int]       				=		{0: "batch", 1: "sequence"}

              return common_inputs






       @property
       def 			_snake_case						(      self      ):
              return self._config.n_layer






       @property
       def 			_snake_case						(      self      ):
              return self._config.n_head






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_ = -1				,     UpperCamelCase_ = -1				,     UpperCamelCase_ = False				,     UpperCamelCase_ = None				,     ):
              __UpperCAmelCase  :				Optional[int]       				=		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()
              __UpperCAmelCase  :				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

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

              __UpperCAmelCase  :				str       				=		common_inputs["attention_mask"]
              if self.use_past:
                     __UpperCAmelCase  :				Any       				=		ordered_inputs["attention_mask"].dtype
                     __UpperCAmelCase  :				Any       				=		torch.cat(
                         [ordered_inputs["attention_mask"], torch.ones(UpperCamelCase_				,     UpperCamelCase_				,     dtype=UpperCamelCase_      )]				,     dim=1      )

              return ordered_inputs







       @property
       def 			_snake_case						(      self      ):
              return 13







'''simple docstring'''




class 		__A       :






       def __init__(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				Any       				=		set_counts
              __UpperCAmelCase  :				int       				=		max(UpperCamelCase_      )
              __UpperCAmelCase  :				List[str]       				=		len(UpperCamelCase_      )
              __UpperCAmelCase  :				Any       				=		[1] * num_sets
              __UpperCAmelCase  :				Any       				=		list(range(UpperCamelCase_      )      )






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				Optional[int]       				=		self.get_parent(UpperCamelCase_      )
              __UpperCAmelCase  :				List[Any]       				=		self.get_parent(UpperCamelCase_      )

              if src_parent == dst_parent:
                     return False

              if self.ranks[dst_parent] >= self.ranks[src_parent]:
                     self.set_counts[dst_parent] += self.set_counts[src_parent]
                     __UpperCAmelCase  :				Optional[Any]       				=		0
                     __UpperCAmelCase  :				List[Any]       				=		dst_parent
                     if self.ranks[dst_parent] == self.ranks[src_parent]:
                            self.ranks[dst_parent] += 1
                     __UpperCAmelCase  :				Union[str, Any]       				=		self.set_counts[dst_parent]
              else:
                     self.set_counts[src_parent] += self.set_counts[dst_parent]
                     __UpperCAmelCase  :				Union[str, Any]       				=		0
                     __UpperCAmelCase  :				Dict       				=		src_parent
                     __UpperCAmelCase  :				Dict       				=		self.set_counts[src_parent]

              __UpperCAmelCase  :				Dict       				=		max(self.max_set				,     UpperCamelCase_      )
              return True







       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              if self.parents[disj_set] == disj_set:
                     return disj_set
              __UpperCAmelCase  :				str       				=		self.get_parent(self.parents[disj_set]      )
              return self.parents[disj_set]






