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 	__snake_case	( unittest.TestCase		):





						'''simple docstring'''






						def __init__(					self     :				Any    ,  _UpperCamelCase     :				Tuple    ,  _UpperCamelCase     :				Optional[int]=13    ,  _UpperCamelCase     :				Optional[int]=7    ,  _UpperCamelCase     :				str=True    ,  _UpperCamelCase     :				Tuple=True    ,  _UpperCamelCase     :				Any=True    ,  _UpperCamelCase     :				int=True    ,  _UpperCamelCase     :				Union[str, Any]=99    ,  _UpperCamelCase     :				Any=32    ,  _UpperCamelCase     :				Dict=5    ,  _UpperCamelCase     :				int=4    ,  _UpperCamelCase     :				List[str]=37    ,  _UpperCamelCase     :				List[str]="gelu"    ,  _UpperCamelCase     :				Any=0.1    ,  _UpperCamelCase     :				Union[str, Any]=0.1    ,  _UpperCamelCase     :				List[Any]=512    ,  _UpperCamelCase     :				Any=16    ,  _UpperCamelCase     :				List[Any]=2    ,  _UpperCamelCase     :				List[Any]=0.0_2    ,  _UpperCamelCase     :				List[str]=4    ,  )				->Dict:


										"""simple docstring"""


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



						def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->Dict:


										"""simple docstring"""


										_lowerCamelCase     :		Dict 						=						ids_tensor([self.batch_size, self.seq_length]    ,  self.vocab_size)

										_lowerCamelCase     :		Any 						=						None
										if self.use_attention_mask:
														_lowerCamelCase     :		Union[str, Any] 						=						random_attention_mask([self.batch_size, self.seq_length])

										_lowerCamelCase     :		Dict 						=						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=_lowerCAmelCase    ,  initializer_range=self.initializer_range    ,  )

										return config, input_ids, token_type_ids, attention_mask




						def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int])				->Any:


										"""simple docstring"""


										_lowerCamelCase     :		Any 						=						self.prepare_config_and_inputs()
										_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase     :		List[Any] 						=						config_and_inputs
										_lowerCamelCase     :		Union[str, Any] 						=						{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
										return config, inputs_dict





@require_flax
class 	__snake_case	( __lowerCAmelCase ,  unittest.TestCase		):





						'''simple docstring'''






						_snake_case    		=       True

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



						def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any])				->Tuple:


										"""simple docstring"""


										_lowerCamelCase     :		Optional[Any] 						=						FlaxRoFormerModelTester(self)




						@slow
						def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any])				->Dict:


										"""simple docstring"""


										for model_class_name in self.all_model_classes:
														_lowerCamelCase     :		Optional[int] 						=						model_class_name.from_pretrained("""junnyu/roformer_chinese_small"""    ,  from_pt=_lowerCAmelCase)
														_lowerCamelCase     :		int 						=						model(np.ones((1, 1)))
														self.assertIsNotNone(_lowerCAmelCase)





@require_flax
class 	__snake_case	( unittest.TestCase		):





						'''simple docstring'''






						@slow
						def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->List[Any]:


										"""simple docstring"""


										_lowerCamelCase     :		Optional[int] 						=						FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""")
										_lowerCamelCase     :		int 						=						jnp.array([[0, 1, 2, 3, 4, 5]])
										_lowerCamelCase     :		str 						=						model(_lowerCAmelCase)[0]

										_lowerCamelCase     :		int 						=						5_0000

										_lowerCamelCase     :		Union[str, Any] 						=						(1, 6, vocab_size)
										self.assertEqual(output.shape    ,  _lowerCAmelCase)

										_lowerCamelCase     :		Dict 						=						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]    ,  _lowerCAmelCase    ,  atol=1E-4))





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
    import PIL


lowerCAmelCase			:							Tuple     =logging.get_logger(__name__)




