"""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 						__A							(			unittest.TestCase		):






     def __init__(		self    ,					a__    ,					a__=13    ,					a__=7    ,					a__=True    ,					a__=True    ,					a__=True    ,					a__=True    ,					a__=99    ,					a__=32    ,					a__=5    ,					a__=4    ,					a__=37    ,					a__="gelu"    ,					a__=0.1    ,					a__=0.1    ,					a__=512    ,					a__=16    ,					a__=2    ,					a__=0.0_2    ,					a__=4    ,					):
       _lowerCAmelCase						:	Dict        =					parent
       _lowerCAmelCase						:	str        =					batch_size
       _lowerCAmelCase						:	Any        =					seq_length
       _lowerCAmelCase						:	Optional[int]        =					is_training
       _lowerCAmelCase						:	Any        =					use_attention_mask
       _lowerCAmelCase						:	Any        =					use_token_type_ids
       _lowerCAmelCase						:	Union[str, Any]        =					use_labels
       _lowerCAmelCase						:	List[Any]        =					vocab_size
       _lowerCAmelCase						:	str        =					hidden_size
       _lowerCAmelCase						:	List[Any]        =					num_hidden_layers
       _lowerCAmelCase						:	Tuple        =					num_attention_heads
       _lowerCAmelCase						:	List[str]        =					intermediate_size
       _lowerCAmelCase						:	Any        =					hidden_act
       _lowerCAmelCase						:	Union[str, Any]        =					hidden_dropout_prob
       _lowerCAmelCase						:	List[str]        =					attention_probs_dropout_prob
       _lowerCAmelCase						:	Dict        =					max_position_embeddings
       _lowerCAmelCase						:	List[str]        =					type_vocab_size
       _lowerCAmelCase						:	Any        =					type_sequence_label_size
       _lowerCAmelCase						:	Dict        =					initializer_range
       _lowerCAmelCase						:	List[str]        =					num_choices






     def    __A  (		self  ):
       _lowerCAmelCase						:	List[Any]        =					ids_tensor([self.batch_size, self.seq_length]    ,					self.vocab_size  )

       _lowerCAmelCase						:	Any        =					None
       if self.use_attention_mask:
         _lowerCAmelCase						:	Optional[Any]        =					random_attention_mask([self.batch_size, self.seq_length]  )

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

       _lowerCAmelCase						:	List[str]        =					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=a__    ,					initializer_range=self.initializer_range    ,					)

       return config, input_ids, token_type_ids, attention_mask






     def    __A  (		self  ):
       _lowerCAmelCase						:	Dict        =					self.prepare_config_and_inputs()
       _lowerCAmelCase						:	Optional[Any]        =					config_and_inputs
       _lowerCAmelCase						:	List[str]        =					{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
       return config, inputs_dict







@require_flax
class 						__A							(			SCREAMING_SNAKE_CASE__   ,     unittest.TestCase		):
     _UpperCamelCase							:      Optional[int]		 =		True

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






     def    __A  (		self  ):
       _lowerCAmelCase						:	Any        =					FlaxRoFormerModelTester(self  )






     @slow
     def    __A  (		self  ):
       for model_class_name in self.all_model_classes:
         _lowerCAmelCase						:	List[Any]        =					model_class_name.from_pretrained("""junnyu/roformer_chinese_small"""    ,					from_pt=a__  )
         _lowerCAmelCase						:	Tuple        =					model(np.ones((1, 1)  )  )
         self.assertIsNotNone(a__  )







@require_flax
class 						__A							(			unittest.TestCase		):






     @slow
     def    __A  (		self  ):
       _lowerCAmelCase						:	Optional[int]        =					FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""  )
       _lowerCAmelCase						:	Dict        =					jnp.array([[0, 1, 2, 3, 4, 5]]  )
       _lowerCAmelCase						:	Union[str, Any]        =					model(a__  )[0]

       _lowerCAmelCase						:	Tuple        =					50000

       _lowerCAmelCase						:	int        =					(1, 6, vocab_size)
       self.assertEqual(output.shape    ,					a__  )

       _lowerCAmelCase						:	List[Any]        =					jnp.array(
           [[[-0.1_2_0_5, -1.0_2_6_5, 0.2_9_2_2], [-1.5_1_3_4, 0.1_9_7_4, 0.1_5_1_9], [-5.0_1_3_5, -3.9_0_0_3, -0.8_4_0_4]]]  )

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





"""simple docstring"""




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import PoolFormerImageProcessor
class 						__A							(			unittest.TestCase		):






     def __init__(		self    ,					a__    ,					a__=7    ,					a__=3    ,					a__=30    ,					a__=400    ,					a__=True    ,					a__=None    ,					a__=0.9    ,					a__=None    ,					a__=True    ,					a__=[0.5, 0.5, 0.5]    ,					a__=[0.5, 0.5, 0.5]    ,					):
       _lowerCAmelCase						:	int        =					size if size is not None else {"""shortest_edge""": 30}
       _lowerCAmelCase						:	Dict        =					crop_size if crop_size is not None else {"""height""": 30, """width""": 30}
       _lowerCAmelCase						:	List[Any]        =					parent
       _lowerCAmelCase						:	Optional[int]        =					batch_size
       _lowerCAmelCase						:	Any        =					num_channels
       _lowerCAmelCase						:	str        =					min_resolution
       _lowerCAmelCase						:	Dict        =					max_resolution
       _lowerCAmelCase						:	str        =					do_resize_and_center_crop
       _lowerCAmelCase						:	List[str]        =					size
       _lowerCAmelCase						:	int        =					crop_pct
       _lowerCAmelCase						:	int        =					crop_size
       _lowerCAmelCase						:	Union[str, Any]        =					do_normalize
       _lowerCAmelCase						:	Tuple        =					image_mean
       _lowerCAmelCase						:	Optional[Any]        =					image_std






