



'''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[int]						 =	"""tf"""
else:
   A_							: int						 =	"""jax"""



class 							lowercase       (  _lowerCamelCase					,					unittest.TestCase				):


 """simple docstring"""


 UpperCAmelCase							=  PerceiverTokenizer
 UpperCAmelCase							=  False







 def _snake_case (							self							)		->   Any:
        super().setUp()
        _UpperCAmelCase  :						Tuple			     = PerceiverTokenizer()
        tokenizer.save_pretrained(self.tmpdirname							)







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







 def _snake_case (							self  ,**a_							)		->   PerceiverTokenizer:
        return self.tokenizer_class.from_pretrained(self.tmpdirname  ,**a_							)







 def _snake_case (							self  ,a_  ,a_=False  ,a_=20  ,a_=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.

        _UpperCAmelCase  :						Union[str, Any]			     = []
        for i in range(len(a_							)							):
               try:
                      _UpperCAmelCase  :						int			     = tokenizer.decode([i]  ,clean_up_tokenization_spaces=a_							)
               except UnicodeDecodeError:
                      pass
               toks.append((i, tok)							)

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

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







 def _snake_case (							self							)		->   int:
        _UpperCAmelCase  :						Any			     = self.perceiver_tokenizer
        _UpperCAmelCase  :						Union[str, Any]			     = """Unicode €."""
        _UpperCAmelCase  :						Dict			     = tokenizer(a_							)
        _UpperCAmelCase  :						Tuple			     = [4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
        self.assertEqual(encoded["""input_ids"""]  ,a_							)

        # decoding
        _UpperCAmelCase  :						Optional[Any]			     = tokenizer.decode(a_							)
        self.assertEqual(a_  ,"""[CLS]Unicode €.[SEP]"""							)

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

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

        self.assertListEqual(a_  ,a_							)

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







 def _snake_case (							self							)		->   Dict:
        _UpperCAmelCase  :						Dict			     = self.perceiver_tokenizer
        _UpperCAmelCase  :						List[str]			     = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
        _UpperCAmelCase  :						str			     = tokenizer(a_  ,padding=a_  ,return_tensors=a_							)
        # check if input_ids are returned and no decoder_input_ids
        self.assertIn("""input_ids"""  ,a_							)
        self.assertIn("""attention_mask"""  ,a_							)
        self.assertNotIn("""decoder_input_ids"""  ,a_							)
        self.assertNotIn("""decoder_attention_mask"""  ,a_							)







 def _snake_case (							self							)		->   Any:
        _UpperCAmelCase  :						Any			     = self.perceiver_tokenizer
        _UpperCAmelCase  :						str			     = [
            """Summary of the text.""",
            """Another summary.""",
        ]
        _UpperCAmelCase  :						Tuple			     = tokenizer(
            text_target=a_  ,max_length=32  ,padding="""max_length"""  ,truncation=a_  ,return_tensors=a_							)
        self.assertEqual(32  ,targets["""input_ids"""].shape[1]							)







 def _snake_case (							self							)		->   List[Any]:
        # safety check on max_len default value so we are sure the test works
        _UpperCAmelCase  :						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
        _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  :						str			     = tempfile.mkdtemp()

                      _UpperCAmelCase  :						int			     = """ He is very happy, UNwant\u00E9d,running"""
                      _UpperCAmelCase  :						Optional[int]			     = tokenizer.encode(a_  ,add_special_tokens=a_							)
                      tokenizer.save_pretrained(a_							)

                      _UpperCAmelCase  :						Union[str, Any]			     = tokenizer.__class__.from_pretrained(a_							)
                      _UpperCAmelCase  :						Union[str, Any]			     = after_tokenizer.encode(a_  ,add_special_tokens=a_							)
                      self.assertListEqual(a_  ,a_							)

                      shutil.rmtree(a_							)

        _UpperCAmelCase  :						Optional[Any]			     = 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  :						Optional[int]			     = tempfile.mkdtemp()

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

                      _UpperCAmelCase  :						Optional[int]			     = tokenizer.__class__.from_pretrained(a_							)
                      _UpperCAmelCase  :						Dict			     = after_tokenizer.encode(a_  ,add_special_tokens=a_							)
                      self.assertListEqual(a_  ,a_							)
                      self.assertIn("""new_additional_special_token"""  ,after_tokenizer.additional_special_tokens							)
                      self.assertEqual(after_tokenizer.model_max_length  ,42							)

                      _UpperCAmelCase  :						Optional[Any]			     = tokenizer.__class__.from_pretrained(a_  ,model_max_length=43							)
                      self.assertEqual(tokenizer.model_max_length  ,43							)

                      shutil.rmtree(a_							)







 def _snake_case (							self							)		->   Union[str, Any]:
        _UpperCAmelCase  :						int			     = []
        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(a_							)

                      with open(os.path.join(a_  ,"""special_tokens_map.json"""							)  ,encoding="""utf-8"""							) as json_file:
                             _UpperCAmelCase  :						Union[str, Any]			     = json.load(a_							)

