


"""simple docstring"""





import os
from pickle import UnpicklingError
from typing import Dict, Tuple

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict

import transformers

from .utils import logging


snake_case__						:   Optional[Any]			 = logging.get_logger(__name__)


def    _snake_case  (  _snake_case :	int       ,      _snake_case :	Union[str, Any]       ,      _snake_case :	List[str]       ,      _snake_case :	Optional[Any]=False	):
      try:
            import torch  # noqa: F401
      except ImportError:
            logger.error(
                '''Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see'''
                ''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'''
                ''' instructions.'''	)
            raise

      if not is_sharded:
            lowerCAmelCase    :		List[str]				=      os.path.abspath(_snake_case	)
            logger.info(f'''Loading PyTorch weights from {pt_path}'''	)

            lowerCAmelCase    :		Dict				=      torch.load(_snake_case       ,      map_location='''cpu'''	)
            logger.info(f'''PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()	):,} parameters.'''	)

            lowerCAmelCase    :		Union[str, Any]				=      convert_pytorch_state_dict_to_flax(_snake_case       ,      _snake_case	)
      else:
            # model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
            lowerCAmelCase    :		Optional[Any]				=      convert_pytorch_sharded_state_dict_to_flax(_snake_case       ,      _snake_case	)
      return flax_state_dict


def    _snake_case  (  _snake_case :	Tuple[str]       ,      _snake_case :	np.ndarray       ,      _snake_case :	Dict[str, jnp.ndarray]       ,      _snake_case :	str       ,      ):

      def is_key_or_prefix_key_in_dict(_snake_case :	Tuple[str]	) -> bool:
            return len(set(_snake_case	) & {key, (model_prefix,) + key}	) > 0

      # layer norm
      lowerCAmelCase    :		str				=      pt_tuple_key[:-1] + ('''scale''',)
      if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(_snake_case	):
            return renamed_pt_tuple_key, pt_tensor

      # batch norm layer mean
      lowerCAmelCase    :		int				=      pt_tuple_key[:-1] + ('''mean''',)
      if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(_snake_case	):
            return renamed_pt_tuple_key, pt_tensor

      # batch norm layer var
      lowerCAmelCase    :		List[Any]				=      pt_tuple_key[:-1] + ('''var''',)
      if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(_snake_case	):
            return renamed_pt_tuple_key, pt_tensor

      # embedding
      lowerCAmelCase    :		Tuple				=      pt_tuple_key[:-1] + ('''embedding''',)
      if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(_snake_case	):
            return renamed_pt_tuple_key, pt_tensor

      # conv layer
      lowerCAmelCase    :		Union[str, Any]				=      pt_tuple_key[:-1] + ('''kernel''',)
      if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(_snake_case	):
            lowerCAmelCase    :		Union[str, Any]				=      pt_tensor.transpose(2       ,      3       ,      1       ,      0	)
            return renamed_pt_tuple_key, pt_tensor

      # linear layer
      lowerCAmelCase    :		Any				=      pt_tuple_key[:-1] + ('''kernel''',)
      if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(_snake_case	):
            lowerCAmelCase    :		Tuple				=      pt_tensor.T
            return renamed_pt_tuple_key, pt_tensor

      # old PyTorch layer norm weight
      lowerCAmelCase    :		str				=      pt_tuple_key[:-1] + ('''weight''',)
      if pt_tuple_key[-1] == "gamma":
            return renamed_pt_tuple_key, pt_tensor

      # old PyTorch layer norm bias
      lowerCAmelCase    :		Tuple				=      pt_tuple_key[:-1] + ('''bias''',)
      if pt_tuple_key[-1] == "beta":
            return renamed_pt_tuple_key, pt_tensor

      # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
      lowerCAmelCase    :		str				=      None
      if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
            lowerCAmelCase    :		Any				=      pt_tuple_key[-2] + '''_g'''
      elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
            lowerCAmelCase    :		Tuple				=      pt_tuple_key[-2] + '''_v'''
      if name is not None:
            lowerCAmelCase    :		Optional[Any]				=      pt_tuple_key[:-3] + (name,)
            return renamed_pt_tuple_key, pt_tensor

      return pt_tuple_key, pt_tensor


def    _snake_case  (  _snake_case :	Union[str, Any]       ,      _snake_case :	Optional[Any]	):
      # convert pytorch tensor to numpy
      lowerCAmelCase    :		Any				=      {k: v.numpy() for k, v in pt_state_dict.items()}

      lowerCAmelCase    :		Any				=      flax_model.base_model_prefix

      # use params dict if the model contains batch norm layers
      if "params" in flax_model.params:
            lowerCAmelCase    :		Dict				=      flax_model.params['''params''']
      else:
            lowerCAmelCase    :		Dict				=      flax_model.params
      lowerCAmelCase    :		Tuple				=      flatten_dict(_snake_case	)

      # add batch_stats keys,values to dict
      if "batch_stats" in flax_model.params:
            lowerCAmelCase    :		Any				=      flatten_dict(flax_model.params['''batch_stats''']	)
            random_flax_state_dict.update(_snake_case	)

      lowerCAmelCase    :		int				=      {}

      lowerCAmelCase    :		Dict				=      (model_prefix not in flax_model_params) and (
          model_prefix in {k.split('''.'''	)[0] for k in pt_state_dict.keys()}
      )
      lowerCAmelCase    :		Dict				=      (model_prefix in flax_model_params) and (
          model_prefix not in {k.split('''.'''	)[0] for k in pt_state_dict.keys()}
      )

      # Need to change some parameters name to match Flax names
      for pt_key, pt_tensor in pt_state_dict.items():
            lowerCAmelCase    :		Union[str, Any]				=      tuple(pt_key.split('''.'''	)	)

            # remove base model prefix if necessary
            lowerCAmelCase    :		int				=      pt_tuple_key[0] == model_prefix
            if load_model_with_head_into_base_model and has_base_model_prefix:
                  lowerCAmelCase    :		int				=      pt_tuple_key[1:]

            # Correctly rename weight parameters
            lowerCAmelCase,					lowerCAmelCase    :		Optional[Any]				=      rename_key_and_reshape_tensor(
                _snake_case       ,      _snake_case       ,      _snake_case       ,      _snake_case	)

            # add model prefix if necessary
            lowerCAmelCase    :		List[Any]				=      (model_prefix,) + flax_key in random_flax_state_dict
            if load_base_model_into_model_with_head and require_base_model_prefix:
                  lowerCAmelCase    :		Optional[Any]				=      (model_prefix,) + flax_key

            if flax_key in random_flax_state_dict:
                  if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                        raise ValueError(
                            f'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
                            f'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''	)

        # add batch stats if the model contains batchnorm layers
            if "batch_stats" in flax_model.params:
                  if "mean" in flax_key[-1] or "var" in flax_key[-1]:
                        lowerCAmelCase    :		Optional[int]				=      jnp.asarray(_snake_case	)
                        continue
                  # remove num_batches_tracked key
                  if "num_batches_tracked" in flax_key[-1]:
                        flax_state_dict.pop(_snake_case       ,      _snake_case	)
                        continue

                  # also add unexpected weight so that warning is thrown
                  lowerCAmelCase    :		List[Any]				=      jnp.asarray(_snake_case	)

            else:
                  # also add unexpected weight so that warning is thrown
                  lowerCAmelCase    :		List[Any]				=      jnp.asarray(_snake_case	)

      return unflatten_dict(_snake_case	)


def    _snake_case  (  _snake_case :	str       ,      _snake_case :	int	):
      import torch