     def    __A  (		self  ):
       return {
           "size": self.size,
           "do_resize_and_center_crop": self.do_resize_and_center_crop,
           "crop_pct": self.crop_pct,
           "crop_size": self.crop_size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
       }







@require_torch
@require_vision
class 						__A							(			SCREAMING_SNAKE_CASE_   ,     unittest.TestCase		):
     _UpperCamelCase							:      List[str]		 =		PoolFormerImageProcessor if is_vision_available() else None






     def    __A  (		self  ):
       _lowerCAmelCase						:	str        =					PoolFormerImageProcessingTester(self  )






     @property
     def    __A  (		self  ):
       return self.image_processor_tester.prepare_image_processor_dict()






     def    __A  (		self  ):
       _lowerCAmelCase						:	List[str]        =					self.image_processing_class(**self.image_processor_dict  )
       self.assertTrue(hasattr(a__    ,					"""do_resize_and_center_crop"""  )  )
       self.assertTrue(hasattr(a__    ,					"""size"""  )  )
       self.assertTrue(hasattr(a__    ,					"""crop_pct"""  )  )
       self.assertTrue(hasattr(a__    ,					"""do_normalize"""  )  )
       self.assertTrue(hasattr(a__    ,					"""image_mean"""  )  )
       self.assertTrue(hasattr(a__    ,					"""image_std"""  )  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	List[str]        =					self.image_processing_class.from_dict(self.image_processor_dict  )
       self.assertEqual(image_processor.size    ,					{"""shortest_edge""": 30}  )
       self.assertEqual(image_processor.crop_size    ,					{"""height""": 30, """width""": 30}  )

       _lowerCAmelCase						:	List[Any]        =					self.image_processing_class.from_dict(self.image_processor_dict    ,					size=42    ,					crop_size=84  )
       self.assertEqual(image_processor.size    ,					{"""shortest_edge""": 42}  )
       self.assertEqual(image_processor.crop_size    ,					{"""height""": 84, """width""": 84}  )






     def    __A  (		self  ):
       pass






     def    __A  (		self  ):
       # Initialize image_processing
       _lowerCAmelCase						:	List[str]        =					self.image_processing_class(**self.image_processor_dict  )
       # create random PIL images
       _lowerCAmelCase						:	Tuple        =					prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=a__  )
       for image in image_inputs:
         self.assertIsInstance(a__    ,					Image.Image  )

       # Test not batched input
       _lowerCAmelCase						:	Tuple        =					image_processing(image_inputs[0]    ,					return_tensors="""pt"""  ).pixel_values

       self.assertEqual(
           encoded_images.shape    ,					(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )    ,					)

       # Test batched
       _lowerCAmelCase						:	List[str]        =					image_processing(a__    ,					return_tensors="""pt"""  ).pixel_values
       self.assertEqual(
           encoded_images.shape    ,					(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )    ,					)






     def    __A  (		self  ):
       # Initialize image_processing
       _lowerCAmelCase						:	Union[str, Any]        =					self.image_processing_class(**self.image_processor_dict  )
       # create random numpy tensors
       _lowerCAmelCase						:	Union[str, Any]        =					prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=a__    ,					numpify=a__  )
       for image in image_inputs:
         self.assertIsInstance(a__    ,					np.ndarray  )

       # Test not batched input
       _lowerCAmelCase						:	Dict        =					image_processing(image_inputs[0]    ,					return_tensors="""pt"""  ).pixel_values
       self.assertEqual(
           encoded_images.shape    ,					(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )    ,					)

       # Test batched
       _lowerCAmelCase						:	int        =					image_processing(a__    ,					return_tensors="""pt"""  ).pixel_values
       self.assertEqual(
           encoded_images.shape    ,					(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )    ,					)






     def    __A  (		self  ):
       # Initialize image_processing
       _lowerCAmelCase						:	Any        =					self.image_processing_class(**self.image_processor_dict  )
       # create random PyTorch tensors
       _lowerCAmelCase						:	Any        =					prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=a__    ,					torchify=a__  )
       for image in image_inputs:
         self.assertIsInstance(a__    ,					torch.Tensor  )

       # Test not batched input
       _lowerCAmelCase						:	str        =					image_processing(image_inputs[0]    ,					return_tensors="""pt"""  ).pixel_values
       self.assertEqual(
           encoded_images.shape    ,					(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )    ,					)

       # Test batched
       _lowerCAmelCase						:	List[str]        =					image_processing(a__    ,					return_tensors="""pt"""  ).pixel_values
       self.assertEqual(
           encoded_images.shape    ,					(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )    ,					)