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       ['pixel_values']



      def __init__(					self     :				Optional[Any]    ,  _UpperCamelCase     :				bool = True    ,  _UpperCamelCase     :				Dict[str, int] = None    ,  _UpperCamelCase     :				PILImageResampling = PIL.Image.BICUBIC    ,  _UpperCamelCase     :				bool = True    ,  _UpperCamelCase     :				Dict[str, int] = None    ,  _UpperCamelCase     :				Union[int, float] = 1 / 255    ,  _UpperCamelCase     :				bool = True    ,  _UpperCamelCase     :				bool = True    ,  _UpperCamelCase     :				Optional[Union[float, List[float]]] = None    ,  _UpperCamelCase     :				Optional[Union[float, List[float]]] = None    ,  **_UpperCamelCase     :				str    ,  )				->None:


          """simple docstring"""


          super().__init__(**_UpperCamelCase)
          _lowerCamelCase     :		Tuple 						=						size if size is not None else {"""height""": 256, """width""": 256}
          _lowerCamelCase     :		Optional[Any] 						=						get_size_dict(_UpperCamelCase)
          _lowerCamelCase     :		Any 						=						crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
          _lowerCamelCase     :		Any 						=						get_size_dict(_UpperCamelCase    ,  param_name="""crop_size""")

          _lowerCamelCase     :		int 						=						do_resize
          _lowerCamelCase     :		int 						=						size
          _lowerCamelCase     :		Optional[int] 						=						resample
          _lowerCamelCase     :		int 						=						do_center_crop
          _lowerCamelCase     :		Optional[Any] 						=						crop_size
          _lowerCamelCase     :		Union[str, Any] 						=						do_rescale
          _lowerCamelCase     :		List[str] 						=						rescale_factor
          _lowerCamelCase     :		List[Any] 						=						do_normalize
          _lowerCamelCase     :		Dict 						=						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          _lowerCamelCase     :		int 						=						image_std if image_std is not None else IMAGENET_STANDARD_STD



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any    ,  _UpperCamelCase     :				np.ndarray    ,  _UpperCamelCase     :				Dict[str, int]    ,  _UpperCamelCase     :				PILImageResampling = PIL.Image.BICUBIC    ,  _UpperCamelCase     :				Optional[Union[str, ChannelDimension]] = None    ,  **_UpperCamelCase     :				Union[str, Any]    ,  )				->np.ndarray:


          """simple docstring"""


          _lowerCamelCase     :		Dict 						=						get_size_dict(_UpperCamelCase)
          if "height" not in size or "width" not in size:
              raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}""")
          return resize(
              _UpperCamelCase    ,  size=(size["""height"""], size["""width"""])    ,  resample=_UpperCamelCase    ,  data_format=_UpperCamelCase    ,  **_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str]    ,  _UpperCamelCase     :				np.ndarray    ,  _UpperCamelCase     :				Dict[str, int]    ,  _UpperCamelCase     :				Optional[Union[str, ChannelDimension]] = None    ,  **_UpperCamelCase     :				List[str]    ,  )				->np.ndarray:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						get_size_dict(_UpperCamelCase)
          if "height" not in size or "width" not in size:
              raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}""")
          return center_crop(_UpperCamelCase    ,  size=(size["""height"""], size["""width"""])    ,  data_format=_UpperCamelCase    ,  **_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any]    ,  _UpperCamelCase     :				np.ndarray    ,  _UpperCamelCase     :				Union[int, float]    ,  _UpperCamelCase     :				Optional[Union[str, ChannelDimension]] = None    ,  **_UpperCamelCase     :				Union[str, Any]    ,  )				->str:


          """simple docstring"""


          return rescale(_UpperCamelCase    ,  scale=_UpperCamelCase    ,  data_format=_UpperCamelCase    ,  **_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any]    ,  _UpperCamelCase     :				np.ndarray    ,  _UpperCamelCase     :				Union[float, List[float]]    ,  _UpperCamelCase     :				Union[float, List[float]]    ,  _UpperCamelCase     :				Optional[Union[str, ChannelDimension]] = None    ,  **_UpperCamelCase     :				Union[str, Any]    ,  )				->np.ndarray:


          """simple docstring"""


          return normalize(_UpperCamelCase    ,  mean=_UpperCamelCase    ,  std=_UpperCamelCase    ,  data_format=_UpperCamelCase    ,  **_UpperCamelCase)




