


"""simple docstring"""







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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCamelCase_       :       Tuple      						=			logging.get_logger(__name__)






class 				__A    (	__snake_case      ):






 """simple docstring"""




 __lowerCAmelCase			 =  ["pixel_values"]


 def __init__(		self			,   __A = True			,   __A = None			,   __A = PILImageResampling.BICUBIC			,   __A = True			,   __A = True			,   __A = 1 / 255			,   __A = None			,   __A = True			,   __A = None			,   __A = None			,   **__A			,   ) ->					None:
     super().__init__(**lowerCamelCase_						)
     a					=size if size is not None else {"""height""": 224, """width""": 224}
     a					=get_size_dict(lowerCamelCase_						)
     a					=crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
     a					=get_size_dict(lowerCamelCase_			,   default_to_square=lowerCamelCase_			,   param_name='''crop_size'''						)

     a					=do_resize
     a					=do_rescale
     a					=do_normalize
     a					=do_center_crop
     a					=crop_size
     a					=size
     a					=resample
     a					=rescale_factor
     a					=image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
     a					=image_std if image_std is not None else IMAGENET_DEFAULT_STD


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A = PILImageResampling.BILINEAR			,   __A = None			,   **__A			,   ) ->					np.ndarray:
     a					=get_size_dict(lowerCamelCase_						)

     if "shortest_edge" in size:
         a					=get_resize_output_image_size(lowerCamelCase_			,   size=size['''shortest_edge''']			,   default_to_square=lowerCamelCase_						)
         # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
     elif "height" in size and "width" in size:
         a					=(size["""height"""], size["""width"""])
     else:
         raise ValueError(f'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'''						)
     return resize(lowerCamelCase_			,   size=lowerCamelCase_			,   resample=lowerCamelCase_			,   data_format=lowerCamelCase_			,   **lowerCamelCase_						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A = None			,   **__A			,   ) ->					np.ndarray:
     a					=get_size_dict(lowerCamelCase_						)
     if "height" not in size or "width" not in size:
         raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''						)
     return center_crop(lowerCamelCase_			,   size=(size['''height'''], size['''width'''])			,   data_format=lowerCamelCase_			,   **lowerCamelCase_						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A = None			,   **__A						) ->					np.ndarray:
     return rescale(lowerCamelCase_			,   scale=lowerCamelCase_			,   data_format=lowerCamelCase_			,   **lowerCamelCase_						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A			,   __A = None			,   **__A			,   ) ->					np.ndarray:
     return normalize(lowerCamelCase_			,   mean=lowerCamelCase_			,   std=lowerCamelCase_			,   data_format=lowerCamelCase_			,   **lowerCamelCase_						)





 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A = None			,   __A = None			,   __A = None			,   __A = None			,   __A = None			,   __A = None			,   __A = None			,   __A = None			,   __A = None			,   __A = None			,   __A = None			,   __A = ChannelDimension.FIRST			,   **__A			,   ) ->					BatchFeature:
     a					=do_resize if do_resize is not None else self.do_resize
     a					=do_rescale if do_rescale is not None else self.do_rescale
     a					=do_normalize if do_normalize is not None else self.do_normalize
     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(lowerCamelCase_			,   param_name='''crop_size'''			,   default_to_square=lowerCamelCase_						)
     a					=resample if resample is not None else self.resample
     a					=rescale_factor if rescale_factor is not None else self.rescale_factor
     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					=size if size is not None else self.size
     a					=get_size_dict(lowerCamelCase_						)

     if not is_batched(lowerCamelCase_						):
         a					=[images]

     if not valid_images(lowerCamelCase_						):
         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.'''						)

     # All transformations expect numpy arrays.
     a					=[to_numpy_array(lowerCamelCase_						) for image in images]

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

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

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

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

     a					=[to_channel_dimension_format(lowerCamelCase_			,   lowerCamelCase_						) for image in images]

     a					={"""pixel_values""": images}
     return BatchFeature(data=lowerCamelCase_			,   tensor_type=lowerCamelCase_						)



