





'''simple docstring'''



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

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


__magic_name__   :						List[str]    =			logging.get_logger(__name__)

__magic_name__   :						List[str]    =			{
    """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json""",
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}


class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						List[str]      =   '''blenderbot-small'''
     UpperCAmelCase__       :						Tuple      =   ['''past_key_values''']
     UpperCAmelCase__       :						List[Any]      =   {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}

     def __init__(					self						,   lowerCamelCase=50_265						,   lowerCamelCase=512						,   lowerCamelCase=8						,   lowerCamelCase=2_048						,   lowerCamelCase=16						,   lowerCamelCase=8						,   lowerCamelCase=2_048						,   lowerCamelCase=16						,   lowerCamelCase=0.0						,   lowerCamelCase=0.0						,   lowerCamelCase=True						,   lowerCamelCase=True						,   lowerCamelCase="gelu"						,   lowerCamelCase=512						,   lowerCamelCase=0.1						,   lowerCamelCase=0.0						,   lowerCamelCase=0.0						,   lowerCamelCase=0.02						,   lowerCamelCase=1						,   lowerCamelCase=False						,   lowerCamelCase=0						,   lowerCamelCase=1						,   lowerCamelCase=2						,   lowerCamelCase=2						,   **lowerCamelCase						,   ):
       _snake_case     		=				vocab_size
       _snake_case     		=				max_position_embeddings
       _snake_case     		=				d_model
       _snake_case     		=				encoder_ffn_dim
       _snake_case     		=				encoder_layers
       _snake_case     		=				encoder_attention_heads
       _snake_case     		=				decoder_ffn_dim
       _snake_case     		=				decoder_layers
       _snake_case     		=				decoder_attention_heads
       _snake_case     		=				dropout
       _snake_case     		=				attention_dropout
       _snake_case     		=				activation_dropout
       _snake_case     		=				activation_function
       _snake_case     		=				init_std
       _snake_case     		=				encoder_layerdrop
       _snake_case     		=				decoder_layerdrop
       _snake_case     		=				use_cache
       _snake_case     		=				encoder_layers
       _snake_case     		=				scale_embedding  # scale factor will be sqrt(d_model) if True

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







class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






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

         if self.use_past:
           _snake_case     		=				{0: "batch"}
           _snake_case     		=				{0: "batch", 1: "past_decoder_sequence + sequence"}
         else:
           _snake_case     		=				{0: "batch", 1: "decoder_sequence"}
           _snake_case     		=				{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.
         _snake_case     		=				OrderedDict(
             [
                 ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                 ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
             ]	)
         if self.use_past:
           _snake_case		,		_snake_case     		=				self.num_layers
           for i in range(lowerCamelCase	):
             _snake_case     		=				{0: "batch", 2: "past_sequence + sequence"}
             _snake_case     		=				{0: "batch", 2: "past_sequence + sequence"}
       else:
         _snake_case     		=				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 	UpperCamelCase(					self	):
       if self.task in ["default", "seq2seq-lm"]:
         _snake_case     		=				super().outputs
       else:
         _snake_case     		=				super(lowerCamelCase						,   self	).outputs
         if self.use_past:
           _snake_case		,		_snake_case     		=				self.num_layers
           for i in range(lowerCamelCase	):
             _snake_case     		=				{0: "batch", 2: "past_sequence + sequence"}
             _snake_case     		=				{0: "batch", 2: "past_sequence + sequence"}
       return common_outputs

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase = -1						,   lowerCamelCase = -1						,   lowerCamelCase = False						,   lowerCamelCase = None						,   ):
       _snake_case     		=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	)

       # Generate decoder inputs
       _snake_case     		=				seq_length if not self.use_past else 1
       _snake_case     		=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	)
       _snake_case     		=				{F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
       _snake_case     		=				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
         _snake_case		,		_snake_case     		=				common_inputs["input_ids"].shape
         _snake_case     		=				common_inputs["decoder_input_ids"].shape[1]
         _snake_case		,		_snake_case     		=				self.num_attention_heads
         _snake_case     		=				(
             batch,
             num_encoder_attention_heads,
             encoder_seq_length,
             self._config.hidden_size // num_encoder_attention_heads,
         )
         _snake_case     		=				decoder_seq_length + 3
         _snake_case     		=				(
             batch,
             num_decoder_attention_heads,
             decoder_past_length,
             self._config.hidden_size // num_decoder_attention_heads,
         )

