
"""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 				lowerCAmelCase_				(      unittest.TestCase    ):



						'''simple docstring'''


						def __init__( self     :      Dict  ,A_     :      int  ,A_     :      Tuple=13  ,A_     :      str=7  ,A_     :      str=True  ,A_     :      Optional[Any]=True  ,A_     :      List[Any]=True  ,A_     :      Optional[Any]=True  ,A_     :      List[Any]=99  ,A_     :      Union[str, Any]=32  ,A_     :      Optional[int]=5  ,A_     :      Optional[int]=4  ,A_     :      Union[str, Any]=37  ,A_     :      List[str]="gelu"  ,A_     :      Tuple=0.1  ,A_     :      str=0.1  ,A_     :      Optional[int]=512  ,A_     :      Union[str, Any]=16  ,A_     :      Dict=2  ,A_     :      Any=0.02  ,A_     :      List[Any]=4  ,)     ->   Optional[Any]:
							A						   =   parent
							A						   =   batch_size
							A						   =   seq_length
							A						   =   is_training
							A						   =   use_attention_mask
							A						   =   use_token_type_ids
							A						   =   use_labels
							A						   =   vocab_size
							A						   =   hidden_size
							A						   =   num_hidden_layers
							A						   =   num_attention_heads
							A						   =   intermediate_size
							A						   =   hidden_act
							A						   =   hidden_dropout_prob
							A						   =   attention_probs_dropout_prob
							A						   =   max_position_embeddings
							A						   =   type_vocab_size
							A						   =   type_sequence_label_size
							A						   =   initializer_range
							A						   =   num_choices


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]      )     ->   int:
							A						   =   ids_tensor([self.batch_size, self.seq_length]  ,self.vocab_size      )

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

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

							A						   =   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 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   Tuple:
							A						   =   self.prepare_config_and_inputs()
							A						   =   config_and_inputs
							A						   =   {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
							return config, inputs_dict







@require_flax
class 				lowerCAmelCase_				(      __SCREAMING_SNAKE_CASE		,					unittest.TestCase    ):



						'''simple docstring'''


						_lowerCamelCase:    List[Any]      =    True

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


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple      )     ->   Union[str, Any]:
							A						   =   FlaxRoFormerModelTester(self      )




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




							for model_class_name in self.all_model_classes:
								A						   =   model_class_name.from_pretrained('junnyu/roformer_chinese_small'  ,from_pt=_a      )
								A						   =   model(np.ones((1, 1)      )      )
								self.assertIsNotNone(_a      )







@require_flax
class 				lowerCAmelCase_				(      unittest.TestCase    ):



						'''simple docstring'''


						@slow
						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   Union[str, Any]:
							A						   =   FlaxRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base'      )
							A						   =   jnp.array([[0, 1, 2, 3, 4, 5]]      )
							A						   =   model(_a      )[0]

							A						   =   5_0000

							A						   =   (1, 6, vocab_size)
							self.assertEqual(output.shape  ,_a      )

							A						   =   jnp.array(
							    [[[-0.12_05, -1.02_65, 0.29_22], [-1.51_34, 0.19_74, 0.15_19], [-5.01_35, -3.90_03, -0.84_04]]]      )

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

"""simple docstring"""


from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    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_torch_available, is_torch_tensor, logging


if is_torch_available():
		import torch


_lowercase							   =				logging.get_logger(__name__)







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						_lowerCamelCase:    Dict      =    ['''pixel_values''']


						def __init__( self     :      Optional[Any]  ,A_     :      bool = True  ,A_     :      Optional[Dict[str, int]] = None  ,A_     :      PILImageResampling = PILImageResampling.BILINEAR  ,A_     :      bool = True  ,A_     :      Dict[str, int] = None  ,A_     :      bool = True  ,A_     :      Union[int, float] = 1 / 255  ,A_     :      bool = True  ,A_     :      Optional[Union[float, List[float]]] = None  ,A_     :      Optional[Union[float, List[float]]] = None  ,**A_     :      Optional[Any]  ,)     ->   None:
							super().__init__(**A_      )
							A						   =   size if size is not None else {'shortest_edge': 256}
							A						   =   get_size_dict(A_  ,default_to_square=A_      )
							A						   =   crop_size if crop_size is not None else {'height': 224, 'width': 224}
							A						   =   get_size_dict(A_  ,param_name='crop_size'      )
							A						   =   do_resize
							A						   =   size
							A						   =   resample
							A						   =   do_center_crop
							A						   =   crop_size
							A						   =   do_rescale
							A						   =   rescale_factor
							A						   =   do_normalize
							A						   =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							A						   =   image_std if image_std is not None else IMAGENET_STANDARD_STD


