




'''simple docstring'''


import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_xglm import XGLMTokenizer
else:
 lowerCAmelCase_			:     Any							    =     None


lowerCAmelCase_			:     int							    =     logging.get_logger(__name__)

lowerCAmelCase_			:     List[Any]							    =     {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase_			:     Union[str, Any]							    =     {
    'vocab_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json',
    },
}

lowerCAmelCase_			:     Optional[int]							    =     {
    'facebook/xglm-564M': 2048,
}







class     SCREAMING_SNAKE_CASE						(	snake_case_							):
      __magic_name__     :					List[str]             =				VOCAB_FILES_NAMES
      __magic_name__     :					Any             =				PRETRAINED_VOCAB_FILES_MAP
      __magic_name__     :					Optional[int]             =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __magic_name__     :					Union[str, Any]             =				['''input_ids''', '''attention_mask''']
      __magic_name__     :					List[Any]             =				XGLMTokenizer






      def __init__(   self      :     Dict	,	lowercase__      :     List[str]=None	,	lowercase__      :     int=None	,	lowercase__      :     Dict="<s>"	,	lowercase__      :     List[str]="</s>"	,	lowercase__      :     Tuple="</s>"	,	lowercase__      :     Tuple="<s>"	,	lowercase__      :     str="<unk>"	,	lowercase__      :     int="<pad>"	,	**lowercase__      :     List[Any]	,	):


           '''simple docstring'''

           a_    :       Optional[Any]    	=     7
           a_    :       Dict    	=     [F"<madeupword{i}>" for i in range(self.num_madeup_words					)]

           a_    :       Optional[Any]    	=     kwargs.get("""additional_special_tokens"""	,	[]					)
           kwargs["additional_special_tokens"] += [
               word for word in madeup_words if word not in kwargs["additional_special_tokens"]
           ]

           super().__init__(
               lowercase__	,	tokenizer_file=lowercase__	,	bos_token=lowercase__	,	eos_token=lowercase__	,	sep_token=lowercase__	,	cls_token=lowercase__	,	unk_token=lowercase__	,	pad_token=lowercase__	,	**lowercase__	,	)

           a_    :       List[Any]    	=     vocab_file
           a_    :       Optional[Any]    	=     False if not self.vocab_file else True






      def        lowercase_	(   self      :     int	,	lowercase__      :     List[int]	,	lowercase__      :     Optional[List[int]] = None					):


           '''simple docstring'''

           if token_ids_a is None:
                return [self.sep_token_id] + token_ids_a
           a_    :       Tuple    	=     [self.sep_token_id]
           return sep + token_ids_a + sep + sep + token_ids_a






      def        lowercase_	(   self      :     int	,	lowercase__      :     List[int]	,	lowercase__      :     Optional[List[int]] = None					):


           '''simple docstring'''

           a_    :       Optional[int]    	=     [self.sep_token_id]

           if token_ids_a is None:
                return len(sep + token_ids_a					) * [0]
           return len(sep + token_ids_a + sep + sep + token_ids_a					) * [0]






      def        lowercase_	(   self      :     Union[str, Any]	,	lowercase__      :     str	,	lowercase__      :     Optional[str] = None					):


           '''simple docstring'''

           if not self.can_save_slow_tokenizer:
                raise ValueError(
                    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                    """tokenizer."""					)

           if not os.path.isdir(lowercase__					):
                logger.error(F"Vocabulary path ({save_directory}) should be a directory."					)
                return
           a_    :       List[Any]    	=     os.path.join(
               lowercase__	,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)

           if os.path.abspath(self.vocab_file					) != os.path.abspath(lowercase__					):
                copyfile(self.vocab_file	,	lowercase__					)

           return (out_vocab_file,)






'''simple docstring'''


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import CLIPImageProcessor







class     SCREAMING_SNAKE_CASE						(	unittest.TestCase							):






      def __init__(   self      :     Tuple	,	lowercase__      :     Union[str, Any]	,	lowercase__      :     Optional[Any]=7	,	lowercase__      :     str=3	,	lowercase__      :     Tuple=18	,	lowercase__      :     Dict=30	,	lowercase__      :     List[Any]=400	,	lowercase__      :     Optional[Any]=True	,	lowercase__      :     Union[str, Any]=None	,	lowercase__      :     Union[str, Any]=True	,	lowercase__      :     Tuple=None	,	lowercase__      :     str=True	,	lowercase__      :     List[Any]=[0.48145466, 0.4578275, 0.40821073]	,	lowercase__      :     Optional[Any]=[0.26862954, 0.26130258, 0.27577711]	,	lowercase__      :     str=True	,	):


