






"""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		:       Dict  				=		'''pt'''
elif is_tf_available():
						__A		:       List[Any]  				=		'''tf'''
else:
						__A		:       Dict  				=		'''jax'''





class 					_UpperCAmelCase       ( __UpperCAmelCase   ,						unittest.TestCase							):
					SCREAMING_SNAKE_CASE_		:				int							    =   PerceiverTokenizer
					SCREAMING_SNAKE_CASE_		:				Any							    =   False




					def   A  (				self     :						Tuple   )  ->  Dict:
							super().setUp()
							lowercase_ : List[Any]							       =   PerceiverTokenizer()
							tokenizer.save_pretrained(self.tmpdirname   )




					@cached_property
					def   A  (				self     :						int   )  ->  Dict:
							return PerceiverTokenizer.from_pretrained('''deepmind/language-perceiver'''   )




					def   A  (				self     :						List[Any]    ,						**A     :						List[Any]   )  ->  Optional[Any]:
							return self.tokenizer_class.from_pretrained(self.tmpdirname    ,						**UpperCamelCase__   )




					def   A  (				self     :						int    ,						A     :						Union[str, Any]    ,						A     :						List[Any]=False    ,						A     :						Optional[int]=20    ,						A     :						List[str]=5   )  ->  Optional[Any]:
							lowercase_ : Optional[Any]							       =   []
							for i in range(len(UpperCamelCase__   )   ):
									try:
											lowercase_ : List[Any]							       =   tokenizer.decode([i]    ,						clean_up_tokenization_spaces=UpperCamelCase__   )
									except UnicodeDecodeError:
											pass
									toks.append((i, tok)   )

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

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




					def   A  (				self     :						Any   )  ->  Dict:
							lowercase_ : Dict							       =   self.perceiver_tokenizer
							lowercase_ : Tuple							       =   '''Unicode €.'''
							lowercase_ : List[Any]							       =   tokenizer(UpperCamelCase__   )
							lowercase_ : Union[str, Any]							       =   [4, 91, 1_16, 1_11, 1_05, 1_17, 1_06, 1_07, 38, 2_32, 1_36, 1_78, 52, 5]
							self.assertEqual(encoded['''input_ids''']    ,						UpperCamelCase__   )

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

							lowercase_ : List[str]							       =   tokenizer('''e è é ê ë'''   )
							lowercase_ : Optional[int]							       =   [4, 1_07, 38, 2_01, 1_74, 38, 2_01, 1_75, 38, 2_01, 1_76, 38, 2_01, 1_77, 5]
							self.assertEqual(encoded['''input_ids''']    ,						UpperCamelCase__   )
							# decoding
							lowercase_ : List[Any]							       =   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   A  (				self     :						Optional[int]   )  ->  List[Any]:
							lowercase_ : Optional[Any]							       =   self.perceiver_tokenizer
							lowercase_ : Union[str, Any]							       =   ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
							# fmt: off
							lowercase_ : str							       =   [4, 71, 38, 1_14, 1_17, 1_16, 1_09, 38, 1_18, 1_03, 1_20, 1_03, 1_09, 1_20, 1_03, 1_18, 1_10, 38, 1_08, 1_17, 1_20, 38, 1_21, 1_23, 1_15, 1_15, 1_03, 1_20, 1_11, 1_28, 1_03, 1_22, 1_11, 1_17, 1_16, 52, 5, 0]
							# fmt: on
							lowercase_ : str							       =   tokenizer(UpperCamelCase__    ,						padding=UpperCamelCase__    ,						return_tensors=UpperCamelCase__   )
							self.assertIsInstance(UpperCamelCase__    ,						UpperCamelCase__   )

							if FRAMEWORK != "jax":
									lowercase_ : str							       =   list(batch.input_ids.numpy()[0]   )
							else:
									lowercase_ : 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   A  (				self     :						Optional[Any]   )  ->  Optional[Any]:
							lowercase_ : Any							       =   self.perceiver_tokenizer
							lowercase_ : int							       =   ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
							lowercase_ : 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     :						Optional[int]   )  ->  Optional[Any]:
							lowercase_ : Optional[int]							       =   self.perceiver_tokenizer
							lowercase_ : Optional[int]							       =   [
							    '''Summary of the text.''',
							    '''Another summary.''',
							]
							lowercase_ : str							       =   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   )  ->  Tuple:
							lowercase_ : Tuple							       =   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
							lowercase_ : 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
											lowercase_ : List[str]							       =   tempfile.mkdtemp()

											lowercase_ : Optional[Any]							       =   ''' He is very happy, UNwant\u00E9d,running'''
											lowercase_ : Dict							       =   tokenizer.encode(UpperCamelCase__    ,						add_special_tokens=UpperCamelCase__   )
											tokenizer.save_pretrained(UpperCamelCase__   )

