"""simple docstring"""






from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


__A			   = logging.get_logger(__name__)


class     UpperCAmelCase						(_UpperCAmelCase  ):



    """simple docstring"""

    _UpperCAmelCase	:int						    = ["pixel_values"]






    def __init__(   self    ,			_UpperCAmelCase = True    ,			_UpperCAmelCase = 1 / 255    ,			_UpperCAmelCase = True    ,			_UpperCAmelCase = 8    ,			**_UpperCAmelCase    ,			):
       super().__init__(**_UpperCAmelCase							)

       lowercase__:   Optional[Any]					=      do_rescale
       lowercase__:   Tuple					=      rescale_factor
       lowercase__:   int					=      do_pad
       lowercase__:   Union[str, Any]					=      pad_size






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase = None    ,			**_UpperCAmelCase							):
       return rescale(_UpperCAmelCase    ,			scale=_UpperCAmelCase    ,			data_format=_UpperCAmelCase    ,			**_UpperCAmelCase							)






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase = None							):
       lowercase__, lowercase__:   int					=      get_image_size(_UpperCAmelCase							)
       lowercase__:   int					=      (old_height // size + 1) * size - old_height
       lowercase__:   Dict					=      (old_width // size + 1) * size - old_width

       return pad(_UpperCAmelCase    ,			((0, pad_height), (0, pad_width))    ,			mode='''symmetric'''    ,			data_format=_UpperCAmelCase							)






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = ChannelDimension.FIRST    ,			**_UpperCAmelCase    ,			):
       lowercase__:   Any					=      do_rescale if do_rescale is not None else self.do_rescale
       lowercase__:   Union[str, Any]					=      rescale_factor if rescale_factor is not None else self.rescale_factor
       lowercase__:   int					=      do_pad if do_pad is not None else self.do_pad
       lowercase__:   int					=      pad_size if pad_size is not None else self.pad_size

       lowercase__:   List[str]					=      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_rescale and rescale_factor is None:
          raise ValueError('''Rescale factor must be specified if do_rescale is True.'''							)

       # All transformations expect numpy arrays.
       lowercase__:   Optional[Any]					=      [to_numpy_array(_UpperCAmelCase							) for image in images]

       if do_rescale:
          lowercase__:   int					=      [self.rescale(image=_UpperCAmelCase    ,			scale=_UpperCAmelCase							) for image in images]

       if do_pad:
          lowercase__:   Optional[Any]					=      [self.pad(_UpperCAmelCase    ,			size=_UpperCAmelCase							) for image in images]

       lowercase__:   List[Any]					=      [to_channel_dimension_format(_UpperCAmelCase    ,			_UpperCAmelCase							) for image in images]

       lowercase__:   List[Any]					=      {'''pixel_values''': images}
       return BatchFeature(data=_UpperCAmelCase    ,			tensor_type=_UpperCAmelCase							)

"""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  ):



    """simple docstring"""

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






    def _snake_case			(   self							):
       lowercase__:   int					=      ids_tensor([self.batch_size, self.seq_length]    ,			self.vocab_size							)

       lowercase__:   List[Any]					=      None
       if self.use_attention_mask:
          lowercase__:   Tuple					=      random_attention_mask([self.batch_size, self.seq_length]							)

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

       lowercase__:   Optional[int]					=      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 _snake_case			(   self							):
       lowercase__:   str					=      self.prepare_config_and_inputs()
       lowercase__, lowercase__, lowercase__, lowercase__:   Optional[Any]					=      config_and_inputs
       lowercase__:   Dict					=      {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
       return config, inputs_dict



@require_flax
class     UpperCAmelCase						(_UpperCAmelCase    ,unittest.TestCase  ):



    """simple docstring"""

    _UpperCAmelCase	:List[str]						    = True

    _UpperCAmelCase	:Union[str, Any]						    = (
        (
            FlaxRoFormerModel,
            FlaxRoFormerForMaskedLM,
            FlaxRoFormerForSequenceClassification,
            FlaxRoFormerForTokenClassification,
            FlaxRoFormerForMultipleChoice,
            FlaxRoFormerForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )






    def _snake_case			(   self							):
       lowercase__:   str					=      FlaxRoFormerModelTester(self							)






    @slow
    def _snake_case			(   self							):
       for model_class_name in self.all_model_classes:
          lowercase__:   Dict					=      model_class_name.from_pretrained('''junnyu/roformer_chinese_small'''    ,			from_pt=_UpperCAmelCase							)
          lowercase__:   int					=      model(np.ones((1, 1)							)							)
          self.assertIsNotNone(_UpperCAmelCase							)



@require_flax
class     UpperCAmelCase						(unittest.TestCase  ):



    """simple docstring"""

    @slow
    def _snake_case			(   self							):
       lowercase__:   Any					=      FlaxRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''							)
       lowercase__:   Optional[int]					=      jnp.array([[0, 1, 2, 3, 4, 5]]							)
       lowercase__:   List[Any]					=      model(_UpperCAmelCase							)[0]

       lowercase__:   str					=      50000

       lowercase__:   Tuple					=      (1, 6, vocab_size)
       self.assertEqual(output.shape    ,			_UpperCAmelCase							)

       lowercase__:   List[Any]					=      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							)							)