      # Load the index
      lowerCAmelCase    :		Tuple				=      {}
      for shard_file in shard_filenames:
            # load using msgpack utils
            lowerCAmelCase    :		Optional[int]				=      torch.load(_snake_case	)
            lowerCAmelCase    :		Dict				=      {k: v.numpy() for k, v in pt_state_dict.items()}

            lowerCAmelCase    :		Union[str, Any]				=      flax_model.base_model_prefix

            # use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
            if "batch_stats" in flax_model.params:
                  lowerCAmelCase    :		Any				=      flax_model.params['''params''']

                  lowerCAmelCase    :		str				=      flatten_dict(_snake_case	)
                  random_flax_state_dict.update(flatten_dict(flax_model.params['''batch_stats''']	)	)
            else:
                  lowerCAmelCase    :		Optional[Any]				=      flax_model.params
                  lowerCAmelCase    :		Dict				=      flatten_dict(_snake_case	)

            lowerCAmelCase    :		Tuple				=      (model_prefix not in flax_model_params) and (
                model_prefix in {k.split('''.'''	)[0] for k in pt_state_dict.keys()}
            )
            lowerCAmelCase    :		Tuple				=      (model_prefix in flax_model_params) and (
                model_prefix not in {k.split('''.'''	)[0] for k in pt_state_dict.keys()}
            )
            # Need to change some parameters name to match Flax names
            for pt_key, pt_tensor in pt_state_dict.items():
                  lowerCAmelCase    :		List[str]				=      tuple(pt_key.split('''.'''	)	)

                  # remove base model prefix if necessary
                  lowerCAmelCase    :		Dict				=      pt_tuple_key[0] == model_prefix
                  if load_model_with_head_into_base_model and has_base_model_prefix:
                        lowerCAmelCase    :		str				=      pt_tuple_key[1:]

                  # Correctly rename weight parameters
                  lowerCAmelCase,					lowerCAmelCase    :		Tuple				=      rename_key_and_reshape_tensor(
                      _snake_case       ,      _snake_case       ,      _snake_case       ,      _snake_case	)
                  # add model prefix if necessary
                  lowerCAmelCase    :		Any				=      (model_prefix,) + flax_key in random_flax_state_dict
                  if load_base_model_into_model_with_head and require_base_model_prefix:
                        lowerCAmelCase    :		List[Any]				=      (model_prefix,) + flax_key

                  if flax_key in random_flax_state_dict:
                        if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                              raise ValueError(
                                  f'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
                                  f'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''	)

            # add batch stats if the model contains batchnorm layers
                  if "batch_stats" in flax_model.params:
                        if "mean" in flax_key[-1]:
                              lowerCAmelCase    :		List[str]				=      jnp.asarray(_snake_case	)
                              continue
                        if "var" in flax_key[-1]:
                              lowerCAmelCase    :		List[str]				=      jnp.asarray(_snake_case	)
                              continue
                        # remove num_batches_tracked key
                        if "num_batches_tracked" in flax_key[-1]:
                              flax_state_dict.pop(_snake_case       ,      _snake_case	)
                              continue

                        # also add unexpected weight so that warning is thrown
                        lowerCAmelCase    :		str				=      jnp.asarray(_snake_case	)

                  else:
                        # also add unexpected weight so that warning is thrown
                        lowerCAmelCase    :		Tuple				=      jnp.asarray(_snake_case	)
      return unflatten_dict(_snake_case	)


def    _snake_case  (  _snake_case :	Optional[Any]       ,      _snake_case :	List[str]	):
      lowerCAmelCase    :		Optional[int]				=      os.path.abspath(_snake_case	)
      logger.info(f'''Loading Flax weights from {flax_checkpoint_path}'''	)

      # import correct flax class
      lowerCAmelCase    :		str				=      getattr(_snake_case       ,      '''Flax''' + model.__class__.__name__	)

      # load flax weight dict
      with open(_snake_case       ,      '''rb'''	) as state_f:
            try:
                  lowerCAmelCase    :		Any				=      from_bytes(_snake_case       ,      state_f.read()	)
            except UnpicklingError:
                  raise EnvironmentError(f'''Unable to convert {flax_checkpoint_path} to Flax deserializable object. '''	)

      return load_flax_weights_in_pytorch_model(_snake_case       ,      _snake_case	)






def    _snake_case  (  _snake_case :	Tuple       ,      _snake_case :	Dict	):

      try:
            import torch  # noqa: F401
      except ImportError:
            logger.error(
                '''Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see'''
                ''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'''
                ''' instructions.'''	)
            raise

