
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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


_UpperCamelCase:					Dict     =logging.get_logger(__name__)


if is_vision_available():
       import PIL





class 						__lowercase(     __A   ):


       """simple docstring"""







       UpperCamelCase_				 =							['pixel_values']



       def __init__(     self					:			Union[str, Any]  ,      _lowerCAmelCase					:			Any = True  ,      _lowerCAmelCase					:			Optional[int] = None  ,      _lowerCAmelCase					:			str = PILImageResampling.BILINEAR  ,      _lowerCAmelCase					:			Any = True  ,      _lowerCAmelCase					:			Union[str, Any] = False  ,      _lowerCAmelCase					:			List[str] = True  ,      _lowerCAmelCase					:			Any = True  ,      _lowerCAmelCase					:			Union[str, Any] = 1 / 255  ,      _lowerCAmelCase					:			str = True  ,      _lowerCAmelCase					:			Dict = None  ,      _lowerCAmelCase					:			Dict = None  ,      **_lowerCAmelCase					:			Any  ,      )					->   List[str]:
         super().__init__(**__lowercase       )

         _lowerCAmelCase						=		size if size is not None else {'height': 2560, 'width': 1920}
         if isinstance(__lowercase  ,      (tuple, list)       ):
           # The previous feature extractor size parameter was in (width, height) format
           _lowerCAmelCase						=		size[::-1]
         _lowerCAmelCase						=		get_size_dict(__lowercase       )

         _lowerCAmelCase						=		do_resize
         _lowerCAmelCase						=		size
         _lowerCAmelCase						=		resample
         _lowerCAmelCase						=		do_thumbnail
         _lowerCAmelCase						=		do_align_long_axis
         _lowerCAmelCase						=		do_pad
         _lowerCAmelCase						=		do_rescale
         _lowerCAmelCase						=		rescale_factor
         _lowerCAmelCase						=		do_normalize
         _lowerCAmelCase						=		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         _lowerCAmelCase						=		image_std if image_std is not None else IMAGENET_STANDARD_STD



       def SCREAMING_SNAKE_CASE_				(     self					:			int  ,      _lowerCAmelCase					:			Optional[int]  ,      _lowerCAmelCase					:			Tuple  ,      _lowerCAmelCase					:			str = None       )					->   Optional[Any]:
         _lowerCAmelCase	,       _lowerCAmelCase						=		get_image_size(__lowercase       )
         _lowerCAmelCase	,       _lowerCAmelCase						=		size['height'], size['width']

         if (output_width < output_height and input_width > input_height) or (
             output_width > output_height and input_width < input_height
         ):
           _lowerCAmelCase						=		np.rotaa(__lowercase  ,      3       )

         if data_format is not None:
           _lowerCAmelCase						=		to_channel_dimension_format(__lowercase  ,      __lowercase       )

         return image



       def SCREAMING_SNAKE_CASE_				(     self					:			Union[str, Any]  ,      _lowerCAmelCase					:			Union[str, Any]  ,      _lowerCAmelCase					:			str  ,      _lowerCAmelCase					:			List[str] = False  ,      _lowerCAmelCase					:			Optional[Any] = None  ,      )					->   int:
         _lowerCAmelCase	,       _lowerCAmelCase						=		size['height'], size['width']
         _lowerCAmelCase	,       _lowerCAmelCase						=		get_image_size(__lowercase       )

         _lowerCAmelCase						=		output_width - input_width
         _lowerCAmelCase						=		output_height - input_height

         if random_padding:
           _lowerCAmelCase						=		np.random.randint(low=0  ,      high=delta_height + 1       )
           _lowerCAmelCase						=		np.random.randint(low=0  ,      high=delta_width + 1       )
         else:
           _lowerCAmelCase						=		delta_height // 2
           _lowerCAmelCase						=		delta_width // 2

         _lowerCAmelCase						=		delta_height - pad_top
         _lowerCAmelCase						=		delta_width - pad_left

         _lowerCAmelCase						=		((pad_top, pad_bottom), (pad_left, pad_right))
         return pad(__lowercase  ,      __lowercase  ,      data_format=__lowercase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]  ,      *_lowerCAmelCase					:			Optional[int]  ,      **_lowerCAmelCase					:			Any       )					->   List[Any]:
         logger.info('pad is deprecated and will be removed in version 4.27. Please use pad_image instead.'       )
         return self.pad_image(*__lowercase  ,      **__lowercase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			str  ,      _lowerCAmelCase					:			Dict  ,      _lowerCAmelCase					:			Any  ,      _lowerCAmelCase					:			List[str] = PILImageResampling.BICUBIC  ,      _lowerCAmelCase					:			List[Any] = None  ,      **_lowerCAmelCase					:			Any  ,      )					->   int:
         _lowerCAmelCase	,       _lowerCAmelCase						=		get_image_size(__lowercase       )
         _lowerCAmelCase	,       _lowerCAmelCase						=		size['height'], size['width']

