



import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class     _A       (	__UpperCAmelCase      ,unittest.TestCase    ):
       UpperCamelCase__  :    Any											=						LEDTokenizer
       UpperCamelCase__  :    str											=						LEDTokenizerFast
       UpperCamelCase__  :    List[Any]											=						True



       def 		_lowerCamelCase			(		self							:     Optional[int]):


           '''simple docstring'''

           super().setUp()
           __a						       =				[
               '''l''',
               '''o''',
               '''w''',
               '''e''',
               '''r''',
               '''s''',
               '''t''',
               '''i''',
               '''d''',
               '''n''',
               '''\u0120''',
               '''\u0120l''',
               '''\u0120n''',
               '''\u0120lo''',
               '''\u0120low''',
               '''er''',
               '''\u0120lowest''',
               '''\u0120newer''',
               '''\u0120wider''',
               '''<unk>''',
           ]
           __a						       =				dict(zip(__SCREAMING_SNAKE_CASE     ,		range(len(__SCREAMING_SNAKE_CASE))))
           __a						       =				['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
           __a						       =				{'''unk_token''': '''<unk>'''}

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



       def 		_lowerCamelCase			(		self							:     str     ,		**__SCREAMING_SNAKE_CASE							:     Dict):


           '''simple docstring'''

           kwargs.update(self.special_tokens_map)
           return self.tokenizer_class.from_pretrained(self.tmpdirname     ,		**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     int     ,		**__SCREAMING_SNAKE_CASE							:     int):


           '''simple docstring'''

           kwargs.update(self.special_tokens_map)
           return self.rust_tokenizer_class.from_pretrained(self.tmpdirname     ,		**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]):


           '''simple docstring'''

           return "lower newer", "lower newer"



       @cached_property
       def 		_lowerCamelCase			(		self							:     Optional[int]):


           '''simple docstring'''

           return LEDTokenizer.from_pretrained('''allenai/led-base-16384''')



       @cached_property
       def 		_lowerCamelCase			(		self							:     Any):


           '''simple docstring'''

           return LEDTokenizerFast.from_pretrained('''allenai/led-base-16384''')



       @require_torch
       def 		_lowerCamelCase			(		self							:     Optional[int]):


           '''simple docstring'''

           __a						       =				['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
           __a						       =				[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               __a						       =				tokenizer(__SCREAMING_SNAKE_CASE     ,		max_length=len(__SCREAMING_SNAKE_CASE)     ,		padding=__SCREAMING_SNAKE_CASE     ,		return_tensors='''pt''')
               self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)

               self.assertEqual((2, 9)     ,		batch.input_ids.shape)
               self.assertEqual((2, 9)     ,		batch.attention_mask.shape)
               __a						       =				batch.input_ids.tolist()[0]
               self.assertListEqual(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)



       @require_torch
       def 		_lowerCamelCase			(		self							:     Union[str, Any]):


           '''simple docstring'''

           __a						       =				['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               __a						       =				tokenizer(__SCREAMING_SNAKE_CASE     ,		padding=__SCREAMING_SNAKE_CASE     ,		return_tensors='''pt''')
               self.assertIn('''input_ids'''     ,		__SCREAMING_SNAKE_CASE)
               self.assertIn('''attention_mask'''     ,		__SCREAMING_SNAKE_CASE)
               self.assertNotIn('''labels'''     ,		__SCREAMING_SNAKE_CASE)
               self.assertNotIn('''decoder_attention_mask'''     ,		__SCREAMING_SNAKE_CASE)



       @require_torch
       def 		_lowerCamelCase			(		self							:     List[Any]):


           '''simple docstring'''

           __a						       =				[
               '''Summary of the text.''',
               '''Another summary.''',
           ]
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               __a						       =				tokenizer(text_target=__SCREAMING_SNAKE_CASE     ,		max_length=32     ,		padding='''max_length'''     ,		return_tensors='''pt''')
               self.assertEqual(32     ,		targets['''input_ids'''].shape[1])



       @require_torch
       def 		_lowerCamelCase			(		self							:     Union[str, Any]):


           '''simple docstring'''

           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               __a						       =				tokenizer(
                   ['''I am a small frog''' * 1_024, '''I am a small frog''']     ,		padding=__SCREAMING_SNAKE_CASE     ,		truncation=__SCREAMING_SNAKE_CASE     ,		return_tensors='''pt''')
               self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
               self.assertEqual(batch.input_ids.shape     ,		(2, 5_122))



       @require_torch
       def 		_lowerCamelCase			(		self							:     List[Any]):


           '''simple docstring'''

           __a						       =				['''A long paragraph for summarization.''']
           __a						       =				[
               '''Summary of the text.''',
           ]
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               __a						       =				tokenizer(__SCREAMING_SNAKE_CASE     ,		return_tensors='''pt''')
               __a						       =				tokenizer(text_target=__SCREAMING_SNAKE_CASE     ,		return_tensors='''pt''')
               __a						       =				inputs['''input_ids''']
               __a						       =				targets['''input_ids''']
               self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item())
               self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item())
               self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item())
               self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item())



