
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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__a         =   logging.get_logger(__name__)
class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	a     :Any       	=      ['pixel_values']
	def __init__(			self    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     bool = True			,				SCREAMING_SNAKE_CASE_    :     Optional[Dict[str, int]] = None			,				SCREAMING_SNAKE_CASE_    :     PILImageResampling = PILImageResampling.BILINEAR			,				SCREAMING_SNAKE_CASE_    :     bool = True			,				SCREAMING_SNAKE_CASE_    :     Dict[str, int] = None			,				SCREAMING_SNAKE_CASE_    :     bool = True			,				SCREAMING_SNAKE_CASE_    :     Union[int, float] = 1 / 2_5_5			,				SCREAMING_SNAKE_CASE_    :     bool = True			,				SCREAMING_SNAKE_CASE_    :     Optional[Union[float, List[float]]] = None			,				SCREAMING_SNAKE_CASE_    :     Optional[Union[float, List[float]]] = None			,				**SCREAMING_SNAKE_CASE_    :     Tuple			,				)			->	None:
					super().__init__(**SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   size if size is not None else {'''shortest_edge''': 2_5_6}
					lowercase_	    =   get_size_dict(SCREAMING_SNAKE_CASE_			,				default_to_square=SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
					lowercase_	    =   get_size_dict(SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   do_resize
					lowercase_	    =   size
					lowercase_	    =   resample
					lowercase_	    =   do_center_crop
					lowercase_	    =   crop_size
					lowercase_	    =   do_rescale
					lowercase_	    =   rescale_factor
					lowercase_	    =   do_normalize
					lowercase_	    =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
					lowercase_	    =   image_std if image_std is not None else IMAGENET_STANDARD_STD
	def 		_lowercase    (			self    :     str			,				SCREAMING_SNAKE_CASE_    :     np.ndarray			,				SCREAMING_SNAKE_CASE_    :     Dict[str, int]			,				SCREAMING_SNAKE_CASE_    :     PILImageResampling = PILImageResampling.BICUBIC			,				SCREAMING_SNAKE_CASE_    :     Optional[Union[str, ChannelDimension]] = None			,				**SCREAMING_SNAKE_CASE_    :     List[str]			,				)			->	np.ndarray:
					lowercase_	    =   get_size_dict(SCREAMING_SNAKE_CASE_			,				default_to_square=SCREAMING_SNAKE_CASE_			)
					if "shortest_edge" not in size:
									raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''			)
					lowercase_	    =   get_resize_output_image_size(SCREAMING_SNAKE_CASE_			,				size=size['''shortest_edge''']			,				default_to_square=SCREAMING_SNAKE_CASE_			)
					return resize(SCREAMING_SNAKE_CASE_			,				size=SCREAMING_SNAKE_CASE_			,				resample=SCREAMING_SNAKE_CASE_			,				data_format=SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     int			,				SCREAMING_SNAKE_CASE_    :     np.ndarray			,				SCREAMING_SNAKE_CASE_    :     Dict[str, int]			,				SCREAMING_SNAKE_CASE_    :     Optional[Union[str, ChannelDimension]] = None			,				**SCREAMING_SNAKE_CASE_    :     Tuple			,				)			->	np.ndarray:
					lowercase_	    =   get_size_dict(SCREAMING_SNAKE_CASE_			)
					return center_crop(SCREAMING_SNAKE_CASE_			,				size=(size['''height'''], size['''width'''])			,				data_format=SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     str			,				SCREAMING_SNAKE_CASE_    :     np.ndarray			,				SCREAMING_SNAKE_CASE_    :     float			,				SCREAMING_SNAKE_CASE_    :     Optional[Union[str, ChannelDimension]] = None			,				**SCREAMING_SNAKE_CASE_    :     List[Any]			)			->	np.ndarray:
					return rescale(SCREAMING_SNAKE_CASE_			,				scale=SCREAMING_SNAKE_CASE_			,				data_format=SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     np.ndarray			,				SCREAMING_SNAKE_CASE_    :     Union[float, List[float]]			,				SCREAMING_SNAKE_CASE_    :     Union[float, List[float]]			,				SCREAMING_SNAKE_CASE_    :     Optional[Union[str, ChannelDimension]] = None			,				**SCREAMING_SNAKE_CASE_    :     Any			,				)			->	np.ndarray:
					return normalize(SCREAMING_SNAKE_CASE_			,				mean=SCREAMING_SNAKE_CASE_			,				std=SCREAMING_SNAKE_CASE_			,				data_format=SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)




