





'''simple docstring'''



import random
import timeit
from functools import wraps
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_tf_available():
 import tensorflow as tf
 from tensorflow.python.framework.errors_impl import ResourceExhaustedError

 from .benchmark_args_tf import TensorFlowBenchmarkArguments

if is_pyanvml_available():
 import pyanvml.pyanvml as nvml

__magic_name__   :						Tuple    =			logging.get_logger(__name__)




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  def run_func(SCREAMING_SNAKE_CASE__       ):
    @wraps(SCREAMING_SNAKE_CASE__       )
    def run_in_eager_mode(*SCREAMING_SNAKE_CASE__					,	**SCREAMING_SNAKE_CASE__       ):
      return func(*SCREAMING_SNAKE_CASE__					,	**SCREAMING_SNAKE_CASE__       )

    @wraps(SCREAMING_SNAKE_CASE__       )
    @tf.function(experimental_compile=SCREAMING_SNAKE_CASE__       )
    def run_in_graph_mode(*SCREAMING_SNAKE_CASE__					,	**SCREAMING_SNAKE_CASE__       ):
      return func(*SCREAMING_SNAKE_CASE__					,	**SCREAMING_SNAKE_CASE__       )

    if do_eager_mode is True:
      if use_xla is not False:
        raise ValueError(
            "Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`."       )
      return run_in_eager_mode
    else:
      return run_in_graph_mode

  return run_func




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				random.Random()
  _snake_case     		=				[rng.randint(0					,	vocab_size - 1       ) for i in range(batch_size * sequence_length       )]
  return tf.constant(SCREAMING_SNAKE_CASE__					,	shape=(batch_size, sequence_length)					,	dtype=tf.intaa       )




class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						TensorFlowBenchmarkArguments
     UpperCAmelCase__       :						PretrainedConfig
     UpperCAmelCase__       :						str      =   "TensorFlow"

     @property
     def 	UpperCamelCase(					self	):
       return tf.__version__

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       # initialize GPU on separate process
       _snake_case     		=				self.args.strategy
       if strategy is None:
         raise ValueError("A device strategy has to be initialized before using TensorFlow."	)
       _snake_case     		=				self._prepare_inference_func(lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	)
       return self._measure_speed(_inference	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       _snake_case     		=				self.args.strategy
       if strategy is None:
         raise ValueError("A device strategy has to be initialized before using TensorFlow."	)
       _snake_case     		=				self._prepare_train_func(lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	)
       return self._measure_speed(_train	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       # initialize GPU on separate process
       if self.args.is_gpu:
         tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]						,   lowerCamelCase	)
       _snake_case     		=				self.args.strategy
       if strategy is None:
         raise ValueError("A device strategy has to be initialized before using TensorFlow."	)
       _snake_case     		=				self._prepare_inference_func(lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	)
       return self._measure_memory(_inference	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       if self.args.is_gpu:
         tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]						,   lowerCamelCase	)
       _snake_case     		=				self.args.strategy
       if strategy is None:
         raise ValueError("A device strategy has to be initialized before using TensorFlow."	)

       _snake_case     		=				self._prepare_train_func(lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	)
       return self._measure_memory(_train	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       _snake_case     		=				self.config_dict[model_name]

       if self.args.fpaa:
         raise NotImplementedError("Mixed precision is currently not supported."	)

       _snake_case     		=				(
           hasattr(lowerCamelCase						,   "architectures"	)
           and isinstance(config.architectures						,   lowerCamelCase	)
           and len(config.architectures	) > 0
       )
       if not self.args.only_pretrain_model and has_model_class_in_config:
         try:
           _snake_case     		=				"TF" + config.architectures[0]  # prepend 'TF' for tensorflow model
           _snake_case     		=				__import__("transformers"						,   fromlist=[model_class]	)
           _snake_case     		=				getattr(lowerCamelCase						,   lowerCamelCase	)
           _snake_case     		=				model_cls(lowerCamelCase	)
         except ImportError:
           raise ImportError(
               F'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
               " set `--only_pretrain_model` or `args.only_pretrain_model=True`."	)
       else:
         _snake_case     		=				TF_MODEL_MAPPING[config.__class__](lowerCamelCase	)