											lowercase_ : int							       =   tokenizer.__class__.from_pretrained(UpperCamelCase__   )
											lowercase_ : Dict							       =   after_tokenizer.encode(UpperCamelCase__    ,						add_special_tokens=UpperCamelCase__   )
											self.assertListEqual(UpperCamelCase__    ,						UpperCamelCase__   )

											shutil.rmtree(UpperCamelCase__   )

							lowercase_ : 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
											lowercase_ : int							       =   tempfile.mkdtemp()

											lowercase_ : Tuple							       =   ''' He is very happy, UNwant\u00E9d,running'''
											tokenizer.add_tokens(['''bim''', '''bambam''']   )
											lowercase_ : Optional[Any]							       =   tokenizer.additional_special_tokens
											additional_special_tokens.append('''new_additional_special_token'''   )
											tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens}   )
											lowercase_ : Dict							       =   tokenizer.encode(UpperCamelCase__    ,						add_special_tokens=UpperCamelCase__   )
											tokenizer.save_pretrained(UpperCamelCase__   )

											lowercase_ : Union[str, Any]							       =   tokenizer.__class__.from_pretrained(UpperCamelCase__   )
											lowercase_ : 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   )

											lowercase_ : List[str]							       =   tokenizer.__class__.from_pretrained(UpperCamelCase__    ,						model_max_length=43   )
											self.assertEqual(tokenizer.model_max_length    ,						43   )

											shutil.rmtree(UpperCamelCase__   )




					def   A  (				self     :						Any   )  ->  List[Any]:
							lowercase_ : 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__   )

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

											with open(os.path.join(UpperCamelCase__    ,						'''tokenizer_config.json'''   )    ,						encoding='''utf-8'''   ) as json_file:
													lowercase_ : Optional[int]							       =   json.load(UpperCamelCase__   )

											lowercase_ : int							       =   [F'''<extra_id_{i}>''' for i in range(1_25   )]

											lowercase_ : Any							       =   added_tokens_extra_ids + [
											    '''an_additional_special_token'''
											]
											lowercase_ : List[str]							       =   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
											lowercase_ : List[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
											lowercase_ : Optional[Any]							       =   added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''    ,						lstrip=UpperCamelCase__   )]
											lowercase_ : Optional[int]							       =   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   )  ->  int:
							lowercase_ : Union[str, Any]							       =   self.perceiver_tokenizer
							self.assertEqual(tokenizer.decode([1_78]   )    ,						'''�'''   )




					def   A  (				self     :						List[Any]   )  ->  Tuple:
							pass




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




					def   A  (				self     :						Optional[int]   )  ->  List[str]:
							pass




					def   A  (				self     :						Any   )  ->  Optional[Any]:
							pass






					def   A  (				self     :						Optional[Any]   )  ->  Any:
							lowercase_ : int							       =   self.get_tokenizers(fast=UpperCamelCase__    ,						do_lower_case=UpperCamelCase__   )
							for tokenizer in tokenizers:
									with self.subTest(F'''{tokenizer.__class__.__name__}'''   ):
											lowercase_ : List[Any]							       =   ['''[CLS]''', '''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''s''', '''t''', '''[SEP]''']
											lowercase_ : Optional[Any]							       =   tokenizer.convert_tokens_to_string(UpperCamelCase__   )

											self.assertIsInstance(UpperCamelCase__    ,						UpperCamelCase__   )









import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
						import torch

						from transformers import AutoModelForImageClassification

if is_vision_available():
						from transformers import AutoImageProcessor


@require_torch
@require_vision
class  A		(  unittest.TestCase     ):



		@slow
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  AutoImageProcessor.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip'''   )
								lowerCAmelCase_      			=  AutoModelForImageClassification.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip'''   )
								model.to(UpperCamelCase__   )

								from datasets import load_dataset

								lowerCAmelCase_      			=  load_dataset('''nielsr/rvlcdip-demo'''   )

								lowerCAmelCase_      			=  dataset['''train'''][0]['''image'''].convert('''RGB'''   )

								lowerCAmelCase_      			=  image_processor(UpperCamelCase__,	return_tensors='''pt'''   ).to(UpperCamelCase__   )

								# forward pass
								with torch.no_grad():
														lowerCAmelCase_      			=  model(**UpperCamelCase__   )
														lowerCAmelCase_      			=  outputs.logits

								lowerCAmelCase_      			=  torch.Size((1, 16)   )
								self.assertEqual(logits.shape,	UpperCamelCase__   )

								lowerCAmelCase_      			=  torch.tensor(
								    [-0.4_158, -0.4_092, -0.4_347],	device=UpperCamelCase__,	dtype=torch.float,	)
								self.assertTrue(torch.allclose(logits[0, :3],	UpperCamelCase__,	atol=1E-4   )   )




