





import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class 					__snake_case	(  __SCREAMING_SNAKE_CASE			,     unittest.TestCase    ):


    '''simple docstring'''







    lowerCamelCase__    :		int				=	GPTaTokenizer
    lowerCamelCase__    :		Any				=	GPTaTokenizerFast
    lowerCamelCase__    :		Optional[int]				=	True
    lowerCamelCase__    :		int				=	{"""add_prefix_space""": True}
    lowerCamelCase__    :		List[str]				=	False



    def  lowercase_					(       self     ):





        '''simple docstring'''

        super().setUp()

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

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



    def  lowercase_					(       self   ,				**A_     ):





        '''simple docstring'''

        kwargs.update(self.special_tokens_map     )
        return GPTaTokenizer.from_pretrained(self.tmpdirname   ,				**A_     )



    def  lowercase_					(       self   ,				**A_     ):





        '''simple docstring'''

        kwargs.update(self.special_tokens_map     )
        return GPTaTokenizerFast.from_pretrained(self.tmpdirname   ,				**A_     )



    def  lowercase_					(       self   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			'''lower newer'''
        SCREAMING_SNAKE_CASE__											=			'''lower newer'''
        return input_text, output_text



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			GPTaTokenizer(self.vocab_file   ,				self.merges_file   ,				**self.special_tokens_map     )
        SCREAMING_SNAKE_CASE__											=			'''lower newer'''
        SCREAMING_SNAKE_CASE__											=			['''\u0120low''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
        SCREAMING_SNAKE_CASE__											=			tokenizer.tokenize(A_   ,				add_prefix_space=A_     )
        self.assertListEqual(A_   ,				A_     )

        SCREAMING_SNAKE_CASE__											=			tokens + [tokenizer.unk_token]
        SCREAMING_SNAKE_CASE__											=			[14, 15, 10, 9, 3, 2, 15, 19]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(A_     )   ,				A_     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        if not self.test_rust_tokenizer:
            return

        SCREAMING_SNAKE_CASE__											=			self.get_tokenizer()
        SCREAMING_SNAKE_CASE__											=			self.get_rust_tokenizer(add_prefix_space=A_     )

        SCREAMING_SNAKE_CASE__											=			'''lower newer'''

        # Testing tokenization
        SCREAMING_SNAKE_CASE__											=			tokenizer.tokenize(A_   ,				add_prefix_space=A_     )
        SCREAMING_SNAKE_CASE__											=			rust_tokenizer.tokenize(A_     )
        self.assertListEqual(A_   ,				A_     )

        # Testing conversion to ids without special tokens
        SCREAMING_SNAKE_CASE__											=			tokenizer.encode(A_   ,				add_special_tokens=A_   ,				add_prefix_space=A_     )
        SCREAMING_SNAKE_CASE__											=			rust_tokenizer.encode(A_   ,				add_special_tokens=A_     )
        self.assertListEqual(A_   ,				A_     )

        # Testing conversion to ids with special tokens
        SCREAMING_SNAKE_CASE__											=			self.get_rust_tokenizer(add_prefix_space=A_     )
        SCREAMING_SNAKE_CASE__											=			tokenizer.encode(A_   ,				add_prefix_space=A_     )
        SCREAMING_SNAKE_CASE__											=			rust_tokenizer.encode(A_     )
        self.assertListEqual(A_   ,				A_     )

        # Testing the unknown token
        SCREAMING_SNAKE_CASE__											=			tokens + [rust_tokenizer.unk_token]
        SCREAMING_SNAKE_CASE__											=			[14, 15, 10, 9, 3, 2, 15, 19]
        self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(A_     )   ,				A_     )



    def  lowercase_					(       self   ,				*A_   ,				**A_     ):





        '''simple docstring'''

        pass



    def  lowercase_					(       self   ,				A_=15     ):





        '''simple docstring'''

        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''     ):
                SCREAMING_SNAKE_CASE__											=			self.rust_tokenizer_class.from_pretrained(A_   ,				**A_     )

                # Simple input
                SCREAMING_SNAKE_CASE__											=			'''This is a simple input'''
                SCREAMING_SNAKE_CASE__											=			['''This is a simple input 1''', '''This is a simple input 2''']
                SCREAMING_SNAKE_CASE__											=			('''This is a simple input''', '''This is a pair''')
                SCREAMING_SNAKE_CASE__											=			[
                    ('''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(A_   ,				tokenizer_r.encode   ,				A_   ,				max_length=A_   ,				padding='''max_length'''     )

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

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

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

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

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



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			GPTaTokenizer.from_pretrained(self.tmpdirname   ,				pad_token='''<pad>'''     )

        # Simple input
        SCREAMING_SNAKE_CASE__											=			'''This is a simple input'''
        SCREAMING_SNAKE_CASE__											=			['''This is a simple input looooooooong''', '''This is a simple input''']
        SCREAMING_SNAKE_CASE__											=			('''This is a simple input''', '''This is a pair''')
        SCREAMING_SNAKE_CASE__											=			[
            ('''This is a simple input loooooong''', '''This is a simple input'''),
            ('''This is a simple pair loooooong''', '''This is a simple pair'''),
        ]

        SCREAMING_SNAKE_CASE__											=			tokenizer.pad_token_id

        SCREAMING_SNAKE_CASE__											=			tokenizer(A_   ,				padding='''max_length'''   ,				max_length=30   ,				return_tensors='''np'''     )
        SCREAMING_SNAKE_CASE__											=			tokenizer(A_   ,				padding=A_   ,				truncate=A_   ,				return_tensors='''np'''     )
        SCREAMING_SNAKE_CASE__											=			tokenizer(*A_   ,				padding='''max_length'''   ,				max_length=60   ,				return_tensors='''np'''     )
        SCREAMING_SNAKE_CASE__											=			tokenizer(A_   ,				padding=A_   ,				truncate=A_   ,				return_tensors='''np'''     )

