
"""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,
)
SCREAMING_SNAKE_CASE_  					= logging.getLogger(__name__)
def       __UpperCAmelCase		(		a_      :		List[str]					)  ->							Optional[Any]:
   np.random.seed(args.seed					)
   torch.manual_seed(args.seed					)
   if args.n_gpu > 0:
      torch.cuda.manual_seed_all(args.seed					)
def       __UpperCAmelCase		(		)  ->							Optional[int]:
   _UpperCAmelCase 						=       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"     ,	)
   _UpperCAmelCase 						=       parser.parse_args()

   _UpperCAmelCase 						=       torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"					)
   _UpperCAmelCase 						=       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
   _UpperCAmelCase 						=       AutoTokenizer.from_pretrained(args.model_name_or_path					)
   _UpperCAmelCase 						=       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_					)
   _UpperCAmelCase 						=       args.prompt if args.prompt else input("Model prompt >>> "					)

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

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

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

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

      # Remove all text after the stop token
      _UpperCAmelCase 						=       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
      _UpperCAmelCase 						=       (
          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


