





import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase_  							=				get_tests_dir('fixtures/test_sentencepiece_no_bos.model')






@require_sentencepiece
@require_tokenizers
class 					lowercase__ (			__lowerCamelCase    ,       unittest.TestCase       ):

   '''simple docstring'''


   a		:							List[str]     	=	PegasusTokenizer
   a		:							Any     	=	PegasusTokenizerFast
   a		:							List[str]     	=	True
   a		:							List[Any]     	=	True




   def    UpperCamelCase__						(					self							)				->   str:






       """simple docstring"""
       super().setUp()

       # We have a SentencePiece fixture for testing
       UpperCamelCase__							:       int      =   PegasusTokenizer(__magic_name__							)
       tokenizer.save_pretrained(self.tmpdirname							)




   @cached_property
   def    UpperCamelCase__						(					self							)				->   List[str]:






       """simple docstring"""
       return PegasusTokenizer.from_pretrained('''google/pegasus-large'''							)




   def    UpperCamelCase__						(					self,	**__magic_name__							)				->   PegasusTokenizer:






       """simple docstring"""
       return PegasusTokenizer.from_pretrained(self.tmpdirname,	**__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__							)				->   List[str]:






       """simple docstring"""
       return ("This is a test", "This is a test")




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






       """simple docstring"""
       UpperCamelCase__							:       Any      =   '''</s>'''
       UpperCamelCase__							:       List[str]      =   1

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(__magic_name__							),	__magic_name__							)
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(__magic_name__							),	__magic_name__							)




   def    UpperCamelCase__						(					self							)				->   Any:






       """simple docstring"""
       UpperCamelCase__							:       List[str]      =   list(self.get_tokenizer().get_vocab().keys()							)

       self.assertEqual(vocab_keys[0],	'''<pad>'''							)
       self.assertEqual(vocab_keys[1],	'''</s>'''							)
       self.assertEqual(vocab_keys[-1],	'''v'''							)
       self.assertEqual(len(__magic_name__							),	1103							)




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






       """simple docstring"""
       self.assertEqual(self.get_tokenizer().vocab_size,	1103							)




   def    UpperCamelCase__						(					self							)				->   List[str]:






       """simple docstring"""
       UpperCamelCase__							:       Tuple      =   self.rust_tokenizer_class.from_pretrained(self.tmpdirname							)
       UpperCamelCase__							:       int      =   self.tokenizer_class.from_pretrained(self.tmpdirname							)
       UpperCamelCase__							:       Union[str, Any]      =   (
           '''Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'''
           ''' </s> <pad> <pad> <pad>'''
       )
       UpperCamelCase__							:       Any      =   rust_tokenizer([raw_input_str],	return_tensors=__magic_name__,	add_special_tokens=__magic_name__							).input_ids[0]
       UpperCamelCase__							:       Any      =   py_tokenizer([raw_input_str],	return_tensors=__magic_name__,	add_special_tokens=__magic_name__							).input_ids[0]
       self.assertListEqual(__magic_name__,	__magic_name__							)




   def    UpperCamelCase__						(					self							)				->   Any:






       """simple docstring"""
       UpperCamelCase__							:       str      =   self._large_tokenizer
       # <mask_1> masks whole sentence while <mask_2> masks single word
       UpperCamelCase__							:       int      =   '''<mask_1> To ensure a <mask_2> flow of bank resolutions.'''
       UpperCamelCase__							:       Optional[int]      =   [2, 413, 615, 114, 3, 1971, 113, 1679, 10710, 107, 1]
       UpperCamelCase__							:       Dict      =   tokenizer([raw_input_str],	return_tensors=__magic_name__							).input_ids[0]
       self.assertListEqual(__magic_name__,	__magic_name__							)




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






       """simple docstring"""
       UpperCamelCase__							:       Optional[int]      =   self._large_tokenizer
       # The tracebacks for the following asserts are **better** without messages or self.assertEqual
       assert tokenizer.vocab_size == 96103
       assert tokenizer.pad_token_id == 0
       assert tokenizer.eos_token_id == 1
       assert tokenizer.offset == 103
       assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
       assert tokenizer.unk_token == "<unk>"
       assert tokenizer.model_max_length == 1024
       UpperCamelCase__							:       Union[str, Any]      =   '''To ensure a smooth flow of bank resolutions.'''
       UpperCamelCase__							:       str      =   [413, 615, 114, 2291, 1971, 113, 1679, 10710, 107, 1]
       UpperCamelCase__							:       Any      =   tokenizer([raw_input_str],	return_tensors=__magic_name__							).input_ids[0]
       self.assertListEqual(__magic_name__,	__magic_name__							)
       assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]							) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]




