





'''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():
  __SCREAMING_SNAKE_CASE	:int	    =							'pt'
elif is_tf_available():
  __SCREAMING_SNAKE_CASE	:Optional[Any]	    =							'tf'
else:
  __SCREAMING_SNAKE_CASE	:str	    =							'jax'







class     A_      (    _a				,		unittest.TestCase     ):
 _lowerCamelCase    :					Any       			=				PerceiverTokenizer
 _lowerCamelCase    :					str       			=				False




 def 	lowercase      (   self			:       str       ):
      super().setUp()
      _UpperCAmelCase					=  PerceiverTokenizer()
      tokenizer.save_pretrained(self.tmpdirname       )




 @cached_property
 def 	lowercase      (   self			:       Dict       ):
      return PerceiverTokenizer.from_pretrained("deepmind/language-perceiver"       )




 def 	lowercase      (   self			:       List[Any]      ,     **snake_case_			:       Union[str, Any]       ):
      return self.tokenizer_class.from_pretrained(self.tmpdirname      ,     **snake_case_       )




 def 	lowercase      (   self			:       List[str]      ,     snake_case_			:       Optional[Any]      ,     snake_case_			:       str=False      ,     snake_case_			:       Union[str, Any]=2_0      ,     snake_case_			:       str=5       ):
      _UpperCAmelCase					=  []
      for i in range(len(snake_case_       )       ):
           try:
                _UpperCAmelCase					=  tokenizer.decode([i]      ,     clean_up_tokenization_spaces=snake_case_       )
           except UnicodeDecodeError:
                pass
           toks.append((i, tok)       )

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

      # Ensure consistency
      _UpperCAmelCase					=  tokenizer.decode(snake_case_      ,     clean_up_tokenization_spaces=snake_case_       )
      if " " not in output_txt and len(snake_case_       ) > 1:
           _UpperCAmelCase					=  (
               tokenizer.decode([toks_ids[0]]      ,     clean_up_tokenization_spaces=snake_case_       )
               + " "
               + tokenizer.decode(toks_ids[1:]      ,     clean_up_tokenization_spaces=snake_case_       )
           )
      if with_prefix_space:
           _UpperCAmelCase					=  " " + output_txt
      _UpperCAmelCase					=  tokenizer.encode(snake_case_      ,     add_special_tokens=snake_case_       )
      return output_txt, output_ids




 def 	lowercase      (   self			:       Tuple       ):
      _UpperCAmelCase					=  self.perceiver_tokenizer
      _UpperCAmelCase					=  "Unicode €."
      _UpperCAmelCase					=  tokenizer(snake_case_       )
      _UpperCAmelCase					=  [4, 9_1, 1_1_6, 1_1_1, 1_0_5, 1_1_7, 1_0_6, 1_0_7, 3_8, 2_3_2, 1_3_6, 1_7_8, 5_2, 5]
      self.assertEqual(encoded["input_ids"]      ,     snake_case_       )

      # decoding
      _UpperCAmelCase					=  tokenizer.decode(snake_case_       )
      self.assertEqual(snake_case_      ,     "[CLS]Unicode €.[SEP]"       )

      _UpperCAmelCase					=  tokenizer("e è é ê ë"       )
      _UpperCAmelCase					=  [4, 1_0_7, 3_8, 2_0_1, 1_7_4, 3_8, 2_0_1, 1_7_5, 3_8, 2_0_1, 1_7_6, 3_8, 2_0_1, 1_7_7, 5]
      self.assertEqual(encoded["input_ids"]      ,     snake_case_       )
      # decoding
      _UpperCAmelCase					=  tokenizer.decode(snake_case_       )
      self.assertEqual(snake_case_      ,     "[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 	lowercase      (   self			:       Tuple       ):
      _UpperCAmelCase					=  self.perceiver_tokenizer
      _UpperCAmelCase					=  ["A long paragraph for summarization.", "Another paragraph for summarization."]
      # fmt: off
      _UpperCAmelCase					=  [4, 7_1, 3_8, 1_1_4, 1_1_7, 1_1_6, 1_0_9, 3_8, 1_1_8, 1_0_3, 1_2_0, 1_0_3, 1_0_9, 1_2_0, 1_0_3, 1_1_8, 1_1_0, 3_8, 1_0_8, 1_1_7, 1_2_0, 3_8, 1_2_1, 1_2_3, 1_1_5, 1_1_5, 1_0_3, 1_2_0, 1_1_1, 1_2_8, 1_0_3, 1_2_2, 1_1_1, 1_1_7, 1_1_6, 5_2, 5, 0]
      # fmt: on
      _UpperCAmelCase					=  tokenizer(snake_case_      ,     padding=snake_case_      ,     return_tensors=snake_case_       )
      self.assertIsInstance(snake_case_      ,     snake_case_       )

