
'''simple docstring'''






import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, ByTaTokenizer
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 :  Any    							=				"""tf"""
else:
  __A :  Union[str, Any]    							=				"""jax"""






class        __snake_case	(       lowerCAmelCase__     ,unittest.TestCase):







    """simple docstring"""



    lowercase       			= ByTaTokenizer
    lowercase       			= False

    def __lowercase       (       self   :  str		)       ->  Tuple:
      super().setUp()
      lowerCAmelCase_      :				Dict      =       ByTaTokenizer()
      tokenizer.save_pretrained(self.tmpdirname		)

    @cached_property
    def __lowercase       (       self   :  Dict		)       ->  Union[str, Any]:
      return ByTaTokenizer.from_pretrained("""google/byt5-small"""		)

    def __lowercase       (       self   :  Optional[int]   ,						**lowerCamelCase   :  Optional[int]		)       ->  List[str]:
      return self.tokenizer_class.from_pretrained(self.tmpdirname   ,						**a__		)

    def __lowercase       (       self   :  Optional[Any]   ,						lowerCamelCase   :  Optional[int]   ,						lowerCamelCase   :  Optional[Any]=False   ,						lowerCamelCase   :  Dict=20   ,						lowerCamelCase   :  Tuple=5		)       ->  Dict:
      # XXX The default common tokenizer tests assume that every ID is decodable on its own.
      # This assumption is invalid for ByT5 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.

      lowerCAmelCase_      :				Optional[Any]      =       []
      for i in range(len(a__		)		):
        try:
          lowerCAmelCase_      :				str      =       tokenizer.decode([i]   ,						clean_up_tokenization_spaces=a__		)
        except UnicodeDecodeError:
          pass
        toks.append((i, tok)		)

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

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

    def __lowercase       (       self   :  int		)       ->  Tuple:
      lowerCAmelCase_      :				Union[str, Any]      =       self.ta_base_tokenizer
      lowerCAmelCase_      :				int      =       tokenizer(["""hi</s>""", """I went to the gym</s>""", """</s>"""]		)
      lowerCAmelCase_      :				Tuple      =       tokenizer(["""hi""", """I went to the gym""", """"""]		)
      self.assertListEqual(batch_with_eos_added["""input_ids"""]   ,						batch_without_eos_added["""input_ids"""]		)

    def __lowercase       (       self   :  Optional[int]		)       ->  int:
      lowerCAmelCase_      :				Optional[Any]      =       self.ta_base_tokenizer
      lowerCAmelCase_      :				List[str]      =       """Unicode €."""
      lowerCAmelCase_      :				Optional[int]      =       tokenizer(a__		)
      lowerCAmelCase_      :				int      =       [88, 1_13, 1_08, 1_02, 1_14, 1_03, 1_04, 35, 2_29, 1_33, 1_75, 49, 1]
      self.assertEqual(encoded["""input_ids"""]   ,						a__		)

      # decoding
      lowerCAmelCase_      :				int      =       tokenizer.decode(a__		)
      self.assertEqual(a__   ,						"""Unicode €.</s>"""		)

      lowerCAmelCase_      :				Optional[int]      =       tokenizer("""e è é ê ë"""		)
      lowerCAmelCase_      :				List[Any]      =       [1_04, 35, 1_98, 1_71, 35, 1_98, 1_72, 35, 1_98, 1_73, 35, 1_98, 1_74, 1]
      self.assertEqual(encoded["""input_ids"""]   ,						a__		)
      # decoding
      lowerCAmelCase_      :				int      =       tokenizer.decode(a__		)
      self.assertEqual(a__   ,						"""e è é ê ë</s>"""		)

      # encode/decode, but with `encode` instead of `__call__`
      self.assertEqual(tokenizer.decode(tokenizer.encode("""e è é ê ë"""		)		)   ,						"""e è é ê ë</s>"""		)

    def __lowercase       (       self   :  Optional[int]		)       ->  Union[str, Any]:
      lowerCAmelCase_      :				List[str]      =       self.ta_base_tokenizer
      lowerCAmelCase_      :				Tuple      =       ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
      # fmt: off
      lowerCAmelCase_      :				Dict      =       [68, 35, 1_11, 1_14, 1_13, 1_06, 35, 1_15, 1_00, 1_17, 1_00, 1_06, 1_17, 1_00, 1_15, 1_07, 35, 1_05, 1_14, 1_17, 35, 1_18, 1_20, 1_12, 1_12, 1_00, 1_17, 1_08, 1_25, 1_00, 1_19, 1_08, 1_14, 1_13, 49, 1, 0]
      # fmt: on
      lowerCAmelCase_      :				Union[str, Any]      =       tokenizer(a__   ,						padding=a__   ,						return_tensors=a__		)
      self.assertIsInstance(a__   ,						a__		)