         # We always resize to the smallest of either the input or output size.
         _lowerCAmelCase						=		min(__lowercase  ,      __lowercase       )
         _lowerCAmelCase						=		min(__lowercase  ,      __lowercase       )

         if height == input_height and width == input_width:
           return image

         if input_height > input_width:
           _lowerCAmelCase						=		int(input_width * height / input_height       )
         elif input_width > input_height:
           _lowerCAmelCase						=		int(input_height * width / input_width       )

         return resize(
             __lowercase  ,      size=(height, width)  ,      resample=__lowercase  ,      reducing_gap=2.0  ,      data_format=__lowercase  ,      **__lowercase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			List[Any]  ,      _lowerCAmelCase					:			List[str]  ,      _lowerCAmelCase					:			Tuple  ,      _lowerCAmelCase					:			List[str] = PILImageResampling.BICUBIC  ,      _lowerCAmelCase					:			str = None  ,      **_lowerCAmelCase					:			str  ,      )					->   Dict:
         _lowerCAmelCase						=		get_size_dict(__lowercase       )
         _lowerCAmelCase						=		min(size['height']  ,      size['width']       )
         _lowerCAmelCase						=		get_resize_output_image_size(__lowercase  ,      size=__lowercase  ,      default_to_square=__lowercase       )
         _lowerCAmelCase						=		resize(__lowercase  ,      size=__lowercase  ,      resample=__lowercase  ,      data_format=__lowercase  ,      **__lowercase       )
         return resized_image



       def SCREAMING_SNAKE_CASE_				(     self					:			Dict  ,      _lowerCAmelCase					:			List[str]  ,      _lowerCAmelCase					:			Any  ,      _lowerCAmelCase					:			List[Any] = None  ,      **_lowerCAmelCase					:			List[str]  ,      )					->   str:
         return rescale(__lowercase  ,      scale=__lowercase  ,      data_format=__lowercase  ,      **__lowercase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			List[Any]  ,      _lowerCAmelCase					:			Tuple  ,      _lowerCAmelCase					:			List[str]  ,      _lowerCAmelCase					:			Optional[int]  ,      _lowerCAmelCase					:			Optional[int] = None  ,      **_lowerCAmelCase					:			Any  ,      )					->   Union[str, Any]:
         return normalize(__lowercase  ,      mean=__lowercase  ,      std=__lowercase  ,      data_format=__lowercase  ,      **__lowercase       )






       def SCREAMING_SNAKE_CASE_				(     self					:			Any  ,      _lowerCAmelCase					:			Optional[Any]  ,      _lowerCAmelCase					:			Any = None  ,      _lowerCAmelCase					:			List[str] = None  ,      _lowerCAmelCase					:			List[str] = None  ,      _lowerCAmelCase					:			List[str] = None  ,      _lowerCAmelCase					:			List[str] = None  ,      _lowerCAmelCase					:			Dict = None  ,      _lowerCAmelCase					:			Dict = False  ,      _lowerCAmelCase					:			Optional[int] = None  ,      _lowerCAmelCase					:			Dict = None  ,      _lowerCAmelCase					:			List[str] = None  ,      _lowerCAmelCase					:			str = None  ,      _lowerCAmelCase					:			Union[str, Any] = None  ,      _lowerCAmelCase					:			str = None  ,      _lowerCAmelCase					:			Dict = ChannelDimension.FIRST  ,      **_lowerCAmelCase					:			Optional[Any]  ,      )					->   Tuple:
         _lowerCAmelCase						=		do_resize if do_resize is not None else self.do_resize
         _lowerCAmelCase						=		size if size is not None else self.size
         if isinstance(__lowercase  ,      (tuple, list)       ):
           # Previous feature extractor had size in (width, height) format
           _lowerCAmelCase						=		size[::-1]
         _lowerCAmelCase						=		get_size_dict(__lowercase       )
         _lowerCAmelCase						=		resample if resample is not None else self.resample
         _lowerCAmelCase						=		do_thumbnail if do_thumbnail is not None else self.do_thumbnail
         _lowerCAmelCase						=		do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
         _lowerCAmelCase						=		do_pad if do_pad is not None else self.do_pad
         _lowerCAmelCase						=		do_rescale if do_rescale is not None else self.do_rescale
         _lowerCAmelCase						=		rescale_factor if rescale_factor is not None else self.rescale_factor
         _lowerCAmelCase						=		do_normalize if do_normalize is not None else self.do_normalize
         _lowerCAmelCase						=		image_mean if image_mean is not None else self.image_mean
         _lowerCAmelCase						=		image_std if image_std is not None else self.image_std