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

      self.assertListEqual(snake_case_      ,     snake_case_       )

      self.assertEqual((2, 3_8)      ,     batch.input_ids.shape       )
      self.assertEqual((2, 3_8)      ,     batch.attention_mask.shape       )




 def 	lowercase      (   self			:       Optional[int]       ):
      _UpperCAmelCase					=  self.perceiver_tokenizer
      _UpperCAmelCase					=  ["A long paragraph for summarization.", "Another paragraph for summarization."]
      _UpperCAmelCase					=  tokenizer(snake_case_      ,     padding=snake_case_      ,     return_tensors=snake_case_       )
      # check if input_ids are returned and no decoder_input_ids
      self.assertIn("input_ids"      ,     snake_case_       )
      self.assertIn("attention_mask"      ,     snake_case_       )
      self.assertNotIn("decoder_input_ids"      ,     snake_case_       )
      self.assertNotIn("decoder_attention_mask"      ,     snake_case_       )




 def 	lowercase      (   self			:       str       ):
      _UpperCAmelCase					=  self.perceiver_tokenizer
      _UpperCAmelCase					=  [
          "Summary of the text.",
          "Another summary.",
      ]
      _UpperCAmelCase					=  tokenizer(
          text_target=snake_case_      ,     max_length=3_2      ,     padding="max_length"      ,     truncation=snake_case_      ,     return_tensors=snake_case_       )
      self.assertEqual(3_2      ,     targets["input_ids"].shape[1]       )




 def 	lowercase      (   self			:       List[str]       ):
      _UpperCAmelCase					=  self.get_tokenizers()
      for tokenizer in tokenizers:
           with self.subTest(f'{tokenizer.__class__.__name__}'       ):
                self.assertNotEqual(tokenizer.model_max_length      ,     4_2       )

        # Now let's start the test
      _UpperCAmelCase					=  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					=  tempfile.mkdtemp()

                _UpperCAmelCase					=  " He is very happy, UNwant\u00E9d,running"
                _UpperCAmelCase					=  tokenizer.encode(snake_case_      ,     add_special_tokens=snake_case_       )
                tokenizer.save_pretrained(snake_case_       )

                _UpperCAmelCase					=  tokenizer.__class__.from_pretrained(snake_case_       )
                _UpperCAmelCase					=  after_tokenizer.encode(snake_case_      ,     add_special_tokens=snake_case_       )
                self.assertListEqual(snake_case_      ,     snake_case_       )

                shutil.rmtree(snake_case_       )

      _UpperCAmelCase					=  self.get_tokenizers(model_max_length=4_2       )
      for tokenizer in tokenizers:
           with self.subTest(f'{tokenizer.__class__.__name__}'       ):
                # Isolate this from the other tests because we save additional tokens/etc
                _UpperCAmelCase					=  tempfile.mkdtemp()

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

                _UpperCAmelCase					=  tokenizer.__class__.from_pretrained(snake_case_       )
                _UpperCAmelCase					=  after_tokenizer.encode(snake_case_      ,     add_special_tokens=snake_case_       )
                self.assertListEqual(snake_case_      ,     snake_case_       )
                self.assertIn("new_additional_special_token"      ,     after_tokenizer.additional_special_tokens       )
                self.assertEqual(after_tokenizer.model_max_length      ,     4_2       )

                _UpperCAmelCase					=  tokenizer.__class__.from_pretrained(snake_case_      ,     model_max_length=4_3       )
                self.assertEqual(tokenizer.model_max_length      ,     4_3       )

                shutil.rmtree(snake_case_       )




 def 	lowercase      (   self			:       Dict       ):
      _UpperCAmelCase					=  []
      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(snake_case_       )

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

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

                _UpperCAmelCase					=  [f'<extra_id_{i}>' for i in range(1_2_5       )]

