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

if is_tf_available():




    class 	__snake_case	( tf.keras.Model		):





          '''simple docstring'''






          def __init__(					self     :				List[Any]    ,  _UpperCamelCase     :				Union[str, Any])				->List[str]:


              """simple docstring"""


              super().__init__()
              _lowerCamelCase     :		Tuple 						=						tokenizer
              _lowerCamelCase     :		str 						=						AutoConfig.from_pretrained(_UpperCamelCase)
              _lowerCamelCase     :		List[str] 						=						TFAutoModel.from_config(_UpperCamelCase)




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


              """simple docstring"""


              _lowerCamelCase     :		Optional[int] 						=						self.tokenizer(_UpperCamelCase)
              _lowerCamelCase     :		Dict 						=						self.bert(**_UpperCamelCase)
              return out["pooler_output"]




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





      '''simple docstring'''






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


          """simple docstring"""


          super().setUp()

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

          _lowerCamelCase     :		List[Any] 						=						[
              """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""",
          ]
          _lowerCamelCase     :		Optional[Any] 						=						list(zip(self.test_sentences    ,  self.test_sentences[::-1]))



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


          """simple docstring"""


          for tokenizer, tf_tokenizer in zip(self.tokenizers    ,  self.tf_tokenizers):
              for test_inputs in (self.test_sentences, self.paired_sentences):
                  _lowerCamelCase     :		Optional[Any] 						=						tokenizer(_UpperCamelCase    ,  return_tensors="""tf"""    ,  padding="""longest""")
                  _lowerCamelCase     :		Tuple 						=						tf_tokenizer(_UpperCamelCase)

                  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     :				Tuple)				->Union[str, Any]:


          """simple docstring"""


          for tf_tokenizer in self.tf_tokenizers:
              _lowerCamelCase     :		Any 						=						tf_tokenizer(self.paired_sentences)
              _lowerCamelCase     :		Optional[int] 						=						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     :				Optional[Any])				->Tuple:


          """simple docstring"""


          for tf_tokenizer in self.tf_tokenizers:
              _lowerCamelCase     :		List[Any] 						=						tf.function(_UpperCamelCase)
              for test_inputs in (self.test_sentences, self.paired_sentences):
                  _lowerCamelCase     :		List[Any] 						=						tf.constant(_UpperCamelCase)
                  _lowerCamelCase     :		Optional[int] 						=						compiled_tokenizer(_UpperCamelCase)
                  _lowerCamelCase     :		Optional[Any] 						=						tf_tokenizer(_UpperCamelCase)

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




      @slow
      def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->Dict:


          """simple docstring"""


          for tf_tokenizer in self.tf_tokenizers:
              _lowerCamelCase     :		Dict 						=						ModelToSave(tokenizer=_UpperCamelCase)
              _lowerCamelCase     :		int 						=						tf.convert_to_tensor(self.test_sentences)
              _lowerCamelCase     :		List[str] 						=						model(_UpperCamelCase)  # Build model with some sample inputs
              with TemporaryDirectory() as tempdir:
                  _lowerCamelCase     :		Any 						=						Path(_UpperCamelCase) / """saved.model"""
                  model.save(_UpperCamelCase)
                  _lowerCamelCase     :		Union[str, Any] 						=						tf.keras.models.load_model(_UpperCamelCase)
              _lowerCamelCase     :		Tuple 						=						loaded_model(_UpperCamelCase)
              # 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)





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)




