






"""simple docstring"""




import numpy as np
from matplotlib import pyplot as plt






def 							lowerCAmelCase			(   UpperCamelCase_:				Optional[Any]       )							->				int:







  '''simple docstring'''







  return 1 / (1 + np.exp(-1 * x       ))




class  lowercase_     :






      def __init__(     self  ,  a_  ,  a_=None  ,  a_=None  ,  a_=False						)						->Optional[int]:







        '''simple docstring'''


        _a					      =						units
        _a					      =						None
        _a					      =						None
        _a					      =						activation
        if learning_rate is None:
          _a					      =						0.3
        _a					      =						learning_rate
        _a					      =						is_input_layer






      def       lowerCamelCase__     (     self  ,  a_						)						->str:







        '''simple docstring'''


        _a					      =						np.asmatrix(np.random.normal(0  ,  0.5  ,  (self.units, back_units)						)						)
        _a					      =						np.asmatrix(np.random.normal(0  ,  0.5  ,  self.units						)						).T
        if self.activation is None:
          _a					      =						sigmoid






      def       lowerCamelCase__     (     self						)						->str:







        '''simple docstring'''


        if self.activation == sigmoid:
          _a					      =						np.dot(self.output  ,  (1 - self.output).T						)
          _a					      =						np.diag(np.diag(a_						)						)
        else:
          _a					      =						1
        return gradient_activation






      def       lowerCamelCase__     (     self  ,  a_						)						->Dict:







        '''simple docstring'''


        _a					      =						xdata
        if self.is_input_layer:
          # input layer
          _a					      =						xdata
          _a					      =						xdata
          return xdata
        else:
          _a					      =						np.dot(self.weight  ,  self.xdata						) - self.bias
          _a					      =						self.activation(self.wx_plus_b						)
          return self.output






      def       lowerCamelCase__     (     self  ,  a_						)						->List[Any]:







        '''simple docstring'''


        _a					      =						self.cal_gradient()  # i * i 维
        _a					      =						np.asmatrix(np.dot(gradient.T  ,  a_						)						)

        _a					      =						np.asmatrix(self.xdata						)
        _a					      =						-1
        _a					      =						self.weight

        _a					      =						np.dot(gradient.T  ,  self._gradient_weight.T						)
        _a					      =						gradient * self._gradient_bias
        _a					      =						np.dot(a_  ,  self._gradient_x						).T
        # upgrade: the Negative gradient direction
        _a					      =						self.weight - self.learn_rate * self.gradient_weight
        _a					      =						self.bias - self.learn_rate * self.gradient_bias.T
        # updates the weights and bias according to learning rate (0.3 if undefined)
        return self.gradient







class  lowercase_     :






      def __init__(     self						)						->Optional[int]:







        '''simple docstring'''


        _a					      =						[]
        _a					      =						[]
        _a					      =						plt.figure()
        _a					      =						self.fig_loss.add_subplot(1  ,  1  ,  1						)






      def       lowerCamelCase__     (     self  ,  a_						)						->Optional[int]:







        '''simple docstring'''


        self.layers.append(a_						)






      def       lowerCamelCase__     (     self						)						->Optional[Any]:







        '''simple docstring'''


        for i, layer in enumerate(self.layers[:]						):
          if i < 1:
            _a					      =						True
          else:
            layer.initializer(self.layers[i - 1].units						)






      def       lowerCamelCase__     (     self						)						->Tuple:







        '''simple docstring'''


        for i, layer in enumerate(self.layers[:]						):
          print(f'''------- layer {i} -------'''						)
          print("weight.shape "  ,  np.shape(layer.weight						)						)
          print("bias.shape "  ,  np.shape(layer.bias						)						)






      def       lowerCamelCase__     (     self  ,  a_  ,  a_  ,  a_  ,  a_						)						->str:







        '''simple docstring'''


        _a					      =						train_round
        _a					      =						accuracy

        self.ax_loss.hlines(self.accuracy  ,  0  ,  self.train_round * 1.1						)

        _a					      =						np.shape(a_						)
        for _ in range(a_						):
          _a					      =						0
          for row in range(x_shape[0]						):
            _a					      =						np.asmatrix(xdata[row, :]						).T
            _a					      =						np.asmatrix(ydata[row, :]						).T

            # forward propagation
            for layer in self.layers:
              _a					      =						layer.forward_propagation(_xdata						)