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

					lowercase_	    =   make_list_of_images(SCREAMING_SNAKE_CASE_			)

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

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

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

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

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

					lowercase_	    =   [to_channel_dimension_format(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			) for image in images]

					lowercase_	    =   {'''pixel_values''': images}
					return BatchFeature(data=SCREAMING_SNAKE_CASE_			,				tensor_type=SCREAMING_SNAKE_CASE_			)








'''simple docstring'''






import json
import os
import re
import unittest

from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class    __magic_name__				(						lowerCAmelCase   ,unittest.TestCase							):
       UpperCAmelCase		 =CodeGenTokenizer
       UpperCAmelCase		 =CodeGenTokenizerFast
       UpperCAmelCase		 =True
       UpperCAmelCase		 ={"add_prefix_space": True}
       UpperCAmelCase		 =False






       def      lowerCAmelCase						(     self)		->    Optional[int]:




          '''simple docstring'''
          super().setUp()

          # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
          _UpperCAmelCase  :  Optional[Any]				       =[
              'l',
              'o',
              'w',
              'e',
              'r',
              's',
              't',
              'i',
              'd',
              'n',
              '\u0120',
              '\u0120l',
              '\u0120n',
              '\u0120lo',
              '\u0120low',
              'er',
              '\u0120lowest',
              '\u0120newer',
              '\u0120wider',
              '<unk>',
              '<|endoftext|>',
          ]
          _UpperCAmelCase  :  Dict				       =dict(zip(snake_case    ,				range(len(snake_case))))
          _UpperCAmelCase  :  List[Any]				       =['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
          _UpperCAmelCase  :  List[Any]				       ={'unk_token': '<unk>'}

          _UpperCAmelCase  :  List[Any]				       =os.path.join(self.tmpdirname    ,				VOCAB_FILES_NAMES['vocab_file'])
          _UpperCAmelCase  :  List[str]				       =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(snake_case) + '\n')
          with open(self.merges_file    ,				'w'    ,				encoding='utf-8') as fp:
             fp.write('\n'.join(snake_case))






       def      lowerCAmelCase						(     self    ,				**snake_case)		->    str:




          '''simple docstring'''
          kwargs.update(self.special_tokens_map)
          return CodeGenTokenizer.from_pretrained(self.tmpdirname    ,				**snake_case)






       def      lowerCAmelCase						(     self    ,				**snake_case)		->    Optional[Any]:




          '''simple docstring'''
          kwargs.update(self.special_tokens_map)
          return CodeGenTokenizerFast.from_pretrained(self.tmpdirname    ,				**snake_case)






       def      lowerCAmelCase						(     self    ,				snake_case)		->    Optional[Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  Union[str, Any]				       ='lower newer'
          _UpperCAmelCase  :  int				       ='lower newer'
          return input_text, output_text






       def      lowerCAmelCase						(     self)		->    int:




          '''simple docstring'''
          _UpperCAmelCase  :  Optional[int]				       =CodeGenTokenizer(self.vocab_file    ,				self.merges_file    ,				**self.special_tokens_map)
          _UpperCAmelCase  :  str				       ='lower newer'
          _UpperCAmelCase  :  Union[str, Any]				       =['\u0120low', 'er', '\u0120', 'n', 'e', 'w', 'er']
          _UpperCAmelCase  :  Any				       =tokenizer.tokenize(snake_case    ,				add_prefix_space=snake_case)
          self.assertListEqual(snake_case    ,				snake_case)

          _UpperCAmelCase  :  Any				       =tokens + [tokenizer.unk_token]
          _UpperCAmelCase  :  List[str]				       =[1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case)    ,				snake_case)






       def      lowerCAmelCase						(     self)		->    Union[str, Any]:




          '''simple docstring'''
          if not self.test_rust_tokenizer:
             return

          _UpperCAmelCase  :  Tuple				       =self.get_tokenizer()
          _UpperCAmelCase  :  Dict				       =self.get_rust_tokenizer(add_prefix_space=snake_case)

          _UpperCAmelCase  :  Union[str, Any]				       ='lower newer'