      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any]    ,  _UpperCamelCase     :				ImageInput    ,  _UpperCamelCase     :				bool = None    ,  _UpperCamelCase     :				Dict[str, int] = None    ,  _UpperCamelCase     :				Tuple=None    ,  _UpperCamelCase     :				bool = None    ,  _UpperCamelCase     :				Dict[str, int] = None    ,  _UpperCamelCase     :				bool = None    ,  _UpperCamelCase     :				float = None    ,  _UpperCamelCase     :				bool = None    ,  _UpperCamelCase     :				Optional[Union[float, List[float]]] = None    ,  _UpperCamelCase     :				Optional[Union[float, List[float]]] = None    ,  _UpperCamelCase     :				Optional[Union[str, TensorType]] = None    ,  _UpperCamelCase     :				ChannelDimension = ChannelDimension.FIRST    ,  **_UpperCamelCase     :				List[Any]    ,  )				->PIL.Image.Image:


          """simple docstring"""


          _lowerCamelCase     :		Any 						=						do_resize if do_resize is not None else self.do_resize
          _lowerCamelCase     :		List[str] 						=						resample if resample is not None else self.resample
          _lowerCamelCase     :		Dict 						=						do_center_crop if do_center_crop is not None else self.do_center_crop
          _lowerCamelCase     :		Optional[Any] 						=						do_rescale if do_rescale is not None else self.do_rescale
          _lowerCamelCase     :		Dict 						=						rescale_factor if rescale_factor is not None else self.rescale_factor
          _lowerCamelCase     :		Dict 						=						do_normalize if do_normalize is not None else self.do_normalize
          _lowerCamelCase     :		int 						=						image_mean if image_mean is not None else self.image_mean
          _lowerCamelCase     :		Dict 						=						image_std if image_std is not None else self.image_std

          _lowerCamelCase     :		Optional[Any] 						=						size if size is not None else self.size
          _lowerCamelCase     :		Optional[int] 						=						get_size_dict(_UpperCamelCase)
          _lowerCamelCase     :		List[str] 						=						crop_size if crop_size is not None else self.crop_size
          _lowerCamelCase     :		Dict 						=						get_size_dict(_UpperCamelCase    ,  param_name="""crop_size""")

          _lowerCamelCase     :		int 						=						make_list_of_images(_UpperCamelCase)

          if not valid_images(_UpperCamelCase):
              raise ValueError(
                  """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                  """torch.Tensor, tf.Tensor or jax.ndarray.""")

          if do_resize and size is None or resample is None:
              raise ValueError("""Size and resample must be specified if do_resize is True.""")

          if do_center_crop and crop_size is None:
              raise ValueError("""Crop size must be specified if do_center_crop is True.""")

          if do_rescale and rescale_factor is None:
              raise ValueError("""Rescale factor must be specified if do_rescale is True.""")

          if do_normalize and (image_mean is None or image_std is None):
              raise ValueError("""Image mean and std must be specified if do_normalize is True.""")

          # All transformations expect numpy arrays.
          _lowerCamelCase     :		Union[str, Any] 						=						[to_numpy_array(_UpperCamelCase) for image in images]

          if do_resize:
              _lowerCamelCase     :		Any 						=						[self.resize(image=_UpperCamelCase    ,  size=_UpperCamelCase    ,  resample=_UpperCamelCase) for image in images]

          if do_center_crop:
              _lowerCamelCase     :		str 						=						[self.center_crop(image=_UpperCamelCase    ,  size=_UpperCamelCase) for image in images]

          if do_rescale:
              _lowerCamelCase     :		Optional[int] 						=						[self.rescale(image=_UpperCamelCase    ,  scale=_UpperCamelCase) for image in images]

          if do_normalize:
              _lowerCamelCase     :		List[str] 						=						[self.normalize(image=_UpperCamelCase    ,  mean=_UpperCamelCase    ,  std=_UpperCamelCase) for image in images]

          _lowerCamelCase     :		List[str] 						=						[to_channel_dimension_format(_UpperCamelCase    ,  _UpperCamelCase) for image in images]

          _lowerCamelCase     :		int 						=						{"""pixel_values""": images}
          return BatchFeature(data=_UpperCamelCase    ,  tensor_type=_UpperCamelCase)