            _a      ,						_a					      =						self.cal_loss(_ydata  ,  _xdata						)
            _a					      =						all_loss + loss

            # back propagation: the input_layer does not upgrade
            for layer in self.layers[:0:-1]:
              _a					      =						layer.back_propagation(a_						)

          _a					      =						all_loss / x_shape[0]
          self.train_mse.append(a_						)

          self.plot_loss()

          if mse < self.accuracy:
            print("----达到精度----"						)
            return mse
        return None






      def       lowerCamelCase__     (     self  ,  a_  ,  a_						)						->List[Any]:







        '''simple docstring'''


        _a					      =						np.sum(np.power((ydata - ydata_)  ,  2						)						)
        _a					      =						2 * (ydata_ - ydata)
        # vector (shape is the same as _ydata.shape)
        return self.loss, self.loss_gradient






      def       lowerCamelCase__     (     self						)						->str:







        '''simple docstring'''


        if self.ax_loss.lines:
          self.ax_loss.lines.remove(self.ax_loss.lines[0]						)
        self.ax_loss.plot(self.train_mse  ,  "r-"						)
        plt.ion()
        plt.xlabel("step"						)
        plt.ylabel("loss"						)
        plt.show()
        plt.pause(0.1						)







def 							lowerCAmelCase			(   )							->				Union[str, Any]:







  '''simple docstring'''







  _a					      =						np.random.randn(10  ,			10       )
  _a					      =						np.asarray(
      [
          [0.8, 0.4],
          [0.4, 0.3],
          [0.34, 0.45],
          [0.67, 0.32],
          [0.88, 0.67],
          [0.78, 0.77],
          [0.55, 0.66],
          [0.55, 0.43],
          [0.54, 0.1],
          [0.1, 0.5],
      ]       )
  _a					      =						BPNN()
  for i in (10, 20, 30, 2):
    model.add_layer(DenseLayer(UpperCamelCase_       )       )
  model.build()
  model.summary()
  model.train(xdata=UpperCamelCase_  ,			ydata=UpperCamelCase_  ,			train_round=100  ,			accuracy=0.01       )


if __name__ == "__main__":
     example()










"""simple docstring"""




import argparse
import time

import torch
import transformers
from measures_util import end_measure, log_measures, start_measure
from transformers import AutoConfig, AutoModelForCausalLM, AutoModelForSeqaSeqLM, AutoTokenizer

from accelerate.utils import compute_module_sizes


UpperCamelCase											=     {
    """gpt-j-6b""": {"""is_causal""": True, """model""": """sgugger/sharded-gpt-j-6B""", """tokenizer""": """EleutherAI/gpt-j-6B"""},
    """gpt-neox""": {"""is_causal""": True, """model""": """EleutherAI/gpt-neox-20b"""},
    """opt""": {"""is_causal""": True, """model""": """facebook/opt-30b"""},
    """T0pp""": {"""is_causal""": False, """model""": """bigscience/T0pp""", """model_revision""": """sharded"""},
}

UpperCamelCase											=     [
    """Hello, my name is""",
    """Are unicorns real? Unicorns are""",
    """For the first time in several years,""",
    """My name is Julien and I am""",
    """The goal of life is""",
    """Whenever I'm sad, I like to""",
]






def 							lowerCAmelCase			(   )							->				Optional[int]:







  '''simple docstring'''







  _a					      =						argparse.ArgumentParser(description="Run and time generations on a big model using Accelerate."       )
  parser.add_argument("model_name"  ,			type=UpperCamelCase_  ,			default=UpperCamelCase_  ,			help="The name of the model to try."       )
  parser.add_argument(
      "--tokenizer_name"  ,			type=UpperCamelCase_  ,			default=UpperCamelCase_  ,			help="The name of the tokenizer (if different from the model."       )
  parser.add_argument("--is_causal"  ,			type=UpperCamelCase_  ,			default=UpperCamelCase_  ,			help="Whether or not the model is causal."       )
  parser.add_argument(
      "--model_revision"  ,			type=UpperCamelCase_  ,			default=UpperCamelCase_  ,			help="The revision to use for the model checkpoint."       )
  parser.add_argument("--torch_dtype"  ,			type=UpperCamelCase_  ,			default=UpperCamelCase_  ,			help="The dtype for the model."       )
  parser.add_argument("--disk_offload"  ,			action="store_true"       )

