



from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


A_		:  int										=  logging.get_logger(__name__)

A_		:  List[str]										=  {
    '''google/bigbird-pegasus-large-arxiv''': (
        '''https://huggingface.co/google/bigbird-pegasus-large-arxiv/resolve/main/config.json'''
    ),
    '''google/bigbird-pegasus-large-pubmed''': (
        '''https://huggingface.co/google/bigbird-pegasus-large-pubmed/resolve/main/config.json'''
    ),
    '''google/bigbird-pegasus-large-bigpatent''': (
        '''https://huggingface.co/google/bigbird-pegasus-large-bigpatent/resolve/main/config.json'''
    ),
    # See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
}



class 		_lowercase		(							UpperCAmelCase__						):
   _UpperCAmelCase  						=      '''bigbird_pegasus'''
   _UpperCAmelCase  						=      ['''past_key_values''']
   _UpperCAmelCase  						=      {
       '''num_attention_heads''': '''encoder_attention_heads''',
       '''hidden_size''': '''d_model''',
       '''attention_probs_dropout_prob''': '''attention_dropout''',
   }

   def __init__(   self      :							int				, __lowerCAmelCase      :							Any=9_6103				, __lowerCAmelCase      :							Optional[Any]=4096				, __lowerCAmelCase      :							List[Any]=16				, __lowerCAmelCase      :							str=4096				, __lowerCAmelCase      :							List[Any]=16				, __lowerCAmelCase      :							Union[str, Any]=16				, __lowerCAmelCase      :							Dict=4096				, __lowerCAmelCase      :							Union[str, Any]=16				, __lowerCAmelCase      :							Tuple=0.0				, __lowerCAmelCase      :							int=0.0				, __lowerCAmelCase      :							List[str]=True				, __lowerCAmelCase      :							str=True				, __lowerCAmelCase      :							List[str]="gelu_new"				, __lowerCAmelCase      :							int=1024				, __lowerCAmelCase      :							Optional[int]=0.1				, __lowerCAmelCase      :							Tuple=0.0				, __lowerCAmelCase      :							Dict=0.0				, __lowerCAmelCase      :							List[Any]=0.0_2				, __lowerCAmelCase      :							Dict=2				, __lowerCAmelCase      :							Dict=0.0				, __lowerCAmelCase      :							List[Any]=True				, __lowerCAmelCase      :							Any=0				, __lowerCAmelCase      :							Dict=2				, __lowerCAmelCase      :							Any=1				, __lowerCAmelCase      :							Any="block_sparse"				, __lowerCAmelCase      :							Dict=64				, __lowerCAmelCase      :							Tuple=3				, __lowerCAmelCase      :							Optional[int]=False				, **__lowerCAmelCase      :							Optional[int]				, )							->					Optional[int]:


    """simple docstring"""






    a							=		vocab_size
    a							=		max_position_embeddings
    a							=		d_model
    a							=		encoder_ffn_dim
    a							=		encoder_layers
    a							=		encoder_attention_heads
    a							=		decoder_ffn_dim
    a							=		decoder_layers
    a							=		decoder_attention_heads
    a							=		dropout
    a							=		attention_dropout
    a							=		activation_dropout
    a							=		activation_function
    a							=		init_std
    a							=		encoder_layerdrop
    a							=		decoder_layerdrop
    a							=		classifier_dropout
    a							=		use_cache
    a							=		encoder_layers
    a							=		scale_embedding  # scale factor will be sqrt(d_model) if True

    # extra config
    a							=		attention_type
    a							=		block_size
    a							=		num_random_blocks
    a							=		use_bias

    super().__init__(
        pad_token_id=__lowerCAmelCase				, bos_token_id=__lowerCAmelCase				, eos_token_id=__lowerCAmelCase				, is_encoder_decoder=__lowerCAmelCase				, decoder_start_token_id=__lowerCAmelCase				, **__lowerCAmelCase				, )



class 		_lowercase		(							UpperCAmelCase__						):

   @property
   def 		A				(   self      :							Dict				)							->					Mapping[str, Mapping[int, str]]:


    """simple docstring"""






    if self.task in ["default", "seq2seq-lm"]:
     a							=		OrderedDict(
         [
             ("input_ids", {0: "batch", 1: "encoder_sequence"}),
             ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
         ]				)

     if self.use_past:
      a							=		{0: "batch"}
      a							=		{0: "batch", 1: "past_decoder_sequence + sequence"}
     else:
      a							=		{0: "batch", 1: "decoder_sequence"}
      a							=		{0: "batch", 1: "decoder_sequence"}

     if self.use_past:
      self.fill_with_past_key_values_(__lowerCAmelCase				, direction="inputs"				)
    elif self.task == "causal-lm":
     # TODO: figure this case out.
     a							=		OrderedDict(
         [
             ("input_ids", {0: "batch", 1: "encoder_sequence"}),
             ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
         ]				)
     if self.use_past:
      a		,	a							=		self.num_layers
      for i in range(__lowerCAmelCase				):
       a							=		{0: "batch", 2: "past_sequence + sequence"}
       a							=		{0: "batch", 2: "past_sequence + sequence"}
    else:
     a							=		OrderedDict(
         [
             ("input_ids", {0: "batch", 1: "encoder_sequence"}),
             ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
             ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
             ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
         ]				)

    return common_inputs

   @property
   def 		A				(   self      :							Optional[Any]				)							->					Mapping[str, Mapping[int, str]]:


    """simple docstring"""






    if self.task in ["default", "seq2seq-lm"]:
     a							=		super().outputs
    else:
     a							=		super(__lowerCAmelCase				, self				).outputs
     if self.use_past:
      a		,	a							=		self.num_layers
      for i in range(__lowerCAmelCase				):
       a							=		{0: "batch", 2: "past_sequence + sequence"}
       a							=		{0: "batch", 2: "past_sequence + sequence"}
    return common_outputs

   def 		A				(   self      :							List[str]				, __lowerCAmelCase      :							PreTrainedTokenizer				, __lowerCAmelCase      :							int = -1				, __lowerCAmelCase      :							int = -1				, __lowerCAmelCase      :							bool = False				, __lowerCAmelCase      :							Optional[TensorType] = None				, )							->					Mapping[str, Any]:


    """simple docstring"""






    a							=		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
        __lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				)

    # Generate decoder inputs
    a							=		seq_length if not self.use_past else 1
    a							=		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
        __lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				)
    a							=		{f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
    a							=		dict(**__lowerCAmelCase				, **__lowerCAmelCase				)

    if self.use_past:
     if not is_torch_available():
      raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."				)
     else:
      import torch
     a		,	a							=		common_inputs["input_ids"].shape
     a							=		common_inputs["decoder_input_ids"].shape[1]
     a		,	a							=		self.num_attention_heads
     a							=		(
         batch,
         num_encoder_attention_heads,
         encoder_seq_length,
         self._config.hidden_size // num_encoder_attention_heads,
     )
     a							=		decoder_seq_length + 3
     a							=		(
         batch,
         num_decoder_attention_heads,
         decoder_past_length,
         self._config.hidden_size // num_decoder_attention_heads,
     )

     a							=		torch.cat(
         [common_inputs["decoder_attention_mask"], torch.ones(__lowerCAmelCase				, __lowerCAmelCase				)]				, dim=1				)

     a							=		[]
     # If the number of encoder and decoder layers are present in the model configuration, both are considered
     a		,	a							=		self.num_layers
     a							=		min(__lowerCAmelCase				, __lowerCAmelCase				)
     a							=		max(__lowerCAmelCase				, __lowerCAmelCase				) - min_num_layers
     a							=		"encoder" if num_encoder_layers > num_decoder_layers else "decoder"

     for _ in range(__lowerCAmelCase				):
      common_inputs["past_key_values"].append(
          (
              torch.zeros(__lowerCAmelCase				),
              torch.zeros(__lowerCAmelCase				),
              torch.zeros(__lowerCAmelCase				),
              torch.zeros(__lowerCAmelCase				),
          )				)
     # TODO: test this.
     a							=		encoder_shape if remaining_side_name == "encoder" else decoder_shape
     for _ in range(__lowerCAmelCase				, __lowerCAmelCase				):
      common_inputs["past_key_values"].append((torch.zeros(__lowerCAmelCase				), torch.zeros(__lowerCAmelCase				))				)
    return common_inputs

   def 		A				(   self      :							Any				, __lowerCAmelCase      :							PreTrainedTokenizer				, __lowerCAmelCase      :							int = -1				, __lowerCAmelCase      :							int = -1				, __lowerCAmelCase      :							bool = False				, __lowerCAmelCase      :							Optional[TensorType] = None				, )							->					Mapping[str, Any]:


    """simple docstring"""






    a							=		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
        __lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				)

    if self.use_past:
     if not is_torch_available():
      raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."				)
     else:
      import torch
     a		,	a							=		common_inputs["input_ids"].shape
     # Not using the same length for past_key_values
     a							=		seqlen + 2
     a		,	a							=		self.num_layers
     a		,	a							=		self.num_attention_heads
     a							=		(
         batch,
         num_encoder_attention_heads,
         past_key_values_length,
         self._config.hidden_size // num_encoder_attention_heads,
     )

     a							=		common_inputs["attention_mask"].dtype
     a							=		torch.cat(
         [common_inputs["attention_mask"], torch.ones(__lowerCAmelCase				, __lowerCAmelCase				, dtype=__lowerCAmelCase				)]				, dim=1				)
     a							=		[
         (torch.zeros(__lowerCAmelCase				), torch.zeros(__lowerCAmelCase				)) for _ in range(__lowerCAmelCase				)
     ]
    return common_inputs

   def 		A				(   self      :							Tuple				, __lowerCAmelCase      :							PreTrainedTokenizer				, __lowerCAmelCase      :							int = -1				, __lowerCAmelCase      :							int = -1				, __lowerCAmelCase      :							bool = False				, __lowerCAmelCase      :							Optional[TensorType] = None				, )							->					Mapping[str, Any]:


    """simple docstring"""






    a							=		compute_effective_axis_dimension(
        __lowerCAmelCase				, fixed_dimension=OnnxConfig.default_fixed_batch				, num_token_to_add=0				)

    # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
    a							=		tokenizer.num_special_tokens_to_add(__lowerCAmelCase				)
    a							=		compute_effective_axis_dimension(
        __lowerCAmelCase				, fixed_dimension=OnnxConfig.default_fixed_sequence				, num_token_to_add=__lowerCAmelCase				)

    # Generate dummy inputs according to compute batch and sequence
    a							=		[" ".join([tokenizer.unk_token]				) * seq_length] * batch_size
    a							=		dict(tokenizer(__lowerCAmelCase				, return_tensors=__lowerCAmelCase				)				)
    return common_inputs

   def 		A				(   self      :							Optional[Any]				, __lowerCAmelCase      :							PreTrainedTokenizer				, __lowerCAmelCase      :							int = -1				, __lowerCAmelCase      :							int = -1				, __lowerCAmelCase      :							bool = False				, __lowerCAmelCase      :							Optional[TensorType] = None				, )							->					Mapping[str, Any]:


    """simple docstring"""






    if self.task in ["default", "seq2seq-lm"]:
     a							=		self._generate_dummy_inputs_for_default_and_seqaseq_lm(
         __lowerCAmelCase				, batch_size=__lowerCAmelCase				, seq_length=__lowerCAmelCase				, is_pair=__lowerCAmelCase				, framework=__lowerCAmelCase				)

    elif self.task == "causal-lm":
     a							=		self._generate_dummy_inputs_for_causal_lm(
         __lowerCAmelCase				, batch_size=__lowerCAmelCase				, seq_length=__lowerCAmelCase				, is_pair=__lowerCAmelCase				, framework=__lowerCAmelCase				)
    else:
     a							=		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         __lowerCAmelCase				, batch_size=__lowerCAmelCase				, seq_length=__lowerCAmelCase				, is_pair=__lowerCAmelCase				, framework=__lowerCAmelCase				)

    return common_inputs




   def 		A				(   self      :							str				, __lowerCAmelCase      :							str				, __lowerCAmelCase      :							Dict				, __lowerCAmelCase      :							Optional[int]				, __lowerCAmelCase      :							List[Any]				)							->					str:


    """simple docstring"""






    if self.task in ["default", "seq2seq-lm"]:
     a							=		super()._flatten_past_key_values_(__lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				)
    else:
     a							=		super(__lowerCAmelCase				, self				)._flatten_past_key_values_(
         __lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				)






from collections import Counter


def 						UpperCAmelCase__   (   UpperCAmelCase__			:list[int]       ):






 '''simple docstring'''

 return sum(socks_by_color // 2 for socks_by_color in Counter(UpperCAmelCase__       ).values()       )


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     A_		:  List[str]										=  [int(x) for x in input('''Enter socks by color :''').rstrip().split()]
     print(F"""sock_merchant({colors}) = {sock_merchant(colors)}""")

