




"""simple docstring"""





import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
					A				=							"""pt"""
elif is_tf_available():
					A				=							"""tf"""
else:
					A				=							"""jax"""



class   a__					(						__magic_name__				,     unittest.TestCase ):
			lowercase_		=						ByTaTokenizer
			lowercase_		=						False


			def    a_  (   self   :     Any):


						"""simple docstring"""




						super().setUp()
						__UpperCAmelCase						:			List[str]												=       ByTaTokenizer()
						tokenizer.save_pretrained(self.tmpdirname)


			@cached_property
			def    a_  (   self   :     List[str]):


						"""simple docstring"""




						return ByTaTokenizer.from_pretrained("google/byt5-small")


			def    a_  (   self   :     Any   ,	**UpperCamelCase_   :     str):


						"""simple docstring"""




						return self.tokenizer_class.from_pretrained(self.tmpdirname   ,	**UpperCamelCase_)


			def    a_  (   self   :     Tuple   ,	UpperCamelCase_   :     Tuple   ,	UpperCamelCase_   :     List[str]=False   ,	UpperCamelCase_   :     Dict=20   ,	UpperCamelCase_   :     Dict=5):


						"""simple docstring"""




						__UpperCAmelCase						:			Optional[Any]												=       []
						for i in range(len(UpperCamelCase_)):
									try:
												__UpperCAmelCase						:			Optional[int]												=       tokenizer.decode([i]   ,	clean_up_tokenization_spaces=UpperCamelCase_)
									except UnicodeDecodeError:
												pass
									toks.append((i, tok))

						__UpperCAmelCase						:			List[str]												=       list(filter(lambda UpperCamelCase_: re.match(r"^[ a-zA-Z]+$"   ,	t[1])   ,	UpperCamelCase_))
						__UpperCAmelCase						:			Dict												=       list(filter(lambda UpperCamelCase_: [t[0]] == tokenizer.encode(t[1]   ,	add_special_tokens=UpperCamelCase_)   ,	UpperCamelCase_))
						if max_length is not None and len(UpperCamelCase_) > max_length:
									__UpperCAmelCase						:			Optional[int]												=       toks[:max_length]
						if min_length is not None and len(UpperCamelCase_) < min_length and len(UpperCamelCase_) > 0:
									while len(UpperCamelCase_) < min_length:
												__UpperCAmelCase						:			Dict												=       toks + toks
        # toks_str = [t[1] for t in toks]
						__UpperCAmelCase						:			Optional[int]												=       [t[0] for t in toks]

						# Ensure consistency
						__UpperCAmelCase						:			List[str]												=       tokenizer.decode(UpperCamelCase_   ,	clean_up_tokenization_spaces=UpperCamelCase_)
						if " " not in output_txt and len(UpperCamelCase_) > 1:
									__UpperCAmelCase						:			Optional[int]												=       (
									    tokenizer.decode([toks_ids[0]]   ,	clean_up_tokenization_spaces=UpperCamelCase_)
									    + " "
									    + tokenizer.decode(toks_ids[1:]   ,	clean_up_tokenization_spaces=UpperCamelCase_)
									)
						if with_prefix_space:
									__UpperCAmelCase						:			Any												=       " " + output_txt
						__UpperCAmelCase						:			str												=       tokenizer.encode(UpperCamelCase_   ,	add_special_tokens=UpperCamelCase_)
						return output_txt, output_ids


			def    a_  (   self   :     Union[str, Any]):


						"""simple docstring"""




						__UpperCAmelCase						:			Any												=       self.ta_base_tokenizer
						__UpperCAmelCase						:			Any												=       tokenizer(["hi</s>", "I went to the gym</s>", "</s>"])
						__UpperCAmelCase						:			List[str]												=       tokenizer(["hi", "I went to the gym", ""])
						self.assertListEqual(batch_with_eos_added["input_ids"]   ,	batch_without_eos_added["input_ids"])


			def    a_  (   self   :     List[str]):


						"""simple docstring"""




