


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


lowerCAmelCase__  :Optional[Any]             =							logging.get_logger(__name__)

lowerCAmelCase__  :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 		__a				(    __snake_case      ):
		_a     :  int           =     """blenderbot-small"""
		_a     :  List[Any]           =     ["""past_key_values"""]
		_a     :  Union[str, Any]           =     {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE=50265      ,   _SCREAMING_SNAKE_CASE=512      ,   _SCREAMING_SNAKE_CASE=8      ,   _SCREAMING_SNAKE_CASE=2048      ,   _SCREAMING_SNAKE_CASE=16      ,   _SCREAMING_SNAKE_CASE=8      ,   _SCREAMING_SNAKE_CASE=2048      ,   _SCREAMING_SNAKE_CASE=16      ,   _SCREAMING_SNAKE_CASE=0.0      ,   _SCREAMING_SNAKE_CASE=0.0      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE="gelu"      ,   _SCREAMING_SNAKE_CASE=512      ,   _SCREAMING_SNAKE_CASE=0.1      ,   _SCREAMING_SNAKE_CASE=0.0      ,   _SCREAMING_SNAKE_CASE=0.0      ,   _SCREAMING_SNAKE_CASE=0.02      ,   _SCREAMING_SNAKE_CASE=1      ,   _SCREAMING_SNAKE_CASE=False      ,   _SCREAMING_SNAKE_CASE=0      ,   _SCREAMING_SNAKE_CASE=1      ,   _SCREAMING_SNAKE_CASE=2      ,   _SCREAMING_SNAKE_CASE=2      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	Any:




									"""simple docstring"""





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

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



class 		__a				(    __snake_case      ):
		@property
		def      UpperCAmelCase__    (      self						)			->	Mapping[str, Mapping[int, str]]:




									"""simple docstring"""





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

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

																if self.use_past:
																							self.fill_with_past_key_values_(UpperCamelCase__      ,   direction='inputs'						)
									elif self.task == "causal-lm":
																# TODO: figure this case out.
																_UpperCAmelCase					=      OrderedDict(
																    [
																        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
																        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
																    ]						)
																if self.use_past:
																							_UpperCAmelCase					=      self.num_layers
																							for i in range(UpperCamelCase__						):
																														_UpperCAmelCase					=      {0: "batch", 2: "past_sequence + sequence"}
																														_UpperCAmelCase					=      {0: "batch", 2: "past_sequence + sequence"}
									else:
																_UpperCAmelCase					=      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						)			->	Mapping[str, Mapping[int, str]]:




									"""simple docstring"""





									if self.task in ["default", "seq2seq-lm"]:
																_UpperCAmelCase					=      super().outputs
									else:
																_UpperCAmelCase					=      super(UpperCamelCase__      ,   self						).outputs
																if self.use_past:
																							_UpperCAmelCase					=      self.num_layers
																							for i in range(UpperCamelCase__						):
																														_UpperCAmelCase					=      {0: "batch", 2: "past_sequence + sequence"}
																														_UpperCAmelCase					=      {0: "batch", 2: "past_sequence + sequence"}
									return common_outputs
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = -1      ,   _SCREAMING_SNAKE_CASE = -1      ,   _SCREAMING_SNAKE_CASE = False      ,   _SCREAMING_SNAKE_CASE = None      ,   )			->	Mapping[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    UpperCamelCase__      ,   UpperCamelCase__      ,   UpperCamelCase__      ,   UpperCamelCase__      ,   UpperCamelCase__						)

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

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

																_UpperCAmelCase					=      torch.cat(
																    [common_inputs['decoder_attention_mask'], torch.ones(UpperCamelCase__      ,   UpperCamelCase__						)]      ,   dim=1						)

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

																for _ in range(UpperCamelCase__						):
																							common_inputs["past_key_values"].append(
																							    (
																							        torch.zeros(UpperCamelCase__						),
																							        torch.zeros(UpperCamelCase__						),
																							        torch.zeros(UpperCamelCase__						),
																							        torch.zeros(UpperCamelCase__						),
																							    )						)
																# TODO: test this.
																_UpperCAmelCase					=      encoder_shape if remaining_side_name == "encoder" else decoder_shape
																for _ in range(UpperCamelCase__      ,   UpperCamelCase__						):
																							common_inputs["past_key_values"].append((torch.zeros(UpperCamelCase__						), torch.zeros(UpperCamelCase__						))						)
									return common_inputs
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = -1      ,   _SCREAMING_SNAKE_CASE = -1      ,   _SCREAMING_SNAKE_CASE = False      ,   _SCREAMING_SNAKE_CASE = None      ,   )			->	Mapping[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    UpperCamelCase__      ,   UpperCamelCase__      ,   UpperCamelCase__      ,   UpperCamelCase__      ,   UpperCamelCase__						)