      # check if we have bf16 weights
      lowerCAmelCase    :		str				=      flatten_dict(jax.tree_util.tree_map(lambda _snake_case	: x.dtype == jnp.bfloataa       ,      _snake_case	)	).values()
      if any(_snake_case	):
            # convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
            # and bf16 is not fully supported in PT yet.
            logger.warning(
                '''Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '''
                '''before loading those in PyTorch model.'''	)
            lowerCAmelCase    :		Union[str, Any]				=      jax.tree_util.tree_map(
                lambda _snake_case	: params.astype(np.floataa	) if params.dtype == jnp.bfloataa else params       ,      _snake_case	)

      lowerCAmelCase    :		Union[str, Any]				=      flatten_dict(_snake_case	)
      lowerCAmelCase    :		Any				=      pt_model.state_dict()

      lowerCAmelCase    :		Optional[Any]				=      (pt_model.base_model_prefix in flax_state) and (
          pt_model.base_model_prefix not in {k.split('''.'''	)[0] for k in pt_model_dict.keys()}
      )
      lowerCAmelCase    :		int				=      (pt_model.base_model_prefix not in flax_state) and (
          pt_model.base_model_prefix in {k.split('''.'''	)[0] for k in pt_model_dict.keys()}
      )

      # keep track of unexpected & missing keys
      lowerCAmelCase    :		Optional[int]				=      []
      lowerCAmelCase    :		Optional[Any]				=      set(pt_model_dict.keys()	)

      for flax_key_tuple, flax_tensor in flax_state_dict.items():
            lowerCAmelCase    :		Dict				=      flax_key_tuple[0] == pt_model.base_model_prefix
            lowerCAmelCase    :		List[str]				=      '''.'''.join((pt_model.base_model_prefix,) + flax_key_tuple	) in pt_model_dict

            # adapt flax_key to prepare for loading from/to base model only
            if load_model_with_head_into_base_model and has_base_model_prefix:
                  lowerCAmelCase    :		List[Any]				=      flax_key_tuple[1:]
            elif load_base_model_into_model_with_head and require_base_model_prefix:
                  lowerCAmelCase    :		Optional[int]				=      (pt_model.base_model_prefix,) + flax_key_tuple

            # rename flax weights to PyTorch format
            if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(_snake_case	) not in pt_model_dict:
                  # conv layer
                  lowerCAmelCase    :		str				=      flax_key_tuple[:-1] + ('''weight''',)
                  lowerCAmelCase    :		List[Any]				=      jnp.transpose(_snake_case       ,      (3, 2, 0, 1)	)
            elif flax_key_tuple[-1] == "kernel" and ".".join(_snake_case	) not in pt_model_dict:
                  # linear layer
                  lowerCAmelCase    :		int				=      flax_key_tuple[:-1] + ('''weight''',)
                  lowerCAmelCase    :		List[Any]				=      flax_tensor.T
            elif flax_key_tuple[-1] in ["scale", "embedding"]:
                  lowerCAmelCase    :		List[Any]				=      flax_key_tuple[:-1] + ('''weight''',)

            # adding batch stats from flax batch norm to pt
            elif "mean" in flax_key_tuple[-1]:
                  lowerCAmelCase    :		List[str]				=      flax_key_tuple[:-1] + ('''running_mean''',)
            elif "var" in flax_key_tuple[-1]:
                  lowerCAmelCase    :		List[Any]				=      flax_key_tuple[:-1] + ('''running_var''',)

            if "batch_stats" in flax_state:
                  lowerCAmelCase    :		Union[str, Any]				=      '''.'''.join(flax_key_tuple[1:]	)  # Remove the params/batch_stats header
            else:
                  lowerCAmelCase    :		List[Any]				=      '''.'''.join(_snake_case	)

