



import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
							import jax.numpy as jnp

							from transformers.models.roformer.modeling_flax_roformer import (
							    FlaxRoFormerForMaskedLM,
							    FlaxRoFormerForMultipleChoice,
							    FlaxRoFormerForQuestionAnswering,
							    FlaxRoFormerForSequenceClassification,
							    FlaxRoFormerForTokenClassification,
							    FlaxRoFormerModel,
							)


class   __lowerCAmelCase  (		unittest.TestCase   ):
					"""simple docstring"""






					def __init__(  self       :							Any					,       _snake_case       :							Optional[Any]					,       _snake_case       :							Tuple=13					,       _snake_case       :							int=7					,       _snake_case       :							Dict=True					,       _snake_case       :							str=True					,       _snake_case       :							Tuple=True					,       _snake_case       :							List[str]=True					,       _snake_case       :							Union[str, Any]=99					,       _snake_case       :							Union[str, Any]=32					,       _snake_case       :							Union[str, Any]=5					,       _snake_case       :							Any=4					,       _snake_case       :							Any=37					,       _snake_case       :							Any="gelu"					,       _snake_case       :							Optional[Any]=0.1					,       _snake_case       :							Any=0.1					,       _snake_case       :							List[str]=5_12					,       _snake_case       :							int=16					,       _snake_case       :							str=2					,       _snake_case       :							Any=0.02					,       _snake_case       :							List[str]=4					,       ):






								"""simple docstring"""


								A__	  =     parent
								A__	  =     batch_size
								A__	  =     seq_length
								A__	  =     is_training
								A__	  =     use_attention_mask
								A__	  =     use_token_type_ids
								A__	  =     use_labels
								A__	  =     vocab_size
								A__	  =     hidden_size
								A__	  =     num_hidden_layers
								A__	  =     num_attention_heads
								A__	  =     intermediate_size
								A__	  =     hidden_act
								A__	  =     hidden_dropout_prob
								A__	  =     attention_probs_dropout_prob
								A__	  =     max_position_embeddings
								A__	  =     type_vocab_size
								A__	  =     type_sequence_label_size
								A__	  =     initializer_range
								A__	  =     num_choices



					def      _a				(  self       :							Tuple       ):






								"""simple docstring"""


								A__	  =     ids_tensor([self.batch_size, self.seq_length]					,       self.vocab_size       )

								A__	  =     None
								if self.use_attention_mask:
											A__	  =     random_attention_mask([self.batch_size, self.seq_length]       )

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

								A__	  =     RoFormerConfig(
								    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					,       is_decoder=_snake_case					,       initializer_range=self.initializer_range					,       )

								return config, input_ids, token_type_ids, attention_mask



					def      _a				(  self       :							Dict       ):






								"""simple docstring"""


								A__	  =     self.prepare_config_and_inputs()
								A__  ,			A__  ,			A__  ,			A__	  =     config_and_inputs
								A__	  =     {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
								return config, inputs_dict







@require_flax
class   __lowerCAmelCase  (		UpperCAmelCase_				,  unittest.TestCase   ):
					"""simple docstring"""






					A__					:		str      							=     True

					A__					:		Optional[Any]      							=     (
					    (
					        FlaxRoFormerModel,
					        FlaxRoFormerForMaskedLM,
					        FlaxRoFormerForSequenceClassification,
					        FlaxRoFormerForTokenClassification,
					        FlaxRoFormerForMultipleChoice,
					        FlaxRoFormerForQuestionAnswering,
					    )
					    if is_flax_available()
					    else ()
					)



					def      _a				(  self       :							Optional[Any]       ):






								"""simple docstring"""


								A__	  =     FlaxRoFormerModelTester(self       )



					@slow
					def      _a				(  self       :							int       ):






								"""simple docstring"""


								for model_class_name in self.all_model_classes:
											A__	  =     model_class_name.from_pretrained('junnyu/roformer_chinese_small'					,       from_pt=_snake_case       )
											A__	  =     model(np.ones((1, 1)       )       )
											self.assertIsNotNone(_snake_case       )







@require_flax
class   __lowerCAmelCase  (		unittest.TestCase   ):
					"""simple docstring"""






					@slow
					def      _a				(  self       :							Tuple       ):






								"""simple docstring"""


								A__	  =     FlaxRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base'       )
								A__	  =     jnp.array([[0, 1, 2, 3, 4, 5]]       )
								A__	  =     model(_snake_case       )[0]

								A__	  =     5_00_00

								A__	  =     (1, 6, vocab_size)
								self.assertEqual(output.shape					,       _snake_case       )

								A__	  =     jnp.array(
								    [[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]]       )

								self.assertTrue(jnp.allclose(output[:, :3, :3]					,       _snake_case					,       atol=1E-4       )       )







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_lowerCamelCase      :					Tuple					    =      {
    '''configuration_squeezebert''': [
        '''SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SqueezeBertConfig''',
        '''SqueezeBertOnnxConfig''',
    ],
    '''tokenization_squeezebert''': ['''SqueezeBertTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase      :					List[str]					    =      ['''SqueezeBertTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase      :					Tuple					    =      [
       '''SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''SqueezeBertForMaskedLM''',
       '''SqueezeBertForMultipleChoice''',
       '''SqueezeBertForQuestionAnswering''',
       '''SqueezeBertForSequenceClassification''',
       '''SqueezeBertForTokenClassification''',
       '''SqueezeBertModel''',
       '''SqueezeBertModule''',
       '''SqueezeBertPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_squeezebert import (
       SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
       SqueezeBertConfig,
       SqueezeBertOnnxConfig,
   )
   from .tokenization_squeezebert import SqueezeBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_squeezebert import (
          SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          SqueezeBertForMaskedLM,
          SqueezeBertForMultipleChoice,
          SqueezeBertForQuestionAnswering,
          SqueezeBertForSequenceClassification,
          SqueezeBertForTokenClassification,
          SqueezeBertModel,
          SqueezeBertModule,
          SqueezeBertPreTrainedModel,
      )

else:
   import sys

   _lowerCamelCase      :					List[str]					    =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


