


'''simple docstring'''




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 		UpperCAmelCase_      (  unittest.TestCase		):





     def __init__( self       :      str    ,      UpperCAmelCase__       :      Union[str, Any]    ,      UpperCAmelCase__       :      List[Any]=1_3    ,      UpperCAmelCase__       :      Tuple=7    ,      UpperCAmelCase__       :      List[Any]=True    ,      UpperCAmelCase__       :      Dict=True    ,      UpperCAmelCase__       :      Optional[Any]=True    ,      UpperCAmelCase__       :      int=True    ,      UpperCAmelCase__       :      int=9_9    ,      UpperCAmelCase__       :      List[str]=3_2    ,      UpperCAmelCase__       :      Tuple=5    ,      UpperCAmelCase__       :      List[str]=4    ,      UpperCAmelCase__       :      Tuple=3_7    ,      UpperCAmelCase__       :      Union[str, Any]="gelu"    ,      UpperCAmelCase__       :      Any=0.1    ,      UpperCAmelCase__       :      Tuple=0.1    ,      UpperCAmelCase__       :      int=5_1_2    ,      UpperCAmelCase__       :      int=1_6    ,      UpperCAmelCase__       :      Optional[int]=2    ,      UpperCAmelCase__       :      Tuple=0.02    ,      UpperCAmelCase__       :      Any=4    ,      )				->  int:
        lowerCAmelCase     =			parent
        lowerCAmelCase     =			batch_size
        lowerCAmelCase     =			seq_length
        lowerCAmelCase     =			is_training
        lowerCAmelCase     =			use_attention_mask
        lowerCAmelCase     =			use_token_type_ids
        lowerCAmelCase     =			use_labels
        lowerCAmelCase     =			vocab_size
        lowerCAmelCase     =			hidden_size
        lowerCAmelCase     =			num_hidden_layers
        lowerCAmelCase     =			num_attention_heads
        lowerCAmelCase     =			intermediate_size
        lowerCAmelCase     =			hidden_act
        lowerCAmelCase     =			hidden_dropout_prob
        lowerCAmelCase     =			attention_probs_dropout_prob
        lowerCAmelCase     =			max_position_embeddings
        lowerCAmelCase     =			type_vocab_size
        lowerCAmelCase     =			type_sequence_label_size
        lowerCAmelCase     =			initializer_range
        lowerCAmelCase     =			num_choices





     def 							__UpperCAmelCase      ( self       :      Dict    )				->  Optional[Any]:
        lowerCAmelCase     =			ids_tensor([self.batch_size, self.seq_length]    ,      self.vocab_size    )

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

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

        lowerCAmelCase     =			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=UpperCAmelCase__    ,      initializer_range=self.initializer_range    ,      )

        return config, input_ids, token_type_ids, attention_mask





     def 							__UpperCAmelCase      ( self       :      Any    )				->  str:
        lowerCAmelCase     =			self.prepare_config_and_inputs()
        lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     =			config_and_inputs
        lowerCAmelCase     =			{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
        return config, inputs_dict




@require_flax
class 		UpperCAmelCase_      (  __lowercase       ,	unittest.TestCase		):
     lowerCamelCase				:	Optional[Any]		=     True

     lowerCamelCase				:	Tuple		=     (
         (
             FlaxRoFormerModel,
             FlaxRoFormerForMaskedLM,
             FlaxRoFormerForSequenceClassification,
             FlaxRoFormerForTokenClassification,
             FlaxRoFormerForMultipleChoice,
             FlaxRoFormerForQuestionAnswering,
         )
         if is_flax_available()
         else ()
     )





     def 							__UpperCAmelCase      ( self       :      str    )				->  str:
        lowerCAmelCase     =			FlaxRoFormerModelTester(self    )





     @slow
     def 							__UpperCAmelCase      ( self       :      List[Any]    )				->  List[Any]:
        for model_class_name in self.all_model_classes:
           lowerCAmelCase     =			model_class_name.from_pretrained('junnyu/roformer_chinese_small'    ,      from_pt=UpperCAmelCase__    )
           lowerCAmelCase     =			model(np.ones((1, 1)    )    )
           self.assertIsNotNone(UpperCAmelCase__    )




@require_flax
class 		UpperCAmelCase_      (  unittest.TestCase		):





     @slow
     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  str:
        lowerCAmelCase     =			FlaxRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base'    )
        lowerCAmelCase     =			jnp.array([[0, 1, 2, 3, 4, 5]]    )
        lowerCAmelCase     =			model(UpperCAmelCase__    )[0]

        lowerCAmelCase     =			5_0_0_0_0

        lowerCAmelCase     =			(1, 6, vocab_size)
        self.assertEqual(output.shape    ,      UpperCAmelCase__    )

        lowerCAmelCase     =			jnp.array(
            [[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]]    )

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












from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
	if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
	from .pipeline_unclip import UnCLIPPipeline
	from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
	from .text_proj import UnCLIPTextProjModel