            # We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
            lowerCAmelCase    :		Optional[int]				=      {}
            # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
            for key in pt_model_dict:
                  lowerCAmelCase    :		str				=      key.split('''.'''	)
                  lowerCAmelCase    :		str				=      None
                  if key_components[-3::2] == ["parametrizations", "original0"]:
                        lowerCAmelCase    :		Any				=      key_components[-2] + '''_g'''
                  elif key_components[-3::2] == ["parametrizations", "original1"]:
                        lowerCAmelCase    :		Optional[int]				=      key_components[-2] + '''_v'''
                  if name is not None:
                        lowerCAmelCase    :		Union[str, Any]				=      key_components[:-3] + [name]
                        lowerCAmelCase    :		List[str]				=      '''.'''.join(_snake_case	)
                        lowerCAmelCase    :		List[str]				=      key

            if flax_key in special_pt_names:
                  lowerCAmelCase    :		Optional[int]				=      special_pt_names[flax_key]

            if flax_key in pt_model_dict:
                  if flax_tensor.shape != pt_model_dict[flax_key].shape:
                        raise ValueError(
                            f'''Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '''
                            f'''to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.'''	)
                  else:
                        # add weight to pytorch dict
                        lowerCAmelCase    :		Union[str, Any]				=      np.asarray(_snake_case	) if not isinstance(_snake_case       ,      np.ndarray	) else flax_tensor
                        lowerCAmelCase    :		List[Any]				=      torch.from_numpy(_snake_case	)
                        # remove from missing keys
                        missing_keys.remove(_snake_case	)
            else:
                  # weight is not expected by PyTorch model
                  unexpected_keys.append(_snake_case	)

      pt_model.load_state_dict(_snake_case	)

      # re-transform missing_keys to list
      lowerCAmelCase    :		Optional[int]				=      list(_snake_case	)

      if len(_snake_case	) > 0:
            logger.warning(
                '''Some weights of the Flax model were not used when initializing the PyTorch model'''
                f''' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'''
                f''' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'''
                ''' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'''
                f''' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'''
                ''' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'''
                ''' FlaxBertForSequenceClassification model).'''	)
      else:
            logger.warning(f'''All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n'''	)
      if len(_snake_case	) > 0:
            logger.warning(
                f'''Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'''
                f''' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'''
                ''' use it for predictions and inference.'''	)
      else:
            logger.warning(
                f'''All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n'''
                '''If your task is similar to the task the model of the checkpoint was trained on, '''
                f'''you can already use {pt_model.__class__.__name__} for predictions without further training.'''	)

      return pt_model





'''simple docstring'''






import unittest

from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

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


if is_torch_available():
 import torch

 from transformers import (
     DebertaForMaskedLM,
     DebertaForQuestionAnswering,
     DebertaForSequenceClassification,
     DebertaForTokenClassification,
     DebertaModel,
 )
 from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST






class 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




  def __init__(      self   :     Optional[Any]					,lowercase__   :     Optional[Any]					,lowercase__   :     Optional[int]=1_3					,lowercase__   :     Any=7					,lowercase__   :     Union[str, Any]=True					,lowercase__   :     Optional[int]=True					,lowercase__   :     List[str]=True					,lowercase__   :     str=True					,lowercase__   :     Dict=9_9					,lowercase__   :     Union[str, Any]=3_2					,lowercase__   :     List[str]=5					,lowercase__   :     int=4					,lowercase__   :     Dict=3_7					,lowercase__   :     Union[str, Any]="gelu"					,lowercase__   :     str=0.1					,lowercase__   :     List[str]=0.1					,lowercase__   :     Any=5_1_2					,lowercase__   :     Optional[int]=1_6					,lowercase__   :     Optional[int]=2					,lowercase__   :     Optional[int]=0.0_2					,lowercase__   :     Dict=False					,lowercase__   :     Optional[int]=True					,lowercase__   :     str="None"					,lowercase__   :     Optional[int]=3					,lowercase__   :     List[Any]=4					,lowercase__   :     Union[str, Any]=None					,):
   __lowercase							  = parent
   __lowercase							  = batch_size
   __lowercase							  = seq_length
   __lowercase							  = is_training
   __lowercase							  = use_input_mask
   __lowercase							  = use_token_type_ids
   __lowercase							  = use_labels
   __lowercase							  = vocab_size
   __lowercase							  = hidden_size
   __lowercase							  = num_hidden_layers
   __lowercase							  = num_attention_heads
   __lowercase							  = intermediate_size
   __lowercase							  = hidden_act
   __lowercase							  = hidden_dropout_prob
   __lowercase							  = attention_probs_dropout_prob
   __lowercase							  = max_position_embeddings
   __lowercase							  = type_vocab_size
   __lowercase							  = type_sequence_label_size
   __lowercase							  = initializer_range
   __lowercase							  = num_labels
   __lowercase							  = num_choices
   __lowercase							  = relative_attention
   __lowercase							  = position_biased_input
   __lowercase							  = pos_att_type
   __lowercase							  = scope






