



"""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.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEncoder,
    BertModel,
    BertPreTrainedModel,
)


lowerCamelCase       	=						logging.getLogger(__name__)
class 				lowercase__       (   SCREAMING_SNAKE_CASE				):







       '''simple docstring'''


       def 					lowercase__    (							self      :  Optional[Any]		,    _UpperCAmelCase      :  int		,    _UpperCAmelCase      :  str		,    _UpperCAmelCase      :  str=None		,    _UpperCAmelCase      :  List[str]=None   )			->						int:


             '''simple docstring'''




             UpperCAmelCase_      			=							self.layer[current_layer](_UpperCAmelCase		,    _UpperCAmelCase		,    head_mask[current_layer]   )

             UpperCAmelCase_      			=							layer_outputs[0]

             return hidden_states



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







       '''simple docstring'''


       def __init__(							self      :  List[str]		,    _UpperCAmelCase      :  Union[str, Any]   )			->						Any:


             '''simple docstring'''




             super().__init__(_UpperCAmelCase   )

             UpperCAmelCase_      			=							BertEncoderWithPabee(_UpperCAmelCase   )

             self.init_weights()
             UpperCAmelCase_      			=							0
             UpperCAmelCase_      			=							0
             UpperCAmelCase_      			=							0

             UpperCAmelCase_      			=							0



       def 					lowercase__    (							self      :  Optional[int]		,    _UpperCAmelCase      :  Dict   )			->						Any:


             '''simple docstring'''




             UpperCAmelCase_      			=							threshold



       def 					lowercase__    (							self      :  List[str]		,    _UpperCAmelCase      :  int   )			->						Any:


             '''simple docstring'''




             UpperCAmelCase_      			=							patience



       def 					lowercase__    (							self      :  Any   )			->						List[Any]:


             '''simple docstring'''




             UpperCAmelCase_      			=							0
             UpperCAmelCase_      			=							0



       def 					lowercase__    (							self      :  List[Any]   )			->						Tuple:


             '''simple docstring'''




             UpperCAmelCase_      			=							self.inference_layers_num / self.inference_instances_num
             UpperCAmelCase_      			=							(
                 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(_UpperCAmelCase   )







       @add_start_docstrings_to_model_forward(_UpperCAmelCase   )
       def 					lowercase__    (							self      :  int		,    _UpperCAmelCase      :  Optional[int]=None		,    _UpperCAmelCase      :  Union[str, Any]=None		,    _UpperCAmelCase      :  Any=None		,    _UpperCAmelCase      :  str=None		,    _UpperCAmelCase      :  Optional[Any]=None		,    _UpperCAmelCase      :  Any=None		,    _UpperCAmelCase      :  Dict=None		,    _UpperCAmelCase      :  str=None		,    _UpperCAmelCase      :  List[str]=None		,    _UpperCAmelCase      :  List[Any]=None		,    _UpperCAmelCase      :  Any=False		,    )			->						Optional[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:
                   UpperCAmelCase_      			=							input_ids.size()
             elif inputs_embeds is not None:
                   UpperCAmelCase_      			=							inputs_embeds.size()[:-1]
             else:
                   raise ValueError("You have to specify either input_ids or inputs_embeds"   )

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

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

             # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
             # ourselves in which case we just need to make it broadcastable to all heads.
             UpperCAmelCase_      			=							self.get_extended_attention_mask(_UpperCAmelCase		,    _UpperCAmelCase		,    _UpperCAmelCase   )

             # If a 2D ou 3D attention mask is provided for the cross-attention
             # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
             if self.config.is_decoder and encoder_hidden_states is not None:
                   UpperCAmelCase_			,   UpperCAmelCase_			,   UpperCAmelCase_      			=							encoder_hidden_states.size()
                   UpperCAmelCase_      			=							(encoder_batch_size, encoder_sequence_length)
                   if encoder_attention_mask is None:
                         UpperCAmelCase_      			=							torch.ones(_UpperCAmelCase		,    device=_UpperCAmelCase   )
                   UpperCAmelCase_      			=							self.invert_attention_mask(_UpperCAmelCase   )
             else:
                   UpperCAmelCase_      			=							None