  _a					      =						parser.parse_args()

  # Sanitize args
  if args.model_name in DEFAULT_MODELS:
    _a					      =						DEFAULT_MODELS[args.model_name]
    _a					      =						defaults["model"]
    if args.tokenizer_name is None:
      _a					      =						defaults.get("tokenizer"  ,			args.model_name       )
    if args.is_causal is None:
      _a					      =						defaults["is_causal"]
    if args.model_revision is None:
      _a					      =						defaults.get("model_revision"  ,			"main"       )

  if args.is_causal is None:
    raise ValueError("Could not infer the default for `--is_causal`, pass either True or False for it."       )
  if args.tokenizer_name is None:
    _a					      =						args.model_name
  if args.model_revision is None:
    _a					      =						"main"

  return args






def 							lowerCAmelCase			(   )							->				str:







  '''simple docstring'''







  transformers.utils.logging.set_verbosity_error()
  _a					      =						parse_args()

  if args.torch_dtype is None:
    _a					      =						AutoConfig.from_pretrained(args.model_name       )
    _a					      =						getattr(UpperCamelCase_  ,			"torch_dtype"  ,			torch.floataa       )
  else:
    _a					      =						getattr(UpperCamelCase_  ,			args.torch_dtype       )
  _a					      =						AutoModelForCausalLM if args.is_causal else AutoModelForSeqaSeqLM
  _a					      =						{
      "torch_dtype": torch_dtype,
      "revision": args.model_revision,
  }
  if args.disk_offload:
    _a					      =						"tmp_offload"
    _a					      =						True

  _a					      =						start_measure()
  _a					      =						model_cls.from_pretrained(args.model_name  ,			device_map="auto"  ,			**UpperCamelCase_       )
  _a					      =						end_measure(UpperCamelCase_       )
  log_measures(UpperCamelCase_  ,			"Model loading"       )

  _a					      =						compute_module_sizes(UpperCamelCase_       )
  _a					      =						{v: 0 for v in model.hf_device_map.values()}
  for module, device in model.hf_device_map.items():
    device_size[device] += module_sizes[module]
  _a					      =						"\n".join([f'''- {device}: {size // 2**20}MiB''' for device, size in device_size.items()]       )
  print(f'''\nTheoretical use:\n{message}'''       )

  _a					      =						AutoTokenizer.from_pretrained(args.tokenizer_name       )

  _a					      =						start_measure()
  _a					      =						[]
  _a					      =						[]
  _a					      =						[]
  for prompt in PROMPTS:
    _a					      =						tokenizer(UpperCamelCase_  ,			return_tensors="pt"       ).to(0       )
    _a					      =						inputs["input_ids"][0].tolist()
    _a					      =						time.time()
    _a					      =						model.generate(inputs["input_ids"]       )
    _a					      =						time.time()
    _a					      =						outputs[0].tolist()
    _a					      =						len(UpperCamelCase_       ) if outputs[: len(UpperCamelCase_       )] != tokens else len(UpperCamelCase_       ) - len(UpperCamelCase_       )
    _a					      =						after_generate - before_generate

    _a					      =						tokenizer.decode(UpperCamelCase_  ,			skip_special_tokens=UpperCamelCase_       )
    texts_outs.append(UpperCamelCase_       )
    generation_times.append(UpperCamelCase_       )
    gen_tokens.append(UpperCamelCase_       )
    print(f'''Prompt: {prompt}\nGeneration {text_out}\nIn {generation_time:.2f}s for {num_gen_tokens} tokens\n'''       )

  _a					      =						end_measure(UpperCamelCase_       )
  log_measures(UpperCamelCase_  ,			"Model generation"       )

  _a					      =						[gen / tok for gen, tok in zip(UpperCamelCase_  ,			UpperCamelCase_       )]
  _a					      =						sum(UpperCamelCase_       ) / len(UpperCamelCase_       )
  print(f'''Average time of generation per token: {avg_gen:.2f}s'''       )
  print(f'''First generation (avg time per token): {generation_times_per_token[0]:.2f}s'''       )
  _a					      =						sum(generation_times_per_token[1:]       ) / (len(UpperCamelCase_       ) - 1)
  print(f'''Average time of generation per token (excluding the first): {avg_gen:.2f}s'''       )


if __name__ == "__main__":
     main()


