




'''simple docstring'''



import argparse
import glob
import inspect
import os
import re
import unittest

from get_test_info import get_test_classes

from tests.test_pipeline_mixin import pipeline_test_mapping


lowerCamelCase_	 =		{}
for task, _ in pipeline_test_mapping.items():
 lowerCamelCase_	 =		{'pt': None, 'tf': None}


# DO **NOT** add item to this set (unless the reason is approved)
lowerCamelCase_	 =		{
    'tests/models/esm/test_modeling_esmfold.py',  # The pipeline test mapping is added to `test_modeling_esm.py`
}
def 				SCREAMING_SNAKE_CASE_				(							__A       :				Dict						)				->				List[str]:

   if "ModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
      return "pt"
   elif "TFModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
      return "tf"
   elif "FlaxModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
      return "flax"
   else:
      return None
def 				SCREAMING_SNAKE_CASE_				(							__A       :				Optional[Any] ,  __A       :				List[Any]						)				->				str:
   # Use the cached results
   if PIPELINE_TEST_MAPPING[task].get(__A ,  __A						) is not None:
      return PIPELINE_TEST_MAPPING[task][framework]

   _SCREAMING_SNAKE_CASE =   pipeline_test_mapping[task]["test"]
   _SCREAMING_SNAKE_CASE =   None

   if framework == "pt":
      _SCREAMING_SNAKE_CASE =   getattr(__A ,  "model_mapping" ,  __A						)
   elif framework == "tf":
      _SCREAMING_SNAKE_CASE =   getattr(__A ,  "tf_model_mapping" ,  __A						)

   if mapping is not None:
      _SCREAMING_SNAKE_CASE =   dict(mapping.items()						)

   # cache the results
   _SCREAMING_SNAKE_CASE =   mapping
   return mapping
def 				SCREAMING_SNAKE_CASE_				(							__A       :				List[Any] ,  __A       :				str						)				->				Tuple:
   _SCREAMING_SNAKE_CASE =   get_framework(__A						)
   if framework is None:
      return None
   _SCREAMING_SNAKE_CASE =   get_mapping_for_task(__A ,  __A						)
   if mapping is None:
      return None

   _SCREAMING_SNAKE_CASE =   list({model_class.config_class for model_class in test_class.all_model_classes}						)
   if len(__A						) != 1:
      raise ValueError("There should be exactly one configuration class from `test_class.all_model_classes`."						)

   # This could be a list/tuple of model classes, but it's rare.
   _SCREAMING_SNAKE_CASE =   mapping.get(config_classes[0] ,  __A						)
   if isinstance(__A ,  (tuple, list)						):
      _SCREAMING_SNAKE_CASE =   sorted(__A ,  key=lambda __A						: x.__name__						)

   return model_class
def 				SCREAMING_SNAKE_CASE_				(							__A       :				List[Any]						)				->				str:
   _SCREAMING_SNAKE_CASE =   [(task, get_model_for_pipeline_test(__A ,  __A						)) for task in pipeline_test_mapping]
   _SCREAMING_SNAKE_CASE =   sorted([(task, model) for task, model in mapping if model is not None] ,  key=lambda __A						: x[0]						)

   return dict(__A						)
def 				SCREAMING_SNAKE_CASE_				(							__A       :				Tuple						)				->				Optional[Any]:
   _SCREAMING_SNAKE_CASE =   get_framework(__A						)
   if framework == "pt":
      _SCREAMING_SNAKE_CASE =   "torch"
   _SCREAMING_SNAKE_CASE =   "{}"

   _SCREAMING_SNAKE_CASE =   get_pipeline_model_mapping(__A						)
   if len(__A						) == 0:
      return ""

   _SCREAMING_SNAKE_CASE =   []
   for task, model_classes in mapping.items():
      if isinstance(__A ,  (tuple, list)						):
         # A list/tuple of model classes
         _SCREAMING_SNAKE_CASE =   "(" + ", ".join([x.__name__ for x in model_classes]						) + ")"
      else:
         # A single model class
         _SCREAMING_SNAKE_CASE =   model_classes.__name__
      texts.append(f"""\"{task}\": {value}"""						)
   _SCREAMING_SNAKE_CASE =   "{" + ", ".join(__A						) + "}"
   _SCREAMING_SNAKE_CASE =   f"""pipeline_model_mapping = {text} if is_{framework}_available() else {default_value}"""

   return text
def 				SCREAMING_SNAKE_CASE_				(							__A       :				int						)				->				str:
   _SCREAMING_SNAKE_CASE =   {"ModelTesterMixin", "TFModelTesterMixin", "FlaxModelTesterMixin"}
   if not issubclass(__A ,  unittest.TestCase						):
      return False
   return len(base_class_names.intersection([x.__name__ for x in test_class.__bases__]						)						) > 0
def 				SCREAMING_SNAKE_CASE_				(							__A       :				List[str]						)				->				List[Any]:
   _SCREAMING_SNAKE_CASE =   [x for x in get_test_classes(__A						) if is_valid_test_class(__A						)]

