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


class 						__lowerCAmelCase	(	lowerCAmelCase_ , unittest.TestCase      ):



      """simple docstring"""







      A__	:			Dict        =    PerceiverTokenizer
      A__	:			str        =    False




      def 		snake_case_			(      self		:       int				):
          super().setUp()
          __lowercase		:			int	    =       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		:       Optional[Any]				,       **_snake_case		:       Tuple				):
          return self.tokenizer_class.from_pretrained(self.tmpdirname				,       **A__				)




      def 		snake_case_			(      self		:       List[str]				,       _snake_case		:       Optional[Any]				,       _snake_case		:       Union[str, Any]=False				,       _snake_case		:       List[str]=20				,       _snake_case		:       Union[str, Any]=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.

          __lowercase		:			int	    =       []
          for i in range(len(A__				)				):
              try:
                  __lowercase		:			List[Any]	    =       tokenizer.decode([i]				,       clean_up_tokenization_spaces=A__				)
              except UnicodeDecodeError:
                  pass
              toks.append((i, tok)				)

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

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




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

          # decoding
          __lowercase		:			List[Any]	    =       tokenizer.decode(A__				)
          self.assertEqual(A__				,       '''[CLS]Unicode €.[SEP]'''				)

          __lowercase		:			Any	    =       tokenizer('''e è é ê ë'''				)
          __lowercase		:			str	    =       [4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
          self.assertEqual(encoded['''input_ids''']				,       A__				)
          # decoding
          __lowercase		:			Tuple	    =       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		:       str				):
          __lowercase		:			int	    =       self.perceiver_tokenizer
          __lowercase		:			Optional[int]	    =       ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
          # fmt: off
          __lowercase		:			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
          __lowercase		:			Union[str, Any]	    =       tokenizer(A__				,       padding=A__				,       return_tensors=A__				)
          self.assertIsInstance(A__				,       A__				)

          if FRAMEWORK != "jax":
              __lowercase		:			Dict	    =       list(batch.input_ids.numpy()[0]				)
          else:
              __lowercase		:			List[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		:       str				):
          __lowercase		:			Tuple	    =       self.perceiver_tokenizer
          __lowercase		:			Tuple	    =       ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
          __lowercase		:			List[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		:       Union[str, Any]				):
          __lowercase		:			Optional[int]	    =       self.perceiver_tokenizer
          __lowercase		:			Optional[Any]	    =       [
              """Summary of the text.""",
              """Another summary.""",
          ]
          __lowercase		:			Optional[Any]	    =       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		:       str				):
          # safety check on max_len default value so we are sure the test works
          __lowercase		:			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
          __lowercase		:			Optional[int]	    =       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		:			Tuple	    =       tempfile.mkdtemp()

                  __lowercase		:			Tuple	    =       """ He is very happy, UNwant\u00E9d,running"""
                  __lowercase		:			Optional[Any]	    =       tokenizer.encode(A__				,       add_special_tokens=A__				)
                  tokenizer.save_pretrained(A__				)

                  __lowercase		:			List[str]	    =       tokenizer.__class__.from_pretrained(A__				)
                  __lowercase		:			int	    =       after_tokenizer.encode(A__				,       add_special_tokens=A__				)
                  self.assertListEqual(A__				,       A__				)

                  shutil.rmtree(A__				)

          __lowercase		:			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
                  __lowercase		:			Optional[Any]	    =       tempfile.mkdtemp()

                  __lowercase		:			Any	    =       """ 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		:			Optional[Any]	    =       tokenizer.encode(A__				,       add_special_tokens=A__				)
                  tokenizer.save_pretrained(A__				)

                  __lowercase		:			List[Any]	    =       tokenizer.__class__.from_pretrained(A__				)
                  __lowercase		:			Optional[int]	    =       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				)

                  __lowercase		:			Dict	    =       tokenizer.__class__.from_pretrained(A__				,       model_max_length=43				)
                  self.assertEqual(tokenizer.model_max_length				,       43				)

                  shutil.rmtree(A__				)




      def 		snake_case_			(      self		:       Optional[int]				):
          __lowercase		:			Dict	    =       []
          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:
                      __lowercase		:			int	    =       json.load(A__				)

