






"""simple docstring"""




import argparse
import logging

import numpy as np
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""",
    datefmt="""%m/%d/%Y %H:%M:%S""",
    level=logging.INFO,
)
UpperCamelCase											=     logging.getLogger(__name__)






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







  '''simple docstring'''







  np.random.seed(args.seed       )
  torch.manual_seed(args.seed       )
  if args.n_gpu > 0:
    torch.cuda.manual_seed_all(args.seed       )






def 							lowerCAmelCase			(   )							->				Optional[int]:







  '''simple docstring'''







  _a					      =						argparse.ArgumentParser()
  parser.add_argument(
      "--model_name_or_path"  ,			default=UpperCamelCase_  ,			type=UpperCamelCase_  ,			required=UpperCamelCase_  ,			)
  parser.add_argument("--prompt"  ,			type=UpperCamelCase_  ,			default=""       )
  parser.add_argument("--length"  ,			type=UpperCamelCase_  ,			default=20       )
  parser.add_argument("--stop_token"  ,			type=UpperCamelCase_  ,			default=UpperCamelCase_  ,			help="Token at which text generation is stopped"       )
  parser.add_argument(
      "--temperature"  ,			type=UpperCamelCase_  ,			default=1.0  ,			help="temperature of 1.0 has no effect, lower tend toward greedy sampling"  ,			)
  parser.add_argument(
      "--repetition_penalty"  ,			type=UpperCamelCase_  ,			default=1.0  ,			help="primarily useful for CTRL model; in that case, use 1.2"       )
  parser.add_argument("--k"  ,			type=UpperCamelCase_  ,			default=0       )
  parser.add_argument("--penalty_alpha"  ,			type=UpperCamelCase_  ,			default=0.0       )
  parser.add_argument("--p"  ,			type=UpperCamelCase_  ,			default=0.9       )

  parser.add_argument("--prefix"  ,			type=UpperCamelCase_  ,			default=""  ,			help="Text added prior to input."       )
  parser.add_argument("--padding_text"  ,			type=UpperCamelCase_  ,			default=""  ,			help="Deprecated, the use of `--prefix` is preferred."       )
  parser.add_argument("--xlm_language"  ,			type=UpperCamelCase_  ,			default=""  ,			help="Optional language when used with the XLM model."       )

  parser.add_argument("--seed"  ,			type=UpperCamelCase_  ,			default=42  ,			help="random seed for initialization"       )
  parser.add_argument("--no_cuda"  ,			action="store_true"  ,			help="Avoid using CUDA when available"       )
  parser.add_argument(
      "--fp16"  ,			action="store_true"  ,			help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"  ,			)
  _a					      =						parser.parse_args()

  _a					      =						torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"       )
  _a					      =						0 if args.no_cuda else torch.cuda.device_count()

  logger.warning(f'''device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}'''       )

  set_seed(UpperCamelCase_       )

  # Initialize the model and tokenizer
  _a					      =						AutoTokenizer.from_pretrained(args.model_name_or_path       )
  _a					      =						AutoModelForCausalLM.from_pretrained(args.model_name_or_path       )

  # tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
  # model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
  model.to(args.device       )

  if args.fpaa:
    model.half()

  logger.info(UpperCamelCase_       )
  _a					      =						args.prompt if args.prompt else input("Model prompt >>> "       )

  _a					      =						tokenizer(UpperCamelCase_  ,			return_tensors="pt"  ,			add_special_tokens=UpperCamelCase_       )
  _a					      =						{key: value.to(args.device       ) for key, value in inputs.items()}

  _a					      =						model.generate(
      **UpperCamelCase_  ,			max_length=args.length + len(inputs["input_ids"][0]       )  ,			penalty_alpha=args.penalty_alpha  ,			top_k=args.k  ,			)

  _a					      =						[]
  for generated_sequence_idx, generated_sequence in enumerate(UpperCamelCase_       ):
    print(f'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='''       )
    _a					      =						generated_sequence.tolist()

