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

import numpy as np

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


if is_torch_available():
       import torch

if is_vision_available():
       import PIL


__SCREAMING_SNAKE_CASE								=logging.get_logger(__name__)



def     lowercase__( __SCREAMING_SNAKE_CASE							:  str  ,    __SCREAMING_SNAKE_CASE							:  Optional[Any]  ,    __SCREAMING_SNAKE_CASE							:  Tuple  ,    __SCREAMING_SNAKE_CASE							:  Optional[int]   ):

     def constraint_to_multiple_of(__SCREAMING_SNAKE_CASE							:  str  ,    __SCREAMING_SNAKE_CASE							:  List[str]  ,    __SCREAMING_SNAKE_CASE							:  Optional[int]=0  ,    __SCREAMING_SNAKE_CASE							:  Optional[Any]=None   ):
          lowercase_			:       List[str]									=			round(val / multiple   ) * multiple

          if max_val is not None and x > max_val:
               lowercase_			:       Dict									=			math.floor(val / multiple   ) * multiple

          if x < min_val:
               lowercase_			:       Optional[Any]									=			math.ceil(val / multiple   ) * multiple

          return x

     lowercase_			:       int									=			(output_size, output_size) if isinstance(a__  ,    a__   ) else output_size

     lowercase_							,						lowercase_			:       Optional[int]									=			get_image_size(a__   )
     lowercase_							,						lowercase_			:       Any									=			output_size

     # determine new height and width
     lowercase_			:       Dict									=			output_height / input_height
     lowercase_			:       Union[str, Any]									=			output_width / input_width

     if keep_aspect_ratio:
          # scale as little as possible
          if abs(1 - scale_width   ) < abs(1 - scale_height   ):
               # fit width
               lowercase_			:       List[Any]									=			scale_width
          else:
               # fit height
               lowercase_			:       List[Any]									=			scale_height

     lowercase_			:       Optional[int]									=			constraint_to_multiple_of(scale_height * input_height  ,    multiple=a__   )
     lowercase_			:       int									=			constraint_to_multiple_of(scale_width * input_width  ,    multiple=a__   )

     return (new_height, new_width)







class  UpperCamelCase	(    lowercase_						):
   lowercase							=						['pixel_values']
   def __init__(    self  ,__UpperCamelCase = True  ,__UpperCamelCase = None  ,__UpperCamelCase = PILImageResampling.BILINEAR  ,__UpperCamelCase = False  ,__UpperCamelCase = 1  ,__UpperCamelCase = True  ,__UpperCamelCase = 1 / 255  ,__UpperCamelCase = True  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,**__UpperCamelCase  ,)			->   None:


        '''simple docstring'''


        super().__init__(**lowercase_    )
        lowercase_			:       Optional[int]									=			size if size is not None else {'height': 384, 'width': 384}
        lowercase_			:       List[Any]									=			get_size_dict(lowercase_    )
        lowercase_			:       int									=			do_resize
        lowercase_			:       Any									=			size
        lowercase_			:       List[str]									=			keep_aspect_ratio
        lowercase_			:       Optional[int]									=			ensure_multiple_of
        lowercase_			:       str									=			resample
        lowercase_			:       List[str]									=			do_rescale
        lowercase_			:       List[Any]									=			rescale_factor
        lowercase_			:       Dict									=			do_normalize
        lowercase_			:       Any									=			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        lowercase_			:       int									=			image_std if image_std is not None else IMAGENET_STANDARD_STD
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase = False  ,__UpperCamelCase = 1  ,__UpperCamelCase = PILImageResampling.BICUBIC  ,__UpperCamelCase = None  ,**__UpperCamelCase  ,)			->   np.ndarray:


        '''simple docstring'''


        lowercase_			:       List[Any]									=			get_size_dict(lowercase_    )
        if "height" not in size or "width" not in size:
             raise ValueError(f'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''    )
        lowercase_			:       Optional[Any]									=			get_resize_output_image_size(
            lowercase_  ,output_size=(size['height'], size['width'])  ,keep_aspect_ratio=lowercase_  ,multiple=lowercase_  ,)
        return resize(lowercase_  ,size=lowercase_  ,resample=lowercase_  ,data_format=lowercase_  ,**lowercase_    )
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase = None  ,**__UpperCamelCase  ,)			->   Union[str, Any]:


        '''simple docstring'''


        return rescale(lowercase_  ,scale=lowercase_  ,data_format=lowercase_  ,**lowercase_    )
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase = None  ,**__UpperCamelCase  ,)			->   np.ndarray:


        '''simple docstring'''


        return normalize(lowercase_  ,mean=lowercase_  ,std=lowercase_  ,data_format=lowercase_  ,**lowercase_    )
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = ChannelDimension.FIRST  ,**__UpperCamelCase  ,)			->   PIL.Image.Image:


        '''simple docstring'''


        lowercase_			:       List[str]									=			do_resize if do_resize is not None else self.do_resize
        lowercase_			:       Dict									=			size if size is not None else self.size
        lowercase_			:       Any									=			get_size_dict(lowercase_    )
        lowercase_			:       Dict									=			keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
        lowercase_			:       Dict									=			ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
        lowercase_			:       Optional[int]									=			resample if resample is not None else self.resample
        lowercase_			:       Tuple									=			do_rescale if do_rescale is not None else self.do_rescale
        lowercase_			:       int									=			rescale_factor if rescale_factor is not None else self.rescale_factor
        lowercase_			:       List[str]									=			do_normalize if do_normalize is not None else self.do_normalize
        lowercase_			:       List[str]									=			image_mean if image_mean is not None else self.image_mean
        lowercase_			:       Optional[int]									=			image_std if image_std is not None else self.image_std

        lowercase_			:       List[Any]									=			make_list_of_images(lowercase_    )

        if not valid_images(lowercase_    ):
             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_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.
        lowercase_			:       str									=			[to_numpy_array(lowercase_    ) for image in images]