   _SCREAMING_SNAKE_CASE =   None
   for test_class in test_classes:
      # If a test class has defined `pipeline_model_mapping`, let's take it
      if getattr(__A ,  "pipeline_model_mapping" ,  __A						) is not None:
         _SCREAMING_SNAKE_CASE =   test_class
         break
    # Take the test class with the shortest name (just a heuristic)
   if target_test_class is None and len(__A						) > 0:
      _SCREAMING_SNAKE_CASE =   sorted(__A ,  key=lambda __A						: (len(x.__name__						), x.__name__)						)[0]

   return target_test_class
def 				SCREAMING_SNAKE_CASE_				(							__A       :				int ,  __A       :				Optional[Any] ,  __A       :				Dict						)				->				Any:
   _SCREAMING_SNAKE_CASE =   start_idx
   for idx, line in enumerate(lines[start_idx:]						):
      _SCREAMING_SNAKE_CASE =   len(__A						) - len(line.lstrip()						)
      if idx == 0 or indent > indent_level or (indent == indent_level and line.strip() == ")"):
         _SCREAMING_SNAKE_CASE =   start_idx + idx
      elif idx > 0 and indent <= indent_level:
         # Outside the definition block of `pipeline_model_mapping`
         break

   return end_idx
def 				SCREAMING_SNAKE_CASE_				(							__A       :				Tuple ,  __A       :				List[str]=False						)				->				Any:
   if getattr(__A ,  "pipeline_model_mapping" ,  __A						) is not None:
      if not overwrite:
         return "", -1

   _SCREAMING_SNAKE_CASE =   get_pipeline_model_mapping_string(__A						)
   if len(__A						) == 0:
      return "", -1
   _SCREAMING_SNAKE_CASE =   line_to_add + "\n"

   # The code defined the class `test_class`
   _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   inspect.getsourcelines(__A						)
   # `inspect` gives the code for an object, including decorator(s) if any.
   # We (only) need the exact line of the class definition.
   for idx, line in enumerate(__A						):
      if line.lstrip().startswith("class "						):
         _SCREAMING_SNAKE_CASE =   class_lines[idx:]
         class_start_line_no += idx
         break
   _SCREAMING_SNAKE_CASE =   class_start_line_no + len(__A						) - 1

   # The index in `class_lines` that starts the definition of `all_model_classes`, `all_generative_model_classes` or
   # `pipeline_model_mapping`. This assumes they are defined in such order, and we take the start index of the last
   # block that appears in a `test_class`.
   _SCREAMING_SNAKE_CASE =   None
   # The indent level of the line at `class_lines[start_idx]` (if defined)
   _SCREAMING_SNAKE_CASE =   0
   # To record if `pipeline_model_mapping` is found in `test_class`.
   _SCREAMING_SNAKE_CASE =   None
   for idx, line in enumerate(__A						):
      if line.strip().startswith("all_model_classes = "						):
         _SCREAMING_SNAKE_CASE =   len(__A						) - len(line.lstrip()						)
         _SCREAMING_SNAKE_CASE =   idx
      elif line.strip().startswith("all_generative_model_classes = "						):
         _SCREAMING_SNAKE_CASE =   len(__A						) - len(line.lstrip()						)
         _SCREAMING_SNAKE_CASE =   idx
      elif line.strip().startswith("pipeline_model_mapping = "						):
         _SCREAMING_SNAKE_CASE =   len(__A						) - len(line.lstrip()						)
         _SCREAMING_SNAKE_CASE =   idx
         _SCREAMING_SNAKE_CASE =   line
         break

   if start_idx is None:
      return "", -1
   # Find the ending index (inclusive) of the above found block.
   _SCREAMING_SNAKE_CASE =   find_block_ending(__A ,  __A ,  __A						)

