





'''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


lowercase_					= logging.get_logger(__name__)


lowercase_					= {
    """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_     (				snake_case_       ):

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


    def __init__(		self ,		A=5_0400 ,		A=2048 ,		A=2048 ,		A=4096 ,		A=28 ,		A=16 ,		A=64 ,		A=None ,		A="gelu_new" ,		A=0.0 ,		A=0.0 ,		A=0.0 ,		A=1e-5 ,		A=0.02 ,		A=True ,		A=5_0256 ,		A=5_0256 ,		A=False ,		**A ,		)		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			vocab_size
        _SCREAMING_SNAKE_CASE									=			n_ctx
        _SCREAMING_SNAKE_CASE									=			n_positions
        _SCREAMING_SNAKE_CASE									=			n_embd
        _SCREAMING_SNAKE_CASE									=			n_layer
        _SCREAMING_SNAKE_CASE									=			n_head
        _SCREAMING_SNAKE_CASE									=			n_inner
        _SCREAMING_SNAKE_CASE									=			rotary_dim
        _SCREAMING_SNAKE_CASE									=			activation_function
        _SCREAMING_SNAKE_CASE									=			resid_pdrop
        _SCREAMING_SNAKE_CASE									=			embd_pdrop
        _SCREAMING_SNAKE_CASE									=			attn_pdrop
        _SCREAMING_SNAKE_CASE									=			layer_norm_epsilon
        _SCREAMING_SNAKE_CASE									=			initializer_range
        _SCREAMING_SNAKE_CASE									=			use_cache

        _SCREAMING_SNAKE_CASE									=			bos_token_id
        _SCREAMING_SNAKE_CASE									=			eos_token_id

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




class 					a_     (				snake_case_       ):

    '''simple docstring'''


    def __init__(		self ,		A ,		A = "default" ,		A = None ,		A = False ,		)		->   str:
        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?
            _SCREAMING_SNAKE_CASE									=			0


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

        return common_inputs


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


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


    def 			snake_case_(		self ,		A ,		A = -1 ,		A = -1 ,		A = False ,		A = None ,		)		->   Mapping[str, Any]:
        _SCREAMING_SNAKE_CASE									=			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()
        _SCREAMING_SNAKE_CASE									=			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

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

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

        return ordered_inputs





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










'''simple docstring'''

import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
lowercase_					= logging.getLogger(__name__)

if __name__ == "__main__":
      lowercase_					= argparse.ArgumentParser(
          description="""Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)"""
      )
      parser.add_argument(
          """--data_file""", type=str, default="""data/dump.bert-base-uncased.pickle""", help="""The binarized dataset."""
      )
      parser.add_argument(
          """--token_counts_dump""", type=str, default="""data/token_counts.bert-base-uncased.pickle""", help="""The dump file."""
      )
      parser.add_argument("""--vocab_size""", default=30_522, type=int)
      lowercase_					= parser.parse_args()

      logger.info(f"""Loading data from {args.data_file}""")
      with open(args.data_file, """rb""") as fp:
            lowercase_					= pickle.load(fp)

      logger.info("""Counting occurrences for MLM.""")
      lowercase_					= Counter()
      for tk_ids in data:
            counter.update(tk_ids)
      lowercase_					= [0] * args.vocab_size
      for k, v in counter.items():
            lowercase_					= v

      logger.info(f"""Dump to {args.token_counts_dump}""")
      with open(args.token_counts_dump, """wb""") as handle:
            pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)