						__UpperCAmelCase						:			str												=       self.ta_base_tokenizer
						__UpperCAmelCase						:			Tuple												=       "Unicode €."
						__UpperCAmelCase						:			List[str]												=       tokenizer(UpperCamelCase_)
						__UpperCAmelCase						:			Optional[int]												=       [88, 113, 108, 102, 114, 103, 104, 35, 229, 133, 175, 49, 1]
						self.assertEqual(encoded["input_ids"]   ,	UpperCamelCase_)

						# decoding
						__UpperCAmelCase						:			Optional[Any]												=       tokenizer.decode(UpperCamelCase_)
						self.assertEqual(UpperCamelCase_   ,	"Unicode €.</s>")

						__UpperCAmelCase						:			Any												=       tokenizer("e è é ê ë")
						__UpperCAmelCase						:			Any												=       [104, 35, 198, 171, 35, 198, 172, 35, 198, 173, 35, 198, 174, 1]
						self.assertEqual(encoded["input_ids"]   ,	UpperCamelCase_)
						# decoding
						__UpperCAmelCase						:			str												=       tokenizer.decode(UpperCamelCase_)
						self.assertEqual(UpperCamelCase_   ,	"e è é ê ë</s>")

						# encode/decode, but with `encode` instead of `__call__`
						self.assertEqual(tokenizer.decode(tokenizer.encode("e è é ê ë"))   ,	"e è é ê ë</s>")


			def    a_  (   self   :     Union[str, Any]):


						"""simple docstring"""




						__UpperCAmelCase						:			str												=       self.ta_base_tokenizer
						__UpperCAmelCase						:			Optional[int]												=       ["A long paragraph for summarization.", "Another paragraph for summarization."]
						# fmt: off
						__UpperCAmelCase						:			List[Any]												=       [68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 1, 0]
						# fmt: on
						__UpperCAmelCase						:			Any												=       tokenizer(UpperCamelCase_   ,	padding=UpperCamelCase_   ,	return_tensors=UpperCamelCase_)
						self.assertIsInstance(UpperCamelCase_   ,	UpperCamelCase_)

						if FRAMEWORK != "jax":
									__UpperCAmelCase						:			str												=       list(batch.input_ids.numpy()[0])
						else:
									__UpperCAmelCase						:			str												=       list(batch.input_ids.tolist()[0])

						self.assertListEqual(UpperCamelCase_   ,	UpperCamelCase_)

						self.assertEqual((2, 37)   ,	batch.input_ids.shape)
						self.assertEqual((2, 37)   ,	batch.attention_mask.shape)


			def    a_  (   self   :     int):


						"""simple docstring"""




						__UpperCAmelCase						:			Tuple												=       self.ta_base_tokenizer
						__UpperCAmelCase						:			Dict												=       ["A long paragraph for summarization.", "Another paragraph for summarization."]
						__UpperCAmelCase						:			Optional[Any]												=       tokenizer(UpperCamelCase_   ,	padding=UpperCamelCase_   ,	return_tensors=UpperCamelCase_)
						# check if input_ids are returned and no decoder_input_ids
						self.assertIn("input_ids"   ,	UpperCamelCase_)
						self.assertIn("attention_mask"   ,	UpperCamelCase_)
						self.assertNotIn("decoder_input_ids"   ,	UpperCamelCase_)
						self.assertNotIn("decoder_attention_mask"   ,	UpperCamelCase_)


			def    a_  (   self   :     List[str]):


						"""simple docstring"""




						__UpperCAmelCase						:			Any												=       self.ta_base_tokenizer
						__UpperCAmelCase						:			Tuple												=       [
						    "Summary of the text.",
						    "Another summary.",
						]
						__UpperCAmelCase						:			Union[str, Any]												=       tokenizer(
						    text_target=UpperCamelCase_   ,	max_length=32   ,	padding="max_length"   ,	truncation=UpperCamelCase_   ,	return_tensors=UpperCamelCase_)
						self.assertEqual(32   ,	targets["input_ids"].shape[1])


			def    a_  (   self   :     Any):


						"""simple docstring"""




						__UpperCAmelCase						:			Union[str, Any]												=       self.ta_base_tokenizer
						__UpperCAmelCase						:			Any												=       ["A long paragraph for summarization. </s>"]
						__UpperCAmelCase						:			str												=       ["Summary of the text. </s>"]
						# fmt: off
						__UpperCAmelCase						:			Optional[Any]												=       [68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 35, 1]
						__UpperCAmelCase						:			Dict												=       [86, 120, 112, 112, 100, 117, 124, 35, 114, 105, 35, 119, 107, 104, 35, 119, 104, 123, 119, 49, 35, 1]
						# fmt: on

