



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 	lowerCamelCase_    ( UpperCAmelCase__						):






      '''simple docstring'''
      a__   =  'codegen'
      a__   =  {
          'max_position_embeddings': 'n_positions',
          'hidden_size': 'n_embd',
          'num_attention_heads': 'n_head',
          'num_hidden_layers': 'n_layer',
      }







      def __init__(	self      :			List[Any]	,	__lowerCamelCase      :			Union[str, Any]=5_04_00	,	__lowerCamelCase      :			Tuple=20_48	,	__lowerCamelCase      :			Dict=20_48	,	__lowerCamelCase      :			List[Any]=40_96	,	__lowerCamelCase      :			str=28	,	__lowerCamelCase      :			Union[str, Any]=16	,	__lowerCamelCase      :			Tuple=64	,	__lowerCamelCase      :			Optional[int]=None	,	__lowerCamelCase      :			List[str]="gelu_new"	,	__lowerCamelCase      :			int=0.0	,	__lowerCamelCase      :			Optional[int]=0.0	,	__lowerCamelCase      :			int=0.0	,	__lowerCamelCase      :			int=1e-5	,	__lowerCamelCase      :			Dict=0.02	,	__lowerCamelCase      :			Optional[Any]=True	,	__lowerCamelCase      :			List[str]=5_02_56	,	__lowerCamelCase      :			Dict=5_02_56	,	__lowerCamelCase      :			int=False	,	**__lowerCamelCase      :			str	,	)      ->				str:
         A							:					Optional[Any]              =						vocab_size
         A							:					Any              =						n_ctx
         A							:					str              =						n_positions
         A							:					Any              =						n_embd
         A							:					List[Any]              =						n_layer
         A							:					Tuple              =						n_head
         A							:					List[Any]              =						n_inner
         A							:					List[Any]              =						rotary_dim
         A							:					Tuple              =						activation_function
         A							:					Any              =						resid_pdrop
         A							:					List[str]              =						embd_pdrop
         A							:					List[Any]              =						attn_pdrop
         A							:					Optional[Any]              =						layer_norm_epsilon
         A							:					Optional[Any]              =						initializer_range
         A							:					Tuple              =						use_cache

         A							:					Any              =						bos_token_id
         A							:					str              =						eos_token_id

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







class 	lowerCamelCase_    ( UpperCAmelCase__						):






      '''simple docstring'''







      def __init__(	self      :			Union[str, Any]	,	__lowerCamelCase      :			PretrainedConfig	,	__lowerCamelCase      :			str = "default"	,	__lowerCamelCase      :			List[PatchingSpec] = None	,	__lowerCamelCase      :			bool = False	,	)      ->				Optional[Any]:
         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							:					Dict              =						0







      @property
      def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any] )      ->				Mapping[str, Mapping[int, str]]:
         A							:					Optional[int]              =						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							:					Optional[Any]              =						{0: """batch""", 1: """sequence"""}

         return common_inputs







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







      @property
      def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				int:
         return self._config.n_head







      def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str]	,	__lowerCamelCase      :			PreTrainedTokenizer	,	__lowerCamelCase      :			int = -1	,	__lowerCamelCase      :			int = -1	,	__lowerCamelCase      :			bool = False	,	__lowerCamelCase      :			Optional[TensorType] = None	,	)      ->				Mapping[str, Any]:
         A							:					Union[str, Any]              =						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							:					int              =						common_inputs["""input_ids"""].shape
               # Not using the same length for past_key_values
               A							:					Dict              =						seqlen + 2
               A							:					List[str]              =						(
                   batch,
                   self.num_attention_heads,
                   past_key_values_length,
                   self._config.hidden_size // self.num_attention_heads,
               )
               A							:					Any              =						[
                   (torch.zeros(__lowerCamelCase ), torch.zeros(__lowerCamelCase )) for _ in range(self.num_layers )
               ]

         A							:					Dict              =						common_inputs["""attention_mask"""]
         if self.use_past:
            A							:					int              =						ordered_inputs["""attention_mask"""].dtype
            A							:					Any              =						torch.cat(
                [ordered_inputs["attention_mask"], torch.ones(__lowerCamelCase	,	__lowerCamelCase	,	dtype=__lowerCamelCase )]	,	dim=1 )

         return ordered_inputs







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




from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase = "x" ,    _lowerCamelCase = 10**-10 ,    _lowerCamelCase = 1 ,    ):
			A							:					str              =						symbols(_lowerCamelCase    )
			A							:					int              =						lambdify(_lowerCamelCase ,    _lowerCamelCase    )
			A							:					List[str]              =						lambdify(_lowerCamelCase ,    diff(_lowerCamelCase ,    _lowerCamelCase    )    )

			A							:					Optional[int]              =						starting_point

			while True:
						if diff_function(_lowerCamelCase    ) != 0:
									A							:					Optional[Any]              =						prev_guess - multiplicity * func(_lowerCamelCase    ) / diff_function(
									    _lowerCamelCase    )
						else:
									raise ZeroDivisionError("Could not find root"    ) from None

						# Precision is checked by comparing the difference of consecutive guesses
						if abs(next_guess - prev_guess    ) < precision:
									return next_guess

						A							:					int              =						next_guess


# Let's Execute
if __name__ == "__main__":
							# Find root of trigonometric function
							# Find value of pi
							print(F"""The root of sin(x) = 0 is {newton_raphson('sin(x)', 2)}""")

							# Find root of polynomial
							# Find fourth Root of 5
							print(F"""The root of x**4 - 5 = 0 is {newton_raphson('x**4 -5', 0.4 +5j)}""")

							# Find value of e
							print(
							    """The root of log(y) - 1 = 0 is """,
							    F"""{newton_raphson('log(y) - 1', 2, variable='y')}""",
							)

							# Exponential Roots
							print(
							    """The root of exp(x) - 1 = 0 is""",
							    F"""{newton_raphson('exp(x) - 1', 10, precision=0.005)}""",
							)

							# Find root of cos(x)
							print(F"""The root of cos(x) = 0 is {newton_raphson('cos(x)', 0)}""")