                _UpperCAmelCase					=  added_tokens_extra_ids + [
                    "an_additional_special_token"
                ]
                _UpperCAmelCase					=  added_tokens_extra_ids + [
                    "an_additional_special_token"
                ]

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

                # 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					=  tokenizer_class.from_pretrained(
                    snake_case_      ,     )
                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
                _UpperCAmelCase					=  added_tokens_extra_ids + [AddedToken("a_new_additional_special_token"      ,     lstrip=snake_case_       )]
                _UpperCAmelCase					=  tokenizer_class.from_pretrained(
                    snake_case_      ,     additional_special_tokens=snake_case_      ,     )

                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 	lowercase      (   self			:       List[str]       ):
      _UpperCAmelCase					=  self.perceiver_tokenizer
      self.assertEqual(tokenizer.decode([1_7_8]       )      ,     "�"       )




 def 	lowercase      (   self			:       int       ):
      pass




 def 	lowercase      (   self			:       Union[str, Any]       ):
      pass




 def 	lowercase      (   self			:       Any       ):
      pass




 def 	lowercase      (   self			:       Optional[int]       ):
      pass




 def 	lowercase      (   self			:       Union[str, Any]       ):
      _UpperCAmelCase					=  self.get_tokenizers(fast=snake_case_      ,     do_lower_case=snake_case_       )
      for tokenizer in tokenizers:
           with self.subTest(f'{tokenizer.__class__.__name__}'       ):
                _UpperCAmelCase					=  ["[CLS]", "t", "h", "i", "s", " ", "i", "s", " ", "a", " ", "t", "e", "s", "t", "[SEP]"]
                _UpperCAmelCase					=  tokenizer.convert_tokens_to_string(snake_case_       )

                self.assertIsInstance(snake_case_      ,     snake_case_       )








import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import PoolFormerImageProcessor






class      UpperCamelCase(	unittest.TestCase ):


					def __init__(    self						:		Tuple  ,     SCREAMING_SNAKE_CASE						:		List[Any]  ,     SCREAMING_SNAKE_CASE						:		Optional[Any]=7  ,     SCREAMING_SNAKE_CASE						:		Dict=3  ,     SCREAMING_SNAKE_CASE						:		Any=3_0  ,     SCREAMING_SNAKE_CASE						:		List[str]=4_0_0  ,     SCREAMING_SNAKE_CASE						:		Any=True  ,     SCREAMING_SNAKE_CASE						:		List[Any]=None  ,     SCREAMING_SNAKE_CASE						:		int=0.9  ,     SCREAMING_SNAKE_CASE						:		Tuple=None  ,     SCREAMING_SNAKE_CASE						:		int=True  ,     SCREAMING_SNAKE_CASE						:		Union[str, Any]=[0.5, 0.5, 0.5]  ,     SCREAMING_SNAKE_CASE						:		Optional[Any]=[0.5, 0.5, 0.5]  ,     )      ->						Union[str, Any]:




											'''simple docstring'''

											__snake_case				=		size if size is not None else {"shortest_edge": 3_0}
											__snake_case				=		crop_size if crop_size is not None else {"height": 3_0, "width": 3_0}
											__snake_case				=		parent
											__snake_case				=		batch_size
											__snake_case				=		num_channels
											__snake_case				=		min_resolution
											__snake_case				=		max_resolution
											__snake_case				=		do_resize_and_center_crop
											__snake_case				=		size
											__snake_case				=		crop_pct
											__snake_case				=		crop_size
											__snake_case				=		do_normalize
											__snake_case				=		image_mean
											__snake_case				=		image_std






					def 				SCREAMING_SNAKE_CASE_	(    self						:		str    )      ->						Any:




											'''simple docstring'''

											return {
											    "size": self.size,
											    "do_resize_and_center_crop": self.do_resize_and_center_crop,
											    "crop_pct": self.crop_pct,
											    "crop_size": self.crop_size,
											    "do_normalize": self.do_normalize,
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											}






@require_torch
@require_vision
class      UpperCamelCase(	_a  ,				unittest.TestCase ):
					snake_case_		:					List[str]    					=						PoolFormerImageProcessor if is_vision_available() else None


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




											'''simple docstring'''

											__snake_case				=		PoolFormerImageProcessingTester(self    )


					@property
					def 				SCREAMING_SNAKE_CASE_	(    self						:		int    )      ->						Dict:




											'''simple docstring'''

											return self.image_processor_tester.prepare_image_processor_dict()