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]  ,A_     :      np.ndarray  ,A_     :      Dict[str, int]  ,A_     :      PILImageResampling = PILImageResampling.BICUBIC  ,A_     :      Optional[Union[str, ChannelDimension]] = None  ,**A_     :      int  ,)     ->   np.ndarray:
							A						   =   get_size_dict(A_  ,default_to_square=A_      )
							if "shortest_edge" not in size:
								raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'      )
							A						   =   get_resize_output_image_size(A_  ,size=size['shortest_edge']  ,default_to_square=A_      )
							return resize(A_  ,size=A_  ,resample=A_  ,data_format=A_  ,**A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]  ,A_     :      np.ndarray  ,A_     :      Dict[str, int]  ,A_     :      Optional[Union[str, ChannelDimension]] = None  ,**A_     :      int  ,)     ->   np.ndarray:
							A						   =   get_size_dict(A_      )
							if "height" not in size or "width" not in size:
								raise ValueError(F'The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}'      )
							return center_crop(A_  ,size=(size['height'], size['width'])  ,data_format=A_  ,**A_      )


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


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]  ,A_     :      np.ndarray  ,A_     :      Union[float, List[float]]  ,A_     :      Union[float, List[float]]  ,A_     :      Optional[Union[str, ChannelDimension]] = None  ,**A_     :      Any  ,)     ->   np.ndarray:
							return normalize(A_  ,mean=A_  ,std=A_  ,data_format=A_  ,**A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any  ,A_     :      ImageInput  ,A_     :      Optional[bool] = None  ,A_     :      Dict[str, int] = None  ,A_     :      PILImageResampling = None  ,A_     :      bool = None  ,A_     :      Dict[str, int] = None  ,A_     :      Optional[bool] = None  ,A_     :      Optional[float] = None  ,A_     :      Optional[bool] = None  ,A_     :      Optional[Union[float, List[float]]] = None  ,A_     :      Optional[Union[float, List[float]]] = None  ,A_     :      Optional[Union[str, TensorType]] = None  ,A_     :      Union[str, ChannelDimension] = ChannelDimension.FIRST  ,**A_     :      Tuple  ,)     ->   List[Any]:
							A						   =   do_resize if do_resize is not None else self.do_resize
							A						   =   size if size is not None else self.size
							A						   =   get_size_dict(A_  ,default_to_square=A_      )
							A						   =   resample if resample is not None else self.resample
							A						   =   do_center_crop if do_center_crop is not None else self.do_center_crop
							A						   =   crop_size if crop_size is not None else self.crop_size
							A						   =   get_size_dict(A_  ,param_name='crop_size'      )
							A						   =   do_rescale if do_rescale is not None else self.do_rescale
							A						   =   rescale_factor if rescale_factor is not None else self.rescale_factor
							A						   =   do_normalize if do_normalize is not None else self.do_normalize
							A						   =   image_mean if image_mean is not None else self.image_mean
							A						   =   image_std if image_std is not None else self.image_std

							A						   =   make_list_of_images(A_      )

							if not valid_images(A_      ):
								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:
								raise ValueError('Size 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.
							A						   =   [to_numpy_array(A_      ) for image in images]

							if do_resize:
								A						   =   [self.resize(image=A_  ,size=A_  ,resample=A_      ) for image in images]

							if do_center_crop:
								A						   =   [self.center_crop(image=A_  ,size=A_      ) for image in images]

							if do_rescale:
								A						   =   [self.rescale(image=A_  ,scale=A_      ) for image in images]

							if do_normalize:
								A						   =   [self.normalize(image=A_  ,mean=A_  ,std=A_      ) for image in images]

							A						   =   [to_channel_dimension_format(A_  ,A_      ) for image in images]

							A						   =   {'pixel_values': images}
							return BatchFeature(data=A_  ,tensor_type=A_      )




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]  ,A_     :      Union[str, Any]  ,A_     :      List[Tuple] = None      )     ->   str:
							A						   =   outputs.logits

							# Resize logits and compute semantic segmentation maps
							if target_sizes is not None:
								if len(A_      ) != len(A_      ):
									raise ValueError(
									    'Make sure that you pass in as many target sizes as the batch dimension of the logits'      )

								if is_torch_tensor(A_      ):
									A						   =   target_sizes.numpy()

								A						   =   []

								for idx in range(len(A_      )      ):
									A						   =   torch.nn.functional.interpolate(
									    logits[idx].unsqueeze(dim=0      )  ,size=target_sizes[idx]  ,mode='bilinear'  ,align_corners=A_      )
									A						   =   resized_logits[0].argmax(dim=0      )
									semantic_segmentation.append(A_      )
							else:
								A						   =   logits.argmax(dim=1      )
								A						   =   [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]      )]

							return semantic_segmentation