    # Decode text
    _a					      =						tokenizer.decode(UpperCamelCase_  ,			clean_up_tokenization_spaces=UpperCamelCase_  ,			add_special_tokens=UpperCamelCase_       )

    # Remove all text after the stop token
    _a					      =						text[: text.find(args.stop_token       ) if args.stop_token else None]

    # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
    _a					      =						(
        prompt_text + text[len(tokenizer.decode(inputs["input_ids"][0]  ,			clean_up_tokenization_spaces=UpperCamelCase_       )       ) :]
    )

    generated_sequences.append(UpperCamelCase_       )
    print(UpperCamelCase_       )

  return generated_sequences


if __name__ == "__main__":
     main()










"""simple docstring"""




import logging

import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.albert.modeling_albert import (
    ALBERT_INPUTS_DOCSTRING,
    ALBERT_START_DOCSTRING,
    AlbertModel,
    AlbertPreTrainedModel,
    AlbertTransformer,
)


UpperCamelCase											=     logging.getLogger(__name__)




class  lowercase_     (_UpperCAmelCase  ):






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







        '''simple docstring'''


        if current_layer == 0:
          _a					      =						self.embedding_hidden_mapping_in(a_						)
        else:
          _a					      =						hidden_states[0]

        _a					      =						int(self.config.num_hidden_layers / self.config.num_hidden_groups						)

        # Index of the hidden group
        _a					      =						int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups)						)

        _a					      =						self.albert_layer_groups[group_idx](
            a_  ,  a_  ,  head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]  ,  )
        _a					      =						layer_group_output[0]

        return (hidden_states,)







@add_start_docstrings(
    '''The bare ALBERT Model transformer with PABEE outputting raw hidden-states without any specific head on top.''',     _UpperCAmelCase,     )
class  lowercase_     (_UpperCAmelCase  ):






      def __init__(     self  ,  a_						)						->Dict:







        '''simple docstring'''


        super().__init__(a_						)

        _a					      =						AlbertTransformerWithPabee(a_						)

        self.init_weights()
        _a					      =						0
        _a					      =						0
        _a					      =						0

        _a					      =						0






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







        '''simple docstring'''


        _a					      =						threshold






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







        '''simple docstring'''


        _a					      =						patience






      def       lowerCamelCase__     (     self						)						->int:







        '''simple docstring'''


        _a					      =						0
        _a					      =						0






      def       lowerCamelCase__     (     self						)						->Tuple:







        '''simple docstring'''


        _a					      =						self.inference_layers_num / self.inference_instances_num
        _a					      =						(
            f'''*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ='''
            f''' {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***'''
        )
        print(a_						)






      @add_start_docstrings_to_model_forward(a_						)
      def       lowerCamelCase__     (     self  ,  a_=None  ,  a_=None  ,  a_=None  ,  a_=None  ,  a_=None  ,  a_=None  ,  a_=None  ,  a_=None  ,  a_=False  ,  )						->int:







        '''simple docstring'''


        if input_ids is not None and inputs_embeds is not None:
          raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time"						)
        elif input_ids is not None:
          _a					      =						input_ids.size()
        elif inputs_embeds is not None:
          _a					      =						inputs_embeds.size()[:-1]
        else:
          raise ValueError("You have to specify either input_ids or inputs_embeds"						)

        _a					      =						input_ids.device if input_ids is not None else inputs_embeds.device

        if attention_mask is None:
          _a					      =						torch.ones(a_  ,  device=a_						)
        if token_type_ids is None:
          _a					      =						torch.zeros(a_  ,  dtype=torch.long  ,  device=a_						)

        _a					      =						attention_mask.unsqueeze(1						).unsqueeze(2						)
        _a					      =						extended_attention_mask.to(dtype=self.dtype						)  # fp16 compatibility
        _a					      =						(1.0 - extended_attention_mask) * -10_000.0
        _a					      =						self.get_head_mask(a_  ,  self.config.num_hidden_layers						)