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

      self.assertListEqual(a__   ,						a__		)

      self.assertEqual((2, 37)   ,						batch.input_ids.shape		)
      self.assertEqual((2, 37)   ,						batch.attention_mask.shape		)

    def __lowercase       (       self   :  List[Any]		)       ->  Optional[Any]:
      lowerCAmelCase_      :				List[str]      =       self.ta_base_tokenizer
      lowerCAmelCase_      :				str      =       ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
      lowerCAmelCase_      :				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 __lowercase       (       self   :  List[str]		)       ->  int:
      lowerCAmelCase_      :				Optional[Any]      =       self.ta_base_tokenizer
      lowerCAmelCase_      :				int      =       [
          """Summary of the text.""",
          """Another summary.""",
      ]
      lowerCAmelCase_      :				str      =       tokenizer(
          text_target=a__   ,						max_length=32   ,						padding="""max_length"""   ,						truncation=a__   ,						return_tensors=a__		)
      self.assertEqual(32   ,						targets["""input_ids"""].shape[1]		)

    def __lowercase       (       self   :  Optional[Any]		)       ->  Optional[int]:
      lowerCAmelCase_      :				Any      =       self.ta_base_tokenizer
      lowerCAmelCase_      :				Union[str, Any]      =       ["""A long paragraph for summarization. </s>"""]
      lowerCAmelCase_      :				str      =       ["""Summary of the text. </s>"""]
      # fmt: off
      lowerCAmelCase_      :				str      =       [68, 35, 1_11, 1_14, 1_13, 1_06, 35, 1_15, 1_00, 1_17, 1_00, 1_06, 1_17, 1_00, 1_15, 1_07, 35, 1_05, 1_14, 1_17, 35, 1_18, 1_20, 1_12, 1_12, 1_00, 1_17, 1_08, 1_25, 1_00, 1_19, 1_08, 1_14, 1_13, 49, 35, 1]
      lowerCAmelCase_      :				int      =       [86, 1_20, 1_12, 1_12, 1_00, 1_17, 1_24, 35, 1_14, 1_05, 35, 1_19, 1_07, 1_04, 35, 1_19, 1_04, 1_23, 1_19, 49, 35, 1]
      # fmt: on

      lowerCAmelCase_      :				Union[str, Any]      =       tokenizer(a__   ,						text_target=a__		)

      self.assertEqual(a__   ,						batch["""input_ids"""][0]		)
      self.assertEqual(a__   ,						batch["""labels"""][0]		)

    def __lowercase       (       self   :  str		)       ->  Dict:
      # safety check on max_len default value so we are sure the test works
      lowerCAmelCase_      :				Union[str, 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
      lowerCAmelCase_      :				Tuple      =       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
          lowerCAmelCase_      :				Any      =       tempfile.mkdtemp()

          lowerCAmelCase_      :				Union[str, Any]      =       """ He is very happy, UNwant\u00E9d,running"""
          lowerCAmelCase_      :				Optional[int]      =       tokenizer.encode(a__   ,						add_special_tokens=a__		)
          tokenizer.save_pretrained(a__		)

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

          shutil.rmtree(a__		)

      lowerCAmelCase_      :				List[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
          lowerCAmelCase_      :				Optional[int]      =       tempfile.mkdtemp()

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

          lowerCAmelCase_      :				int      =       tokenizer.__class__.from_pretrained(a__		)
          lowerCAmelCase_      :				List[str]      =       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		)

          lowerCAmelCase_      :				Optional[int]      =       tokenizer.__class__.from_pretrained(a__   ,						model_max_length=43		)
          self.assertEqual(tokenizer.model_max_length   ,						43		)

          shutil.rmtree(a__		)

    def __lowercase       (       self   :  Union[str, Any]		)       ->  Optional[int]:
      lowerCAmelCase_      :				Tuple      =       []
      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:
            lowerCAmelCase_      :				List[Any]      =       json.load(a__		)

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

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

          lowerCAmelCase_      :				Optional[int]      =       added_tokens_extra_ids + [
              """an_additional_special_token"""
          ]
          lowerCAmelCase_      :				int      =       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
          lowerCAmelCase_      :				List[str]      =       tokenizer_class.from_pretrained(
              a__   ,						)
          self.assertIn(
              """an_additional_special_token"""   ,						tokenizer_without_change_in_init.additional_special_tokens		)
          # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
          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
          lowerCAmelCase_      :				Any      =       added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token"""   ,						lstrip=a__		)]
          lowerCAmelCase_      :				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 __lowercase       (       self   :  str		)       ->  Union[str, Any]:
      lowerCAmelCase_      :				List[Any]      =       []
      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__		)

          lowerCAmelCase_      :				Tuple      =       tokenizer_class.from_pretrained(a__		)

          self.assertTrue(tokenizer.decode([2_55]		) == """"""		)

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

    def __lowercase       (       self   :  List[str]		)       ->  Union[str, Any]:
      pass

    def __lowercase       (       self   :  Optional[int]		)       ->  Dict:
      pass