       # encoder-decoder has vocab size saved differently
       _snake_case     		=				config.vocab_size if hasattr(lowerCamelCase						,   "vocab_size"	) else config.encoder.vocab_size
       _snake_case     		=				random_input_ids(lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	)

       @run_with_tf_optimizations(self.args.eager_mode						,   self.args.use_xla	)
       def encoder_decoder_forward():
         return model(lowerCamelCase						,   decoder_input_ids=lowerCamelCase						,   training=lowerCamelCase	)

       @run_with_tf_optimizations(self.args.eager_mode						,   self.args.use_xla	)
       def encoder_forward():
         return model(lowerCamelCase						,   training=lowerCamelCase	)

       _snake_case     		=				encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

       return _inference

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       _snake_case     		=				self.config_dict[model_name]

       if self.args.eager_mode is not False:
         raise ValueError("Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`."	)

       if self.args.fpaa:
         raise NotImplementedError("Mixed precision is currently not supported."	)

       _snake_case     		=				(
           hasattr(lowerCamelCase						,   "architectures"	)
           and isinstance(config.architectures						,   lowerCamelCase	)
           and len(config.architectures	) > 0
       )
       if not self.args.only_pretrain_model and has_model_class_in_config:
         try:
           _snake_case     		=				"TF" + config.architectures[0]  # prepend 'TF' for tensorflow model
           _snake_case     		=				__import__("transformers"						,   fromlist=[model_class]	)
           _snake_case     		=				getattr(lowerCamelCase						,   lowerCamelCase	)
           _snake_case     		=				model_cls(lowerCamelCase	)
         except ImportError:
           raise ImportError(
               F'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
               " set `--only_pretrain_model` or `args.only_pretrain_model=True`."	)
       else:
         _snake_case     		=				TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowerCamelCase	)

       # encoder-decoder has vocab size saved differently
       _snake_case     		=				config.vocab_size if hasattr(lowerCamelCase						,   "vocab_size"	) else config.encoder.vocab_size
       _snake_case     		=				random_input_ids(lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	)

       @run_with_tf_optimizations(self.args.eager_mode						,   self.args.use_xla	)
       def encoder_decoder_train():
         _snake_case     		=				model(lowerCamelCase						,   decoder_input_ids=lowerCamelCase						,   labels=lowerCamelCase						,   training=lowerCamelCase	)[0]
         _snake_case     		=				tf.gradients(lowerCamelCase						,   model.trainable_variables	)
         return gradients

       @run_with_tf_optimizations(self.args.eager_mode						,   self.args.use_xla	)
       def encoder_train():
         _snake_case     		=				model(lowerCamelCase						,   labels=lowerCamelCase						,   training=lowerCamelCase	)[0]
         _snake_case     		=				tf.gradients(lowerCamelCase						,   model.trainable_variables	)
         return gradients

       _snake_case     		=				encoder_decoder_train if config.is_encoder_decoder else encoder_train

       return _train

     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       with self.args.strategy.scope():
         try:
           if self.args.is_tpu or self.args.use_xla:
             # run additional 10 times to stabilize compilation for tpu
             logger.info("Do inference on TPU. Running model 5 times to stabilize compilation"	)
             timeit.repeat(lowerCamelCase						,   repeat=1						,   number=5	)

           # as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
           _snake_case     		=				timeit.repeat(
               lowerCamelCase						,   repeat=self.args.repeat						,   number=10						,   )

           return min(lowerCamelCase	) / 10.0
         except ResourceExhaustedError as e:
           self.print_fn(F'''Doesn\'t fit on GPU. {e}'''	)






