






"""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():
    _UpperCamelCase		:  List[Any]						=		'pt'
elif is_tf_available():
    _UpperCamelCase		:  List[str]						=		'tf'
else:
    _UpperCamelCase		:  Optional[Any]						=		'jax'

class        a     (   a_,				unittest.TestCase	):
    UpperCAmelCase_ :     Dict	     =PerceiverTokenizer
    UpperCAmelCase_ :     Optional[int]	     =False







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







    @cached_property
    def 			UpperCamelCase_      (			self       ):
      return PerceiverTokenizer.from_pretrained('deepmind/language-perceiver'       )







    def 			UpperCamelCase_      (			self							,		**_lowerCamelCase       ):
      return self.tokenizer_class.from_pretrained(self.tmpdirname							,		**_lowerCamelCase       )







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase=False							,		_lowerCamelCase=2_0							,		_lowerCamelCase=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							    =       []
      for i in range(len(_lowerCamelCase       )       ):
        try:
          lowercase							    =       tokenizer.decode([i]							,		clean_up_tokenization_spaces=_lowerCamelCase       )
        except UnicodeDecodeError:
          pass
        toks.append((i, tok)       )

      lowercase							    =       list(filter(lambda _lowerCamelCase       : re.match(R'^[ a-zA-Z]+$'							,		t[1]       )							,		_lowerCamelCase       )       )
      lowercase							    =       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:
        lowercase							    =       toks[:max_length]
      if min_length is not None and len(_lowerCamelCase       ) < min_length and len(_lowerCamelCase       ) > 0:
        while len(_lowerCamelCase       ) < min_length:
          lowercase							    =       toks + toks
        # toks_str = [t[1] for t in toks]
      lowercase							    =       [t[0] for t in toks]

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







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.perceiver_tokenizer
      lowercase							    =       'Unicode €.'
      lowercase							    =       tokenizer(_lowerCamelCase       )
      lowercase							    =       [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']							,		_lowerCamelCase       )

      # decoding
      lowercase							    =       tokenizer.decode(_lowerCamelCase       )
      self.assertEqual(_lowerCamelCase							,		'[CLS]Unicode €.[SEP]'       )

      lowercase							    =       tokenizer('e è é ê ë'       )
      lowercase							    =       [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']							,		_lowerCamelCase       )
      # decoding
      lowercase							    =       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 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.perceiver_tokenizer
      lowercase							    =       ['A long paragraph for summarization.', 'Another paragraph for summarization.']
      # fmt: off
      lowercase							    =       [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
      lowercase							    =       tokenizer(_lowerCamelCase							,		padding=_lowerCamelCase							,		return_tensors=_lowerCamelCase       )
      self.assertIsInstance(_lowerCamelCase							,		_lowerCamelCase       )

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

      self.assertListEqual(_lowerCamelCase							,		_lowerCamelCase       )

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







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.perceiver_tokenizer
      lowercase							    =       ['A long paragraph for summarization.', 'Another paragraph for summarization.']
      lowercase							    =       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 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.perceiver_tokenizer
      lowercase							    =       [
          'Summary of the text.',
          'Another summary.',
      ]
      lowercase							    =       tokenizer(
          text_target=_lowerCamelCase							,		max_length=3_2							,		padding='max_length'							,		truncation=_lowerCamelCase							,		return_tensors=_lowerCamelCase       )
      self.assertEqual(3_2							,		targets['input_ids'].shape[1]       )







    def 			UpperCamelCase_      (			self       ):
      # safety check on max_len default value so we are sure the test works
      lowercase							    =       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
      lowercase							    =       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							    =       tempfile.mkdtemp()

          lowercase							    =       ' He is very happy, UNwant\u00E9d,running'
          lowercase							    =       tokenizer.encode(_lowerCamelCase							,		add_special_tokens=_lowerCamelCase       )
          tokenizer.save_pretrained(_lowerCamelCase       )

          lowercase							    =       tokenizer.__class__.from_pretrained(_lowerCamelCase       )
          lowercase							    =       after_tokenizer.encode(_lowerCamelCase							,		add_special_tokens=_lowerCamelCase       )
          self.assertListEqual(_lowerCamelCase							,		_lowerCamelCase       )

          shutil.rmtree(_lowerCamelCase       )

      lowercase							    =       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
          lowercase							    =       tempfile.mkdtemp()

