
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     =  logging.get_logger(__name__)


_A     =  {
    "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      (			__a ):
    _lowercase    	='''codegen'''
    _lowercase    	={
        '''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       ,       )       ->   Dict:
       lowerCAmelCase_      			=			vocab_size
       lowerCAmelCase_      			=			n_ctx
       lowerCAmelCase_      			=			n_positions
       lowerCAmelCase_      			=			n_embd
       lowerCAmelCase_      			=			n_layer
       lowerCAmelCase_      			=			n_head
       lowerCAmelCase_      			=			n_inner
       lowerCAmelCase_      			=			rotary_dim
       lowerCAmelCase_      			=			activation_function
       lowerCAmelCase_      			=			resid_pdrop
       lowerCAmelCase_      			=			embd_pdrop
       lowerCAmelCase_      			=			attn_pdrop
       lowerCAmelCase_      			=			layer_norm_epsilon
       lowerCAmelCase_      			=			initializer_range
       lowerCAmelCase_      			=			use_cache

       lowerCAmelCase_      			=			bos_token_id
       lowerCAmelCase_      			=			eos_token_id

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





class 			_lowerCAmelCase      (			__a ):




    def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase = "default"       ,       _UpperCamelCase = None       ,       _UpperCamelCase = False       ,       )       ->   List[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?
          lowerCAmelCase_      			=			0




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

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

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

       return ordered_inputs




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





import itertools
import string
from collections.abc import Generator, Iterable






def      lowerCamelCase__       (       __lowerCAmelCase						:						Iterable[str]					,							__lowerCAmelCase						:						int       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			iter(__lowerCAmelCase       )
			while True:
						lowerCAmelCase_      			=			tuple(itertools.islice(__lowerCAmelCase					,							__lowerCAmelCase       )       )
						if not chunk:
									return
						yield chunk






def      lowerCamelCase__       (       __lowerCAmelCase						:						str       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			"".join([c.upper() for c in dirty if c in string.ascii_letters]       )
			lowerCAmelCase_      			=			""

			if len(__lowerCAmelCase       ) < 2:
						return dirty

			for i in range(len(__lowerCAmelCase       ) - 1       ):
						clean += dirty[i]

						if dirty[i] == dirty[i + 1]:
									clean += "X"

			clean += dirty[-1]

			if len(__lowerCAmelCase       ) & 1:
						clean += "X"

			return clean






def      lowerCamelCase__       (       __lowerCAmelCase						:						str       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			"ABCDEFGHIKLMNOPQRSTUVWXYZ"
			# we're using a list instead of a '2d' array because it makes the math
			# for setting up the table and doing the actual encoding/decoding simpler
			lowerCAmelCase_      			=			[]

			# copy key chars into the table if they are in `alphabet` ignoring duplicates
			for char in key.upper():
						if char not in table and char in alphabet:
									table.append(__lowerCAmelCase       )

    # fill the rest of the table in with the remaining alphabet chars
			for char in alphabet:
						if char not in table:
									table.append(__lowerCAmelCase       )

			return table






def      lowerCamelCase__       (       __lowerCAmelCase						:						str					,							__lowerCAmelCase						:						str       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			generate_table(__lowerCAmelCase       )
			lowerCAmelCase_      			=			prepare_input(__lowerCAmelCase       )
			lowerCAmelCase_      			=			""

			# https://en.wikipedia.org/wiki/Playfair_cipher#Description
			for chara, chara in chunker(__lowerCAmelCase					,							2       ):
						lowerCAmelCase_			,						lowerCAmelCase_      			=			divmod(table.index(__lowerCAmelCase       )					,							5       )
						lowerCAmelCase_			,						lowerCAmelCase_      			=			divmod(table.index(__lowerCAmelCase       )					,							5       )

						if rowa == rowa:
									ciphertext += table[rowa * 5 + (cola + 1) % 5]
									ciphertext += table[rowa * 5 + (cola + 1) % 5]
						elif cola == cola:
									ciphertext += table[((rowa + 1) % 5) * 5 + cola]
									ciphertext += table[((rowa + 1) % 5) * 5 + cola]
						else:  # rectangle
									ciphertext += table[rowa * 5 + cola]
									ciphertext += table[rowa * 5 + cola]

			return ciphertext






def      lowerCamelCase__       (       __lowerCAmelCase						:						str					,							__lowerCAmelCase						:						str       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			generate_table(__lowerCAmelCase       )
			lowerCAmelCase_      			=			""

			# https://en.wikipedia.org/wiki/Playfair_cipher#Description
			for chara, chara in chunker(__lowerCAmelCase					,							2       ):
						lowerCAmelCase_			,						lowerCAmelCase_      			=			divmod(table.index(__lowerCAmelCase       )					,							5       )
						lowerCAmelCase_			,						lowerCAmelCase_      			=			divmod(table.index(__lowerCAmelCase       )					,							5       )

						if rowa == rowa:
									plaintext += table[rowa * 5 + (cola - 1) % 5]
									plaintext += table[rowa * 5 + (cola - 1) % 5]
						elif cola == cola:
									plaintext += table[((rowa - 1) % 5) * 5 + cola]
									plaintext += table[((rowa - 1) % 5) * 5 + cola]
						else:  # rectangle
									plaintext += table[rowa * 5 + cola]
									plaintext += table[rowa * 5 + cola]

			return plaintext