           '''simple docstring'''

           a_    :       Dict    	=     size if size is not None else {"""shortest_edge""": 20}
           a_    :       Any    	=     crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
           a_    :       List[str]    	=     parent
           a_    :       Dict    	=     batch_size
           a_    :       Any    	=     num_channels
           a_    :       Optional[int]    	=     image_size
           a_    :       Union[str, Any]    	=     min_resolution
           a_    :       Tuple    	=     max_resolution
           a_    :       int    	=     do_resize
           a_    :       List[str]    	=     size
           a_    :       Optional[int]    	=     do_center_crop
           a_    :       str    	=     crop_size
           a_    :       str    	=     do_normalize
           a_    :       Any    	=     image_mean
           a_    :       List[str]    	=     image_std
           a_    :       Any    	=     do_convert_rgb






      def        lowercase_	(   self      :     Optional[int]					):


           '''simple docstring'''

           return {
               "do_resize": self.do_resize,
               "size": self.size,
               "do_center_crop": self.do_center_crop,
               "crop_size": self.crop_size,
               "do_normalize": self.do_normalize,
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_convert_rgb": self.do_convert_rgb,
           }






      def        lowercase_	(   self      :     str	,	lowercase__      :     Union[str, Any]=False	,	lowercase__      :     Union[str, Any]=False	,	lowercase__      :     Union[str, Any]=False					):


           '''simple docstring'''

           assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

           if equal_resolution:
                a_    :       Union[str, Any]    	=     []
                for i in range(self.batch_size					):
                     image_inputs.append(
                         np.random.randint(
                             255	,	size=(self.num_channels, self.max_resolution, self.max_resolution)	,	dtype=np.uinta					)					)
           else:
                a_    :       int    	=     []
                for i in range(self.batch_size					):
                     a_	,    a_    :       Any    	=     np.random.choice(np.arange(self.min_resolution	,	self.max_resolution					)	,	2					)
                     image_inputs.append(np.random.randint(255	,	size=(self.num_channels, width, height)	,	dtype=np.uinta					)					)

           if not numpify and not torchify:
                # PIL expects the channel dimension as last dimension
                a_    :       Union[str, Any]    	=     [Image.fromarray(np.moveaxis(lowercase__	,	0	,	-1					)					) for x in image_inputs]

           if torchify:
                a_    :       List[Any]    	=     [torch.from_numpy(lowercase__					) for x in image_inputs]

           return image_inputs







@require_torch
@require_vision
class     SCREAMING_SNAKE_CASE						(	snake_case_       ,			unittest.TestCase							):
      __magic_name__     :					int             =				CLIPImageProcessor if is_vision_available() else None






      def        lowercase_	(   self      :     List[str]					):


           '''simple docstring'''

           a_    :       Dict    	=     CLIPImageProcessingTester(self					)






      @property
      def        lowercase_	(   self      :     Optional[int]					):


           '''simple docstring'''

           return self.image_processor_tester.prepare_image_processor_dict()






      def        lowercase_	(   self      :     Optional[Any]					):


           '''simple docstring'''

           a_    :       List[Any]    	=     self.image_processing_class(**self.image_processor_dict					)
           self.assertTrue(hasattr(lowercase__	,	"""do_resize"""					)					)
           self.assertTrue(hasattr(lowercase__	,	"""size"""					)					)
           self.assertTrue(hasattr(lowercase__	,	"""do_center_crop"""					)					)
           self.assertTrue(hasattr(lowercase__	,	"""center_crop"""					)					)
           self.assertTrue(hasattr(lowercase__	,	"""do_normalize"""					)					)
           self.assertTrue(hasattr(lowercase__	,	"""image_mean"""					)					)
           self.assertTrue(hasattr(lowercase__	,	"""image_std"""					)					)
           self.assertTrue(hasattr(lowercase__	,	"""do_convert_rgb"""					)					)






      def        lowercase_	(   self      :     Dict					):


           '''simple docstring'''

           a_    :       Optional[Any]    	=     self.image_processing_class.from_dict(self.image_processor_dict					)
           self.assertEqual(image_processor.size	,	{"""shortest_edge""": 20}					)
           self.assertEqual(image_processor.crop_size	,	{"""height""": 18, """width""": 18}					)

           a_    :       Optional[int]    	=     self.image_processing_class.from_dict(self.image_processor_dict	,	size=42	,	crop_size=84					)
           self.assertEqual(image_processor.size	,	{"""shortest_edge""": 42}					)
           self.assertEqual(image_processor.crop_size	,	{"""height""": 84, """width""": 84}					)






      def        lowercase_	(   self      :     List[str]					):


           '''simple docstring'''

           pass






      def        lowercase_	(   self      :     Tuple					):