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

    def __lowercase       (       self   :  Optional[Any]		)       ->  Optional[int]:
      # The default common tokenizer tests uses invalid tokens for ByT5 that can only accept one-character strings
      # and special added tokens as tokens
      lowerCAmelCase_      :				Optional[Any]      =       self.get_tokenizers(fast=a__   ,						do_lower_case=a__		)
      for tokenizer in tokenizers:
        with self.subTest(F'{tokenizer.__class__.__name__}'		):
          lowerCAmelCase_      :				Optional[Any]      =       ["""t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """x""", """t""", """</s>"""]
          lowerCAmelCase_      :				Tuple      =       tokenizer.convert_tokens_to_string(a__		)

          self.assertIsInstance(a__   ,						a__		)





    def __lowercase       (       self   :  Any		)       ->  Optional[Any]:
      lowerCAmelCase_      :				Tuple      =       self.get_tokenizers()
      for tokenizer in tokenizers:
        with self.subTest(F'{tokenizer.__class__.__name__}'		):
          lowerCAmelCase_      :				List[str]      =       [
              """bos_token""",
              """eos_token""",
              """unk_token""",
              """sep_token""",
              """pad_token""",
              """cls_token""",
              """mask_token""",
          ]

          lowerCAmelCase_      :				int      =       0
          lowerCAmelCase_      :				List[str]      =       tokenizer.convert_ids_to_tokens(
              a__   ,						skip_special_tokens=a__		)

          for attr in attributes_list:
            setattr(a__   ,						attr + """_id"""   ,						a__		)
            self.assertEqual(getattr(a__   ,						a__		)   ,						a__		)
            self.assertEqual(getattr(a__   ,						attr + """_id"""		)   ,						a__		)

            setattr(a__   ,						attr + """_id"""   ,						a__		)
            self.assertEqual(getattr(a__   ,						a__		)   ,						a__		)
            self.assertEqual(getattr(a__   ,						attr + """_id"""		)   ,						a__		)

          setattr(a__   ,						"""additional_special_tokens_ids"""   ,						[]		)
          self.assertListEqual(getattr(a__   ,						"""additional_special_tokens"""		)   ,						[]		)
          self.assertListEqual(getattr(a__   ,						"""additional_special_tokens_ids"""		)   ,						[]		)

          setattr(a__   ,						"""additional_special_tokens_ids"""   ,						[token_id_to_test_setters]		)
          self.assertListEqual(getattr(a__   ,						"""additional_special_tokens"""		)   ,						[token_to_test_setters]		)
          self.assertListEqual(getattr(a__   ,						"""additional_special_tokens_ids"""		)   ,						[token_id_to_test_setters]		)







'''simple docstring'''






import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class        __snake_case	(       unittest.TestCase):







    """simple docstring"""



    @property
    def __lowercase       (       self   :  str		)       ->  List[Any]:
      torch.manual_seed(0		)
      lowerCAmelCase_      :				Optional[Any]      =       UNetaDModel(
          block_out_channels=(32, 64)   ,						layers_per_block=2   ,						sample_size=32   ,						in_channels=3   ,						out_channels=3   ,						down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")   ,						up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")   ,						)
      return model





    def __lowercase       (       self   :  Tuple		)       ->  Optional[Any]:
      lowerCAmelCase_      :				Optional[int]      =       self.dummy_uncond_unet
      lowerCAmelCase_      :				Tuple      =       PNDMScheduler()

      lowerCAmelCase_      :				List[Any]      =       PNDMPipeline(unet=lowerCamelCase   ,						scheduler=lowerCamelCase		)
      pndm.to(lowerCamelCase		)
      pndm.set_progress_bar_config(disable=lowerCamelCase		)

      lowerCAmelCase_      :				Dict      =       torch.manual_seed(0		)
      lowerCAmelCase_      :				List[Any]      =       pndm(generator=lowerCamelCase   ,						num_inference_steps=20   ,						output_type="""numpy"""		).images

      lowerCAmelCase_      :				str      =       torch.manual_seed(0		)
      lowerCAmelCase_      :				int      =       pndm(generator=lowerCamelCase   ,						num_inference_steps=20   ,						output_type="""numpy"""   ,						return_dict=lowerCamelCase		)[0]

      lowerCAmelCase_      :				Optional[int]      =       image[0, -3:, -3:, -1]
      lowerCAmelCase_      :				Optional[Any]      =       image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      lowerCAmelCase_      :				int      =       np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]		)

      assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2
      assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1E-2







@slow
@require_torch
class        __snake_case	(       unittest.TestCase):







    """simple docstring"""



    def __lowercase       (       self   :  str		)       ->  Tuple:
      lowerCAmelCase_      :				str      =       """google/ddpm-cifar10-32"""

      lowerCAmelCase_      :				Dict      =       UNetaDModel.from_pretrained(lowerCamelCase		)
      lowerCAmelCase_      :				Dict      =       PNDMScheduler()

      lowerCAmelCase_      :				Union[str, Any]      =       PNDMPipeline(unet=lowerCamelCase   ,						scheduler=lowerCamelCase		)
      pndm.to(lowerCamelCase		)
      pndm.set_progress_bar_config(disable=lowerCamelCase		)
      lowerCAmelCase_      :				Any      =       torch.manual_seed(0		)
      lowerCAmelCase_      :				Union[str, Any]      =       pndm(generator=lowerCamelCase   ,						output_type="""numpy"""		).images

      lowerCAmelCase_      :				Optional[int]      =       image[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      lowerCAmelCase_      :				List[Any]      =       np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]		)

      assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2