         _snake_case     		=				torch.cat(
             [common_inputs["decoder_attention_mask"], torch.ones(lowerCamelCase						,   lowerCamelCase	)]						,   dim=1	)

         _snake_case     		=				[]
         # If the number of encoder and decoder layers are present in the model configuration, both are considered
         _snake_case		,		_snake_case     		=				self.num_layers
         _snake_case     		=				min(lowerCamelCase						,   lowerCamelCase	)
         _snake_case     		=				max(lowerCamelCase						,   lowerCamelCase	) - min_num_layers
         _snake_case     		=				"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.
         _snake_case     		=				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 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase = -1						,   lowerCamelCase = -1						,   lowerCamelCase = False						,   lowerCamelCase = None						,   ):
       _snake_case     		=				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
         _snake_case		,		_snake_case     		=				common_inputs["input_ids"].shape
         # Not using the same length for past_key_values
         _snake_case     		=				seqlen + 2
         _snake_case		,		_snake_case     		=				self.num_layers
         _snake_case		,		_snake_case     		=				self.num_attention_heads
         _snake_case     		=				(
             batch,
             num_encoder_attention_heads,
             past_key_values_length,
             self._config.hidden_size // num_encoder_attention_heads,
         )

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

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase = -1						,   lowerCamelCase = -1						,   lowerCamelCase = False						,   lowerCamelCase = None						,   ):
       # Copied from OnnxConfig.generate_dummy_inputs
       # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
       # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
       _snake_case     		=				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
       _snake_case     		=				tokenizer.num_special_tokens_to_add(lowerCamelCase	)
       _snake_case     		=				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
       _snake_case     		=				[" ".join([tokenizer.unk_token]	) * seq_length] * batch_size
       _snake_case     		=				dict(tokenizer(lowerCamelCase						,   return_tensors=lowerCamelCase	)	)
       return common_inputs

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase = -1						,   lowerCamelCase = -1						,   lowerCamelCase = False						,   lowerCamelCase = None						,   ):
       if self.task in ["default", "seq2seq-lm"]:
         _snake_case     		=				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":
         _snake_case     		=				self._generate_dummy_inputs_for_causal_lm(
             lowerCamelCase						,   batch_size=lowerCamelCase						,   seq_length=lowerCamelCase						,   is_pair=lowerCamelCase						,   framework=lowerCamelCase	)
       else:
         _snake_case     		=				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 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       if self.task in ["default", "seq2seq-lm"]:
         _snake_case     		=				super()._flatten_past_key_values_(lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	)
       else:
         _snake_case     		=				super(lowerCamelCase						,   self	)._flatten_past_key_values_(
             lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	)









'''simple docstring'''



import math


class       __SCREAMING_SNAKE_CASE	:



     '''simple docstring'''






     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase	):
       _snake_case     		=				0.0
       _snake_case     		=				0.0
       for i in range(len(lowerCamelCase	)	):
         da += math.pow((sample[i] - weights[0][i])						,   2	)
         da += math.pow((sample[i] - weights[1][i])						,   2	)
         return 0 if da > da else 1
       return 0






     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       for i in range(len(lowerCamelCase	)	):
         weights[j][i] += alpha * (sample[i] - weights[j][i])
       return weights







def 				snake_case_      (		):







  '''simple docstring'''



  _snake_case     		=				[[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

  # weight initialization ( n, C )
  _snake_case     		=				[[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

  # training
  _snake_case     		=				SelfOrganizingMap()
  _snake_case     		=				3
  _snake_case     		=				0.5

  for _ in range(SCREAMING_SNAKE_CASE__       ):
    for j in range(len(SCREAMING_SNAKE_CASE__       )       ):
      # training sample
      _snake_case     		=				training_samples[j]

      # Compute the winning vector
      _snake_case     		=				self_organizing_map.get_winner(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )

      # Update the winning vector
      _snake_case     		=				self_organizing_map.update(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )

    # classify test sample
  _snake_case     		=				[0, 0, 0, 1]
  _snake_case     		=				self_organizing_map.get_winner(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )

  # results
  print(f'''Clusters that the test sample belongs to : {winner}'''       )
  print(f'''Weights that have been trained : {weights}'''       )


# running the main() function
if __name__ == "__main__":
 main()