   # Extract `is_xxx_available()` from existing blocks: some models require specific libraries like `timm` and use
   # `is_timm_available()` instead of `is_torch_available()`.
   # Keep leading and trailing whitespaces
   _SCREAMING_SNAKE_CASE =   re.compile(R"\s(is_\S+?_available\(\))\s"						)
   for line in class_lines[start_idx : end_idx + 1]:
      _SCREAMING_SNAKE_CASE =   r.search(__A						)
      if backend_condition is not None:
         # replace the leading and trailing whitespaces to the space character " ".
         _SCREAMING_SNAKE_CASE =   " " + backend_condition[0][1:-1] + " "
         _SCREAMING_SNAKE_CASE =   r.sub(__A ,  __A						)
         break

   if def_line is None:
      # `pipeline_model_mapping` is not defined. The target index is set to the ending index (inclusive) of
      # `all_model_classes` or `all_generative_model_classes`.
      _SCREAMING_SNAKE_CASE =   end_idx
   else:
      # `pipeline_model_mapping` is defined. The target index is set to be one **BEFORE** its start index.
      _SCREAMING_SNAKE_CASE =   start_idx - 1
      # mark the lines of the currently existing `pipeline_model_mapping` to be removed.
      for idx in range(__A ,  end_idx + 1						):
         # These lines are going to be removed before writing to the test file.
         _SCREAMING_SNAKE_CASE =   None  # noqa

    # Make sure the test class is a subclass of `PipelineTesterMixin`.
   _SCREAMING_SNAKE_CASE =   [x.__name__ for x in test_class.__bases__]
   if "PipelineTesterMixin" not in parent_classes:
      # Put `PipelineTesterMixin` just before `unittest.TestCase`
      _SCREAMING_SNAKE_CASE =   [x for x in parent_classes if x != "TestCase"] + ["PipelineTesterMixin"]
      if "TestCase" in parent_classes:
         # Here we **assume** the original string is always with `unittest.TestCase`.
         _parent_classes.append("unittest.TestCase"						)
      _SCREAMING_SNAKE_CASE =   ", ".join(_parent_classes						)
      for idx, line in enumerate(__A						):
         # Find the ending of the declaration of `test_class`
         if line.strip().endswith("):"						):
            # mark the lines of the declaration of `test_class` to be removed
            for _idx in range(idx + 1						):
               _SCREAMING_SNAKE_CASE =   None  # noqa
            break
        # Add the new, one-line, class declaration for `test_class`
      _SCREAMING_SNAKE_CASE =   f"""class {test_class.__name__}({parent_classes}):\n"""

   # Add indentation
   _SCREAMING_SNAKE_CASE =   " " * indent_level + line_to_add
   # Insert `pipeline_model_mapping` to `class_lines`.
   # (The line at `target_idx` should be kept by definition!)
   _SCREAMING_SNAKE_CASE =   class_lines[: target_idx + 1] + [line_to_add] + class_lines[target_idx + 1 :]
   # Remove the lines that are marked to be removed
   _SCREAMING_SNAKE_CASE =   [x for x in class_lines if x is not None]

   # Move from test class to module (in order to write to the test file)
   _SCREAMING_SNAKE_CASE =   inspect.getsourcelines(inspect.getmodule(__A						)						)[0]
   # Be careful with the 1-off between line numbers and array indices
   _SCREAMING_SNAKE_CASE =   module_lines[: class_start_line_no - 1] + class_lines + module_lines[class_end_line_no:]
   _SCREAMING_SNAKE_CASE =   "".join(__A						)

   _SCREAMING_SNAKE_CASE =   inspect.getsourcefile(__A						)
   with open(__A ,  "w" ,  encoding="UTF-8" ,  newline="\n"						) as fp:
      fp.write(__A						)

   return line_to_add
def 				SCREAMING_SNAKE_CASE_				(							__A       :				List[Any] ,  __A       :				Dict=False						)				->				Any:
   _SCREAMING_SNAKE_CASE =   find_test_class(__A						)
   if test_class:
      add_pipeline_model_mapping(__A ,  overwrite=__A						)


if __name__ == "__main__":
 lowerCamelCase_	 =		argparse.ArgumentParser()
 parser.add_argument(
     '--test_file', type=str, help='A path to the test file, starting with the repository\'s `tests` directory.'
 )
 parser.add_argument(
     '--all',
     action='store_true',
     help='If to check and modify all test files.',
 )
 parser.add_argument(
     '--overwrite',
     action='store_true',
     help='If to overwrite a test class if it has already defined `pipeline_model_mapping`.',
 )
 lowerCamelCase_	 =		parser.parse_args()

 if not args.all and not args.test_file:
  raise ValueError('Please specify either `test_file` or pass `--all` to check/modify all test files.')
 elif args.all and args.test_file:
  raise ValueError('Only one of `--test_file` and `--all` could be specified.')