   @require_torch
   def    UpperCamelCase__						(					self							)				->   Any:






       """simple docstring"""
       UpperCamelCase__							:       List[str]      =   ['''This is going to be way too long.''' * 150, '''short example''']
       UpperCamelCase__							:       Optional[int]      =   ['''not super long but more than 5 tokens''', '''tiny''']
       UpperCamelCase__							:       Optional[Any]      =   self._large_tokenizer(__magic_name__,	padding=__magic_name__,	truncation=__magic_name__,	return_tensors='''pt'''							)
       UpperCamelCase__							:       Dict      =   self._large_tokenizer(
           text_target=__magic_name__,	max_length=5,	padding=__magic_name__,	truncation=__magic_name__,	return_tensors='''pt'''							)

       assert batch.input_ids.shape == (2, 1024)
       assert batch.attention_mask.shape == (2, 1024)
       assert targets["input_ids"].shape == (2, 5)
       assert len(__magic_name__							) == 2  # input_ids, attention_mask.




   @slow
   def    UpperCamelCase__						(					self							)				->   Dict:






       """simple docstring"""
       UpperCamelCase__							:       List[str]      =   {'''input_ids''': [[38979, 143, 18485, 606, 130, 26669, 87686, 121, 54189, 1129, 111, 26669, 87686, 121, 9114, 14787, 121, 13249, 158, 592, 956, 121, 14621, 31576, 143, 62613, 108, 9688, 930, 43430, 11562, 62613, 304, 108, 11443, 897, 108, 9314, 17415, 63399, 108, 11443, 7614, 18316, 118, 4284, 7148, 12430, 143, 1400, 25703, 158, 111, 4284, 7148, 11772, 143, 21297, 1064, 158, 122, 204, 3506, 1754, 1133, 14787, 1581, 115, 33224, 4482, 111, 1355, 110, 29173, 317, 50833, 108, 20147, 94665, 111, 77198, 107, 1], [110, 62613, 117, 638, 112, 1133, 121, 20098, 1355, 79050, 13872, 135, 1596, 53541, 1352, 141, 13039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1235, 2799, 18289, 17780, 204, 109, 9474, 1296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=__magic_name__,	model_name='''google/bigbird-pegasus-large-arxiv''',	revision='''ba85d0851d708441f91440d509690f1ab6353415''',	)






@require_sentencepiece
@require_tokenizers
class 					lowercase__ (			__lowerCamelCase    ,       unittest.TestCase       ):

   '''simple docstring'''


   a		:							str     	=	PegasusTokenizer
   a		:							int     	=	PegasusTokenizerFast
   a		:							int     	=	True
   a		:							List[Any]     	=	True




   def    UpperCamelCase__						(					self							)				->   str:






       """simple docstring"""
       super().setUp()

       # We have a SentencePiece fixture for testing
       UpperCamelCase__							:       Union[str, Any]      =   PegasusTokenizer(__magic_name__,	offset=0,	mask_token_sent=__magic_name__,	mask_token='''[MASK]'''							)
       tokenizer.save_pretrained(self.tmpdirname							)




   @cached_property
   def    UpperCamelCase__						(					self							)				->   List[Any]:






       """simple docstring"""
       return PegasusTokenizer.from_pretrained('''google/bigbird-pegasus-large-arxiv'''							)




   def    UpperCamelCase__						(					self,	**__magic_name__							)				->   PegasusTokenizer:






       """simple docstring"""
       return PegasusTokenizer.from_pretrained(self.tmpdirname,	**__magic_name__							)




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






       """simple docstring"""
       return ("This is a test", "This is a test")




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






       """simple docstring"""
       UpperCamelCase__							:       Tuple      =   self.rust_tokenizer_class.from_pretrained(self.tmpdirname							)
       UpperCamelCase__							:       Tuple      =   self.tokenizer_class.from_pretrained(self.tmpdirname							)
       UpperCamelCase__							:       Any      =   (
           '''Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'''
           ''' <pad> <pad> <pad>'''
       )
       UpperCamelCase__							:       Dict      =   rust_tokenizer([raw_input_str],	return_tensors=__magic_name__,	add_special_tokens=__magic_name__							).input_ids[0]
       UpperCamelCase__							:       Dict      =   py_tokenizer([raw_input_str],	return_tensors=__magic_name__,	add_special_tokens=__magic_name__							).input_ids[0]
       self.assertListEqual(__magic_name__,	__magic_name__							)




