






'''simple docstring'''







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

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

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
			_A	:			str         ='''pt'''
elif is_tf_available():
			_A	:			Optional[Any]         ='''tf'''
else:
			_A	:			Tuple         ='''jax'''

class _lowercase	(				_lowercase				,      unittest.TestCase		):
							a				      =       PerceiverTokenizer
							a				      =       False




							def 		lowerCamelCase_				(							self:				str     ):
														super().setUp()
														lowerCamelCase__					:	Optional[int]         =       PerceiverTokenizer()
														tokenizer.save_pretrained(self.tmpdirname     )




							@cached_property
							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														return PerceiverTokenizer.from_pretrained("""deepmind/language-perceiver"""     )




							def 		lowerCamelCase_				(							self:				Optional[int]			,							**UpperCamelCase__:				str     ):
														return self.tokenizer_class.from_pretrained(self.tmpdirname			,							**UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				List[Any]			,							UpperCamelCase__:				Tuple			,							UpperCamelCase__:				List[str]=False			,							UpperCamelCase__:				Any=20			,							UpperCamelCase__:				str=5     ):
														# XXX The default common tokenizer tests assume that every ID is decodable on its own.
														# This assumption is invalid for Perceiver because single bytes might not be
														# valid utf-8 (byte 128 for instance).
														# Here we're overriding the smallest possible method to provide
														# a clean sequence without making the same assumption.

														lowerCamelCase__					:	List[str]         =       []
														for i in range(len(UpperCamelCase__     )     ):
																					try:
																												lowerCamelCase__					:	str         =       tokenizer.decode([i]			,							clean_up_tokenization_spaces=UpperCamelCase__     )
																					except UnicodeDecodeError:
																												pass
																					toks.append((i, tok)     )

														lowerCamelCase__					:	Any         =       list(filter(lambda UpperCamelCase__     : re.match(R"""^[ a-zA-Z]+$"""			,							t[1]     )			,							UpperCamelCase__     )     )
														lowerCamelCase__					:	str         =       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:
																					lowerCamelCase__					:	str         =       toks[:max_length]
														if min_length is not None and len(UpperCamelCase__     ) < min_length and len(UpperCamelCase__     ) > 0:
																					while len(UpperCamelCase__     ) < min_length:
																												lowerCamelCase__					:	Any         =       toks + toks
        # toks_str = [t[1] for t in toks]
														lowerCamelCase__					:	Optional[Any]         =       [t[0] for t in toks]

														# Ensure consistency
														lowerCamelCase__					:	List[Any]         =       tokenizer.decode(UpperCamelCase__			,							clean_up_tokenization_spaces=UpperCamelCase__     )
														if " " not in output_txt and len(UpperCamelCase__     ) > 1:
																					lowerCamelCase__					:	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:
																					lowerCamelCase__					:	List[Any]         =       """ """ + output_txt
														lowerCamelCase__					:	str         =       tokenizer.encode(UpperCamelCase__			,							add_special_tokens=UpperCamelCase__     )
														return output_txt, output_ids




							def 		lowerCamelCase_				(							self:				Union[str, Any]     ):
														lowerCamelCase__					:	Union[str, Any]         =       self.perceiver_tokenizer
														lowerCamelCase__					:	List[str]         =       """Unicode €."""
														lowerCamelCase__					:	Any         =       tokenizer(UpperCamelCase__     )
														lowerCamelCase__					:	str         =       [4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
														self.assertEqual(encoded["""input_ids"""]			,							UpperCamelCase__     )

														# decoding
														lowerCamelCase__					:	Tuple         =       tokenizer.decode(UpperCamelCase__     )
														self.assertEqual(UpperCamelCase__			,							"""[CLS]Unicode €.[SEP]"""     )

														lowerCamelCase__					:	Dict         =       tokenizer("""e è é ê ë"""     )
														lowerCamelCase__					:	str         =       [4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
														self.assertEqual(encoded["""input_ids"""]			,							UpperCamelCase__     )
														# decoding
														lowerCamelCase__					:	List[str]         =       tokenizer.decode(UpperCamelCase__     )
														self.assertEqual(UpperCamelCase__			,							"""[CLS]e è é ê ë[SEP]"""     )

														# encode/decode, but with `encode` instead of `__call__`
														self.assertEqual(tokenizer.decode(tokenizer.encode("""e è é ê ë"""     )     )			,							"""[CLS]e è é ê ë[SEP]"""     )




							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														lowerCamelCase__					:	Optional[Any]         =       self.perceiver_tokenizer
														lowerCamelCase__					:	List[Any]         =       ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
														# fmt: off
														lowerCamelCase__					:	Optional[Any]         =       [4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
														# fmt: on
														lowerCamelCase__					:	Optional[Any]         =       tokenizer(UpperCamelCase__			,							padding=UpperCamelCase__			,							return_tensors=UpperCamelCase__     )
														self.assertIsInstance(UpperCamelCase__			,							UpperCamelCase__     )

														if FRAMEWORK != "jax":
																					lowerCamelCase__					:	Union[str, Any]         =       list(batch.input_ids.numpy()[0]     )
														else:
																					lowerCamelCase__					:	Dict         =       list(batch.input_ids.tolist()[0]     )

														self.assertListEqual(UpperCamelCase__			,							UpperCamelCase__     )

														self.assertEqual((2, 38)			,							batch.input_ids.shape     )
														self.assertEqual((2, 38)			,							batch.attention_mask.shape     )




