






"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_     :				Any         =				{
    """configuration_rembert""": ["""REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RemBertConfig""", """RemBertOnnxConfig"""]
}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_     :				Any         =				["""RemBertTokenizer"""]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_     :				Dict         =				["""RemBertTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_     :				List[Any]         =				[
      """REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """RemBertForCausalLM""",
      """RemBertForMaskedLM""",
      """RemBertForMultipleChoice""",
      """RemBertForQuestionAnswering""",
      """RemBertForSequenceClassification""",
      """RemBertForTokenClassification""",
      """RemBertLayer""",
      """RemBertModel""",
      """RemBertPreTrainedModel""",
      """load_tf_weights_in_rembert""",
  ]


try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_     :				Dict         =				[
      """TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFRemBertForCausalLM""",
      """TFRemBertForMaskedLM""",
      """TFRemBertForMultipleChoice""",
      """TFRemBertForQuestionAnswering""",
      """TFRemBertForSequenceClassification""",
      """TFRemBertForTokenClassification""",
      """TFRemBertLayer""",
      """TFRemBertModel""",
      """TFRemBertPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_rembert import RemBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_rembert_fast import RemBertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_rembert import (
        REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        RemBertForCausalLM,
        RemBertForMaskedLM,
        RemBertForMultipleChoice,
        RemBertForQuestionAnswering,
        RemBertForSequenceClassification,
        RemBertForTokenClassification,
        RemBertLayer,
        RemBertModel,
        RemBertPreTrainedModel,
        load_tf_weights_in_rembert,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_rembert import (
        TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFRemBertForCausalLM,
        TFRemBertForMaskedLM,
        TFRemBertForMultipleChoice,
        TFRemBertForQuestionAnswering,
        TFRemBertForSequenceClassification,
        TFRemBertForTokenClassification,
        TFRemBertLayer,
        TFRemBertModel,
        TFRemBertPreTrainedModel,
    )


else:
  import sys

  UpperCAmelCase_     :				Tuple         =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)










"""simple docstring"""






import unittest

import numpy as np

from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
    expand_dims,
    flatten_dict,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    reshape,
    squeeze,
    transpose,
)


if is_flax_available():
  import jax.numpy as jnp

if is_tf_available():
  import tensorflow as tf

if is_torch_available():
  import torch




class 							lowerCAmelCase__       (    unittest.TestCase				):






      '''simple docstring'''

      def _SCREAMING_SNAKE_CASE				(		self  :						str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						str							=	{
              '''task_specific_params''': {
                  '''summarization''': {'''length_penalty''': 1.0, '''max_length''': 128, '''min_length''': 12, '''num_beams''': 4},
                  '''summarization_cnn''': {'''length_penalty''': 2.0, '''max_length''': 142, '''min_length''': 56, '''num_beams''': 4},
                  '''summarization_xsum''': {'''length_penalty''': 1.0, '''max_length''': 62, '''min_length''': 11, '''num_beams''': 6},
              }
          }
          SCREAMING_SNAKE_CASE_   :						List[str]							=	{
              '''task_specific_params.summarization.length_penalty''': 1.0,
              '''task_specific_params.summarization.max_length''': 128,
              '''task_specific_params.summarization.min_length''': 12,
              '''task_specific_params.summarization.num_beams''': 4,
              '''task_specific_params.summarization_cnn.length_penalty''': 2.0,
              '''task_specific_params.summarization_cnn.max_length''': 142,
              '''task_specific_params.summarization_cnn.min_length''': 56,
              '''task_specific_params.summarization_cnn.num_beams''': 4,
              '''task_specific_params.summarization_xsum.length_penalty''': 1.0,
              '''task_specific_params.summarization_xsum.max_length''': 62,
              '''task_specific_params.summarization_xsum.min_length''': 11,
              '''task_specific_params.summarization_xsum.num_beams''': 6,
          }

          self.assertEqual(flatten_dict(lowercase_)		,			lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	np.random.randn(3		,			4)
          self.assertTrue(np.allclose(transpose(lowercase_)		,			x.transpose()))