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

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

                      _UpperCAmelCase  :						Optional[Any]			     = added_tokens_extra_ids + [
                          """an_additional_special_token"""
                      ]
                      _UpperCAmelCase  :						List[str]			     = added_tokens_extra_ids + [
                          """an_additional_special_token"""
                      ]

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

                      # 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  :						Optional[Any]			     = tokenizer_class.from_pretrained(
                          a_  ,)
                      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  :						int			     = added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token"""  ,lstrip=a_							)]
                      _UpperCAmelCase  :						List[str]			     = tokenizer_class.from_pretrained(
                          a_  ,additional_special_tokens=a_  ,)

                      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							)		->   Tuple:
        _UpperCAmelCase  :						Tuple			     = self.perceiver_tokenizer
        self.assertEqual(tokenizer.decode([178]							)  ,"""�"""							)







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







 def _snake_case (							self							)		->   Any:
        pass







 def _snake_case (							self							)		->   Dict:
        pass







 def _snake_case (							self							)		->   Dict:
        pass







 def _snake_case (							self							)		->   Any:
        # The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
        # strings and special added tokens as tokens
        _UpperCAmelCase  :						Any			     = self.get_tokenizers(fast=a_  ,do_lower_case=a_							)
        for tokenizer in tokenizers:
               with self.subTest(f'''{tokenizer.__class__.__name__}'''							):
                      _UpperCAmelCase  :						Optional[int]			     = ["""[CLS]""", """t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """s""", """t""", """[SEP]"""]
                      _UpperCAmelCase  :						Dict			     = tokenizer.convert_tokens_to_string(a_							)

                      self.assertIsInstance(a_  ,a_							)








'''simple docstring'''






import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest



class 							lowercase       (  _lowerCamelCase				):


 """simple docstring"""


 UpperCAmelCase							=  (IPNDMScheduler,)
 UpperCAmelCase							=  (("""num_inference_steps""", 50),)







 def _snake_case (							self  ,**a_							)		->   Optional[int]:
        _UpperCAmelCase  :						str			     = {"""num_train_timesteps""": 1_000}
        config.update(**a_							)
        return config







 def _snake_case (							self  ,a_=0  ,**a_							)		->   List[str]:
        _UpperCAmelCase  :						Any			     = dict(self.forward_default_kwargs							)
        _UpperCAmelCase  :						Any			     = kwargs.pop("""num_inference_steps"""  ,a_							)
        _UpperCAmelCase  :						Union[str, Any]			     = self.dummy_sample
        _UpperCAmelCase  :						Union[str, Any]			     = 0.1 * sample
        _UpperCAmelCase  :						List[str]			     = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
               _UpperCAmelCase  :						str			     = self.get_scheduler_config(**a_							)
               _UpperCAmelCase  :						List[str]			     = scheduler_class(**a_							)
               scheduler.set_timesteps(a_							)
               # copy over dummy past residuals
               _UpperCAmelCase  :						str			     = dummy_past_residuals[:]

               if time_step is None:
                      _UpperCAmelCase  :						List[str]			     = scheduler.timesteps[len(scheduler.timesteps							) // 2]

               with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(a_							)
                      _UpperCAmelCase  :						List[str]			     = scheduler_class.from_pretrained(a_							)
                      new_scheduler.set_timesteps(a_							)
                      # copy over dummy past residuals
                      _UpperCAmelCase  :						Tuple			     = dummy_past_residuals[:]

               _UpperCAmelCase  :						Dict			     = scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample
               _UpperCAmelCase  :						Any			     = new_scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample

               assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"

               _UpperCAmelCase  :						str			     = scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample
               _UpperCAmelCase  :						Any			     = new_scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample

               assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"







 def _snake_case (							self							)		->   Any:
        pass







 def _snake_case (							self  ,a_=0  ,**a_							)		->   Dict:
        _UpperCAmelCase  :						str			     = dict(self.forward_default_kwargs							)
        _UpperCAmelCase  :						Union[str, Any]			     = kwargs.pop("""num_inference_steps"""  ,a_							)
        _UpperCAmelCase  :						Optional[int]			     = self.dummy_sample
        _UpperCAmelCase  :						Tuple			     = 0.1 * sample
        _UpperCAmelCase  :						Optional[Any]			     = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
               _UpperCAmelCase  :						Optional[Any]			     = self.get_scheduler_config()
               _UpperCAmelCase  :						Dict			     = scheduler_class(**a_							)
               scheduler.set_timesteps(a_							)