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

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

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

        lowercase_			:       Optional[int]									=			[to_channel_dimension_format(lowercase_  ,lowercase_    ) for image in images]

        lowercase_			:       List[str]									=			{'pixel_values': images}
        return BatchFeature(data=lowercase_  ,tensor_type=lowercase_    )







   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase = None    )			->   Optional[Any]:


        '''simple docstring'''


        lowercase_			:       int									=			outputs.logits

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

             if is_torch_tensor(lowercase_    ):
                  lowercase_			:       Dict									=			target_sizes.numpy()

             lowercase_			:       List[Any]									=			[]

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

        return semantic_segmentation


"""simple docstring"""







import unittest

import numpy as np

from transformers import DistilBertConfig, 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.distilbert.modeling_flax_distilbert import (
           FlaxDistilBertForMaskedLM,
           FlaxDistilBertForMultipleChoice,
           FlaxDistilBertForQuestionAnswering,
           FlaxDistilBertForSequenceClassification,
           FlaxDistilBertForTokenClassification,
           FlaxDistilBertModel,
       )







class  UpperCamelCase	(    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  ,)			->   str:


        '''simple docstring'''


        lowercase_			:       str									=			parent
        lowercase_			:       List[str]									=			batch_size
        lowercase_			:       Optional[Any]									=			seq_length
        lowercase_			:       str									=			is_training
        lowercase_			:       Any									=			use_attention_mask
        lowercase_			:       int									=			use_token_type_ids
        lowercase_			:       Union[str, Any]									=			use_labels
        lowercase_			:       int									=			vocab_size
        lowercase_			:       Union[str, Any]									=			hidden_size
        lowercase_			:       Optional[int]									=			num_hidden_layers
        lowercase_			:       Dict									=			num_attention_heads
        lowercase_			:       Dict									=			intermediate_size
        lowercase_			:       str									=			hidden_act
        lowercase_			:       Union[str, Any]									=			hidden_dropout_prob
        lowercase_			:       Dict									=			attention_probs_dropout_prob
        lowercase_			:       Union[str, Any]									=			max_position_embeddings
        lowercase_			:       List[Any]									=			type_vocab_size
        lowercase_			:       str									=			type_sequence_label_size
        lowercase_			:       Optional[Any]									=			initializer_range
        lowercase_			:       Any									=			num_choices
   def     _UpperCAmelCase     (    self    )			->   List[Any]:


        '''simple docstring'''


        lowercase_			:       Optional[Any]									=			ids_tensor([self.batch_size, self.seq_length]  ,self.vocab_size    )

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

        lowercase_			:       Optional[Any]									=			DistilBertConfig(
            vocab_size=self.vocab_size  ,dim=self.hidden_size  ,n_layers=self.num_hidden_layers  ,n_heads=self.num_attention_heads  ,hidden_dim=self.intermediate_size  ,hidden_act=self.hidden_act  ,dropout=self.hidden_dropout_prob  ,attention_dropout=self.attention_probs_dropout_prob  ,max_position_embeddings=self.max_position_embeddings  ,initializer_range=self.initializer_range  ,tie_weights_=__UpperCamelCase  ,)

        return config, input_ids, attention_mask







   def     _UpperCAmelCase     (    self    )			->   Dict:


        '''simple docstring'''


        lowercase_			:       Any									=			self.prepare_config_and_inputs()
        lowercase_							,						lowercase_							,						lowercase_			:       str									=			config_and_inputs
        lowercase_			:       List[str]									=			{'input_ids': input_ids, 'attention_mask': attention_mask}
        return config, inputs_dict







@require_flax
class  UpperCamelCase	(    lowercase_		,   unittest.TestCase						):
   lowercase							=						(
       (
           FlaxDistilBertModel,
           FlaxDistilBertForMaskedLM,
           FlaxDistilBertForMultipleChoice,
           FlaxDistilBertForQuestionAnswering,
           FlaxDistilBertForSequenceClassification,
           FlaxDistilBertForTokenClassification,
           FlaxDistilBertForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )
   def     _UpperCAmelCase     (    self    )			->   List[Any]:


        '''simple docstring'''


        lowercase_			:       Optional[Any]									=			FlaxDistilBertModelTester(self    )







   @slow
   def     _UpperCAmelCase     (    self    )			->   List[str]:


        '''simple docstring'''


        for model_class_name in self.all_model_classes:
             lowercase_			:       Optional[int]									=			model_class_name.from_pretrained('distilbert-base-uncased'    )
             lowercase_			:       List[str]									=			model(np.ones((1, 1)    )    )
             self.assertIsNotNone(__UpperCamelCase    )







@require_flax
class  UpperCamelCase	(    unittest.TestCase						):
   @slow
   def     _UpperCAmelCase     (    self    )			->   str:


        '''simple docstring'''


        lowercase_			:       Optional[int]									=			FlaxDistilBertModel.from_pretrained('distilbert-base-uncased'    )
        lowercase_			:       Tuple									=			np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]    )
        lowercase_			:       Optional[int]									=			np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]    )
        lowercase_			:       Any									=			model(__UpperCamelCase  ,attention_mask=__UpperCamelCase    )[0]
        lowercase_			:       Tuple									=			(1, 11, 768)
        self.assertEqual(output.shape  ,__UpperCamelCase    )
        lowercase_			:       Union[str, Any]									=			np.array([[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]]    )

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