          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	np.random.randn(3		,			4		,			5)
          self.assertTrue(np.allclose(transpose(lowercase_		,			axes=(1, 2, 0))		,			x.transpose((1, 2, 0))))
      @require_torch
      def _SCREAMING_SNAKE_CASE				(		self  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						str							=	np.random.randn(3		,			4)
          SCREAMING_SNAKE_CASE_   :						str							=	torch.tensor(lowercase_)
          self.assertTrue(np.allclose(transpose(lowercase_)		,			transpose(lowercase_).numpy()))

          SCREAMING_SNAKE_CASE_   :						List[Any]							=	np.random.randn(3		,			4		,			5)
          SCREAMING_SNAKE_CASE_   :						Dict							=	torch.tensor(lowercase_)
          self.assertTrue(np.allclose(transpose(lowercase_		,			axes=(1, 2, 0))		,			transpose(lowercase_		,			axes=(1, 2, 0)).numpy()))
      @require_tf
      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	np.random.randn(3		,			4)
          SCREAMING_SNAKE_CASE_   :						str							=	tf.constant(lowercase_)
          self.assertTrue(np.allclose(transpose(lowercase_)		,			transpose(lowercase_).numpy()))

          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	np.random.randn(3		,			4		,			5)
          SCREAMING_SNAKE_CASE_   :						int							=	tf.constant(lowercase_)
          self.assertTrue(np.allclose(transpose(lowercase_		,			axes=(1, 2, 0))		,			transpose(lowercase_		,			axes=(1, 2, 0)).numpy()))
      @require_flax
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Tuple							=	np.random.randn(3		,			4)
          SCREAMING_SNAKE_CASE_   :						Any							=	jnp.array(lowercase_)
          self.assertTrue(np.allclose(transpose(lowercase_)		,			np.asarray(transpose(lowercase_))))

          SCREAMING_SNAKE_CASE_   :						Dict							=	np.random.randn(3		,			4		,			5)
          SCREAMING_SNAKE_CASE_   :						Any							=	jnp.array(lowercase_)
          self.assertTrue(np.allclose(transpose(lowercase_		,			axes=(1, 2, 0))		,			np.asarray(transpose(lowercase_		,			axes=(1, 2, 0)))))
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Dict							=	np.random.randn(3		,			4)
          self.assertTrue(np.allclose(reshape(lowercase_		,			(4, 3))		,			np.reshape(lowercase_		,			(4, 3))))

          SCREAMING_SNAKE_CASE_   :						Dict							=	np.random.randn(3		,			4		,			5)
          self.assertTrue(np.allclose(reshape(lowercase_		,			(12, 5))		,			np.reshape(lowercase_		,			(12, 5))))
      @require_torch
      def _SCREAMING_SNAKE_CASE				(		self  :						Union[str, Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	np.random.randn(3		,			4)
          SCREAMING_SNAKE_CASE_   :						List[str]							=	torch.tensor(lowercase_)
          self.assertTrue(np.allclose(reshape(lowercase_		,			(4, 3))		,			reshape(lowercase_		,			(4, 3)).numpy()))

          SCREAMING_SNAKE_CASE_   :						int							=	np.random.randn(3		,			4		,			5)
          SCREAMING_SNAKE_CASE_   :						int							=	torch.tensor(lowercase_)
          self.assertTrue(np.allclose(reshape(lowercase_		,			(12, 5))		,			reshape(lowercase_		,			(12, 5)).numpy()))
      @require_tf
      def _SCREAMING_SNAKE_CASE				(		self  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	np.random.randn(3		,			4)
          SCREAMING_SNAKE_CASE_   :						int							=	tf.constant(lowercase_)
          self.assertTrue(np.allclose(reshape(lowercase_		,			(4, 3))		,			reshape(lowercase_		,			(4, 3)).numpy()))

          SCREAMING_SNAKE_CASE_   :						List[Any]							=	np.random.randn(3		,			4		,			5)
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	tf.constant(lowercase_)
          self.assertTrue(np.allclose(reshape(lowercase_		,			(12, 5))		,			reshape(lowercase_		,			(12, 5)).numpy()))
      @require_flax
      def _SCREAMING_SNAKE_CASE				(		self  :						Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Any							=	np.random.randn(3		,			4)
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	jnp.array(lowercase_)
          self.assertTrue(np.allclose(reshape(lowercase_		,			(4, 3))		,			np.asarray(reshape(lowercase_		,			(4, 3)))))