						__UpperCAmelCase						:			List[str]												=       tokenizer(UpperCamelCase_   ,	text_target=UpperCamelCase_)

						self.assertEqual(UpperCamelCase_   ,	batch["input_ids"][0])
						self.assertEqual(UpperCamelCase_   ,	batch["labels"][0])


			def    a_  (   self   :     Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase						:			List[str]												=       self.get_tokenizers()
						for tokenizer in tokenizers:
									with self.subTest(F"{tokenizer.__class__.__name__}"):
												self.assertNotEqual(tokenizer.model_max_length   ,	42)

        # Now let's start the test
						__UpperCAmelCase						:			str												=       self.get_tokenizers()
						for tokenizer in tokenizers:
									with self.subTest(F"{tokenizer.__class__.__name__}"):
												# Isolate this from the other tests because we save additional tokens/etc
												__UpperCAmelCase						:			List[Any]												=       tempfile.mkdtemp()

												__UpperCAmelCase						:			int												=       " He is very happy, UNwant\u00E9d,running"
												__UpperCAmelCase						:			str												=       tokenizer.encode(UpperCamelCase_   ,	add_special_tokens=UpperCamelCase_)
												tokenizer.save_pretrained(UpperCamelCase_)

												__UpperCAmelCase						:			Tuple												=       tokenizer.__class__.from_pretrained(UpperCamelCase_)
												__UpperCAmelCase						:			Optional[Any]												=       after_tokenizer.encode(UpperCamelCase_   ,	add_special_tokens=UpperCamelCase_)
												self.assertListEqual(UpperCamelCase_   ,	UpperCamelCase_)

												shutil.rmtree(UpperCamelCase_)

						__UpperCAmelCase						:			Tuple												=       self.get_tokenizers(model_max_length=42)
						for tokenizer in tokenizers:
									with self.subTest(F"{tokenizer.__class__.__name__}"):
												# Isolate this from the other tests because we save additional tokens/etc
												__UpperCAmelCase						:			Union[str, Any]												=       tempfile.mkdtemp()

												__UpperCAmelCase						:			Any												=       " He is very happy, UNwant\u00E9d,running"
												tokenizer.add_tokens(["bim", "bambam"])
												__UpperCAmelCase						:			Optional[int]												=       tokenizer.additional_special_tokens
												additional_special_tokens.append("new_additional_special_token")
												tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
												__UpperCAmelCase						:			Optional[Any]												=       tokenizer.encode(UpperCamelCase_   ,	add_special_tokens=UpperCamelCase_)
												tokenizer.save_pretrained(UpperCamelCase_)

												__UpperCAmelCase						:			Union[str, Any]												=       tokenizer.__class__.from_pretrained(UpperCamelCase_)
												__UpperCAmelCase						:			List[Any]												=       after_tokenizer.encode(UpperCamelCase_   ,	add_special_tokens=UpperCamelCase_)
												self.assertListEqual(UpperCamelCase_   ,	UpperCamelCase_)
												self.assertIn("new_additional_special_token"   ,	after_tokenizer.additional_special_tokens)
												self.assertEqual(after_tokenizer.model_max_length   ,	42)

												__UpperCAmelCase						:			List[Any]												=       tokenizer.__class__.from_pretrained(UpperCamelCase_   ,	model_max_length=43)
												self.assertEqual(tokenizer.model_max_length   ,	43)

												shutil.rmtree(UpperCamelCase_)


			def    a_  (   self   :     Dict):


						"""simple docstring"""




						__UpperCAmelCase						:			Tuple												=       []
						if self.test_slow_tokenizer:
									tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()))

						if self.test_rust_tokenizer:
									tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()))

						for tokenizer_class, tokenizer_utils in tokenizer_list:
									with tempfile.TemporaryDirectory() as tmp_dir:
												tokenizer_utils.save_pretrained(UpperCamelCase_)

												with open(os.path.join(UpperCamelCase_   ,	"special_tokens_map.json")   ,	encoding="utf-8") as json_file:
															__UpperCAmelCase						:			int												=       json.load(UpperCamelCase_)

												with open(os.path.join(UpperCamelCase_   ,	"tokenizer_config.json")   ,	encoding="utf-8") as json_file:
															__UpperCAmelCase						:			Dict												=       json.load(UpperCamelCase_)