  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple			):
   __lowercase							  = ids_tensor([self.batch_size, self.seq_length]					,self.vocab_size			)

   __lowercase							  = None
   if self.use_input_mask:
    __lowercase							  = ids_tensor([self.batch_size, self.seq_length]					,vocab_size=2			)

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

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

   __lowercase							  = self.get_config()

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






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   return DebertaConfig(
       vocab_size=self.vocab_size					,hidden_size=self.hidden_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					,initializer_range=self.initializer_range					,relative_attention=self.relative_attention					,position_biased_input=self.position_biased_input					,pos_att_type=self.pos_att_type					,)






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]			):
   __lowercase							  = self.get_config()
   __lowercase							  = 3_0_0
   return config






  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]					,lowercase__   :     Optional[Any]			):
   self.parent.assertListEqual(list(result.loss.size()			)					,[]			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]					,lowercase__   :     List[Any]					,lowercase__   :     Dict					,lowercase__   :     List[Any]					,lowercase__   :     Tuple					,lowercase__   :     Dict					,lowercase__   :     str					,lowercase__   :     Union[str, Any]			):
   __lowercase							  = DebertaModel(config=lowercase__			)
   model.to(lowercase__			)
   model.eval()
   __lowercase							  = model(lowercase__					,attention_mask=lowercase__					,token_type_ids=lowercase__			)[0]
   __lowercase							  = model(lowercase__					,token_type_ids=lowercase__			)[0]
   __lowercase							  = model(lowercase__			)[0]

   self.parent.assertListEqual(list(sequence_output.size()			)					,[self.batch_size, self.seq_length, self.hidden_size]			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple					,lowercase__   :     str					,lowercase__   :     Tuple					,lowercase__   :     Optional[Any]					,lowercase__   :     Dict					,lowercase__   :     int					,lowercase__   :     Tuple					,lowercase__   :     int			):
   __lowercase							  = DebertaForMaskedLM(config=lowercase__			)
   model.to(lowercase__			)
   model.eval()
   __lowercase							  = model(lowercase__					,attention_mask=lowercase__					,token_type_ids=lowercase__					,labels=lowercase__			)

   self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.seq_length, self.vocab_size)			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     int					,lowercase__   :     Optional[int]					,lowercase__   :     Optional[int]					,lowercase__   :     int					,lowercase__   :     Union[str, Any]					,lowercase__   :     Dict					,lowercase__   :     List[Any]					,lowercase__   :     Optional[Any]			):
   __lowercase							  = self.num_labels
   __lowercase							  = DebertaForSequenceClassification(lowercase__			)
   model.to(lowercase__			)
   model.eval()
   __lowercase							  = model(lowercase__					,attention_mask=lowercase__					,token_type_ids=lowercase__					,labels=lowercase__			)
   self.parent.assertListEqual(list(result.logits.size()			)					,[self.batch_size, self.num_labels]			)
   self.check_loss_output(lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]					,lowercase__   :     Optional[int]					,lowercase__   :     Optional[int]					,lowercase__   :     Optional[int]					,lowercase__   :     Tuple					,lowercase__   :     List[str]					,lowercase__   :     List[str]					,lowercase__   :     Optional[int]			):
   __lowercase							  = self.num_labels
   __lowercase							  = DebertaForTokenClassification(config=lowercase__			)
   model.to(lowercase__			)
   model.eval()
   __lowercase							  = model(lowercase__					,attention_mask=lowercase__					,token_type_ids=lowercase__					,labels=lowercase__			)
   self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.seq_length, self.num_labels)			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any					,lowercase__   :     Any					,lowercase__   :     Any					,lowercase__   :     Dict					,lowercase__   :     Any					,lowercase__   :     Optional[Any]					,lowercase__   :     int					,lowercase__   :     List[str]			):
   __lowercase							  = DebertaForQuestionAnswering(config=lowercase__			)
   model.to(lowercase__			)
   model.eval()
   __lowercase							  = model(
       lowercase__					,attention_mask=lowercase__					,token_type_ids=lowercase__					,start_positions=lowercase__					,end_positions=lowercase__					,)
   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 					SCREAMING_SNAKE_CASE      (      self   :     Dict			):
   __lowercase							  = self.prepare_config_and_inputs()
   (
       (
       __lowercase
   )  ,	(
       __lowercase
   )  ,	(
       __lowercase
   )  ,	(
       __lowercase
   )  ,	(
       __lowercase
   )  ,	(
       __lowercase
   )  ,	(
       __lowercase
   )  ,	
   )							  = config_and_inputs
   __lowercase							  = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
   return config, inputs_dict






