



"""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():
		_lowercase			:			Union[str, Any]										=						"pt"
elif is_tf_available():
		_lowercase			:			int										=						"tf"
else:
		_lowercase			:			Union[str, Any]										=						"jax"

class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_		,       unittest.TestCase       ):


					'''simple docstring'''





					_a             =							PerceiverTokenizer
					_a             =							False



					def 		snake_case	(	self							:  Optional[Any]					)-> Optional[int]:
								super().setUp()
								lowerCamelCase__		:		Optional[Any]		     =PerceiverTokenizer()
								tokenizer.save_pretrained(self.tmpdirname					)



					@cached_property
					def 		snake_case	(	self							:  str					)-> Optional[int]:
								return PerceiverTokenizer.from_pretrained('''deepmind/language-perceiver'''					)



					def 		snake_case	(	self							:  List[str],  **lowerCamelCase							:  int					)-> PerceiverTokenizer:
								return self.tokenizer_class.from_pretrained(self.tmpdirname,  **lowerCamelCase					)



					def 		snake_case	(	self							:  List[Any],  lowerCamelCase							:  List[str],  lowerCamelCase							:  str=False,  lowerCamelCase							:  Dict=20,  lowerCamelCase							:  str=5					)-> Tuple[str, list]:
								# 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__		:		Optional[int]		     =[]
								for i in range(len(lowerCamelCase					)					):
											try:
														lowerCamelCase__		:		List[Any]		     =tokenizer.decode([i],  clean_up_tokenization_spaces=lowerCamelCase					)
											except UnicodeDecodeError:
														pass
											toks.append((i, tok)					)

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

								# Ensure consistency
								lowerCamelCase__		:		Dict		     =tokenizer.decode(lowerCamelCase,  clean_up_tokenization_spaces=lowerCamelCase					)
								if " " not in output_txt and len(lowerCamelCase					) > 1:
											lowerCamelCase__		:		Optional[int]		     =(
											    tokenizer.decode([toks_ids[0]],  clean_up_tokenization_spaces=lowerCamelCase					)
											    + ''' '''
											    + tokenizer.decode(toks_ids[1:],  clean_up_tokenization_spaces=lowerCamelCase					)
											)
								if with_prefix_space:
											lowerCamelCase__		:		Tuple		     =''' ''' + output_txt
								lowerCamelCase__		:		Union[str, Any]		     =tokenizer.encode(lowerCamelCase,  add_special_tokens=lowerCamelCase					)
								return output_txt, output_ids



					def 		snake_case	(	self							:  str					)-> Dict:
								lowerCamelCase__		:		int		     =self.perceiver_tokenizer
								lowerCamelCase__		:		List[Any]		     ='''Unicode €.'''
								lowerCamelCase__		:		List[Any]		     =tokenizer(lowerCamelCase					)
								lowerCamelCase__		:		Optional[Any]		     =[4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
								self.assertEqual(encoded['''input_ids'''],  lowerCamelCase					)

								# decoding
								lowerCamelCase__		:		str		     =tokenizer.decode(lowerCamelCase					)
								self.assertEqual(lowerCamelCase,  '''[CLS]Unicode €.[SEP]'''					)

								lowerCamelCase__		:		int		     =tokenizer('''e è é ê ë'''					)
								lowerCamelCase__		:		int		     =[4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
								self.assertEqual(encoded['''input_ids'''],  lowerCamelCase					)
								# decoding
								lowerCamelCase__		:		str		     =tokenizer.decode(lowerCamelCase					)
								self.assertEqual(lowerCamelCase,  '''[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 		snake_case	(	self							:  str					)-> List[Any]:
								lowerCamelCase__		:		Tuple		     =self.perceiver_tokenizer
								lowerCamelCase__		:		str		     =['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
								# fmt: off
								lowerCamelCase__		:		int		     =[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__		:		int		     =tokenizer(lowerCamelCase,  padding=lowerCamelCase,  return_tensors=lowerCamelCase					)
								self.assertIsInstance(lowerCamelCase,  lowerCamelCase					)

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

								self.assertListEqual(lowerCamelCase,  lowerCamelCase					)

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



					def 		snake_case	(	self							:  Any					)-> Any:
								lowerCamelCase__		:		Tuple		     =self.perceiver_tokenizer
								lowerCamelCase__		:		Union[str, Any]		     =['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
								lowerCamelCase__		:		str		     =tokenizer(lowerCamelCase,  padding=lowerCamelCase,  return_tensors=lowerCamelCase					)
								# check if input_ids are returned and no decoder_input_ids
								self.assertIn('''input_ids''',  lowerCamelCase					)
								self.assertIn('''attention_mask''',  lowerCamelCase					)
								self.assertNotIn('''decoder_input_ids''',  lowerCamelCase					)
								self.assertNotIn('''decoder_attention_mask''',  lowerCamelCase					)



					def 		snake_case	(	self							:  List[Any]					)-> List[Any]:
								lowerCamelCase__		:		List[str]		     =self.perceiver_tokenizer
								lowerCamelCase__		:		int		     =[
								    '''Summary of the text.''',
								    '''Another summary.''',
								]
								lowerCamelCase__		:		Any		     =tokenizer(
								    text_target=lowerCamelCase,  max_length=32,  padding='''max_length''',  truncation=lowerCamelCase,  return_tensors=lowerCamelCase					)
								self.assertEqual(32,  targets['''input_ids'''].shape[1]					)