          # Testing tokenization
          _UpperCAmelCase  :  List[str]				       =tokenizer.tokenize(snake_case    ,				add_prefix_space=snake_case)
          _UpperCAmelCase  :  Optional[int]				       =rust_tokenizer.tokenize(snake_case)
          self.assertListEqual(snake_case    ,				snake_case)

          # Testing conversion to ids without special tokens
          _UpperCAmelCase  :  str				       =tokenizer.encode(snake_case    ,				add_special_tokens=snake_case    ,				add_prefix_space=snake_case)
          _UpperCAmelCase  :  Optional[int]				       =rust_tokenizer.encode(snake_case    ,				add_special_tokens=snake_case)
          self.assertListEqual(snake_case    ,				snake_case)

          # Testing conversion to ids with special tokens
          _UpperCAmelCase  :  Dict				       =self.get_rust_tokenizer(add_prefix_space=snake_case)
          _UpperCAmelCase  :  Tuple				       =tokenizer.encode(snake_case    ,				add_prefix_space=snake_case)
          _UpperCAmelCase  :  int				       =rust_tokenizer.encode(snake_case)
          self.assertListEqual(snake_case    ,				snake_case)

          # Testing the unknown token
          _UpperCAmelCase  :  List[str]				       =tokens + [rust_tokenizer.unk_token]
          _UpperCAmelCase  :  Union[str, Any]				       =[1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
          self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(snake_case)    ,				snake_case)






       def      lowerCAmelCase						(     self    ,				*snake_case    ,				**snake_case)		->    Any:




          '''simple docstring'''
          # It's very difficult to mix/test pretokenization with byte-level
          # And get both CodeGen and Roberta to work at the same time (mostly an issue of adding a space before the string)
          pass






       def      lowerCAmelCase						(     self    ,				snake_case=1_5)		->    Optional[int]:




          '''simple docstring'''
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
                _UpperCAmelCase  :  Optional[Any]				       =self.rust_tokenizer_class.from_pretrained(snake_case    ,				**snake_case)

                # Simple input
                _UpperCAmelCase  :  List[str]				       ='This is a simple input'
                _UpperCAmelCase  :  Optional[Any]				       =['This is a simple input 1', 'This is a simple input 2']
                _UpperCAmelCase  :  int				       =('This is a simple input', 'This is a pair')
                _UpperCAmelCase  :  Union[str, Any]				       =[
                    ('This is a simple input 1', 'This is a simple input 2'),
                    ('This is a simple pair 1', 'This is a simple pair 2'),
                ]

                # Simple input tests
                self.assertRaises(snake_case    ,				tokenizer_r.encode    ,				snake_case    ,				max_length=snake_case    ,				padding='max_length')

                # Simple input
                self.assertRaises(snake_case    ,				tokenizer_r.encode_plus    ,				snake_case    ,				max_length=snake_case    ,				padding='max_length')

                # Simple input
                self.assertRaises(
                    snake_case    ,				tokenizer_r.batch_encode_plus    ,				snake_case    ,				max_length=snake_case    ,				padding='max_length'    ,				)

                # Pair input
                self.assertRaises(snake_case    ,				tokenizer_r.encode    ,				snake_case    ,				max_length=snake_case    ,				padding='max_length')

                # Pair input
                self.assertRaises(snake_case    ,				tokenizer_r.encode_plus    ,				snake_case    ,				max_length=snake_case    ,				padding='max_length')

                # Pair input
                self.assertRaises(
                    snake_case    ,				tokenizer_r.batch_encode_plus    ,				snake_case    ,				max_length=snake_case    ,				padding='max_length'    ,				)






       def      lowerCAmelCase						(     self)		->    Optional[Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  Any				       =CodeGenTokenizer.from_pretrained(self.tmpdirname    ,				pad_token='<pad>')

          # Simple input
          _UpperCAmelCase  :  List[Any]				       ='This is a simple input'
          _UpperCAmelCase  :  Any				       =['This is a simple input looooooooong', 'This is a simple input']
          _UpperCAmelCase  :  List[str]				       =('This is a simple input', 'This is a pair')
          _UpperCAmelCase  :  Optional[int]				       =[
              ('This is a simple input loooooong', 'This is a simple input'),
              ('This is a simple pair loooooong', 'This is a simple pair'),
          ]

