


"""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


A	:  List[str]            =     get_tests_dir("fixtures/test_sentencepiece_no_bos.model")
@require_sentencepiece
@require_tokenizers
class     _UpperCamelCase    (  lowerCAmelCase__			,unittest.TestCase						):
							'''simple docstring'''







							__UpperCAmelCase		:       Union[str, Any]							 =PegasusTokenizer
							__UpperCAmelCase		:       str							 =PegasusTokenizerFast
							__UpperCAmelCase		:       List[str]							 =True
							__UpperCAmelCase		:       List[Any]							 =True

							def      snake_case							(	self      ):
										super().setUp()

										# We have a SentencePiece fixture for testing
										__lowerCAmelCase							       =     PegasusTokenizer(__a      )
										tokenizer.save_pretrained(self.tmpdirname      )

							@cached_property
							def      snake_case							(	self      ):
										return PegasusTokenizer.from_pretrained("google/pegasus-large"      )

							def      snake_case							(	self       ,  **__a      ):
										return PegasusTokenizer.from_pretrained(self.tmpdirname       ,  **__a      )

							def      snake_case							(	self       ,  __a      ):
										return ("This is a test", "This is a test")

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     "</s>"
										__lowerCAmelCase							       =     1

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

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     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      )       ,  11_03      )

							def      snake_case							(	self      ):
										self.assertEqual(self.get_tokenizer().vocab_size       ,  11_03      )

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     self.rust_tokenizer_class.from_pretrained(self.tmpdirname      )
										__lowerCAmelCase							       =     self.tokenizer_class.from_pretrained(self.tmpdirname      )
										__lowerCAmelCase							       =     (
										    "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>"
										)
										__lowerCAmelCase							       =     rust_tokenizer([raw_input_str]       ,  return_tensors=__a       ,  add_special_tokens=__a      ).input_ids[0]
										__lowerCAmelCase							       =     py_tokenizer([raw_input_str]       ,  return_tensors=__a       ,  add_special_tokens=__a      ).input_ids[0]
										self.assertListEqual(__a       ,  __a      )

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     self._large_tokenizer
										# <mask_1> masks whole sentence while <mask_2> masks single word
										__lowerCAmelCase							       =     "<mask_1> To ensure a <mask_2> flow of bank resolutions."
										__lowerCAmelCase							       =     [2, 4_13, 6_15, 1_14, 3, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
										__lowerCAmelCase							       =     tokenizer([raw_input_str]       ,  return_tensors=__a      ).input_ids[0]
										self.assertListEqual(__a       ,  __a      )

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     self._large_tokenizer
										# The tracebacks for the following asserts are **better** without messages or self.assertEqual
										assert tokenizer.vocab_size == 9_61_03
										assert tokenizer.pad_token_id == 0
										assert tokenizer.eos_token_id == 1
										assert tokenizer.offset == 1_03
										assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_05
										assert tokenizer.unk_token == "<unk>"
										assert tokenizer.model_max_length == 10_24
										__lowerCAmelCase							       =     "To ensure a smooth flow of bank resolutions."
										__lowerCAmelCase							       =     [4_13, 6_15, 1_14, 22_91, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
										__lowerCAmelCase							       =     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      snake_case							(	self      ):
										__lowerCAmelCase							       =     ["This is going to be way too long." * 1_50, "short example"]
										__lowerCAmelCase							       =     ["not super long but more than 5 tokens", "tiny"]
										__lowerCAmelCase							       =     self._large_tokenizer(__a       ,  padding=__a       ,  truncation=__a       ,  return_tensors="pt"      )
										__lowerCAmelCase							       =     self._large_tokenizer(
										    text_target=__a       ,  max_length=5       ,  padding=__a       ,  truncation=__a       ,  return_tensors="pt"      )

										assert batch.input_ids.shape == (2, 10_24)
										assert batch.attention_mask.shape == (2, 10_24)
										assert targets["input_ids"].shape == (2, 5)
										assert len(__a      ) == 2  # input_ids, attention_mask.