               # copy over dummy past residuals (must be after setting timesteps)
               _UpperCAmelCase  :						Any			     = dummy_past_residuals[:]

               if time_step is None:
                      _UpperCAmelCase  :						Optional[int]			     = scheduler.timesteps[len(scheduler.timesteps							) // 2]

               with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(a_							)
                      _UpperCAmelCase  :						str			     = scheduler_class.from_pretrained(a_							)
                      # copy over dummy past residuals
                      new_scheduler.set_timesteps(a_							)

                      # copy over dummy past residual (must be after setting timesteps)
                      _UpperCAmelCase  :						Dict			     = dummy_past_residuals[:]

               _UpperCAmelCase  :						Tuple			     = scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample
               _UpperCAmelCase  :						str			     = new_scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample

               assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"

               _UpperCAmelCase  :						str			     = scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample
               _UpperCAmelCase  :						str			     = new_scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample

               assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"







 def _snake_case (							self  ,**a_							)		->   List[Any]:
        _UpperCAmelCase  :						int			     = self.scheduler_classes[0]
        _UpperCAmelCase  :						List[str]			     = self.get_scheduler_config(**a_							)
        _UpperCAmelCase  :						List[Any]			     = scheduler_class(**a_							)

        _UpperCAmelCase  :						List[Any]			     = 10
        _UpperCAmelCase  :						Dict			     = self.dummy_model()
        _UpperCAmelCase  :						Dict			     = self.dummy_sample_deter
        scheduler.set_timesteps(a_							)

        for i, t in enumerate(scheduler.timesteps							):
               _UpperCAmelCase  :						Optional[Any]			     = model(a_  ,a_							)
               _UpperCAmelCase  :						Tuple			     = scheduler.step(a_  ,a_  ,a_							).prev_sample

        for i, t in enumerate(scheduler.timesteps							):
               _UpperCAmelCase  :						List[str]			     = model(a_  ,a_							)
               _UpperCAmelCase  :						int			     = scheduler.step(a_  ,a_  ,a_							).prev_sample

        return sample







 def _snake_case (							self							)		->   Optional[Any]:
        _UpperCAmelCase  :						int			     = dict(self.forward_default_kwargs							)

        _UpperCAmelCase  :						Optional[Any]			     = kwargs.pop("""num_inference_steps"""  ,a_							)

        for scheduler_class in self.scheduler_classes:
               _UpperCAmelCase  :						int			     = self.get_scheduler_config()
               _UpperCAmelCase  :						List[str]			     = scheduler_class(**a_							)

               _UpperCAmelCase  :						Optional[int]			     = self.dummy_sample
               _UpperCAmelCase  :						Tuple			     = 0.1 * sample

               if num_inference_steps is not None and hasattr(a_  ,"""set_timesteps"""							):
                      scheduler.set_timesteps(a_							)
               elif num_inference_steps is not None and not hasattr(a_  ,"""set_timesteps"""							):
                      _UpperCAmelCase  :						Optional[Any]			     = num_inference_steps

               # copy over dummy past residuals (must be done after set_timesteps)
               _UpperCAmelCase  :						Union[str, Any]			     = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
               _UpperCAmelCase  :						Optional[int]			     = dummy_past_residuals[:]

               _UpperCAmelCase  :						Tuple			     = scheduler.timesteps[5]
               _UpperCAmelCase  :						Optional[Any]			     = scheduler.timesteps[6]

               _UpperCAmelCase  :						Optional[Any]			     = scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample
               _UpperCAmelCase  :						Optional[int]			     = scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample

               self.assertEqual(output_a.shape  ,sample.shape							)
               self.assertEqual(output_a.shape  ,output_a.shape							)

               _UpperCAmelCase  :						Dict			     = scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample
               _UpperCAmelCase  :						Any			     = scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample

               self.assertEqual(output_a.shape  ,sample.shape							)
               self.assertEqual(output_a.shape  ,output_a.shape							)







 def _snake_case (							self							)		->   List[Any]:
        for timesteps in [100, 1_000]:
               self.check_over_configs(num_train_timesteps=a_  ,time_step=a_							)







 def _snake_case (							self							)		->   int:
        for t, num_inference_steps in zip([1, 5, 10]  ,[10, 50, 100]							):
               self.check_over_forward(num_inference_steps=a_  ,time_step=a_							)







 def _snake_case (							self							)		->   Optional[Any]:
        _UpperCAmelCase  :						Union[str, Any]			     = self.full_loop()
        _UpperCAmelCase  :						Any			     = torch.mean(torch.abs(a_							)							)

        assert abs(result_mean.item() - 2_540_529							) < 10