          _UpperCAmelCase  :  List[Any]				       =tokenizer.pad_token_id

          _UpperCAmelCase  :  Union[str, Any]				       =tokenizer(snake_case    ,				padding='max_length'    ,				max_length=3_0    ,				return_tensors='np')
          _UpperCAmelCase  :  List[Any]				       =tokenizer(snake_case    ,				padding=snake_case    ,				truncate=snake_case    ,				return_tensors='np')
          _UpperCAmelCase  :  Optional[Any]				       =tokenizer(*snake_case    ,				padding='max_length'    ,				max_length=6_0    ,				return_tensors='np')
          _UpperCAmelCase  :  List[Any]				       =tokenizer(snake_case    ,				padding=snake_case    ,				truncate=snake_case    ,				return_tensors='np')

          # s
          # test single string max_length padding
          self.assertEqual(out_s['input_ids'].shape[-1]    ,				3_0)
          self.assertTrue(pad_token_id in out_s['input_ids'])
          self.assertTrue(0 in out_s['attention_mask'])

          # s2
          # test automatic padding
          self.assertEqual(out_sa['input_ids'].shape[-1]    ,				3_3)
          # long slice doesn't have padding
          self.assertFalse(pad_token_id in out_sa['input_ids'][0])
          self.assertFalse(0 in out_sa['attention_mask'][0])
          # short slice does have padding
          self.assertTrue(pad_token_id in out_sa['input_ids'][1])
          self.assertTrue(0 in out_sa['attention_mask'][1])

          # p
          # test single pair max_length padding
          self.assertEqual(out_p['input_ids'].shape[-1]    ,				6_0)
          self.assertTrue(pad_token_id in out_p['input_ids'])
          self.assertTrue(0 in out_p['attention_mask'])

          # p2
          # test automatic padding pair
          self.assertEqual(out_pa['input_ids'].shape[-1]    ,				5_2)
          # long slice pair doesn't have padding
          self.assertFalse(pad_token_id in out_pa['input_ids'][0])
          self.assertFalse(0 in out_pa['attention_mask'][0])
          # short slice pair does have padding
          self.assertTrue(pad_token_id in out_pa['input_ids'][1])
          self.assertTrue(0 in out_pa['attention_mask'][1])






       def      lowerCAmelCase						(     self)		->    Optional[int]:




          '''simple docstring'''
          _UpperCAmelCase  :  Any				       ='$$$'
          _UpperCAmelCase  :  Any				       =CodeGenTokenizer.from_pretrained(self.tmpdirname    ,				bos_token=snake_case    ,				add_bos_token=snake_case)

          _UpperCAmelCase  :  Optional[Any]				       ='This is a simple input'
          _UpperCAmelCase  :  Any				       =['This is a simple input 1', 'This is a simple input 2']

          _UpperCAmelCase  :  int				       =tokenizer.bos_token_id

          _UpperCAmelCase  :  Optional[Any]				       =tokenizer(snake_case)
          _UpperCAmelCase  :  Tuple				       =tokenizer(snake_case)

          self.assertEqual(out_s.input_ids[0]    ,				snake_case)
          self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids))

          _UpperCAmelCase  :  Optional[int]				       =tokenizer.decode(out_s.input_ids)
          _UpperCAmelCase  :  Any				       =tokenizer.batch_decode(out_sa.input_ids)

          self.assertEqual(decode_s.split()[0]    ,				snake_case)
          self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa))






       @slow
       def      lowerCAmelCase						(     self)		->    Optional[int]:




          '''simple docstring'''
          _UpperCAmelCase  :  Optional[Any]				       =CodeGenTokenizer.from_pretrained('Salesforce/codegen-350M-mono')

          _UpperCAmelCase  :  Optional[Any]				       ='\nif len_a > len_b:\n    result = a\nelse:\n    result = b\n\n\n\n#'
          _UpperCAmelCase  :  Tuple				       ='\nif len_a > len_b:      result = a\nelse:      result = b'

          _UpperCAmelCase  :  Optional[int]				       =tokenizer.encode(snake_case)
          _UpperCAmelCase  :  List[str]				       =['^#', re.escape('<|endoftext|>'), '^\'\'\'', '^"""', '\n\n\n']
          _UpperCAmelCase  :  Dict				       =tokenizer.decode(snake_case    ,				truncate_before_pattern=snake_case)
          self.assertEqual(snake_case    ,				snake_case)






       def      lowerCAmelCase						(     self)		->    Optional[Any]:




          '''simple docstring'''
          pass