"""simple docstring"""







import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
       import tensorflow as tf

if is_tensorflow_text_available():
       from transformers.models.bert import TFBertTokenizer


lowerCamelCase_       :       str      						=  ["""bert-base-uncased""", """bert-base-cased"""]
lowerCamelCase_       :       List[str]      						=  """hf-internal-testing/tiny-bert-tf-only"""

if is_tf_available():






       class 				__A    (	tf.keras.Model      ):






        """simple docstring"""




        def __init__(		self			,   __A						) ->					Dict:
            super().__init__()
            a					=tokenizer
            a					=AutoConfig.from_pretrained(__A						)
            a					=TFAutoModel.from_config(__A						)





        def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					int:
            a					=self.tokenizer(__A						)
            a					=self.bert(**__A						)
            return out["pooler_output"]






@require_tf
@require_tensorflow_text
class 				__A    (	unittest.TestCase      ):






 """simple docstring"""




 def 				SCREAMING_SNAKE_CASE							(		self						) ->					str:
     super().setUp()

     a					=[
         BertTokenizer.from_pretrained(__A						) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
     ]  # repeat for when fast_bert_tokenizer=false
     a					=[TFBertTokenizer.from_pretrained(__A						) for checkpoint in TOKENIZER_CHECKPOINTS] + [
         TFBertTokenizer.from_pretrained(__A			,   use_fast_bert_tokenizer=__A						)
         for checkpoint in TOKENIZER_CHECKPOINTS
     ]
     assert len(self.tokenizers						) == len(self.tf_tokenizers						)

     a					=[
         '''This is a straightforward English test sentence.''',
         '''This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.''',
         '''Now we\'re going to add some Chinese: 一 二 三 一二三''',
         '''And some much more rare Chinese: 齉 堃 齉堃''',
         '''Je vais aussi écrire en français pour tester les accents''',
         '''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
     ]
     a					=list(zip(self.test_sentences			,   self.test_sentences[::-1]						)						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[int]:
     for tokenizer, tf_tokenizer in zip(self.tokenizers			,   self.tf_tokenizers						):
         for test_inputs in (self.test_sentences, self.paired_sentences):
             a					=tokenizer(__A			,   return_tensors='''tf'''			,   padding='''longest'''						)
             a					=tf_tokenizer(__A						)

             for key in python_outputs.keys():
                 self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape						)						)
                 self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]			,   tf.intaa						) == tf_outputs[key]						)						)


 @slow
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					str:
     for tf_tokenizer in self.tf_tokenizers:
         a					=tf_tokenizer(self.paired_sentences						)
         a					=tf_tokenizer(
             text=[sentence[0] for sentence in self.paired_sentences]			,   text_pair=[sentence[1] for sentence in self.paired_sentences]			,   )
         for key in merged_outputs.keys():
             self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]			,   tf.intaa						) == separated_outputs[key]						)						)


 @slow
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[str]:
     for tf_tokenizer in self.tf_tokenizers:
         a					=tf.function(__A						)
         for test_inputs in (self.test_sentences, self.paired_sentences):
             a					=tf.constant(__A						)
             a					=compiled_tokenizer(__A						)
             a					=tf_tokenizer(__A						)

             for key in eager_outputs.keys():
                 self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]						)						)





 @slow
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Tuple:
     for tf_tokenizer in self.tf_tokenizers:
         a					=ModelToSave(tokenizer=__A						)
         a					=tf.convert_to_tensor(self.test_sentences						)
         a					=model(__A						)  # Build model with some sample inputs
         with TemporaryDirectory() as tempdir:
             a					=Path(__A						) / '''saved.model'''
             model.save(__A						)
             a					=tf.keras.models.load_model(__A						)
         a					=loaded_model(__A						)
         # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
         self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output						)						)			,   1E-5						)