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

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

                  __lowercase		:			Optional[Any]	    =       added_tokens_extra_ids + [
                      """an_additional_special_token"""
                  ]
                  __lowercase		:			Dict	    =       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
                  __lowercase		:			List[str]	    =       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
                  __lowercase		:			Tuple	    =       added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''				,       lstrip=A__				)]
                  __lowercase		:			Tuple	    =       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		:       Optional[int]				):
          __lowercase		:			Any	    =       self.perceiver_tokenizer
          self.assertEqual(tokenizer.decode([178]				)				,       '''�'''				)




      def 		snake_case_			(      self		:       int				):
          pass




      def 		snake_case_			(      self		:       Tuple				):
          pass




      def 		snake_case_			(      self		:       str				):
          pass




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




      def 		snake_case_			(      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
          __lowercase		:			int	    =       self.get_tokenizers(fast=A__				,       do_lower_case=A__				)
          for tokenizer in tokenizers:
              with self.subTest(F'{tokenizer.__class__.__name__}'				):
                  __lowercase		:			Tuple	    =       ["""[CLS]""", """t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """s""", """t""", """[SEP]"""]
                  __lowercase		:			Dict	    =       tokenizer.convert_tokens_to_string(A__				)

                  self.assertIsInstance(A__				,       A__				)




import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest


class 						__lowerCAmelCase	(	lowerCAmelCase_      ):



      """simple docstring"""







      A__	:			Optional[Any]        =    (DDPMScheduler,)




      def 		snake_case_			(      self		:       Dict				,       **_snake_case		:       List[str]				):
          __lowercase		:			Tuple	    =       {
              '''num_train_timesteps''': 1000,
              '''beta_start''': 0.00_01,
              '''beta_end''': 0.02,
              '''beta_schedule''': '''linear''',
              '''variance_type''': '''fixed_small''',
              '''clip_sample''': True,
          }

          config.update(**_snake_case				)
          return config




      def 		snake_case_			(      self		:       Union[str, Any]				):
          for timesteps in [1, 5, 100, 1000]:
              self.check_over_configs(num_train_timesteps=_snake_case				)




      def 		snake_case_			(      self		:       Dict				):
          for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]				,       [0.0_02, 0.02, 0.2, 2]				):
              self.check_over_configs(beta_start=_snake_case				,       beta_end=_snake_case				)




      def 		snake_case_			(      self		:       Optional[Any]				):
          for schedule in ["linear", "squaredcos_cap_v2"]:
              self.check_over_configs(beta_schedule=_snake_case				)




      def 		snake_case_			(      self		:       Optional[int]				):
          for variance in ["fixed_small", "fixed_large", "other"]:
              self.check_over_configs(variance_type=_snake_case				)




      def 		snake_case_			(      self		:       Tuple				):
          for clip_sample in [True, False]:
              self.check_over_configs(clip_sample=_snake_case				)




      def 		snake_case_			(      self		:       Any				):
          self.check_over_configs(thresholding=_snake_case				)
          for threshold in [0.5, 1.0, 2.0]:
              for prediction_type in ["epsilon", "sample", "v_prediction"]:
                  self.check_over_configs(
                      thresholding=_snake_case				,       prediction_type=_snake_case				,       sample_max_value=_snake_case				,       )




      def 		snake_case_			(      self		:       List[str]				):
          for prediction_type in ["epsilon", "sample", "v_prediction"]:
              self.check_over_configs(prediction_type=_snake_case				)




      def 		snake_case_			(      self		:       Dict				):
          for t in [0, 500, 999]:
              self.check_over_forward(time_step=_snake_case				)




      def 		snake_case_			(      self		:       Optional[Any]				):
          __lowercase		:			int	    =       self.scheduler_classes[0]
          __lowercase		:			str	    =       self.get_scheduler_config()
          __lowercase		:			Union[str, Any]	    =       scheduler_class(**_snake_case				)

          assert torch.sum(torch.abs(scheduler._get_variance(0				) - 0.0				)				) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(487				) - 0.0_09_79				)				) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(999				) - 0.02				)				) < 1E-5




      def 		snake_case_			(      self		:       Tuple				):
          __lowercase		:			str	    =       self.scheduler_classes[0]
          __lowercase		:			str	    =       self.get_scheduler_config()
          __lowercase		:			int	    =       scheduler_class(**_snake_case				)

          __lowercase		:			List[Any]	    =       len(_snake_case				)

          __lowercase		:			Dict	    =       self.dummy_model()
          __lowercase		:			Tuple	    =       self.dummy_sample_deter
          __lowercase		:			List[str]	    =       torch.manual_seed(0				)

          for t in reversed(range(_snake_case				)				):
              # 1. predict noise residual
              __lowercase		:			Optional[int]	    =       model(_snake_case				,       _snake_case				)

              # 2. predict previous mean of sample x_t-1
              __lowercase		:			int	    =       scheduler.step(_snake_case				,       _snake_case				,       _snake_case				,       generator=_snake_case				).prev_sample

              # if t > 0:
              #     noise = self.dummy_sample_deter
              #     variance = scheduler.get_variance(t) ** (0.5) * noise
              #
              # sample = pred_prev_sample + variance
              __lowercase		:			Any	    =       pred_prev_sample