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




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  Tuple							=			(
      (
          DebertaModel,
          DebertaForMaskedLM,
          DebertaForSequenceClassification,
          DebertaForTokenClassification,
          DebertaForQuestionAnswering,
      )
      if is_torch_available()
      else ()
  )
  SCREAMING_SNAKE_CASE    :  Union[str, Any]							=			(
      {
          'feature-extraction': DebertaModel,
          'fill-mask': DebertaForMaskedLM,
          'question-answering': DebertaForQuestionAnswering,
          'text-classification': DebertaForSequenceClassification,
          'token-classification': DebertaForTokenClassification,
          'zero-shot': DebertaForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )

  SCREAMING_SNAKE_CASE    :  Tuple							=			True
  SCREAMING_SNAKE_CASE    :  List[str]							=			False
  SCREAMING_SNAKE_CASE    :  List[str]							=			False
  SCREAMING_SNAKE_CASE    :  Any							=			False
  SCREAMING_SNAKE_CASE    :  List[Any]							=			False






  def 					SCREAMING_SNAKE_CASE      (      self   :     int			):
   __lowercase							  = DebertaModelTester(self			)
   __lowercase							  = ConfigTester(self					,config_class=lowercase__					,hidden_size=3_7			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     int			):
   self.config_tester.run_common_tests()






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]			):
   __lowercase							  = self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_deberta_model(*lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]			):
   __lowercase							  = self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_deberta_for_sequence_classification(*lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict			):
   __lowercase							  = self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_deberta_for_masked_lm(*lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]			):
   __lowercase							  = self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_deberta_for_question_answering(*lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict			):
   __lowercase							  = self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_deberta_for_token_classification(*lowercase__			)






  @slow
  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple			):
   for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    __lowercase							  = DebertaModel.from_pretrained(lowercase__			)
    self.assertIsNotNone(lowercase__			)






@require_torch
@require_sentencepiece
@require_tokenizers
class 			lowercase_	(unittest.TestCase      ):




  """simple docstring"""




  @unittest.skip(reason='''Model not available yet'''			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     str			):
   pass






  @slow
  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]			):
   __lowercase							  = DebertaModel.from_pretrained('''microsoft/deberta-base'''			)

   __lowercase							  = torch.tensor([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]			)
   __lowercase							  = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]			)
   with torch.no_grad():
    __lowercase							  = model(lowercase__					,attention_mask=lowercase__			)[0]
   # compare the actual values for a slice.
   __lowercase							  = torch.tensor(
       [[[-0.5_9_8_6, -0.8_0_5_5, -0.8_4_6_2], [1.4_4_8_4, -0.9_3_4_8, -0.8_0_5_9], [0.3_1_2_3, 0.0_0_3_2, -1.4_1_3_1]]]			)
   self.assertTrue(torch.allclose(output[:, 1:4, 1:4]					,lowercase__					,atol=1e-4			)					,F"{output[:, 1:4, 1:4]}"			)