					def 		snake_case	(	self							:  Optional[Any]					)-> Any:
								# safety check on max_len default value so we are sure the test works
								lowerCamelCase__		:		List[Any]		     =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__		:		Union[str, 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__		:		List[str]		     =tempfile.mkdtemp()

														lowerCamelCase__		:		Union[str, Any]		     =''' He is very happy, UNwant\u00E9d,running'''
														lowerCamelCase__		:		int		     =tokenizer.encode(lowerCamelCase,  add_special_tokens=lowerCamelCase					)
														tokenizer.save_pretrained(lowerCamelCase					)

														lowerCamelCase__		:		Any		     =tokenizer.__class__.from_pretrained(lowerCamelCase					)
														lowerCamelCase__		:		str		     =after_tokenizer.encode(lowerCamelCase,  add_special_tokens=lowerCamelCase					)
														self.assertListEqual(lowerCamelCase,  lowerCamelCase					)

														shutil.rmtree(lowerCamelCase					)

								lowerCamelCase__		:		List[str]		     =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__		:		Tuple		     =tempfile.mkdtemp()

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

														lowerCamelCase__		:		Union[str, Any]		     =tokenizer.__class__.from_pretrained(lowerCamelCase					)
														lowerCamelCase__		:		Dict		     =after_tokenizer.encode(lowerCamelCase,  add_special_tokens=lowerCamelCase					)
														self.assertListEqual(lowerCamelCase,  lowerCamelCase					)
														self.assertIn('''new_additional_special_token''',  after_tokenizer.additional_special_tokens					)
														self.assertEqual(after_tokenizer.model_max_length,  42					)

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

														shutil.rmtree(lowerCamelCase					)



					def 		snake_case	(	self							:  List[str]					)-> Union[str, Any]:
								lowerCamelCase__		:		Optional[Any]		     =[]
								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(lowerCamelCase					)

														with open(os.path.join(lowerCamelCase,  '''special_tokens_map.json'''					),  encoding='''utf-8'''					) as json_file:
																	lowerCamelCase__		:		List[Any]		     =json.load(lowerCamelCase					)

														with open(os.path.join(lowerCamelCase,  '''tokenizer_config.json'''					),  encoding='''utf-8'''					) as json_file:
																	lowerCamelCase__		:		Tuple		     =json.load(lowerCamelCase					)

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

														lowerCamelCase__		:		Dict		     =added_tokens_extra_ids + [
														    '''an_additional_special_token'''
														]
														lowerCamelCase__		:		Optional[Any]		     =added_tokens_extra_ids + [
														    '''an_additional_special_token'''
														]

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

														# 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(
														    lowerCamelCase,  )
														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__		:		Optional[int]		     =added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token''',  lstrip=lowerCamelCase					)]
														lowerCamelCase__		:		Any		     =tokenizer_class.from_pretrained(
														    lowerCamelCase,  additional_special_tokens=lowerCamelCase,  )

														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 		snake_case	(	self							:  int					)-> Optional[Any]:
								lowerCamelCase__		:		int		     =self.perceiver_tokenizer
								self.assertEqual(tokenizer.decode([178]					),  '''�'''					)



					def 		snake_case	(	self							:  Any					)-> Union[str, Any]:
								pass



					def 		snake_case	(	self							:  Union[str, Any]					)-> int:
								pass



					def 		snake_case	(	self							:  int					)-> str:
								pass



					def 		snake_case	(	self							:  Optional[Any]					)-> List[str]:
								pass




					def 		snake_case	(	self							:  str					)-> 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[str]		     =self.get_tokenizers(fast=lowerCamelCase,  do_lower_case=lowerCamelCase					)
								for tokenizer in tokenizers:
											with self.subTest(F'''{tokenizer.__class__.__name__}'''					):
														lowerCamelCase__		:		Dict		     =['''[CLS]''', '''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''s''', '''t''', '''[SEP]''']
														lowerCamelCase__		:		Any		     =tokenizer.convert_tokens_to_string(lowerCamelCase					)

														self.assertIsInstance(lowerCamelCase,  lowerCamelCase					)









"""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


_lowercase			:			Tuple										=						False

class 	__SCREAMING_SNAKE_CASE     (				unittest.TestCase       ):


					'''simple docstring'''





					pass





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


					'''simple docstring'''





					def 		snake_case	(	self							:  List[Any]					)-> Dict:
								lowerCamelCase__		:		str		     =VersatileDiffusionImageVariationPipeline.from_pretrained('''shi-labs/versatile-diffusion'''					)
								pipe.to(lowerCamelCase					)
								pipe.set_progress_bar_config(disable=lowerCamelCase					)

								lowerCamelCase__		:		int		     =load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''					)
								lowerCamelCase__		:		Dict		     =torch.manual_seed(0					)
								lowerCamelCase__		:		str		     =pipe(
								    image=lowerCamelCase,  generator=lowerCamelCase,  guidance_scale=7.5,  num_inference_steps=50,  output_type='''numpy''',  ).images

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

								assert image.shape == (1, 512, 512, 3)
								lowerCamelCase__		:		List[Any]		     =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