        _a					      =						self.embeddings(
            a_  ,  position_ids=a_  ,  token_type_ids=a_  ,  inputs_embeds=a_						)
        _a					      =						embedding_output

        if self.training:
          _a					      =						[]
          for i in range(self.config.num_hidden_layers						):
            _a					      =						self.encoder.adaptive_forward(
                a_  ,  current_layer=a_  ,  attention_mask=a_  ,  head_mask=a_  ,  )

            _a					      =						self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]						)						)
            _a					      =						output_layers[i](output_dropout(a_						)						)
            res.append(a_						)
        elif self.patience == 0:  # Use all layers for inference
          _a					      =						self.encoder(a_  ,  a_  ,  head_mask=a_						)
          _a					      =						self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]						)						)
          _a					      =						[output_layers[self.config.num_hidden_layers - 1](a_						)]
        else:
          _a					      =						0
          _a					      =						None
          _a					      =						0
          for i in range(self.config.num_hidden_layers						):
            calculated_layer_num += 1
            _a					      =						self.encoder.adaptive_forward(
                a_  ,  current_layer=a_  ,  attention_mask=a_  ,  head_mask=a_  ,  )

            _a					      =						self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]						)						)
            _a					      =						output_layers[i](a_						)
            if regression:
              _a					      =						logits.detach()
              if patient_result is not None:
                _a					      =						patient_result.detach()
              if (patient_result is not None) and torch.abs(patient_result - labels						) < self.regression_threshold:
                patient_counter += 1
              else:
                _a					      =						0
            else:
              _a					      =						logits.detach().argmax(dim=1						)
              if patient_result is not None:
                _a					      =						patient_result.detach().argmax(dim=1						)
              if (patient_result is not None) and torch.all(labels.eq(a_						)						):
                patient_counter += 1
              else:
                _a					      =						0

            _a					      =						logits
            if patient_counter == self.patience:
              break
          _a					      =						[patient_result]
          self.inference_layers_num += calculated_layer_num
          self.inference_instances_num += 1

        return res







@add_start_docstrings(
    '''Albert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of
    the pooled output) e.g. for GLUE tasks. ''',     _UpperCAmelCase,     )
class  lowercase_     (_UpperCAmelCase  ):






      def __init__(     self  ,  a_						)						->Dict:







        '''simple docstring'''


        super().__init__(a_						)
        _a					      =						config.num_labels

        _a					      =						AlbertModelWithPabee(a_						)
        _a					      =						nn.Dropout(config.classifier_dropout_prob						)
        _a					      =						nn.ModuleList(
            [nn.Linear(config.hidden_size  ,  self.config.num_labels						) for _ in range(config.num_hidden_layers						)]						)

        self.init_weights()






      @add_start_docstrings_to_model_forward(a_						)
      def       lowerCamelCase__     (     self  ,  a_=None  ,  a_=None  ,  a_=None  ,  a_=None  ,  a_=None  ,  a_=None  ,  a_=None  ,  )						->List[Any]:







        '''simple docstring'''


        _a					      =						self.albert(
            input_ids=a_  ,  attention_mask=a_  ,  token_type_ids=a_  ,  position_ids=a_  ,  head_mask=a_  ,  inputs_embeds=a_  ,  output_dropout=self.dropout  ,  output_layers=self.classifiers  ,  regression=self.num_labels == 1  ,  )

        _a					      =						(logits[-1],)

        if labels is not None:
          _a					      =						None
          _a					      =						0
          for ix, logits_item in enumerate(a_						):
            if self.num_labels == 1:
              #  We are doing regression
              _a					      =						MSELoss()
              _a					      =						loss_fct(logits_item.view(-1						)  ,  labels.view(-1						)						)
            else:
              _a					      =						CrossEntropyLoss()
              _a					      =						loss_fct(logits_item.view(-1  ,  self.num_labels						)  ,  labels.view(-1						)						)
            if total_loss is None:
              _a					      =						loss
            else:
              total_loss += loss * (ix + 1)
            total_weights += ix + 1
          _a					      =						(total_loss / total_weights,) + outputs

        return outputs


