


'''simple docstring'''
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


__snake_case							      =     get_tests_dir('''fixtures/test_sentencepiece_no_bos.model''')
@require_sentencepiece
@require_tokenizers
class    lowercase	(							__snake_case	,		unittest.TestCase					):

		"""simple docstring"""

		_a     =				PegasusTokenizer
		_a     =				PegasusTokenizerFast
		_a     =				True
		_a     =				True




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							super().setUp()

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




		@cached_property
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


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




		def     lowerCAmelCase__				(		self    ,   **UpperCamelCase_							):

							'''simple docstring'''


							return PegasusTokenizer.from_pretrained(self.tmpdirname    ,   **a_							)




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


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




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Dict												= '''</s>'''
							UpperCamelCase__   :str												= 1

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




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Optional[Any]												= 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(a_							)    ,   1103							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							self.assertEqual(self.get_tokenizer().vocab_size    ,   1103							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :str												= self.rust_tokenizer_class.from_pretrained(self.tmpdirname							)
							UpperCamelCase__   :List[str]												= self.tokenizer_class.from_pretrained(self.tmpdirname							)
							UpperCamelCase__   :Optional[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__   :Optional[Any]												= rust_tokenizer([raw_input_str]    ,   return_tensors=a_    ,   add_special_tokens=a_							).input_ids[0]
							UpperCamelCase__   :Dict												= py_tokenizer([raw_input_str]    ,   return_tensors=a_    ,   add_special_tokens=a_							).input_ids[0]
							self.assertListEqual(a_    ,   a_							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


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




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :List[Any]												= 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__   :Optional[int]												= '''To ensure a smooth flow of bank resolutions.'''
							UpperCamelCase__   :List[str]												= [413, 615, 114, 2291, 1971, 113, 1679, 10710, 107, 1]
							UpperCamelCase__   :Union[str, Any]												= tokenizer([raw_input_str]    ,   return_tensors=a_							).input_ids[0]
							self.assertListEqual(a_    ,   a_							)
							assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]							) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]




		@require_torch
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :List[Any]												= ['''This is going to be way too long.''' * 150, '''short example''']
							UpperCamelCase__   :Optional[Any]												= ['''not super long but more than 5 tokens''', '''tiny''']
							UpperCamelCase__   :List[Any]												= self._large_tokenizer(a_    ,   padding=a_    ,   truncation=a_    ,   return_tensors='''pt'''							)
							UpperCamelCase__   :Dict												= self._large_tokenizer(
							    text_target=a_    ,   max_length=5    ,   padding=a_    ,   truncation=a_    ,   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(a_							) == 2  # input_ids, attention_mask.




		@slow
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Union[str, Any]												= {'''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=a_    ,   model_name='''google/bigbird-pegasus-large-arxiv'''    ,   revision='''ba85d0851d708441f91440d509690f1ab6353415'''    ,   )



@require_sentencepiece
@require_tokenizers
class    lowercase	(							__snake_case	,		unittest.TestCase					):

		"""simple docstring"""

		_a     =				PegasusTokenizer
		_a     =				PegasusTokenizerFast
		_a     =				True
		_a     =				True




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							super().setUp()

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




		@cached_property
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


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




		def     lowerCAmelCase__				(		self    ,   **UpperCamelCase_							):

							'''simple docstring'''


							return PegasusTokenizer.from_pretrained(self.tmpdirname    ,   **a_							)




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


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




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Tuple												= self.rust_tokenizer_class.from_pretrained(self.tmpdirname							)
							UpperCamelCase__   :List[str]												= self.tokenizer_class.from_pretrained(self.tmpdirname							)
							UpperCamelCase__   :Union[str, 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__   :List[str]												= rust_tokenizer([raw_input_str]    ,   return_tensors=a_    ,   add_special_tokens=a_							).input_ids[0]
							UpperCamelCase__   :List[str]												= py_tokenizer([raw_input_str]    ,   return_tensors=a_    ,   add_special_tokens=a_							).input_ids[0]
							self.assertListEqual(a_    ,   a_							)