          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	np.random.randn(3		,			4		,			5)
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	jnp.array(lowercase_)
          self.assertTrue(np.allclose(reshape(lowercase_		,			(12, 5))		,			np.asarray(reshape(lowercase_		,			(12, 5)))))
      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[Any]							=	np.random.randn(1		,			3		,			4)
          self.assertTrue(np.allclose(squeeze(lowercase_)		,			np.squeeze(lowercase_)))

          SCREAMING_SNAKE_CASE_   :						str							=	np.random.randn(1		,			4		,			1		,			5)
          self.assertTrue(np.allclose(squeeze(lowercase_		,			axis=2)		,			np.squeeze(lowercase_		,			axis=2)))
      @require_torch
      def _SCREAMING_SNAKE_CASE				(		self  :						Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	np.random.randn(1		,			3		,			4)
          SCREAMING_SNAKE_CASE_   :						int							=	torch.tensor(lowercase_)
          self.assertTrue(np.allclose(squeeze(lowercase_)		,			squeeze(lowercase_).numpy()))

          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	np.random.randn(1		,			4		,			1		,			5)
          SCREAMING_SNAKE_CASE_   :						str							=	torch.tensor(lowercase_)
          self.assertTrue(np.allclose(squeeze(lowercase_		,			axis=2)		,			squeeze(lowercase_		,			axis=2).numpy()))
      @require_tf
      def _SCREAMING_SNAKE_CASE				(		self  :						List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						int							=	np.random.randn(1		,			3		,			4)
          SCREAMING_SNAKE_CASE_   :						Dict							=	tf.constant(lowercase_)
          self.assertTrue(np.allclose(squeeze(lowercase_)		,			squeeze(lowercase_).numpy()))

          SCREAMING_SNAKE_CASE_   :						List[Any]							=	np.random.randn(1		,			4		,			1		,			5)
          SCREAMING_SNAKE_CASE_   :						Any							=	tf.constant(lowercase_)
          self.assertTrue(np.allclose(squeeze(lowercase_		,			axis=2)		,			squeeze(lowercase_		,			axis=2).numpy()))
      @require_flax
      def _SCREAMING_SNAKE_CASE				(		self  :						int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	np.random.randn(1		,			3		,			4)
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	jnp.array(lowercase_)
          self.assertTrue(np.allclose(squeeze(lowercase_)		,			np.asarray(squeeze(lowercase_))))

          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	np.random.randn(1		,			4		,			1		,			5)
          SCREAMING_SNAKE_CASE_   :						str							=	jnp.array(lowercase_)
          self.assertTrue(np.allclose(squeeze(lowercase_		,			axis=2)		,			np.asarray(squeeze(lowercase_		,			axis=2))))
      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						int							=	np.random.randn(3		,			4)
          self.assertTrue(np.allclose(expand_dims(lowercase_		,			axis=1)		,			np.expand_dims(lowercase_		,			axis=1)))
      @require_torch
      def _SCREAMING_SNAKE_CASE				(		self  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						int							=	np.random.randn(3		,			4)
          SCREAMING_SNAKE_CASE_   :						int							=	torch.tensor(lowercase_)
          self.assertTrue(np.allclose(expand_dims(lowercase_		,			axis=1)		,			expand_dims(lowercase_		,			axis=1).numpy()))
      @require_tf
      def _SCREAMING_SNAKE_CASE				(		self  :						str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[str]							=	np.random.randn(3		,			4)
          SCREAMING_SNAKE_CASE_   :						Any							=	tf.constant(lowercase_)
          self.assertTrue(np.allclose(expand_dims(lowercase_		,			axis=1)		,			expand_dims(lowercase_		,			axis=1).numpy()))



      @require_flax
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Tuple							=	np.random.randn(3		,			4)
          SCREAMING_SNAKE_CASE_   :						int							=	jnp.array(lowercase_)
          self.assertTrue(np.allclose(expand_dims(lowercase_		,			axis=1)		,			np.asarray(expand_dims(lowercase_		,			axis=1))))