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

          lowercase							    =       tokenizer.__class__.from_pretrained(_lowerCamelCase       )
          lowercase							    =       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							,		4_2       )

          lowercase							    =       tokenizer.__class__.from_pretrained(_lowerCamelCase							,		model_max_length=4_3       )
          self.assertEqual(tokenizer.model_max_length							,		4_3       )

          shutil.rmtree(_lowerCamelCase       )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       []
      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:
            lowercase							    =       json.load(_lowerCamelCase       )

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

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

          lowercase							    =       added_tokens_extra_ids + [
              'an_additional_special_token'
          ]
          lowercase							    =       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
          lowercase							    =       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
          lowercase							    =       added_tokens_extra_ids + [AddedToken('a_new_additional_special_token'							,		lstrip=_lowerCamelCase       )]
          lowercase							    =       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 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.perceiver_tokenizer
      self.assertEqual(tokenizer.decode([1_7_8]       )							,		'�'       )







    def 			UpperCamelCase_      (			self       ):
      pass







    def 			UpperCamelCase_      (			self       ):
      pass







    def 			UpperCamelCase_      (			self       ):
      pass







    def 			UpperCamelCase_      (			self       ):
      pass







    def 			UpperCamelCase_      (			self       ):
      # The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
      # strings and special added tokens as tokens
      lowercase							    =       self.get_tokenizers(fast=_lowerCamelCase							,		do_lower_case=_lowerCamelCase       )
      for tokenizer in tokenizers:
        with self.subTest(F'{tokenizer.__class__.__name__}'       ):
          lowercase							    =       ['[CLS]', 't', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 't', 'e', 's', 't', '[SEP]']
          lowercase							    =       tokenizer.convert_tokens_to_string(_lowerCamelCase       )

          self.assertIsInstance(_lowerCamelCase							,		_lowerCamelCase       )










"""simple docstring"""



# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput

class        a     (   a_	):







    def __init__(			self							,		_lowerCamelCase							,		_lowerCamelCase       ):
      super().__init__()
      self.register_modules(unet=_lowerCamelCase							,		scheduler=_lowerCamelCase       )







    @torch.no_grad()
    def __call__(			self							,		_lowerCamelCase = 1							,		_lowerCamelCase = None							,		_lowerCamelCase = 5_0							,		_lowerCamelCase = "pil"							,		_lowerCamelCase = True							,		**_lowerCamelCase							,		):
      lowercase							    =       torch.randn(
          (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)							,		generator=_lowerCamelCase							,		)
      lowercase							    =       image.to(self.device       )

      # set step values
      self.scheduler.set_timesteps(_lowerCamelCase       )

      for t in self.progress_bar(self.scheduler.timesteps       ):
        # 1. predict noise model_output
        lowercase							    =       self.unet(_lowerCamelCase							,		_lowerCamelCase       ).sample

        # 2. predict previous mean of image x_t-1 and add variance depending on eta
        # eta corresponds to η in paper and should be between [0, 1]
        # do x_t -> x_t-1
        lowercase							    =       self.scheduler.step(_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase       ).prev_sample

      lowercase							    =       (image / 2 + 0.5).clamp(0							,		1       )
      lowercase							    =       image.cpu().permute(0							,		2							,		3							,		1       ).numpy()
      if output_type == "pil":
        lowercase							    =       self.numpy_to_pil(_lowerCamelCase       )

      if not return_dict:
        return (image,), "This is a local test"

      return ImagePipelineOutput(images=_lowerCamelCase       ), "This is a local test"