           '''simple docstring'''

           a_    :       str    	=     self.image_processing_class(**self.image_processor_dict					)
           # create random PIL images
           a_    :       str    	=     self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__					)
           for image in image_inputs:
                self.assertIsInstance(lowercase__	,	Image.Image					)

           # Test not batched input
           a_    :       Optional[Any]    	=     image_processing(image_inputs[0]	,	return_tensors="""pt"""					).pixel_values
           self.assertEqual(
               encoded_images.shape	,	(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )	,	)

           # Test batched
           a_    :       str    	=     image_processing(lowercase__	,	return_tensors="""pt"""					).pixel_values
           self.assertEqual(
               encoded_images.shape	,	(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )	,	)






      def        lowercase_	(   self      :     Tuple					):


           '''simple docstring'''

           a_    :       Optional[Any]    	=     self.image_processing_class(**self.image_processor_dict					)
           # create random numpy tensors
           a_    :       Optional[int]    	=     self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__	,	numpify=lowercase__					)
           for image in image_inputs:
                self.assertIsInstance(lowercase__	,	np.ndarray					)

           # Test not batched input
           a_    :       Tuple    	=     image_processing(image_inputs[0]	,	return_tensors="""pt"""					).pixel_values
           self.assertEqual(
               encoded_images.shape	,	(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )	,	)

           # Test batched
           a_    :       str    	=     image_processing(lowercase__	,	return_tensors="""pt"""					).pixel_values
           self.assertEqual(
               encoded_images.shape	,	(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )	,	)






      def        lowercase_	(   self      :     str					):


           '''simple docstring'''

           a_    :       Dict    	=     self.image_processing_class(**self.image_processor_dict					)
           # create random PyTorch tensors
           a_    :       Optional[int]    	=     self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__	,	torchify=lowercase__					)
           for image in image_inputs:
                self.assertIsInstance(lowercase__	,	torch.Tensor					)

           # Test not batched input
           a_    :       Optional[int]    	=     image_processing(image_inputs[0]	,	return_tensors="""pt"""					).pixel_values
           self.assertEqual(
               encoded_images.shape	,	(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )	,	)

           # Test batched
           a_    :       int    	=     image_processing(lowercase__	,	return_tensors="""pt"""					).pixel_values
           self.assertEqual(
               encoded_images.shape	,	(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )	,	)







@require_torch
@require_vision
class     SCREAMING_SNAKE_CASE						(	snake_case_       ,			unittest.TestCase							):
      __magic_name__     :					List[str]             =				CLIPImageProcessor if is_vision_available() else None






      def        lowercase_	(   self      :     str					):


           '''simple docstring'''

           a_    :       str    	=     CLIPImageProcessingTester(self	,	num_channels=4					)
           a_    :       Dict    	=     3






      @property
      def        lowercase_	(   self      :     Any					):


           '''simple docstring'''

           return self.image_processor_tester.prepare_image_processor_dict()






      def        lowercase_	(   self      :     List[Any]					):


           '''simple docstring'''

           a_    :       int    	=     self.image_processing_class(**self.image_processor_dict					)
           self.assertTrue(hasattr(lowercase__	,	"""do_resize"""					)					)
           self.assertTrue(hasattr(lowercase__	,	"""size"""					)					)
           self.assertTrue(hasattr(lowercase__	,	"""do_center_crop"""					)					)
           self.assertTrue(hasattr(lowercase__	,	"""center_crop"""					)					)
           self.assertTrue(hasattr(lowercase__	,	"""do_normalize"""					)					)
           self.assertTrue(hasattr(lowercase__	,	"""image_mean"""					)					)
           self.assertTrue(hasattr(lowercase__	,	"""image_std"""					)					)
           self.assertTrue(hasattr(lowercase__	,	"""do_convert_rgb"""					)					)






      def        lowercase_	(   self      :     int					):


           '''simple docstring'''

           pass






      def        lowercase_	(   self      :     Tuple					):


           '''simple docstring'''

           a_    :       List[Any]    	=     self.image_processing_class(**self.image_processor_dict					)
           # create random PIL images
           a_    :       Union[str, Any]    	=     self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__					)
           for image in image_inputs:
                self.assertIsInstance(lowercase__	,	Image.Image					)

           # Test not batched input
           a_    :       Optional[Any]    	=     image_processing(image_inputs[0]	,	return_tensors="""pt"""					).pixel_values
           self.assertEqual(
               encoded_images.shape	,	(
                   1,
                   self.expected_encoded_image_num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )	,	)

           # Test batched
           a_    :       List[str]    	=     image_processing(lowercase__	,	return_tensors="""pt"""					).pixel_values
           self.assertEqual(
               encoded_images.shape	,	(
                   self.image_processor_tester.batch_size,
                   self.expected_encoded_image_num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )	,	)