												__UpperCAmelCase						:			Dict												=       [F"<extra_id_{i}>" for i in range(125)]

												__UpperCAmelCase						:			Dict												=       added_tokens_extra_ids + [
												    "an_additional_special_token"
												]
												__UpperCAmelCase						:			int												=       added_tokens_extra_ids + [
												    "an_additional_special_token"
												]

												with open(os.path.join(UpperCamelCase_   ,	"special_tokens_map.json")   ,	"w"   ,	encoding="utf-8") as outfile:
															json.dump(UpperCamelCase_   ,	UpperCamelCase_)
												with open(os.path.join(UpperCamelCase_   ,	"tokenizer_config.json")   ,	"w"   ,	encoding="utf-8") as outfile:
															json.dump(UpperCamelCase_   ,	UpperCamelCase_)

												# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
												# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
												# "special_tokens_map.json" files
												__UpperCAmelCase						:			List[Any]												=       tokenizer_class.from_pretrained(
												    UpperCamelCase_   ,	)
												self.assertIn(
												    "an_additional_special_token"   ,	tokenizer_without_change_in_init.additional_special_tokens)
												# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
												self.assertEqual(
												    ["an_additional_special_token"]   ,	tokenizer_without_change_in_init.convert_ids_to_tokens(
												        tokenizer_without_change_in_init.convert_tokens_to_ids(["an_additional_special_token"]))   ,	)

												# Now we test that we can change the value of additional_special_tokens in the from_pretrained
												__UpperCAmelCase						:			Any												=       added_tokens_extra_ids + [AddedToken("a_new_additional_special_token"   ,	lstrip=UpperCamelCase_)]
												__UpperCAmelCase						:			Optional[Any]												=       tokenizer_class.from_pretrained(
												    UpperCamelCase_   ,	additional_special_tokens=UpperCamelCase_   ,	)

												self.assertIn("a_new_additional_special_token"   ,	tokenizer.additional_special_tokens)
												self.assertEqual(
												    ["a_new_additional_special_token"]   ,	tokenizer.convert_ids_to_tokens(
												        tokenizer.convert_tokens_to_ids(["a_new_additional_special_token"]))   ,	)


			def    a_  (   self   :     int):


						"""simple docstring"""




						__UpperCAmelCase						:			List[str]												=       []
						if self.test_slow_tokenizer:
									tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()))

						if self.test_rust_tokenizer:
									tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()))

						for tokenizer_class, tokenizer_utils in tokenizer_list:
									with tempfile.TemporaryDirectory() as tmp_dir:
												tokenizer_utils.save_pretrained(UpperCamelCase_)

												__UpperCAmelCase						:			Any												=       tokenizer_class.from_pretrained(UpperCamelCase_)

												self.assertTrue(tokenizer.decode([255]) == "")


			def    a_  (   self   :     List[str]):


						"""simple docstring"""




						pass


			def    a_  (   self   :     List[Any]):


						"""simple docstring"""




						pass


			def    a_  (   self   :     Any):


						"""simple docstring"""




						pass


			def    a_  (   self   :     Tuple):


						"""simple docstring"""




						pass


			def    a_  (   self   :     Optional[Any]):


						"""simple docstring"""




						__UpperCAmelCase						:			Union[str, Any]												=       self.get_tokenizers(fast=UpperCamelCase_   ,	do_lower_case=UpperCamelCase_)
						for tokenizer in tokenizers:
									with self.subTest(F"{tokenizer.__class__.__name__}"):
												__UpperCAmelCase						:			Optional[Any]												=       ["t", "h", "i", "s", " ", "i", "s", " ", "a", " ", "t", "e", "x", "t", "</s>"]
												__UpperCAmelCase						:			Optional[Any]												=       tokenizer.convert_tokens_to_string(UpperCamelCase_)

												self.assertIsInstance(UpperCamelCase_   ,	UpperCamelCase_)


			def    a_  (   self   :     Dict):