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Dict    )      ->						List[str]:




											'''simple docstring'''

											__snake_case				=		self.image_processing_class(**self.image_processor_dict    )
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE  ,     "do_resize_and_center_crop"    )    )
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE  ,     "size"    )    )
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE  ,     "crop_pct"    )    )
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE  ,     "do_normalize"    )    )
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE  ,     "image_mean"    )    )
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE  ,     "image_std"    )    )


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




											'''simple docstring'''

											__snake_case				=		self.image_processing_class.from_dict(self.image_processor_dict    )
											self.assertEqual(image_processor.size  ,     {"shortest_edge": 3_0}    )
											self.assertEqual(image_processor.crop_size  ,     {"height": 3_0, "width": 3_0}    )

											__snake_case				=		self.image_processing_class.from_dict(self.image_processor_dict  ,     size=4_2  ,     crop_size=8_4    )
											self.assertEqual(image_processor.size  ,     {"shortest_edge": 4_2}    )
											self.assertEqual(image_processor.crop_size  ,     {"height": 8_4, "width": 8_4}    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[int]    )      ->						List[Any]:




											'''simple docstring'''

											pass


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[Any]    )      ->						Dict:




											'''simple docstring'''

											__snake_case				=		self.image_processing_class(**self.image_processor_dict    )
											# create random PIL images
											__snake_case				=		prepare_image_inputs(self.image_processor_tester  ,     equal_resolution=SCREAMING_SNAKE_CASE    )
											for image in image_inputs:
																	self.assertIsInstance(SCREAMING_SNAKE_CASE  ,     Image.Image    )

											# Test not batched input
											__snake_case				=		image_processing(image_inputs[0]  ,     return_tensors="pt"    ).pixel_values

											self.assertEqual(
											    encoded_images.shape  ,     (
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["height"],
											        self.image_processor_tester.crop_size["width"],
											    )  ,     )

											# Test batched
											__snake_case				=		image_processing(SCREAMING_SNAKE_CASE  ,     return_tensors="pt"    ).pixel_values
											self.assertEqual(
											    encoded_images.shape  ,     (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["height"],
											        self.image_processor_tester.crop_size["width"],
											    )  ,     )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[Any]    )      ->						Optional[int]:




											'''simple docstring'''

											__snake_case				=		self.image_processing_class(**self.image_processor_dict    )
											# create random numpy tensors
											__snake_case				=		prepare_image_inputs(self.image_processor_tester  ,     equal_resolution=SCREAMING_SNAKE_CASE  ,     numpify=SCREAMING_SNAKE_CASE    )
											for image in image_inputs:
																	self.assertIsInstance(SCREAMING_SNAKE_CASE  ,     np.ndarray    )

											# Test not batched input
											__snake_case				=		image_processing(image_inputs[0]  ,     return_tensors="pt"    ).pixel_values
											self.assertEqual(
											    encoded_images.shape  ,     (
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["height"],
											        self.image_processor_tester.crop_size["width"],
											    )  ,     )

											# Test batched
											__snake_case				=		image_processing(SCREAMING_SNAKE_CASE  ,     return_tensors="pt"    ).pixel_values
											self.assertEqual(
											    encoded_images.shape  ,     (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["height"],
											        self.image_processor_tester.crop_size["width"],
											    )  ,     )






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




											'''simple docstring'''

											__snake_case				=		self.image_processing_class(**self.image_processor_dict    )
											# create random PyTorch tensors
											__snake_case				=		prepare_image_inputs(self.image_processor_tester  ,     equal_resolution=SCREAMING_SNAKE_CASE  ,     torchify=SCREAMING_SNAKE_CASE    )
											for image in image_inputs:
																	self.assertIsInstance(SCREAMING_SNAKE_CASE  ,     torch.Tensor    )

											# Test not batched input
											__snake_case				=		image_processing(image_inputs[0]  ,     return_tensors="pt"    ).pixel_values
											self.assertEqual(
											    encoded_images.shape  ,     (
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["height"],
											        self.image_processor_tester.crop_size["width"],
											    )  ,     )

											# Test batched
											__snake_case				=		image_processing(SCREAMING_SNAKE_CASE  ,     return_tensors="pt"    ).pixel_values
											self.assertEqual(
											    encoded_images.shape  ,     (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["height"],
											        self.image_processor_tester.crop_size["width"],
											    )  ,     )