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

																_UpperCAmelCase					=      common_inputs["attention_mask"].dtype
																_UpperCAmelCase					=      torch.cat(
																    [common_inputs['attention_mask'], torch.ones(UpperCamelCase__      ,   UpperCamelCase__      ,   dtype=UpperCamelCase__						)]      ,   dim=1						)
																_UpperCAmelCase					=      [
																    (torch.zeros(UpperCamelCase__						), torch.zeros(UpperCamelCase__						)) for _ in range(UpperCamelCase__						)
																]
									return common_inputs
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = -1      ,   _SCREAMING_SNAKE_CASE = -1      ,   _SCREAMING_SNAKE_CASE = False      ,   _SCREAMING_SNAKE_CASE = None      ,   )			->	Mapping[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      compute_effective_axis_dimension(
									    UpperCamelCase__      ,   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
									_UpperCAmelCase					=      tokenizer.num_special_tokens_to_add(UpperCamelCase__						)
									_UpperCAmelCase					=      compute_effective_axis_dimension(
									    UpperCamelCase__      ,   fixed_dimension=OnnxConfig.default_fixed_sequence      ,   num_token_to_add=UpperCamelCase__						)

									# Generate dummy inputs according to compute batch and sequence
									_UpperCAmelCase					=      [" ".join([tokenizer.unk_token]						) * seq_length] * batch_size
									_UpperCAmelCase					=      dict(tokenizer(UpperCamelCase__      ,   return_tensors=UpperCamelCase__						)						)
									return common_inputs
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = -1      ,   _SCREAMING_SNAKE_CASE = -1      ,   _SCREAMING_SNAKE_CASE = False      ,   _SCREAMING_SNAKE_CASE = None      ,   )			->	Mapping[str, Any]:




									"""simple docstring"""





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

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

									return common_inputs






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	List[Any]:




									"""simple docstring"""





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




import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential

if __name__ == "__main__":
		lowerCAmelCase__  :Dict             =							pd.read_csv('''sample_data.csv''', header=None)
		lowerCAmelCase__  :int             =							df.shape[:1][0]
		# If you're using some other dataset input the target column
		lowerCAmelCase__  :Union[str, Any]             =							df.iloc[:, 1:2]
		lowerCAmelCase__  :Optional[int]             =							actual_data.values.reshape(len_data, 1)
		lowerCAmelCase__  :Tuple             =							MinMaxScaler().fit_transform(actual_data)
		lowerCAmelCase__  :str             =							1_0
		lowerCAmelCase__  :Optional[Any]             =							5
		lowerCAmelCase__  :List[str]             =							2_0
		lowerCAmelCase__  :Any             =							len_data - periods * look_back
		lowerCAmelCase__  :Union[str, Any]             =							actual_data[:division]
		lowerCAmelCase__  :Tuple             =							actual_data[division - look_back :]
		lowerCAmelCase__      ,  lowerCAmelCase__  :Optional[int]             =							[], []
		lowerCAmelCase__      ,  lowerCAmelCase__  :str             =							[], []

		for i in range(0, len(train_data) - forward_days - look_back + 1):
				train_x.append(train_data[i : i + look_back])
				train_y.append(train_data[i + look_back : i + look_back + forward_days])
		for i in range(0, len(test_data) - forward_days - look_back + 1):
				test_x.append(test_data[i : i + look_back])
				test_y.append(test_data[i + look_back : i + look_back + forward_days])
		lowerCAmelCase__  :Optional[Any]             =							np.array(train_x)
		lowerCAmelCase__  :Any             =							np.array(test_x)
		lowerCAmelCase__  :Dict             =							np.array([list(i.ravel()) for i in train_y])
		lowerCAmelCase__  :Tuple             =							np.array([list(i.ravel()) for i in test_y])

		lowerCAmelCase__  :Optional[int]             =							Sequential()
		model.add(LSTM(1_2_8, input_shape=(look_back, 1), return_sequences=True))
		model.add(LSTM(6_4, input_shape=(1_2_8, 1)))
		model.add(Dense(forward_days))
		model.compile(loss='''mean_squared_error''', optimizer='''adam''')
		lowerCAmelCase__  :List[Any]             =							model.fit(
		    x_train, y_train, epochs=1_5_0, verbose=1, shuffle=True, batch_size=4
		)
		lowerCAmelCase__  :Optional[Any]             =							model.predict(x_test)