						"""simple docstring"""




						__UpperCAmelCase						:			Optional[Any]												=       self.get_tokenizers()
						for tokenizer in tokenizers:
									with self.subTest(F"{tokenizer.__class__.__name__}"):
												__UpperCAmelCase						:			List[Any]												=       [
												    "bos_token",
												    "eos_token",
												    "unk_token",
												    "sep_token",
												    "pad_token",
												    "cls_token",
												    "mask_token",
												]

												__UpperCAmelCase						:			Dict												=       0
												__UpperCAmelCase						:			str												=       tokenizer.convert_ids_to_tokens(
												    UpperCamelCase_   ,	skip_special_tokens=UpperCamelCase_)

												for attr in attributes_list:
															setattr(UpperCamelCase_   ,	attr + "_id"   ,	UpperCamelCase_)
															self.assertEqual(getattr(UpperCamelCase_   ,	UpperCamelCase_)   ,	UpperCamelCase_)
															self.assertEqual(getattr(UpperCamelCase_   ,	attr + "_id")   ,	UpperCamelCase_)

															setattr(UpperCamelCase_   ,	attr + "_id"   ,	UpperCamelCase_)
															self.assertEqual(getattr(UpperCamelCase_   ,	UpperCamelCase_)   ,	UpperCamelCase_)
															self.assertEqual(getattr(UpperCamelCase_   ,	attr + "_id")   ,	UpperCamelCase_)

												setattr(UpperCamelCase_   ,	"additional_special_tokens_ids"   ,	[])
												self.assertListEqual(getattr(UpperCamelCase_   ,	"additional_special_tokens")   ,	[])
												self.assertListEqual(getattr(UpperCamelCase_   ,	"additional_special_tokens_ids")   ,	[])

												setattr(UpperCamelCase_   ,	"additional_special_tokens_ids"   ,	[token_id_to_test_setters])
												self.assertListEqual(getattr(UpperCamelCase_   ,	"additional_special_tokens")   ,	[token_to_test_setters])
												self.assertListEqual(getattr(UpperCamelCase_   ,	"additional_special_tokens_ids")   ,	[token_id_to_test_setters])










'''simple docstring'''
import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
 import onnxruntime as ort


@nightly
@require_onnxruntime
@require_torch_gpu
class 							_UpperCAmelCase    (		unittest.TestCase      ):




    @property
    def 					lowerCAmelCase__					(      self	:							Union[str, Any]      ):






       '''simple docstring'''






       return (
           "CUDAExecutionProvider",
           {
               "gpu_mem_limit": "15000000000",  # 15GB
               "arena_extend_strategy": "kSameAsRequested",
           },
       )




    @property
    def 					lowerCAmelCase__					(      self	:							List[Any]      ):






       '''simple docstring'''






       lowercase_						:     Union[str, Any]							     =  ort.SessionOptions()
       lowercase_						:     Optional[int]							     =  False
       return options





    def 					lowerCAmelCase__					(      self	:							str      ):






       '''simple docstring'''






       lowercase_						:     List[Any]							     =  load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/in_paint/overture-creations-5sI6fQgYIuo.png"      )
       lowercase_						:     Tuple							     =  load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"      )
       lowercase_						:     List[str]							     =  load_numpy(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy"      )

       # using the PNDM scheduler by default
       lowercase_						:     List[Any]							     =  OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
           "CompVis/stable-diffusion-v1-4"	,  revision="onnx"	,  safety_checker=a	,  feature_extractor=a	,  provider=self.gpu_provider	,  sess_options=self.gpu_options	,  )
       pipe.set_progress_bar_config(disable=a      )

       lowercase_						:     List[str]							     =  "A red cat sitting on a park bench"

       lowercase_						:     int							     =  np.random.RandomState(0      )
       lowercase_						:     int							     =  pipe(
           prompt=a	,  image=a	,  mask_image=a	,  strength=0.75	,  guidance_scale=7.5	,  num_inference_steps=1_5	,  generator=a	,  output_type="np"	,  )

       lowercase_						:     Tuple							     =  output.images[0]

       assert image.shape == (5_1_2, 5_1_2, 3)
       assert np.abs(expected_image - image      ).max() < 1e-2