             # Prepare head mask if needed
             # 1.0 in head_mask indicate we keep the head
             # attention_probs has shape bsz x n_heads x N x N
             # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
             # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
             UpperCAmelCase_      			=							self.get_head_mask(_UpperCAmelCase		,    self.config.num_hidden_layers   )

             UpperCAmelCase_      			=							self.embeddings(
                 input_ids=_UpperCAmelCase		,    position_ids=_UpperCAmelCase		,    token_type_ids=_UpperCAmelCase		,    inputs_embeds=_UpperCAmelCase   )
             UpperCAmelCase_      			=							embedding_output

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

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

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

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

             return res



@add_start_docstrings(
    '''Bert 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. '''    , SCREAMING_SNAKE_CASE    , )
class 				lowercase__       (   SCREAMING_SNAKE_CASE				):







       '''simple docstring'''


       def __init__(							self      :  str		,    _UpperCAmelCase      :  List[Any]   )			->						List[str]:


             '''simple docstring'''




             super().__init__(_UpperCAmelCase   )
             UpperCAmelCase_      			=							config.num_labels

             UpperCAmelCase_      			=							BertModelWithPabee(_UpperCAmelCase   )
             UpperCAmelCase_      			=							nn.Dropout(config.hidden_dropout_prob   )
             UpperCAmelCase_      			=							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(_UpperCAmelCase   )
       def 					lowercase__    (							self      :  List[str]		,    _UpperCAmelCase      :  Tuple=None		,    _UpperCAmelCase      :  Optional[Any]=None		,    _UpperCAmelCase      :  str=None		,    _UpperCAmelCase      :  Dict=None		,    _UpperCAmelCase      :  Tuple=None		,    _UpperCAmelCase      :  List[str]=None		,    _UpperCAmelCase      :  str=None		,    )			->						int:


             '''simple docstring'''




             UpperCAmelCase_      			=							self.bert(
                 input_ids=_UpperCAmelCase		,    attention_mask=_UpperCAmelCase		,    token_type_ids=_UpperCAmelCase		,    position_ids=_UpperCAmelCase		,    head_mask=_UpperCAmelCase		,    inputs_embeds=_UpperCAmelCase		,    output_dropout=self.dropout		,    output_layers=self.classifiers		,    regression=self.num_labels == 1		,    )

             UpperCAmelCase_      			=							(logits[-1],)

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

             return outputs









"""simple docstring"""





import argparse
import glob
import importlib.util
import os
import re

import black
from doc_builder.style_doc import style_docstrings_in_code


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
lowerCamelCase       	=						"""src/diffusers"""
lowerCamelCase       	=						"""."""


# This is to make sure the diffusers module imported is the one in the repo.
lowerCamelCase       	=						importlib.util.spec_from_file_location(
    """diffusers""",
    os.path.join(DIFFUSERS_PATH, """__init__.py"""),
    submodule_search_locations=[DIFFUSERS_PATH],
)
lowerCamelCase       	=						spec.loader.load_module()
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__				):
      return line.startswith(lowerCAmelCase__				) or len(lowerCAmelCase__				) <= 1 or re.search(r"^\s*\)(\s*->.*:|:)\s*$"     ,      lowerCAmelCase__				) is not None
def        a__     (				lowerCAmelCase__				):
      UpperCAmelCase_      			=							object_name.split("."				)
      UpperCAmelCase_      			=							0

      # First let's find the module where our object lives.
      UpperCAmelCase_      			=							parts[i]
      while i < len(lowerCAmelCase__				) and not os.path.isfile(os.path.join(lowerCAmelCase__     ,      f"""{module}.py"""				)				):
            i += 1
            if i < len(lowerCAmelCase__				):
                  UpperCAmelCase_      			=							os.path.join(lowerCAmelCase__     ,      parts[i]				)
      if i >= len(lowerCAmelCase__				):
            raise ValueError(f"""`object_name` should begin with the name of a module of diffusers but got {object_name}."""				)

      with open(os.path.join(lowerCAmelCase__     ,      f"""{module}.py"""				)     ,      "r"     ,      encoding="utf-8"     ,      newline="\n"				) as f:
            UpperCAmelCase_      			=							f.readlines()