 lowerCamelCase_	 =		[]
 if args.test_file:
  lowerCamelCase_	 =		[args.test_file]
 else:
  lowerCamelCase_	 =		os.path.join('tests', 'models', '**', 'test_modeling_*.py')
  for test_file in glob.glob(pattern):
   # `Flax` is not concerned at this moment
   if not test_file.startswith('test_modeling_flax_'):
    test_files.append(test_file)

 for test_file in test_files:
  if test_file in TEST_FILE_TO_IGNORE:
   print(f'''[SKIPPED] {test_file} is skipped as it is in `TEST_FILE_TO_IGNORE` in the file {__file__}.''')
   continue
  add_pipeline_model_mapping_to_test_file(test_file, overwrite=args.overwrite)







'''simple docstring'''



import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     RemBertConfig,
     RemBertForCausalLM,
     RemBertForMaskedLM,
     RemBertForMultipleChoice,
     RemBertForQuestionAnswering,
     RemBertForSequenceClassification,
     RemBertForTokenClassification,
     RemBertModel,
 )
 from transformers.models.rembert.modeling_rembert import REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST







class 	lowercase_					:


   """simple docstring"""

   def __init__(					self      :			Any						,   __lowerCamelCase      :			Any						,   __lowerCamelCase      :			Union[str, Any]=1_3						,   __lowerCamelCase      :			str=7						,   __lowerCamelCase      :			Any=True						,   __lowerCamelCase      :			List[Any]=True						,   __lowerCamelCase      :			str=True						,   __lowerCamelCase      :			Union[str, Any]=True						,   __lowerCamelCase      :			List[Any]=9_9						,   __lowerCamelCase      :			List[str]=3_2						,   __lowerCamelCase      :			List[Any]=1_8						,   __lowerCamelCase      :			List[Any]=4_3						,   __lowerCamelCase      :			int=5						,   __lowerCamelCase      :			Any=4						,   __lowerCamelCase      :			List[str]=3_7						,   __lowerCamelCase      :			Any="gelu"						,   __lowerCamelCase      :			List[str]=0.1						,   __lowerCamelCase      :			List[Any]=0.1						,   __lowerCamelCase      :			Union[str, Any]=5_1_2						,   __lowerCamelCase      :			int=1_6						,   __lowerCamelCase      :			str=2						,   __lowerCamelCase      :			Optional[int]=0.0_2						,   __lowerCamelCase      :			Dict=3						,   __lowerCamelCase      :			str=4						,   __lowerCamelCase      :			Union[str, Any]=None						,   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   parent
      _SCREAMING_SNAKE_CASE =   batch_size
      _SCREAMING_SNAKE_CASE =   seq_length
      _SCREAMING_SNAKE_CASE =   is_training
      _SCREAMING_SNAKE_CASE =   use_input_mask
      _SCREAMING_SNAKE_CASE =   use_token_type_ids
      _SCREAMING_SNAKE_CASE =   use_labels
      _SCREAMING_SNAKE_CASE =   vocab_size
      _SCREAMING_SNAKE_CASE =   hidden_size
      _SCREAMING_SNAKE_CASE =   input_embedding_size
      _SCREAMING_SNAKE_CASE =   output_embedding_size
      _SCREAMING_SNAKE_CASE =   num_hidden_layers
      _SCREAMING_SNAKE_CASE =   num_attention_heads
      _SCREAMING_SNAKE_CASE =   intermediate_size
      _SCREAMING_SNAKE_CASE =   hidden_act
      _SCREAMING_SNAKE_CASE =   hidden_dropout_prob
      _SCREAMING_SNAKE_CASE =   attention_probs_dropout_prob
      _SCREAMING_SNAKE_CASE =   max_position_embeddings
      _SCREAMING_SNAKE_CASE =   type_vocab_size
      _SCREAMING_SNAKE_CASE =   type_sequence_label_size
      _SCREAMING_SNAKE_CASE =   initializer_range
      _SCREAMING_SNAKE_CASE =   num_labels
      _SCREAMING_SNAKE_CASE =   num_choices
      _SCREAMING_SNAKE_CASE =   scope



   def 							lowerCAmelCase_    (					self      :			int					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   ids_tensor([self.batch_size, self.seq_length]						,   self.vocab_size					)