        # s
        # test single string max_length padding
        self.assertEqual(out_s['''input_ids'''].shape[-1]   ,				30     )
        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]   ,				33     )
        # 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]   ,				60     )
        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]   ,				52     )
        # 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  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			'''$$$'''
        SCREAMING_SNAKE_CASE__											=			GPTaTokenizer.from_pretrained(self.tmpdirname   ,				bos_token=A_   ,				add_bos_token=A_     )

        SCREAMING_SNAKE_CASE__											=			'''This is a simple input'''
        SCREAMING_SNAKE_CASE__											=			['''This is a simple input 1''', '''This is a simple input 2''']

        SCREAMING_SNAKE_CASE__											=			tokenizer.bos_token_id

        SCREAMING_SNAKE_CASE__											=			tokenizer(A_     )
        SCREAMING_SNAKE_CASE__											=			tokenizer(A_     )

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

        SCREAMING_SNAKE_CASE__											=			tokenizer.decode(out_s.input_ids     )
        SCREAMING_SNAKE_CASE__											=			tokenizer.batch_decode(out_sa.input_ids     )

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



    def  lowercase_					(       self     ):





        '''simple docstring'''

        pass




    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			[self.get_tokenizer(do_lower_case=A_   ,				add_bos_token=A_     )]
        for tokenizer in tokenizers:
            with self.subTest(f'''{tokenizer.__class__.__name__}'''     ):
                SCREAMING_SNAKE_CASE__											=			'''Encode this.'''
                SCREAMING_SNAKE_CASE__											=			'''This one too please.'''
                SCREAMING_SNAKE_CASE__											=			tokenizer.encode(A_   ,				add_special_tokens=A_     )
                encoded_sequence += tokenizer.encode(A_   ,				add_special_tokens=A_     )
                SCREAMING_SNAKE_CASE__											=			tokenizer.encode_plus(
                    A_   ,				A_   ,				add_special_tokens=A_   ,				return_special_tokens_mask=A_   ,				)
                SCREAMING_SNAKE_CASE__											=			encoded_sequence_dict['''input_ids''']
                SCREAMING_SNAKE_CASE__											=			encoded_sequence_dict['''special_tokens_mask''']
                self.assertEqual(len(A_     )   ,				len(A_     )     )

                SCREAMING_SNAKE_CASE__											=			[
                    (x if not special_tokens_mask[i] else None) for i, x in enumerate(A_     )
                ]
                SCREAMING_SNAKE_CASE__											=			[x for x in filtered_sequence if x is not None]
                self.assertEqual(A_   ,				A_     )





@require_tokenizers
class 					__snake_case	(  unittest.TestCase    ):


    '''simple docstring'''







    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			AutoTokenizer.from_pretrained('''facebook/opt-350m'''   ,				from_slow=A_     )
        SCREAMING_SNAKE_CASE__											=			'''A photo of a cat'''

        SCREAMING_SNAKE_CASE__											=			tokenizer.encode(
            A_   ,				)
        self.assertEqual(A_   ,				[2, 2_50, 13_45, 9, 10, 47_58]     )
        tokenizer.save_pretrained('''test_opt'''     )

        SCREAMING_SNAKE_CASE__											=			AutoTokenizer.from_pretrained('''./test_opt'''     )
        SCREAMING_SNAKE_CASE__											=			tokenizer.encode(
            A_   ,				)
        self.assertEqual(A_   ,				[2, 2_50, 13_45, 9, 10, 47_58]     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			AutoTokenizer.from_pretrained('''facebook/opt-350m'''   ,				use_slow=A_     )
        SCREAMING_SNAKE_CASE__											=			'''A photo of a cat'''

        SCREAMING_SNAKE_CASE__											=			tokenizer.encode(
            A_   ,				)
        # Same as above
        self.assertEqual(A_   ,				[2, 2_50, 13_45, 9, 10, 47_58]     )




    @unittest.skip('''This test is failing because of a bug in the fast tokenizer'''     )
    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			AutoTokenizer.from_pretrained('''facebook/opt-350m'''   ,				from_slow=A_     )

        SCREAMING_SNAKE_CASE__											=			'''bos'''
        SCREAMING_SNAKE_CASE__											=			tokenizer.get_vocab()['''bos''']

        SCREAMING_SNAKE_CASE__											=			'''A photo of a cat'''
        SCREAMING_SNAKE_CASE__											=			tokenizer.encode(
            A_   ,				)
        # We changed the bos token
        self.assertEqual(A_   ,				[3_19_57, 2_50, 13_45, 9, 10, 47_58]     )
        tokenizer.save_pretrained('''./tok'''     )
        SCREAMING_SNAKE_CASE__											=			AutoTokenizer.from_pretrained('''./tok'''     )
        self.assertTrue(tokenizer.is_fast     )
        SCREAMING_SNAKE_CASE__											=			tokenizer.encode(
            A_   ,				)
        self.assertEqual(A_   ,				[3_19_57, 2_50, 13_45, 9, 10, 47_58]     )


from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
		if not (is_transformers_available() and is_torch_available()):
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
		from .camera import create_pan_cameras
		from .pipeline_shap_e import ShapEPipeline
		from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
		from .renderer import (
		    BoundingBoxVolume,
		    ImportanceRaySampler,
		    MLPNeRFModelOutput,
		    MLPNeRSTFModel,
		    ShapEParamsProjModel,
		    ShapERenderer,
		    StratifiedRaySampler,
		    VoidNeRFModel,
		)