
"""simple docstring"""
from collections import Counter
import datasets
import transformers
from transformers.convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS

from transformers.utils import logging

logging.set_verbosity_info()

_a   :      Union[str, Any]       =				{
    name: (getattr(transformers, name), getattr(transformers, name + """Fast""")) for name in SLOW_TO_FAST_CONVERTERS
}

_a   :      Any       =				datasets.load_dataset("""xnli""", split="""test+validation""")

_a   :      Any       =				0
_a   :      int       =				0
_a   :      Optional[Any]       =				0
_a   :      Dict       =				0
def 					a__ ( a :					Dict							,  a :					Tuple							,  a :					Optional[Any]							,  a :					Optional[int]		):

  """simple docstring"""







  if spm_diff == list(reversed(a		)		):
    # AAA -> AA+A vs A+AA case.
    return True
  elif len(a		) == len(a		) and fast.decode(a		) == fast.decode(a		):
    # Second order OK
    # Barrich -> Barr + ich vs Bar + rich
    return True
  _snake_case  :    Any            = slow.encode(slow.decode(a		)		)
  _snake_case  :    Union[str, Any]            = fast.encode(fast.decode(a		)		)
  if spm_reencoded != spm_diff and spm_reencoded == tok_reencoded:
    # Type 3 error.
    # Snehagatha ->
    #       Sne, h, aga, th, a
    #       Sne, ha, gat, ha
    # Encoding the wrong with sp does not even recover what spm gave us
    # It fits tokenizer however...
    return True
  return False
def 					a__ ( a :					str							,  a :					str							,  a :					Dict		):

  """simple docstring"""







  _snake_case  :    Optional[int]            = fast.encode_plus(a		)[0]
  _snake_case  :    List[Any]            = enc.offsets
  _snake_case      ,       _snake_case  :    Dict            = offsets[idx], offsets[idx - 1]
  if curr is not None and line[curr[0] : curr[1]] == "\u200f":
    return True
  if prev is not None and line[prev[0] : prev[1]] == "\u200f":
    return True
def 					a__ ( a :					Optional[Any]							,  a :					List[str]							,  a :					List[Any]							,  a :					Optional[Any]							,  a :					Any		):

  """simple docstring"""







  for i, (spm_id, tok_id) in enumerate(zip(a							,  a		)		):
    if spm_id != tok_id:
      break
  _snake_case  :    Any            = i
  for i, (spm_id, tok_id) in enumerate(zip(reversed(a		)							,  reversed(a		)		)		):
    if spm_id != tok_id:
      break
  _snake_case  :    Dict            = len(a		) - i

  _snake_case  :    int            = spm_ids[first:last]
  _snake_case  :    Union[str, Any]            = tok_ids[first:last]

  if check_diff(a							,  a							,  a							,  a		):
    return True

  if check_LTR_mark(a							,  a							,  a		):
    return True

  if last - first > 5:
    # We might have twice a single problem, attempt to subdivide the disjointed tokens into smaller problems
    _snake_case  :    str            = Counter(spm_ids[first:last]		)
    _snake_case  :    List[Any]            = Counter(tok_ids[first:last]		)

    _snake_case  :    Optional[Any]            = {spm_ for (spm_, si) in spms.items() if toks.get(spm_							,  0		) == si}
    _snake_case  :    str            = 3
    for i in range(last - first - min_width		):
      if all(spm_ids[first + i + j] in removable_tokens for j in range(a		)		):
        _snake_case  :    Dict            = [
            k
            for k in range(last - first - min_width		)
            if tok_ids[first + k : first + k + min_width] == spm_ids[first + i : first + i + min_width]
        ]
        for j in possible_matches:
          if check_diff(spm_ids[first : first + i]							,  tok_ids[first : first + j]							,  a							,  a		) and check_details(
              a							,  spm_ids[first + i : last]							,  tok_ids[first + j : last]							,  a							,  a							,  ):
            return True

  print(f'Spm: {[fast.decode([spm_ids[i]]		) for i in range(a							,  a		)]}'		)
  try:
    print(f'Tok: {[fast.decode([tok_ids[i]]		) for i in range(a							,  a		)]}'		)
  except Exception:
    pass

  _snake_case  :    str            = fast.decode(spm_ids[:first]		)
  _snake_case  :    Optional[Any]            = fast.decode(spm_ids[last:]		)
  _snake_case  :    Optional[Any]            = fast.decode(spm_ids[first:last]		)
  print()
  print(a		)
  return False
def 					a__ ( a :					str							,  a :					Optional[int]							,  a :					Optional[int]		):

  """simple docstring"""







  global perfect
  global imperfect
  global wrong
  global total

  _snake_case  :    List[str]            = slow.encode(a		)
  _snake_case  :    List[str]            = fast.encode(a		)

  _snake_case  :    List[str]            = False
  total += 1

  if slow_ids != fast_ids:
    if check_details(a							,  a							,  a							,  a							,  a		):
      _snake_case  :    Optional[int]            = True
      imperfect += 1
    else:
      wrong += 1
  else:
    perfect += 1

  if total % 10_000 == 0:
    print(f'({perfect} / {imperfect} / {wrong} ----- {perfect + imperfect + wrong})'		)

  if skip_assert:
    return

  assert (
      slow_ids == fast_ids
  ), f'line {text} : \n\n{slow_ids}\n{fast_ids}\n\n{slow.tokenize(a		)}\n{fast.tokenize(a		)}'
def 					a__ ( a :					str							,  a :					Any		):

  """simple docstring"""







  global batch_total
  for i in range(len(a		)		):
    # premise, all languages
    for text in dataset[i]["premise"].values():
      test_string(a							,  a							,  a		)

    # hypothesis, all languages
    for text in dataset[i]["hypothesis"]["translation"]:
      test_string(a							,  a							,  a		)


if __name__ == "__main__":
    for name, (slow_class, fast_class) in TOKENIZER_CLASSES.items():
        _a   :      str       =				list(slow_class.max_model_input_sizes.keys())
        for checkpoint in checkpoint_names:
            _a   :      Optional[Any]       =				0
            _a   :      Tuple       =				0
            _a   :      Dict       =				0
            _a   :      List[str]       =				0

            print(f'========================== Checking {name}: {checkpoint} ==========================')
            _a   :      Union[str, Any]       =				slow_class.from_pretrained(checkpoint, force_download=True)
            _a   :      int       =				fast_class.from_pretrained(checkpoint, force_download=True)
            test_tokenizer(slow, fast)
            print(f'Accuracy {perfect * 100 / total:.2f}')









"""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