      _SCREAMING_SNAKE_CASE =   None
      if self.use_input_mask:
         _SCREAMING_SNAKE_CASE =   random_attention_mask([self.batch_size, self.seq_length]					)

      _SCREAMING_SNAKE_CASE =   None
      if self.use_token_type_ids:
         _SCREAMING_SNAKE_CASE =   ids_tensor([self.batch_size, self.seq_length]						,   self.type_vocab_size					)

      _SCREAMING_SNAKE_CASE =   None
      _SCREAMING_SNAKE_CASE =   None
      _SCREAMING_SNAKE_CASE =   None
      if self.use_labels:
         _SCREAMING_SNAKE_CASE =   ids_tensor([self.batch_size]						,   self.type_sequence_label_size					)
         _SCREAMING_SNAKE_CASE =   ids_tensor([self.batch_size, self.seq_length]						,   self.num_labels					)
         _SCREAMING_SNAKE_CASE =   ids_tensor([self.batch_size]						,   self.num_choices					)

      _SCREAMING_SNAKE_CASE =   RemBertConfig(
          vocab_size=self.vocab_size						,   hidden_size=self.hidden_size						,   input_embedding_size=self.input_embedding_size						,   output_embedding_size=self.output_embedding_size						,   num_hidden_layers=self.num_hidden_layers						,   num_attention_heads=self.num_attention_heads						,   intermediate_size=self.intermediate_size						,   hidden_act=self.hidden_act						,   hidden_dropout_prob=self.hidden_dropout_prob						,   attention_probs_dropout_prob=self.attention_probs_dropout_prob						,   max_position_embeddings=self.max_position_embeddings						,   type_vocab_size=self.type_vocab_size						,   is_decoder=__lowerCamelCase						,   initializer_range=self.initializer_range						,   )

      return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



   def 							lowerCAmelCase_    (					self      :			List[str]					):

      """simple docstring"""
      (
          (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), 
      ) =   self.prepare_config_and_inputs()

      _SCREAMING_SNAKE_CASE =   True
      _SCREAMING_SNAKE_CASE =   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
      _SCREAMING_SNAKE_CASE =   ids_tensor([self.batch_size, self.seq_length]						,   vocab_size=2					)

      return (
          config,
          input_ids,
          token_type_ids,
          input_mask,
          sequence_labels,
          token_labels,
          choice_labels,
          encoder_hidden_states,
          encoder_attention_mask,
      )



   def 							lowerCAmelCase_    (					self      :			int						,   __lowerCamelCase      :			List[str]						,   __lowerCamelCase      :			int						,   __lowerCamelCase      :			List[Any]						,   __lowerCamelCase      :			Any						,   __lowerCamelCase      :			List[str]						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			Dict					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   RemBertModel(config=__lowerCamelCase					)
      model.to(__lowerCamelCase					)
      model.eval()
      _SCREAMING_SNAKE_CASE =   model(__lowerCamelCase						,   attention_mask=__lowerCamelCase						,   token_type_ids=__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   model(__lowerCamelCase						,   token_type_ids=__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   model(__lowerCamelCase					)
      self.parent.assertEqual(result.last_hidden_state.shape						,   (self.batch_size, self.seq_length, self.hidden_size)					)



   def 							lowerCAmelCase_    (					self      :			List[str]						,   __lowerCamelCase      :			str						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			str						,   __lowerCamelCase      :			Any						,   __lowerCamelCase      :			str						,   __lowerCamelCase      :			Any						,   __lowerCamelCase      :			Optional[int]						,   __lowerCamelCase      :			Tuple						,   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   True
      _SCREAMING_SNAKE_CASE =   RemBertModel(__lowerCamelCase					)
      model.to(__lowerCamelCase					)
      model.eval()
      _SCREAMING_SNAKE_CASE =   model(
          __lowerCamelCase						,   attention_mask=__lowerCamelCase						,   token_type_ids=__lowerCamelCase						,   encoder_hidden_states=__lowerCamelCase						,   encoder_attention_mask=__lowerCamelCase						,   )
      _SCREAMING_SNAKE_CASE =   model(
          __lowerCamelCase						,   attention_mask=__lowerCamelCase						,   token_type_ids=__lowerCamelCase						,   encoder_hidden_states=__lowerCamelCase						,   )
      _SCREAMING_SNAKE_CASE =   model(__lowerCamelCase						,   attention_mask=__lowerCamelCase						,   token_type_ids=__lowerCamelCase					)
      self.parent.assertEqual(result.last_hidden_state.shape						,   (self.batch_size, self.seq_length, self.hidden_size)					)