							@slow
							def      snake_case							(	self      ):
										# fmt: off
										__lowerCAmelCase							       =     {"input_ids": [[3_89_79, 1_43, 1_84_85, 6_06, 1_30, 2_66_69, 8_76_86, 1_21, 5_41_89, 11_29, 1_11, 2_66_69, 8_76_86, 1_21, 91_14, 1_47_87, 1_21, 1_32_49, 1_58, 5_92, 9_56, 1_21, 1_46_21, 3_15_76, 1_43, 6_26_13, 1_08, 96_88, 9_30, 4_34_30, 1_15_62, 6_26_13, 3_04, 1_08, 1_14_43, 8_97, 1_08, 93_14, 1_74_15, 6_33_99, 1_08, 1_14_43, 76_14, 1_83_16, 1_18, 42_84, 71_48, 1_24_30, 1_43, 14_00, 2_57_03, 1_58, 1_11, 42_84, 71_48, 1_17_72, 1_43, 2_12_97, 10_64, 1_58, 1_22, 2_04, 35_06, 17_54, 11_33, 1_47_87, 15_81, 1_15, 3_32_24, 44_82, 1_11, 13_55, 1_10, 2_91_73, 3_17, 5_08_33, 1_08, 2_01_47, 9_46_65, 1_11, 7_71_98, 1_07, 1], [1_10, 6_26_13, 1_17, 6_38, 1_12, 11_33, 1_21, 2_00_98, 13_55, 7_90_50, 1_38_72, 1_35, 15_96, 5_35_41, 13_52, 1_41, 1_30_39, 55_42, 1_24, 3_02, 5_18, 1_11, 2_68, 29_56, 1_15, 1_49, 44_27, 1_07, 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_39, 12_35, 27_99, 1_82_89, 1_77_80, 2_04, 1_09, 94_74, 12_96, 1_07, 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     _UpperCamelCase    (  lowerCAmelCase__			,unittest.TestCase						):
							'''simple docstring'''







							__UpperCAmelCase		:       Tuple							 =PegasusTokenizer
							__UpperCAmelCase		:       str							 =PegasusTokenizerFast
							__UpperCAmelCase		:       str							 =True
							__UpperCAmelCase		:       int							 =True

							def      snake_case							(	self      ):
										super().setUp()

										# We have a SentencePiece fixture for testing
										__lowerCAmelCase							       =     PegasusTokenizer(__a       ,  offset=0       ,  mask_token_sent=__a       ,  mask_token="[MASK]"      )
										tokenizer.save_pretrained(self.tmpdirname      )

							@cached_property
							def      snake_case							(	self      ):
										return PegasusTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv"      )

							def      snake_case							(	self       ,  **__a      ):
										return PegasusTokenizer.from_pretrained(self.tmpdirname       ,  **__a      )

							def      snake_case							(	self       ,  __a      ):
										return ("This is a test", "This is a test")

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     self.rust_tokenizer_class.from_pretrained(self.tmpdirname      )
										__lowerCAmelCase							       =     self.tokenizer_class.from_pretrained(self.tmpdirname      )
										__lowerCAmelCase							       =     (
										    "Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"
										    " <pad> <pad> <pad>"
										)
										__lowerCAmelCase							       =     rust_tokenizer([raw_input_str]       ,  return_tensors=__a       ,  add_special_tokens=__a      ).input_ids[0]
										__lowerCAmelCase							       =     py_tokenizer([raw_input_str]       ,  return_tensors=__a       ,  add_special_tokens=__a      ).input_ids[0]
										self.assertListEqual(__a       ,  __a      )

							@require_torch
							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     ["This is going to be way too long." * 10_00, "short example"]
										__lowerCAmelCase							       =     ["not super long but more than 5 tokens", "tiny"]
										__lowerCAmelCase							       =     self._large_tokenizer(__a       ,  padding=__a       ,  truncation=__a       ,  return_tensors="pt"      )
										__lowerCAmelCase							       =     self._large_tokenizer(
										    text_target=__a       ,  max_length=5       ,  padding=__a       ,  truncation=__a       ,  return_tensors="pt"      )

										assert batch.input_ids.shape == (2, 40_96)
										assert batch.attention_mask.shape == (2, 40_96)
										assert targets["input_ids"].shape == (2, 5)
										assert len(__a      ) == 2  # input_ids, attention_mask.