		@require_torch
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Optional[int]												= ['''This is going to be way too long.''' * 1000, '''short example''']
							UpperCamelCase__   :Optional[Any]												= ['''not super long but more than 5 tokens''', '''tiny''']
							UpperCamelCase__   :str												= self._large_tokenizer(a_    ,   padding=a_    ,   truncation=a_    ,   return_tensors='''pt'''							)
							UpperCamelCase__   :List[Any]												= self._large_tokenizer(
							    text_target=a_    ,   max_length=5    ,   padding=a_    ,   truncation=a_    ,   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(a_							) == 2  # input_ids, attention_mask.




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Dict												= (
							    '''This is an example string that is used to test the original TF implementation against the HF'''
							    ''' implementation'''
							)

							UpperCamelCase__   :Optional[Any]												= self._large_tokenizer(a_							).input_ids

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



"""simple docstring"""
import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()



@slow
@require_torch_gpu
class 			_UpperCAmelCase (							unittest.TestCase				):






		'''simple docstring'''

		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Tuple						=				StableDiffusionKDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''					)
			__snake_case	:						List[str]						=				sd_pipe.to(a_					)
			sd_pipe.set_progress_bar_config(disable=a_					)

			sd_pipe.set_scheduler('''sample_euler'''					)

			__snake_case	:						Tuple						=				'''A painting of a squirrel eating a burger'''
			__snake_case	:						str						=				torch.manual_seed(0					)
			__snake_case	:						int						=				sd_pipe([prompt]				,       generator=a_				,       guidance_scale=9.0				,       num_inference_steps=20				,       output_type='''np'''					)

			__snake_case	:						List[str]						=				output.images

			__snake_case	:						Tuple						=				image[0, -3:, -3:, -1]

			assert image.shape == (1, 5_12, 5_12, 3)
			__snake_case	:						Dict						=				np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339]					)

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



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Optional[Any]						=				StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''					)
			__snake_case	:						Union[str, Any]						=				sd_pipe.to(a_					)
			sd_pipe.set_progress_bar_config(disable=a_					)

			sd_pipe.set_scheduler('''sample_euler'''					)

			__snake_case	:						str						=				'''A painting of a squirrel eating a burger'''
			__snake_case	:						List[str]						=				torch.manual_seed(0					)
			__snake_case	:						Dict						=				sd_pipe([prompt]				,       generator=a_				,       guidance_scale=9.0				,       num_inference_steps=20				,       output_type='''np'''					)

			__snake_case	:						Any						=				output.images

			__snake_case	:						Tuple						=				image[0, -3:, -3:, -1]

			assert image.shape == (1, 5_12, 5_12, 3)
			__snake_case	:						Tuple						=				np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112]					)

			assert np.abs(image_slice.flatten() - expected_slice					).max() < 5E-1



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Any						=				StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''					)
			__snake_case	:						Union[str, Any]						=				sd_pipe.to(a_					)
			sd_pipe.set_progress_bar_config(disable=a_					)

			sd_pipe.set_scheduler('''sample_dpmpp_2m'''					)

			__snake_case	:						int						=				'''A painting of a squirrel eating a burger'''
			__snake_case	:						Optional[Any]						=				torch.manual_seed(0					)
			__snake_case	:						Dict						=				sd_pipe(
			    [prompt]				,       generator=a_				,       guidance_scale=7.5				,       num_inference_steps=15				,       output_type='''np'''				,       use_karras_sigmas=a_				,       )

			__snake_case	:						Optional[int]						=				output.images

			__snake_case	:						Any						=				image[0, -3:, -3:, -1]

			assert image.shape == (1, 5_12, 5_12, 3)
			__snake_case	:						Any						=				np.array(
			    [0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048]					)

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