   def 							lowerCAmelCase_    (					self      :			List[str]						,   __lowerCamelCase      :			List[str]						,   __lowerCamelCase      :			Optional[Any]						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			Union[str, Any]						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			List[Any]						,   __lowerCamelCase      :			List[Any]						,   __lowerCamelCase      :			Dict						,   __lowerCamelCase      :			List[str]						,   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   RemBertForCausalLM(config=__lowerCamelCase					)
      model.to(__lowerCamelCase					)
      model.eval()
      _SCREAMING_SNAKE_CASE =   model(__lowerCamelCase						,   attention_mask=__lowerCamelCase						,   token_type_ids=__lowerCamelCase						,   labels=__lowerCamelCase					)
      self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.seq_length, self.vocab_size)					)



   def 							lowerCAmelCase_    (					self      :			Tuple						,   __lowerCamelCase      :			Optional[int]						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			List[str]						,   __lowerCamelCase      :			Any						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			int						,   __lowerCamelCase      :			Any					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   RemBertForMaskedLM(config=__lowerCamelCase					)
      model.to(__lowerCamelCase					)
      model.eval()
      _SCREAMING_SNAKE_CASE =   model(__lowerCamelCase						,   attention_mask=__lowerCamelCase						,   token_type_ids=__lowerCamelCase						,   labels=__lowerCamelCase					)
      self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.seq_length, self.vocab_size)					)



   def 							lowerCAmelCase_    (					self      :			Tuple						,   __lowerCamelCase      :			int						,   __lowerCamelCase      :			Union[str, Any]						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			List[Any]						,   __lowerCamelCase      :			Optional[int]						,   __lowerCamelCase      :			Optional[Any]						,   __lowerCamelCase      :			Union[str, Any]						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			Optional[int]						,   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   True
      _SCREAMING_SNAKE_CASE =   True
      _SCREAMING_SNAKE_CASE =   RemBertForCausalLM(config=__lowerCamelCase					)
      model.to(__lowerCamelCase					)
      model.eval()

      # first forward pass
      _SCREAMING_SNAKE_CASE =   model(
          __lowerCamelCase						,   attention_mask=__lowerCamelCase						,   encoder_hidden_states=__lowerCamelCase						,   encoder_attention_mask=__lowerCamelCase						,   use_cache=__lowerCamelCase						,   )
      _SCREAMING_SNAKE_CASE =   outputs.past_key_values

      # create hypothetical multiple next token and extent to next_input_ids
      _SCREAMING_SNAKE_CASE =   ids_tensor((self.batch_size, 3)						,   config.vocab_size					)
      _SCREAMING_SNAKE_CASE =   ids_tensor((self.batch_size, 3)						,   vocab_size=2					)

      # append to next input_ids and
      _SCREAMING_SNAKE_CASE =   torch.cat([input_ids, next_tokens]						,   dim=-1					)
      _SCREAMING_SNAKE_CASE =   torch.cat([input_mask, next_mask]						,   dim=-1					)

      _SCREAMING_SNAKE_CASE =   model(
          __lowerCamelCase						,   attention_mask=__lowerCamelCase						,   encoder_hidden_states=__lowerCamelCase						,   encoder_attention_mask=__lowerCamelCase						,   output_hidden_states=__lowerCamelCase						,   )["hidden_states"][0]
      _SCREAMING_SNAKE_CASE =   model(
          __lowerCamelCase						,   attention_mask=__lowerCamelCase						,   encoder_hidden_states=__lowerCamelCase						,   encoder_attention_mask=__lowerCamelCase						,   past_key_values=__lowerCamelCase						,   output_hidden_states=__lowerCamelCase						,   )["hidden_states"][0]

      # select random slice
      _SCREAMING_SNAKE_CASE =   ids_tensor((1,)						,   output_from_past.shape[-1]					).item()
      _SCREAMING_SNAKE_CASE =   output_from_no_past[:, -3:, random_slice_idx].detach()
      _SCREAMING_SNAKE_CASE =   output_from_past[:, :, random_slice_idx].detach()

      self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]					)