     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       logger.info(
           "Note that TensorFlow allocates more memory than "
           "it might need to speed up computation. "
           "The memory reported here corresponds to the memory "
           "reported by `nvidia-smi`, which can vary depending "
           "on total available memory on the GPU that is used."	)
       with self.args.strategy.scope():
         try:
           if self.args.trace_memory_line_by_line:
             if not self.args.eager_mode:
               raise ValueError(
                   "`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory"
                   " consumption line by line."	)
             _snake_case     		=				start_memory_tracing("transformers"	)

           if self.args.is_tpu:
             # tpu
             raise NotImplementedError(
                 "Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking"
                 " with `args.memory=False`"	)
           elif self.args.is_gpu:
             # gpu
             if not is_pyanvml_available():
               logger.warning(
                   "py3nvml not installed, we won't log GPU memory usage. "
                   "Install py3nvml (pip install py3nvml) to log information about GPU."	)
               _snake_case     		=				"N/A"
             else:
               logger.info(
                   "Measuring total GPU usage on GPU device. Make sure to not have additional processes"
                   " running on the same GPU."	)
               # init nvml
               nvml.nvmlInit()
               func()
               _snake_case     		=				nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx	)
               _snake_case     		=				nvml.nvmlDeviceGetMemoryInfo(lowerCamelCase	)
               _snake_case     		=				meminfo.used
               _snake_case     		=				Memory(lowerCamelCase	)
               # shutdown nvml
               nvml.nvmlShutdown()
           else:
             # cpu
             if self.args.trace_memory_line_by_line:
               logger.info(
                   "When enabling line by line tracing, the max peak memory for CPU is inaccurate in"
                   " TensorFlow."	)
               _snake_case     		=				None
             else:
               _snake_case     		=				measure_peak_memory_cpu(lowerCamelCase	)
               _snake_case     		=				Memory(lowerCamelCase	) if isinstance(lowerCamelCase						,   lowerCamelCase	) else memory_bytes
           if self.args.trace_memory_line_by_line:
             _snake_case     		=				stop_memory_tracing(lowerCamelCase	)
             if memory is None:
               _snake_case     		=				summary.total
           else:
             _snake_case     		=				None

           return memory, summary
         except ResourceExhaustedError as e:
           self.print_fn(F'''Doesn\'t fit on GPU. {e}'''	)
           return "N/A", None









'''simple docstring'''



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__":
 __magic_name__   :						Union[str, Any]    =			pd.read_csv("""sample_data.csv""", header=None)
 __magic_name__   :						Optional[Any]    =			df.shape[:1][0]
 # If you're using some other dataset input the target column
 __magic_name__   :						str    =			df.iloc[:, 1:2]
 __magic_name__   :						Dict    =			actual_data.values.reshape(len_data, 1)
 __magic_name__   :						Tuple    =			MinMaxScaler().fit_transform(actual_data)
 __magic_name__   :						Union[str, Any]    =			10
 __magic_name__   :						Optional[int]    =			5
 __magic_name__   :						Any    =			20
 __magic_name__   :						int    =			len_data - periods * look_back
 __magic_name__   :						Union[str, Any]    =			actual_data[:division]
 __magic_name__   :						int    =			actual_data[division - look_back :]
 __magic_name__   ,    __magic_name__   :						List[str]    =			[], []
 __magic_name__   ,    __magic_name__   :						Tuple    =			[], []

 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])
 __magic_name__   :						List[str]    =			np.array(train_x)
 __magic_name__   :						List[str]    =			np.array(test_x)
 __magic_name__   :						int    =			np.array([list(i.ravel()) for i in train_y])
 __magic_name__   :						int    =			np.array([list(i.ravel()) for i in test_y])

 __magic_name__   :						Tuple    =			Sequential()
 model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True))
 model.add(LSTM(64, input_shape=(128, 1)))
 model.add(Dense(forward_days))
 model.compile(loss="""mean_squared_error""", optimizer="""adam""")
 __magic_name__   :						int    =			model.fit(
     x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
 )
 __magic_name__   :						int    =			model.predict(x_test)