   @require_torch
   def    UpperCamelCase__						(					self							)				->   Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:       List[str]      =   ['''This is going to be way too long.''' * 1000, '''short example''']
       UpperCamelCase__							:       Dict      =   ['''not super long but more than 5 tokens''', '''tiny''']
       UpperCamelCase__							:       int      =   self._large_tokenizer(__magic_name__,	padding=__magic_name__,	truncation=__magic_name__,	return_tensors='''pt'''							)
       UpperCamelCase__							:       str      =   self._large_tokenizer(
           text_target=__magic_name__,	max_length=5,	padding=__magic_name__,	truncation=__magic_name__,	return_tensors='''pt'''							)

       assert batch.input_ids.shape == (2, 4096)
       assert batch.attention_mask.shape == (2, 4096)
       assert targets["input_ids"].shape == (2, 5)
       assert len(__magic_name__							) == 2  # input_ids, attention_mask.




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






       """simple docstring"""
       UpperCamelCase__							:       List[str]      =   (
           '''This is an example string that is used to test the original TF implementation against the HF'''
           ''' implementation'''
       )

       UpperCamelCase__							:       str      =   self._large_tokenizer(__magic_name__							).input_ids

       self.assertListEqual(
           __magic_name__,	[182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 25016, 3137, 464, 109, 26955, 3137, 1],	)








from manim import *






class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   def    UpperCamelCase__						(					self							)				->   List[str]:






       """simple docstring"""
       UpperCamelCase__							:       Any      =   Rectangle(height=0.5,	width=0.5							)
       UpperCamelCase__							:       Any      =   Rectangle(height=0.25,	width=0.25							)
       UpperCamelCase__							:       Dict      =   Rectangle(height=0.46,	width=0.46							).set_stroke(width=0							)

       UpperCamelCase__							:       List[Any]      =   [mem.copy() for i in range(6							)]
       UpperCamelCase__							:       Optional[int]      =   [mem.copy() for i in range(6							)]
       UpperCamelCase__							:       str      =   VGroup(*__magic_name__							).arrange(__magic_name__,	buff=0							)
       UpperCamelCase__							:       List[str]      =   VGroup(*__magic_name__							).arrange(__magic_name__,	buff=0							)
       UpperCamelCase__							:       Tuple      =   VGroup(__magic_name__,	__magic_name__							).arrange(__magic_name__,	buff=0							)
       UpperCamelCase__							:       List[str]      =   Text('''CPU''',	font_size=24							)
       UpperCamelCase__							:       Any      =   Group(__magic_name__,	__magic_name__							).arrange(__magic_name__,	buff=0.5,	aligned_edge=__magic_name__							)
       cpu.move_to([-2.5, -0.5, 0]							)
       self.add(__magic_name__							)

       UpperCamelCase__							:       int      =   [mem.copy() for i in range(4							)]
       UpperCamelCase__							:       str      =   VGroup(*__magic_name__							).arrange(__magic_name__,	buff=0							)
       UpperCamelCase__							:       Tuple      =   Text('''GPU''',	font_size=24							)
       UpperCamelCase__							:       str      =   Group(__magic_name__,	__magic_name__							).arrange(__magic_name__,	buff=0.5,	aligned_edge=__magic_name__							)
       gpu.move_to([-1, -1, 0]							)
       self.add(__magic_name__							)

       UpperCamelCase__							:       str      =   [mem.copy() for i in range(6							)]
       UpperCamelCase__							:       Tuple      =   VGroup(*__magic_name__							).arrange(__magic_name__,	buff=0							)

       UpperCamelCase__							:       Tuple      =   Text('''Model''',	font_size=24							)
       UpperCamelCase__							:       Tuple      =   Group(__magic_name__,	__magic_name__							).arrange(__magic_name__,	buff=0.5,	aligned_edge=__magic_name__							)
       model.move_to([3, -1.0, 0]							)
       self.add(__magic_name__							)

       UpperCamelCase__							:       Union[str, Any]      =   []
       UpperCamelCase__							:       Any      =   []
       UpperCamelCase__							:       List[Any]      =   []

       for i, rect in enumerate(__magic_name__							):
           rect.set_stroke(__magic_name__							)

           UpperCamelCase__							:       Tuple      =   Rectangle(height=0.46 / 4,	width=0.46 / 3							).set_stroke(width=0.0							).set_fill(__magic_name__,	opacity=0.7							)

           if i == 0:
               cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT							),	buff=0.02,	direction=__magic_name__							)
               cpu_target.set_x(cpu_target.get_x() + 0.1							)
           elif i == 3:
               cpu_target.next_to(model_cpu_arr[0],	direction=__magic_name__,	buff=0.0							)
           else:
               cpu_target.next_to(model_cpu_arr[i - 1],	direction=__magic_name__,	buff=0.0							)
           self.add(__magic_name__							)
           model_cpu_arr.append(__magic_name__							)

       self.add(*__magic_name__,	*__magic_name__,	*__magic_name__							)