							def 		lowerCamelCase_				(							self:				int     ):
														lowerCamelCase__					:	Optional[Any]         =       self.perceiver_tokenizer
														lowerCamelCase__					:	int         =       ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
														lowerCamelCase__					:	Optional[int]         =       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 		lowerCamelCase_				(							self:				List[Any]     ):
														lowerCamelCase__					:	List[str]         =       self.perceiver_tokenizer
														lowerCamelCase__					:	Dict         =       [
														    """Summary of the text.""",
														    """Another summary.""",
														]
														lowerCamelCase__					:	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 		lowerCamelCase_				(							self:				str     ):
														# safety check on max_len default value so we are sure the test works
														lowerCamelCase__					:	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
														lowerCamelCase__					:	Any         =       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
																												lowerCamelCase__					:	Optional[int]         =       tempfile.mkdtemp()

																												lowerCamelCase__					:	int         =       """ He is very happy, UNwant\u00E9d,running"""
																												lowerCamelCase__					:	Any         =       tokenizer.encode(UpperCamelCase__			,							add_special_tokens=UpperCamelCase__     )
																												tokenizer.save_pretrained(UpperCamelCase__     )

																												lowerCamelCase__					:	Union[str, Any]         =       tokenizer.__class__.from_pretrained(UpperCamelCase__     )
																												lowerCamelCase__					:	Optional[int]         =       after_tokenizer.encode(UpperCamelCase__			,							add_special_tokens=UpperCamelCase__     )
																												self.assertListEqual(UpperCamelCase__			,							UpperCamelCase__     )

																												shutil.rmtree(UpperCamelCase__     )

														lowerCamelCase__					:	Dict         =       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
																												lowerCamelCase__					:	Optional[int]         =       tempfile.mkdtemp()

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

																												lowerCamelCase__					:	Optional[Any]         =       tokenizer.__class__.from_pretrained(UpperCamelCase__     )
																												lowerCamelCase__					:	int         =       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     )

																												lowerCamelCase__					:	Optional[int]         =       tokenizer.__class__.from_pretrained(UpperCamelCase__			,							model_max_length=43     )
																												self.assertEqual(tokenizer.model_max_length			,							43     )

																												shutil.rmtree(UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Optional[Any]     ):
														lowerCamelCase__					:	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:
																																			lowerCamelCase__					:	Optional[Any]         =       json.load(UpperCamelCase__     )

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

																												lowerCamelCase__					:	int         =       [F'''<extra_id_{i}>''' for i in range(125     )]

																												lowerCamelCase__					:	str         =       added_tokens_extra_ids + [
																												    """an_additional_special_token"""
																												]
																												lowerCamelCase__					:	Any         =       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
																												lowerCamelCase__					:	Union[str, Any]         =       tokenizer_class.from_pretrained(
																												    UpperCamelCase__			,							)
																												self.assertIn(
																												    """an_additional_special_token"""			,							tokenizer_without_change_in_init.additional_special_tokens     )
																												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
																												lowerCamelCase__					:	int         =       added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token"""			,							lstrip=UpperCamelCase__     )]
																												lowerCamelCase__					:	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 		lowerCamelCase_				(							self:				Dict     ):
														lowerCamelCase__					:	Union[str, Any]         =       self.perceiver_tokenizer
														self.assertEqual(tokenizer.decode([178]     )			,							"""�"""     )




							def 		lowerCamelCase_				(							self:				Optional[Any]     ):
														pass




							def 		lowerCamelCase_				(							self:				Optional[Any]     ):
														pass




							def 		lowerCamelCase_				(							self:				str     ):
														pass




							def 		lowerCamelCase_				(							self:				List[Any]     ):
														pass




							def 		lowerCamelCase_				(							self:				Union[str, Any]     ):
														# The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
														# strings and special added tokens as tokens
														lowerCamelCase__					:	List[Any]         =       self.get_tokenizers(fast=UpperCamelCase__			,							do_lower_case=UpperCamelCase__     )
														for tokenizer in tokenizers:
																					with self.subTest(F'''{tokenizer.__class__.__name__}'''     ):
																												lowerCamelCase__					:	List[str]         =       ["""[CLS]""", """t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """s""", """t""", """[SEP]"""]
																												lowerCamelCase__					:	Tuple         =       tokenizer.convert_tokens_to_string(UpperCamelCase__     )

																												self.assertIsInstance(UpperCamelCase__			,							UpperCamelCase__     )









'''simple docstring'''







import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


_A	:			Union[str, Any]         =False

class _lowercase	(				unittest.TestCase		):
							pass



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




							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														lowerCamelCase__					:	Tuple         =       VersatileDiffusionImageVariationPipeline.from_pretrained("""shi-labs/versatile-diffusion"""     )
														pipe.to(UpperCamelCase__     )
														pipe.set_progress_bar_config(disable=UpperCamelCase__     )

														lowerCamelCase__					:	Optional[int]         =       load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""     )
														lowerCamelCase__					:	List[Any]         =       torch.manual_seed(0     )
														lowerCamelCase__					:	List[Any]         =       pipe(
														    image=UpperCamelCase__			,							generator=UpperCamelCase__			,							guidance_scale=7.5			,							num_inference_steps=50			,							output_type="""numpy"""			,							).images

														lowerCamelCase__					:	List[str]         =       image[0, 253:256, 253:256, -1]

														assert image.shape == (1, 512, 512, 3)
														lowerCamelCase__					:	Tuple         =       np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]     )

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