          __lowercase		:			str	    =       torch.sum(torch.abs(_snake_case				)				)
          __lowercase		:			Any	    =       torch.mean(torch.abs(_snake_case				)				)

          assert abs(result_sum.item() - 2_58.96_06				) < 1E-2
          assert abs(result_mean.item() - 0.33_72				) < 1E-3




      def 		snake_case_			(      self		:       Tuple				):
          __lowercase		:			str	    =       self.scheduler_classes[0]
          __lowercase		:			str	    =       self.get_scheduler_config(prediction_type='''v_prediction'''				)
          __lowercase		:			Union[str, Any]	    =       scheduler_class(**_snake_case				)

          __lowercase		:			List[Any]	    =       len(_snake_case				)

          __lowercase		:			List[Any]	    =       self.dummy_model()
          __lowercase		:			str	    =       self.dummy_sample_deter
          __lowercase		:			Dict	    =       torch.manual_seed(0				)

          for t in reversed(range(_snake_case				)				):
              # 1. predict noise residual
              __lowercase		:			Tuple	    =       model(_snake_case				,       _snake_case				)

              # 2. predict previous mean of sample x_t-1
              __lowercase		:			Optional[int]	    =       scheduler.step(_snake_case				,       _snake_case				,       _snake_case				,       generator=_snake_case				).prev_sample

              # if t > 0:
              #     noise = self.dummy_sample_deter
              #     variance = scheduler.get_variance(t) ** (0.5) * noise
              #
              # sample = pred_prev_sample + variance
              __lowercase		:			Any	    =       pred_prev_sample

          __lowercase		:			int	    =       torch.sum(torch.abs(_snake_case				)				)
          __lowercase		:			List[Any]	    =       torch.mean(torch.abs(_snake_case				)				)

          assert abs(result_sum.item() - 2_02.02_96				) < 1E-2
          assert abs(result_mean.item() - 0.26_31				) < 1E-3




      def 		snake_case_			(      self		:       str				):
          __lowercase		:			int	    =       self.scheduler_classes[0]
          __lowercase		:			List[str]	    =       self.get_scheduler_config()
          __lowercase		:			List[str]	    =       scheduler_class(**_snake_case				)

          __lowercase		:			Optional[Any]	    =       [100, 87, 50, 1, 0]

          scheduler.set_timesteps(timesteps=_snake_case				)

          __lowercase		:			int	    =       scheduler.timesteps

          for i, timestep in enumerate(_snake_case				):
              if i == len(_snake_case				) - 1:
                  __lowercase		:			Optional[Any]	    =       -1
              else:
                  __lowercase		:			List[str]	    =       timesteps[i + 1]

              __lowercase		:			Optional[Any]	    =       scheduler.previous_timestep(_snake_case				)
              __lowercase		:			str	    =       prev_t.item()

              self.assertEqual(_snake_case				,       _snake_case				)




      def 		snake_case_			(      self		:       List[Any]				):
          __lowercase		:			Tuple	    =       self.scheduler_classes[0]
          __lowercase		:			Optional[Any]	    =       self.get_scheduler_config()
          __lowercase		:			Union[str, Any]	    =       scheduler_class(**_snake_case				)

          __lowercase		:			Any	    =       [100, 87, 50, 51, 0]

          with self.assertRaises(_snake_case				,       msg='''`custom_timesteps` must be in descending order.'''				):
              scheduler.set_timesteps(timesteps=_snake_case				)




      def 		snake_case_			(      self		:       List[Any]				):
          __lowercase		:			Optional[int]	    =       self.scheduler_classes[0]
          __lowercase		:			Union[str, Any]	    =       self.get_scheduler_config()
          __lowercase		:			int	    =       scheduler_class(**_snake_case				)

          __lowercase		:			Dict	    =       [100, 87, 50, 1, 0]
          __lowercase		:			str	    =       len(_snake_case				)

          with self.assertRaises(_snake_case				,       msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''				):
              scheduler.set_timesteps(num_inference_steps=_snake_case				,       timesteps=_snake_case				)




      def 		snake_case_			(      self		:       str				):
          __lowercase		:			List[str]	    =       self.scheduler_classes[0]
          __lowercase		:			Optional[int]	    =       self.get_scheduler_config()
          __lowercase		:			int	    =       scheduler_class(**_snake_case				)

          __lowercase		:			Optional[int]	    =       [scheduler.config.num_train_timesteps]

          with self.assertRaises(
              _snake_case				,       msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''				,       ):
              scheduler.set_timesteps(timesteps=_snake_case				)