       UpperCamelCase__							:       Tuple      =   [mem.copy() for i in range(6							)]
       UpperCamelCase__							:       Dict      =   VGroup(*__magic_name__							).arrange(__magic_name__,	buff=0							)

       UpperCamelCase__							:       List[str]      =   Text('''Loaded Checkpoint''',	font_size=24							)
       UpperCamelCase__							:       Optional[Any]      =   Group(__magic_name__,	__magic_name__							).arrange(__magic_name__,	buff=0.5,	aligned_edge=__magic_name__							)
       checkpoint.move_to([3, 0.5, 0]							)

       self.add(__magic_name__							)

       UpperCamelCase__							:       Tuple      =   []
       UpperCamelCase__							:       List[str]      =   []

       for i, rect in enumerate(__magic_name__							):
           UpperCamelCase__							:       Optional[int]      =   fill.copy().set_fill(__magic_name__,	opacity=0.7							)
           target.move_to(__magic_name__							)
           ckpt_arr.append(__magic_name__							)

           UpperCamelCase__							:       int      =   target.copy()
           if i < 5:
               cpu_target.move_to(cpu_left_col_base[i + 1]							)
           else:
               cpu_target.move_to(cpu_right_col_base[i - 5]							)
           ckpt_cpu_arr.append(__magic_name__							)
       self.add(*__magic_name__,	*__magic_name__							)

       UpperCamelCase__							:       List[str]      =   Square(side_length=2.2							)
       key.move_to([-5, 2, 0]							)

       UpperCamelCase__							:       List[str]      =   MarkupText(
           f"<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model",	font_size=18,	)

       key_text.move_to([-5, 2.4, 0]							)

       self.add(__magic_name__,	__magic_name__							)

       UpperCamelCase__							:       Any      =   MarkupText(
           f"<span fgcolor='{BLUE}'>●</span> Checkpoint",	font_size=18,	)

       blue_text.next_to(__magic_name__,	DOWN * 2.4,	aligned_edge=key_text.get_left()							)
       self.add(__magic_name__							)

       UpperCamelCase__							:       Dict      =   MarkupText(
           f"Based on the passed in configuration, weights are stored in\na variety of np.memmaps on disk or to a particular device.",	font_size=24,	)
       step_a.move_to([2, 2, 0]							)

       UpperCamelCase__							:       Any      =   [meta_mem.copy() for i in range(6							)]
       UpperCamelCase__							:       int      =   [meta_mem.copy() for i in range(6							)]
       UpperCamelCase__							:       Optional[int]      =   VGroup(*__magic_name__							).arrange(__magic_name__,	buff=0							)
       UpperCamelCase__							:       Union[str, Any]      =   VGroup(*__magic_name__							).arrange(__magic_name__,	buff=0							)
       UpperCamelCase__							:       Union[str, Any]      =   VGroup(__magic_name__,	__magic_name__							).arrange(__magic_name__,	buff=0							)
       UpperCamelCase__							:       Any      =   Text('''Disk''',	font_size=24							)
       UpperCamelCase__							:       List[Any]      =   Group(__magic_name__,	__magic_name__							).arrange(__magic_name__,	buff=0.5,	aligned_edge=__magic_name__							)
       disk.move_to([-4.0, -1.25, 0]							)
       self.play(Write(__magic_name__,	run_time=3							),	Write(__magic_name__,	run_time=1							),	Create(__magic_name__,	run_time=1							)							)

       UpperCamelCase__							:       Union[str, Any]      =   []
       for i, rect in enumerate(__magic_name__							):
           UpperCamelCase__							:       Union[str, Any]      =   rect.copy()
           target.generate_target()
           target.target.move_to(disk_left_col_base[i]							).scale(0.5							)
           animations.append(MoveToTarget(__magic_name__,	run_time=1.5							)							)
       self.play(*__magic_name__							)

       self.play(FadeOut(__magic_name__							)							)

       UpperCamelCase__							:       Optional[int]      =   MarkupText(f"Then, the checkpoint is removed from memory\nthrough garbage collection.",	font_size=24							)
       step_a.move_to([2, 2, 0]							)

       self.play(Write(__magic_name__,	run_time=3							)							)

       self.play(
           FadeOut(__magic_name__,	__magic_name__,	*__magic_name__,	*__magic_name__							),	)

       self.wait()