         _lowerCAmelCase						=		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:
           raise ValueError('Size 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_pad and size is None:
           raise ValueError('Size must be specified if do_pad 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						=		[to_numpy_array(__lowercase       ) for image in images]

         if do_align_long_axis:
           _lowerCAmelCase						=		[self.align_long_axis(__lowercase  ,      size=__lowercase       ) for image in images]

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

         if do_thumbnail:
           _lowerCAmelCase						=		[self.thumbnail(image=__lowercase  ,      size=__lowercase       ) for image in images]

         if do_pad:
           _lowerCAmelCase						=		[self.pad_image(image=__lowercase  ,      size=__lowercase  ,      random_padding=__lowercase       ) for image in images]

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

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

         _lowerCAmelCase						=		[to_channel_dimension_format(__lowercase  ,      __lowercase       ) for image in images]

         _lowerCAmelCase						=		{'pixel_values': images}
         return BatchFeature(data=__lowercase  ,      tensor_type=__lowercase       )




'''simple docstring'''

import inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin


class    a__					(			__A				,    unittest.TestCase   ):







				"""simple docstring"""



				__UpperCamelCase     :		Any						=						SpeechaTextaTokenizer
				__UpperCamelCase     :		str						=						False




				def        _snake_case	(self		):
										super().setUp()

										__lowerCAmelCase											=					'''<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab'''.split(''' '''		)
										__lowerCAmelCase											=					['''he re</w> 123''', '''here a 1456''']
										__lowerCAmelCase											=					dict(zip(__lowercase							,      range(len(__lowercase		)		)		)		)

										__lowerCAmelCase											=					{'''pad_token''': '''<pad>''', '''unk_token''': '''<unk>''', '''bos_token''': '''<s>''', '''eos_token''': '''</s>'''}

										__lowerCAmelCase											=					tempfile.mkdtemp()
										__lowerCAmelCase											=					os.path.join(self.tmpdirname							,      VOCAB_FILES_NAMES['''vocab_file''']		)
										__lowerCAmelCase											=					os.path.join(self.tmpdirname							,      VOCAB_FILES_NAMES['''merges_file''']		)

										with open(self.vocab_file							,      '''w'''							,      encoding='''utf-8'''		) as fp:
																fp.write(json.dumps(__lowercase		) + '''\n'''		)

										with open(self.merges_file							,      '''w'''		) as fp:
																fp.write('''\n'''.join(__lowercase		)		)




				def        _snake_case	(self		):
										__lowerCAmelCase											=					list(self.get_tokenizer().get_vocab().keys()		)

										self.assertEqual(vocab_keys[0]							,      '''<s>'''		)
										self.assertEqual(vocab_keys[1]							,      '''<pad>'''		)
										self.assertEqual(vocab_keys[-1]							,      '''vocab'''		)
										self.assertEqual(len(__lowercase		)							,      14		)




				def        _snake_case	(self		):
										self.assertEqual(self.get_tokenizer().vocab_size							,      14		)




				def        _snake_case	(self		):
										__lowerCAmelCase											=					SpeechaTextaTokenizer.from_pretrained(self.tmpdirname		)

										# make sure @@ is correctly concatenated
										__lowerCAmelCase											=					[4, 6, 8, 7, 10]  # ["here@@", "couple", "words", "of@@", "the"]
										__lowerCAmelCase											=					tokenizer.decode(__lowercase		)

										self.assertTrue(output_string == '''herecouple words ofthe'''		)




				def        _snake_case	(self		):
										__lowerCAmelCase											=					SpeechaTextaTokenizer.from_pretrained(self.tmpdirname		)

										with tempfile.TemporaryDirectory() as tmp_dirname:
																tokenizer.save_pretrained(__lowercase		)
																os.remove(os.path.join(__lowercase							,      '''merges.txt'''		)		)

																# load tokenizer without merges file should not throw an error
																__lowerCAmelCase											=					SpeechaTextaTokenizer.from_pretrained(__lowercase		)

										with tempfile.TemporaryDirectory() as tmp_dirname:
																# save tokenizer and load again
																tokenizer.save_pretrained(__lowercase		)
																__lowerCAmelCase											=					SpeechaTextaTokenizer.from_pretrained(__lowercase		)

										self.assertIsNotNone(__lowercase		)




				def        _snake_case	(self		):
										if not self.test_slow_tokenizer:
																return

										__lowerCAmelCase											=					inspect.signature(self.tokenizer_class.__init__		)
										__lowerCAmelCase											=					self.get_tokenizer()

										for parameter_name, parameter in signature.parameters.items():
																if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
																						self.assertIn(__lowercase							,      tokenizer.init_kwargs		)