      # test that outputs are equal for slice
      self.parent.assertTrue(torch.allclose(__lowerCamelCase						,   __lowerCamelCase						,   atol=1e-3					)					)



   def 							lowerCAmelCase_    (					self      :			List[Any]						,   __lowerCamelCase      :			List[Any]						,   __lowerCamelCase      :			Optional[int]						,   __lowerCamelCase      :			Optional[int]						,   __lowerCamelCase      :			List[Any]						,   __lowerCamelCase      :			str						,   __lowerCamelCase      :			str						,   __lowerCamelCase      :			Optional[Any]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   RemBertForQuestionAnswering(config=__lowerCamelCase					)
      model.to(__lowerCamelCase					)
      model.eval()
      _SCREAMING_SNAKE_CASE =   model(
          __lowerCamelCase						,   attention_mask=__lowerCamelCase						,   token_type_ids=__lowerCamelCase						,   start_positions=__lowerCamelCase						,   end_positions=__lowerCamelCase						,   )
      self.parent.assertEqual(result.start_logits.shape						,   (self.batch_size, self.seq_length)					)
      self.parent.assertEqual(result.end_logits.shape						,   (self.batch_size, self.seq_length)					)



   def 							lowerCAmelCase_    (					self      :			Optional[int]						,   __lowerCamelCase      :			int						,   __lowerCamelCase      :			int						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			Optional[int]						,   __lowerCamelCase      :			int						,   __lowerCamelCase      :			Optional[Any]						,   __lowerCamelCase      :			Dict					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.num_labels
      _SCREAMING_SNAKE_CASE =   RemBertForSequenceClassification(__lowerCamelCase					)
      model.to(__lowerCamelCase					)
      model.eval()
      _SCREAMING_SNAKE_CASE =   model(__lowerCamelCase						,   attention_mask=__lowerCamelCase						,   token_type_ids=__lowerCamelCase						,   labels=__lowerCamelCase					)
      self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.num_labels)					)



   def 							lowerCAmelCase_    (					self      :			Tuple						,   __lowerCamelCase      :			List[str]						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			Any						,   __lowerCamelCase      :			Any						,   __lowerCamelCase      :			Optional[int]						,   __lowerCamelCase      :			List[Any]						,   __lowerCamelCase      :			Union[str, Any]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.num_labels
      _SCREAMING_SNAKE_CASE =   RemBertForTokenClassification(config=__lowerCamelCase					)
      model.to(__lowerCamelCase					)
      model.eval()
      _SCREAMING_SNAKE_CASE =   model(__lowerCamelCase						,   attention_mask=__lowerCamelCase						,   token_type_ids=__lowerCamelCase						,   labels=__lowerCamelCase					)
      self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.seq_length, self.num_labels)					)



   def 							lowerCAmelCase_    (					self      :			Optional[int]						,   __lowerCamelCase      :			Dict						,   __lowerCamelCase      :			Optional[int]						,   __lowerCamelCase      :			Optional[int]						,   __lowerCamelCase      :			str						,   __lowerCamelCase      :			Union[str, Any]						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			int					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.num_choices
      _SCREAMING_SNAKE_CASE =   RemBertForMultipleChoice(config=__lowerCamelCase					)
      model.to(__lowerCamelCase					)
      model.eval()
      _SCREAMING_SNAKE_CASE =   input_ids.unsqueeze(1					).expand(-1						,   self.num_choices						,   -1					).contiguous()
      _SCREAMING_SNAKE_CASE =   token_type_ids.unsqueeze(1					).expand(-1						,   self.num_choices						,   -1					).contiguous()
      _SCREAMING_SNAKE_CASE =   input_mask.unsqueeze(1					).expand(-1						,   self.num_choices						,   -1					).contiguous()
      _SCREAMING_SNAKE_CASE =   model(
          __lowerCamelCase						,   attention_mask=__lowerCamelCase						,   token_type_ids=__lowerCamelCase						,   labels=__lowerCamelCase						,   )
      self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.num_choices)					)



   def 							lowerCAmelCase_    (					self      :			Optional[int]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.prepare_config_and_inputs()
      (
          (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), 
      ) =   config_and_inputs
      _SCREAMING_SNAKE_CASE =   {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
      return config, inputs_dict







@require_torch
class 	lowercase_					(				A  ,						A  ,						unittest.TestCase     ):