      # Now let's find the class / func in the code!
      UpperCAmelCase_      			=							""
      UpperCAmelCase_      			=							0
      for name in parts[i + 1 :]:
            while (
                line_index < len(lowerCAmelCase__				) and re.search(rf"""^{indent}(class|def)\s+{name}(\(|\:)"""     ,      lines[line_index]				) is None
            ):
                  line_index += 1
            indent += "    "
            line_index += 1

      if line_index >= len(lowerCAmelCase__				):
            raise ValueError(f""" {object_name} does not match any function or class in {module}."""				)

      # We found the beginning of the class / func, now let's find the end (when the indent diminishes).
      UpperCAmelCase_      			=							line_index
      while line_index < len(lowerCAmelCase__				) and _should_continue(lines[line_index]     ,      lowerCAmelCase__				):
            line_index += 1
      # Clean up empty lines at the end (if any).
      while len(lines[line_index - 1]				) <= 1:
            line_index -= 1

      UpperCAmelCase_      			=							lines[start_index:line_index]
      return "".join(lowerCAmelCase__				)


lowerCamelCase       	=						re.compile(r"""^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)""")
lowerCamelCase       	=						re.compile(r"""^\s*(\S+)->(\S+)(\s+.*|$)""")
lowerCamelCase       	=						re.compile(r"""<FILL\s+[^>]*>""")
def        a__     (				lowerCAmelCase__				):
      UpperCAmelCase_      			=							code.split("\n"				)
      UpperCAmelCase_      			=							0
      while idx < len(lowerCAmelCase__				) and len(lines[idx]				) == 0:
            idx += 1
      if idx < len(lowerCAmelCase__				):
            return re.search(r"^(\s*)\S"     ,      lines[idx]				).groups()[0]
      return ""
def        a__     (				lowerCAmelCase__				):
      UpperCAmelCase_      			=							len(get_indent(lowerCAmelCase__				)				) > 0
      if has_indent:
            UpperCAmelCase_      			=							f"""class Bla:\n{code}"""
      UpperCAmelCase_      			=							black.Mode(target_versions={black.TargetVersion.PYaa}     ,      line_length=119     ,      preview=lowerCAmelCase__				)
      UpperCAmelCase_      			=							black.format_str(lowerCAmelCase__     ,      mode=lowerCAmelCase__				)
      UpperCAmelCase_			,   UpperCAmelCase_      			=							style_docstrings_in_code(lowerCAmelCase__				)
      return result[len("class Bla:\n"				) :] if has_indent else result
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__=False				):
      with open(lowerCAmelCase__     ,      "r"     ,      encoding="utf-8"     ,      newline="\n"				) as f:
            UpperCAmelCase_      			=							f.readlines()
      UpperCAmelCase_      			=							[]
      UpperCAmelCase_      			=							0
      # Not a for loop cause `lines` is going to change (if `overwrite=True`).
      while line_index < len(lowerCAmelCase__				):
            UpperCAmelCase_      			=							_re_copy_warning.search(lines[line_index]				)
            if search is None:
                  line_index += 1
                  continue

            # There is some copied code here, let's retrieve the original.
            UpperCAmelCase_			,   UpperCAmelCase_			,   UpperCAmelCase_      			=							search.groups()
            UpperCAmelCase_      			=							find_code_in_diffusers(lowerCAmelCase__				)
            UpperCAmelCase_      			=							get_indent(lowerCAmelCase__				)

            UpperCAmelCase_      			=							line_index + 1 if indent == theoretical_indent else line_index + 2
            UpperCAmelCase_      			=							theoretical_indent
            UpperCAmelCase_      			=							start_index

            # Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
            UpperCAmelCase_      			=							True
            while line_index < len(lowerCAmelCase__				) and should_continue:
                  line_index += 1
                  if line_index >= len(lowerCAmelCase__				):
                        break
                  UpperCAmelCase_      			=							lines[line_index]
                  UpperCAmelCase_      			=							_should_continue(lowerCAmelCase__     ,      lowerCAmelCase__				) and re.search(f"""^{indent}# End copy"""     ,      lowerCAmelCase__				) is None
            # Clean up empty lines at the end (if any).
            while len(lines[line_index - 1]				) <= 1:
                  line_index -= 1

            UpperCAmelCase_      			=							lines[start_index:line_index]
            UpperCAmelCase_      			=							"".join(lowerCAmelCase__				)

            # Remove any nested `Copied from` comments to avoid circular copies
            UpperCAmelCase_      			=							[line for line in theoretical_code.split("\n"				) if _re_copy_warning.search(lowerCAmelCase__				) is None]
            UpperCAmelCase_      			=							"\n".join(lowerCAmelCase__				)

            # Before comparing, use the `replace_pattern` on the original code.
            if len(lowerCAmelCase__				) > 0:
                  UpperCAmelCase_      			=							replace_pattern.replace("with"     ,      ""				).split(","				)
                  UpperCAmelCase_      			=							[_re_replace_pattern.search(lowerCAmelCase__				) for p in patterns]
                  for pattern in patterns:
                        if pattern is None:
                              continue
                        UpperCAmelCase_			,   UpperCAmelCase_			,   UpperCAmelCase_      			=							pattern.groups()
                        UpperCAmelCase_      			=							re.sub(lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__				)
                        if option.strip() == "all-casing":
                              UpperCAmelCase_      			=							re.sub(obja.lower()     ,      obja.lower()     ,      lowerCAmelCase__				)
                              UpperCAmelCase_      			=							re.sub(obja.upper()     ,      obja.upper()     ,      lowerCAmelCase__				)

            # Blackify after replacement. To be able to do that, we need the header (class or function definition)
            # from the previous line
                  UpperCAmelCase_      			=							blackify(lines[start_index - 1] + theoretical_code				)
                  UpperCAmelCase_      			=							theoretical_code[len(lines[start_index - 1]				) :]

            # Test for a diff and act accordingly.
            if observed_code != theoretical_code:
                  diffs.append([object_name, start_index]				)
                  if overwrite:
                        UpperCAmelCase_      			=							lines[:start_index] + [theoretical_code] + lines[line_index:]
                        UpperCAmelCase_      			=							start_index + 1

      if overwrite and len(lowerCAmelCase__				) > 0:
            # Warn the user a file has been modified.
            print(f"""Detected changes, rewriting {filename}."""				)
            with open(lowerCAmelCase__     ,      "w"     ,      encoding="utf-8"     ,      newline="\n"				) as f:
                  f.writelines(lowerCAmelCase__				)
      return diffs
def        a__     (				lowerCAmelCase__ = False				):
      UpperCAmelCase_      			=							glob.glob(os.path.join(lowerCAmelCase__     ,      "**/*.py"				)     ,      recursive=lowerCAmelCase__				)
      UpperCAmelCase_      			=							[]
      for filename in all_files:
            UpperCAmelCase_      			=							is_copy_consistent(lowerCAmelCase__     ,      lowerCAmelCase__				)
            diffs += [f"""- {filename}: copy does not match {d[0]} at line {d[1]}""" for d in new_diffs]
      if not overwrite and len(lowerCAmelCase__				) > 0:
            UpperCAmelCase_      			=							"\n".join(lowerCAmelCase__				)
            raise Exception(
                "Found the following copy inconsistencies:\n"
                + diff
                + "\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them."				)


if __name__ == "__main__":
  lowerCamelCase       	=						argparse.ArgumentParser()
  parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
  lowerCamelCase       	=						parser.parse_args()

  check_copies(args.fix_and_overwrite)




