





'''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 			SCREAMING_SNAKE_CASE   (unittest.TestCase							):


     def __init__(						self						,     _UpperCAmelCase						,     _UpperCAmelCase=13						,     _UpperCAmelCase=7						,     _UpperCAmelCase=True						,     _UpperCAmelCase=True						,     _UpperCAmelCase=True						,     _UpperCAmelCase=True						,     _UpperCAmelCase=99						,     _UpperCAmelCase=32						,     _UpperCAmelCase=5						,     _UpperCAmelCase=4						,     _UpperCAmelCase=37						,     _UpperCAmelCase="gelu"						,     _UpperCAmelCase=0.1						,     _UpperCAmelCase=0.1						,     _UpperCAmelCase=512						,     _UpperCAmelCase=16						,     _UpperCAmelCase=2						,     _UpperCAmelCase=0.02						,     _UpperCAmelCase=4						,     ):

          '''simple docstring'''






          __A						:			Optional[Any]								=	parent
          __A						:			Tuple								=	batch_size
          __A						:			str								=	seq_length
          __A						:			Tuple								=	is_training
          __A						:			List[Any]								=	use_attention_mask
          __A						:			Union[str, Any]								=	use_token_type_ids
          __A						:			List[Any]								=	use_labels
          __A						:			int								=	vocab_size
          __A						:			int								=	hidden_size
          __A						:			str								=	num_hidden_layers
          __A						:			int								=	num_attention_heads
          __A						:			Any								=	intermediate_size
          __A						:			List[Any]								=	hidden_act
          __A						:			Dict								=	hidden_dropout_prob
          __A						:			str								=	attention_probs_dropout_prob
          __A						:			Dict								=	max_position_embeddings
          __A						:			Tuple								=	type_vocab_size
          __A						:			List[Any]								=	type_sequence_label_size
          __A						:			Any								=	initializer_range
          __A						:			Optional[Any]								=	num_choices


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Optional[int]								=	ids_tensor([self.batch_size, self.seq_length]						,     self.vocab_size)

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

          __A						:			List[Any]								=	None
          if self.use_token_type_ids:
               __A						:			Dict								=	ids_tensor([self.batch_size, self.seq_length]						,     self.type_vocab_size)

          __A						:			Optional[Any]								=	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 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Any								=	self.prepare_config_and_inputs()
          __A			,__A			,__A			,__A						:			int								=	config_and_inputs
          __A						:			Tuple								=	{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
          return config, inputs_dict




@require_flax
class 			SCREAMING_SNAKE_CASE   (a__ ,		unittest.TestCase							):
     lowerCAmelCase		      =	True

     lowerCAmelCase		      =	(
         (
             FlaxRoFormerModel,
             FlaxRoFormerForMaskedLM,
             FlaxRoFormerForSequenceClassification,
             FlaxRoFormerForTokenClassification,
             FlaxRoFormerForMultipleChoice,
             FlaxRoFormerForQuestionAnswering,
         )
         if is_flax_available()
         else ()
     )


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			str								=	FlaxRoFormerModelTester(self)


     @slow
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          for model_class_name in self.all_model_classes:
               __A						:			Dict								=	model_class_name.from_pretrained('junnyu/roformer_chinese_small'						,     from_pt=_UpperCAmelCase)
               __A						:			Tuple								=	model(np.ones((1, 1)))
               self.assertIsNotNone(_UpperCAmelCase)




@require_flax
class 			SCREAMING_SNAKE_CASE   (unittest.TestCase							):


     @slow
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			int								=	FlaxRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base')
          __A						:			List[Any]								=	jnp.array([[0, 1, 2, 3, 4, 5]])
          __A						:			int								=	model(_UpperCAmelCase)[0]

          __A						:			Optional[int]								=	5_0000

          __A						:			Optional[Any]								=	(1, 6, vocab_size)
          self.assertEqual(output.shape						,     _UpperCAmelCase)

          __A						:			Dict								=	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]						,     _UpperCAmelCase						,     atol=1e-4))






'''simple docstring'''




import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('''9.1.0'''):
  lowercase__   :				Dict      =   {
      '''linear''': PIL.Image.Resampling.BILINEAR,
      '''bilinear''': PIL.Image.Resampling.BILINEAR,
      '''bicubic''': PIL.Image.Resampling.BICUBIC,
      '''lanczos''': PIL.Image.Resampling.LANCZOS,
      '''nearest''': PIL.Image.Resampling.NEAREST,
  }
else:
  lowercase__   :				Any      =   {
      '''linear''': PIL.Image.LINEAR,
      '''bilinear''': PIL.Image.BILINEAR,
      '''bicubic''': PIL.Image.BICUBIC,
      '''lanczos''': PIL.Image.LANCZOS,
      '''nearest''': PIL.Image.NEAREST,
  }






def 	_lowerCAmelCase      (  __snake_case  :			Any      )					->					Optional[Any]:
     __A						:			Dict								=	(images / 2 + 0.5).clamp(0			,							1      )
     __A						:			str								=	images.cpu().permute(0			,							2			,							3			,							1      ).float().numpy()
     __A						:			Dict								=	numpy_to_pil(__snake_case      )
     return images







def 	_lowerCAmelCase      (  __snake_case  :			List[Any]      )					->					Optional[Any]:
     if images.ndim == 3:
          __A						:			List[Any]								=	images[None, ...]
     __A						:			List[str]								=	(images * 2_55).round().astype('uint8'      )
     if images.shape[-1] == 1:
          # special case for grayscale (single channel) images
          __A						:			str								=	[Image.fromarray(image.squeeze()			,							mode='L'      ) for image in images]
     else:
          __A						:			str								=	[Image.fromarray(__snake_case      ) for image in images]

     return pil_images