   """simple docstring"""

   lowerCamelCase_       						=		(
       (
           RemBertModel,
           RemBertForMaskedLM,
           RemBertForCausalLM,
           RemBertForMultipleChoice,
           RemBertForQuestionAnswering,
           RemBertForSequenceClassification,
           RemBertForTokenClassification,
       )
       if is_torch_available()
       else ()
   )
   lowerCamelCase_       						=		(RemBertForCausalLM,) if is_torch_available() else ()
   lowerCamelCase_       						=		(
       {
           '''feature-extraction''': RemBertModel,
           '''fill-mask''': RemBertForMaskedLM,
           '''question-answering''': RemBertForQuestionAnswering,
           '''text-classification''': RemBertForSequenceClassification,
           '''text-generation''': RemBertForCausalLM,
           '''token-classification''': RemBertForTokenClassification,
           '''zero-shot''': RemBertForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )



   def 							lowerCAmelCase_    (					self      :			Any					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   RemBertModelTester(self					)
      _SCREAMING_SNAKE_CASE =   ConfigTester(self						,   config_class=__lowerCamelCase						,   hidden_size=3_7					)



   def 							lowerCAmelCase_    (					self      :			int					):

      """simple docstring"""
      self.config_tester.run_common_tests()



   def 							lowerCAmelCase_    (					self      :			Dict					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Optional[Any]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
         _SCREAMING_SNAKE_CASE =   type
         self.model_tester.create_and_check_model(*__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Optional[int]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Dict					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_multiple_choice(*__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			List[Any]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_decoder_model_past_large_inputs(*__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Dict					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Tuple					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Optional[int]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Any					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_model_as_decoder(*__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Tuple					):

      """simple docstring"""
      # This regression test was failing with PyTorch < 1.3
      (
          (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), 
      ) =   self.model_tester.prepare_config_and_inputs_for_decoder()

      _SCREAMING_SNAKE_CASE =   None

      self.model_tester.create_and_check_model_as_decoder(
          __lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase						,   )



   @slow
   def 							lowerCAmelCase_    (					self      :			Optional[Any]					):

      """simple docstring"""
      for model_name in REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _SCREAMING_SNAKE_CASE =   RemBertModel.from_pretrained(__lowerCamelCase					)
         self.assertIsNotNone(__lowerCamelCase					)







@require_torch
class 	lowercase_					(				unittest.TestCase     ):


   """simple docstring"""

   @slow
   def 							lowerCAmelCase_    (					self      :			List[Any]					):

      """simple docstring"""
      # Test exact values at the last hidden layer
      _SCREAMING_SNAKE_CASE =   RemBertModel.from_pretrained("google/rembert"					)
      _SCREAMING_SNAKE_CASE =   torch.tensor([[3_1_2, 5_6_4_9_8, 3_1_3, 2_1_2_5, 3_1_3]]					)
      _SCREAMING_SNAKE_CASE =   torch.tensor([[0, 0, 0, 1, 1]]					)
      with torch.no_grad():
         _SCREAMING_SNAKE_CASE =   model(__lowerCamelCase						,   token_type_ids=__lowerCamelCase						,   output_hidden_states=__lowerCamelCase					)

      _SCREAMING_SNAKE_CASE =   1_1_5_2

      _SCREAMING_SNAKE_CASE =   torch.Size((1, 5, hidden_size)					)
      self.assertEqual(output["last_hidden_state"].shape						,   __lowerCamelCase					)

      _SCREAMING_SNAKE_CASE =   torch.tensor(
          [
              [
                  [0.0_7_5_4, -0.2_0_2_2, 0.1_9_0_4],
                  [-0.3_3_5_4, -0.3_6_9_2, -0.4_7_9_1],
                  [-0.2_3_1_4, -0.6_7_2_9, -0.0_7_4_9],
                  [-0.0_3_9_6, -0.3_1_0_5, -0.4_2_3_4],
                  [-0.1_5_7_1, -0.0_5_2_5, 0.5_3_5_3],
              ]
          ]					)

      # Running on the original tf implementation gives slightly different results here.
      # Not clear why this variations is present
      # TODO: Find reason for discrepancy
      # expected_original_implementation = [[
      #     [0.07630594074726105, -0.20146065950393677, 0.19107051193714142],
      #     [-0.3405614495277405, -0.36971670389175415, -0.4808273911476135],
      #     [-0.22587086260318756, -0.6656315922737122, -0.07844287157058716],
      #     [-0.04145475849509239, -0.3077218234539032, -0.42316967248916626],
      #     [-0.15887849032878876, -0.054529931396245956, 0.5356100797653198]
      # ]]

      self.assertTrue(torch.allclose(output["last_hidden_state"][:, :, :3]						,   __lowerCamelCase						,   atol=1e-4					)					)