       @require_torch
       def 		_lowerCamelCase			(		self							:     Tuple):


           '''simple docstring'''

           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               __a						       =				['''Summary of the text.''', '''Another summary.''']
               __a						       =				[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

               __a						       =				tokenizer(__SCREAMING_SNAKE_CASE     ,		padding=__SCREAMING_SNAKE_CASE)
               __a						       =				[[0] * len(__SCREAMING_SNAKE_CASE) for x in encoded_output['''input_ids''']]
               __a						       =				tokenizer.pad(__SCREAMING_SNAKE_CASE)
               self.assertSequenceEqual(outputs['''global_attention_mask''']     ,		__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

           pass





       def 		_lowerCamelCase			(		self							:     Tuple):


           '''simple docstring'''

           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'):
                   __a						       =				self.rust_tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE)
                   __a						       =				self.tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE)
                   __a						       =				'''A, <mask> AllenNLP sentence.'''
                   __a						       =				tokenizer_r.encode_plus(__SCREAMING_SNAKE_CASE     ,		add_special_tokens=__SCREAMING_SNAKE_CASE     ,		return_token_type_ids=__SCREAMING_SNAKE_CASE)
                   __a						       =				tokenizer_p.encode_plus(__SCREAMING_SNAKE_CASE     ,		add_special_tokens=__SCREAMING_SNAKE_CASE     ,		return_token_type_ids=__SCREAMING_SNAKE_CASE)
                   self.assertEqual(sum(tokens_r['''token_type_ids'''])     ,		sum(tokens_p['''token_type_ids''']))
                   self.assertEqual(
                       sum(tokens_r['''attention_mask''']) / len(tokens_r['''attention_mask'''])     ,		sum(tokens_p['''attention_mask''']) / len(tokens_p['''attention_mask'''])     ,		)

                   __a						       =				tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids'''])
                   __a						       =				tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids'''])
                   self.assertSequenceEqual(tokens_p['''input_ids''']     ,		[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2])
                   self.assertSequenceEqual(tokens_r['''input_ids''']     ,		[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2])

                   self.assertSequenceEqual(
                       __SCREAMING_SNAKE_CASE     ,		['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''])
                   self.assertSequenceEqual(
                       __SCREAMING_SNAKE_CASE     ,		['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''])












import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


a__:      List[str]									=		False




class     SCREAMING_SNAKE_CASE__ (						unittest.TestCase      ):
 pass




@nightly
@require_torch_gpu
class     SCREAMING_SNAKE_CASE__ (						unittest.TestCase      ):



 def 				UpperCamelCase			(							self ):
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()



 def 				UpperCamelCase			(							self ):
        A__											=						VersatileDiffusionTextToImagePipeline.from_pretrained('''shi-labs/versatile-diffusion''' )
        # remove text_unet
        pipe.remove_unused_weights()
        pipe.to(__lowerCamelCase )
        pipe.set_progress_bar_config(disable=__lowerCamelCase )

        A__											=						'''A painting of a squirrel eating a burger '''
        A__											=						torch.manual_seed(0 )
        A__											=						pipe(
            prompt=__lowerCamelCase,generator=__lowerCamelCase,guidance_scale=7.5,num_inference_steps=2,output_type='''numpy''' ).images

        with tempfile.TemporaryDirectory() as tmpdirname:
               pipe.save_pretrained(__lowerCamelCase )
               A__											=						VersatileDiffusionTextToImagePipeline.from_pretrained(__lowerCamelCase )
        pipe.to(__lowerCamelCase )
        pipe.set_progress_bar_config(disable=__lowerCamelCase )

        A__											=						generator.manual_seed(0 )
        A__											=						pipe(
            prompt=__lowerCamelCase,generator=__lowerCamelCase,guidance_scale=7.5,num_inference_steps=2,output_type='''numpy''' ).images

        assert np.abs(image - new_image ).sum() < 1E-5, "Models don't have the same forward pass"




 def 				UpperCamelCase			(							self ):
        A__											=						VersatileDiffusionTextToImagePipeline.from_pretrained(
            '''shi-labs/versatile-diffusion''',torch_dtype=torch.floataa )
        pipe.to(__lowerCamelCase )
        pipe.set_progress_bar_config(disable=__lowerCamelCase )

        A__											=						'''A painting of a squirrel eating a burger '''
        A__											=						torch.manual_seed(0 )
        A__											=						pipe(
            prompt=__lowerCamelCase,generator=__lowerCamelCase,guidance_scale=7.5,num_inference_steps=50,output_type='''numpy''' ).images

        A__											=						image[0, 253:256, 253:256, -1]

        assert image.shape == (1, 512, 512, 3)
        A__											=						np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778] )

        assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2