							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     (
										    "This is an example string that is used to test the original TF implementation against the HF"
										    " implementation"
										)

										__lowerCAmelCase							       =     self._large_tokenizer(__a      ).input_ids

										self.assertListEqual(
										    __a       ,  [1_82, 1_17, 1_42, 5_87, 42_11, 1_20, 1_17, 2_63, 1_12, 8_04, 1_09, 8_56, 2_50_16, 31_37, 4_64, 1_09, 2_69_55, 31_37, 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      snake_case							(	self      ):
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     StableDiffusionKDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"      )
										__lowerCAmelCase							       =     sd_pipe.to(__a      )
										sd_pipe.set_progress_bar_config(disable=__a      )

										sd_pipe.set_scheduler("sample_euler"      )

										__lowerCAmelCase							       =     "A painting of a squirrel eating a burger"
										__lowerCAmelCase							       =     torch.manual_seed(0      )
										__lowerCAmelCase							       =     sd_pipe([prompt]       ,  generator=__a       ,  guidance_scale=9.0       ,  num_inference_steps=20       ,  output_type="np"      )

										__lowerCAmelCase							       =     output.images

										__lowerCAmelCase							       =     image[0, -3:, -3:, -1]

										assert image.shape == (1, 5_12, 5_12, 3)
										__lowerCAmelCase							       =     np.array([0.0_4_4_7, 0.0_4_9_2, 0.0_4_6_8, 0.0_4_0_8, 0.0_3_8_3, 0.0_4_0_8, 0.0_3_5_4, 0.0_3_8_0, 0.0_3_3_9]      )

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

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"      )
										__lowerCAmelCase							       =     sd_pipe.to(__a      )
										sd_pipe.set_progress_bar_config(disable=__a      )

										sd_pipe.set_scheduler("sample_euler"      )

										__lowerCAmelCase							       =     "A painting of a squirrel eating a burger"
										__lowerCAmelCase							       =     torch.manual_seed(0      )
										__lowerCAmelCase							       =     sd_pipe([prompt]       ,  generator=__a       ,  guidance_scale=9.0       ,  num_inference_steps=20       ,  output_type="np"      )

										__lowerCAmelCase							       =     output.images

										__lowerCAmelCase							       =     image[0, -3:, -3:, -1]

										assert image.shape == (1, 5_12, 5_12, 3)
										__lowerCAmelCase							       =     np.array([0.1_2_3_7, 0.1_3_2_0, 0.1_4_3_8, 0.1_3_5_9, 0.1_3_9_0, 0.1_1_3_2, 0.1_2_7_7, 0.1_1_7_5, 0.1_1_1_2]      )

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



							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"      )
										__lowerCAmelCase							       =     sd_pipe.to(__a      )
										sd_pipe.set_progress_bar_config(disable=__a      )

										sd_pipe.set_scheduler("sample_dpmpp_2m"      )

										__lowerCAmelCase							       =     "A painting of a squirrel eating a burger"
										__lowerCAmelCase							       =     torch.manual_seed(0      )
										__lowerCAmelCase							       =     sd_pipe(
										    [prompt]       ,  generator=__a       ,  guidance_scale=7.5       ,  num_inference_steps=15       ,  output_type="np"       ,  use_karras_sigmas=__a       ,  )

										__lowerCAmelCase							       =     output.images

										__lowerCAmelCase							       =     image[0, -3:, -3:, -1]

										assert image.shape == (1, 5_12, 5_12, 3)
										__lowerCAmelCase							       =     np.array(
										    [0.1_1_3_8_1_6_8_9, 0.1_2_1_1_2_9_2_1, 0.1_3_8_9_4_5_7, 0.1_2_5_4_9_6_0_6, 0.1_2_4_4_9_6_4, 0.1_0_8_3_1_5_1_7, 0.1_1_5_6_2_8_6_6, 0.1_0_8_6_7_8_1_6, 0.1_0_4_9_9_0_4_8]      )

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




