





'''simple docstring'''






from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 					UpperCAmelCase	(     snake_case_  ):




      def __init__(     self     :					int     ,     __snake_case     :					Tuple     ,     __snake_case     :					List[Any]    )							-> Union[str, Any]:
            super().__init__()

            # make sure scheduler can always be converted to DDIM
            _lowerCAmelCase					=				DDIMScheduler.from_config(scheduler.config    )

            self.register_modules(unet=__snake_case     ,     scheduler=__snake_case    )




      @torch.no_grad()
      def __call__(     self     :					Any     ,     __snake_case     :					int = 1     ,     __snake_case     :					Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,     __snake_case     :					float = 0.0     ,     __snake_case     :					int = 50     ,     __snake_case     :					Optional[bool] = None     ,     __snake_case     :					Optional[str] = "pil"     ,     __snake_case     :					bool = True     ,     )							-> Union[ImagePipelineOutput, Tuple]:

            # Sample gaussian noise to begin loop
            if isinstance(self.unet.config.sample_size     ,     __snake_case    ):
                  _lowerCAmelCase					=				(
                      batch_size,
                      self.unet.config.in_channels,
                      self.unet.config.sample_size,
                      self.unet.config.sample_size,
                  )
            else:
                  _lowerCAmelCase					=				(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

            if isinstance(__snake_case     ,     __snake_case    ) and len(__snake_case    ) != batch_size:
                  raise ValueError(
                      f"You have passed a list of generators of length {len(__snake_case    )}, but requested an effective batch"
                      f" size of {batch_size}. Make sure the batch size matches the length of the generators."    )

            _lowerCAmelCase					=				randn_tensor(__snake_case     ,     generator=__snake_case     ,     device=self.device     ,     dtype=self.unet.dtype    )

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

            for t in self.progress_bar(self.scheduler.timesteps    ):
                  # 1. predict noise model_output
                  _lowerCAmelCase					=				self.unet(__snake_case     ,     __snake_case    ).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
                  _lowerCAmelCase					=				self.scheduler.step(
                      __snake_case     ,     __snake_case     ,     __snake_case     ,     eta=__snake_case     ,     use_clipped_model_output=__snake_case     ,     generator=__snake_case    ).prev_sample

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

            if not return_dict:
                  return (image,)

            return ImagePipelineOutput(images=__snake_case    )













'''simple docstring'''






import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin




class 					UpperCAmelCase	(     snake_case_				,						unittest.TestCase  ):
      _lowercase:     List[str]			=  DebertaTokenizer
      _lowercase:     Union[str, Any]			=  True
      _lowercase:     Union[str, Any]			=  DebertaTokenizerFast




      def 							lowercase__       (     self     :					Any    )							-> List[Any]:
            super().setUp()

            # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
            _lowerCAmelCase					=				[
                """l""",
                """o""",
                """w""",
                """e""",
                """r""",
                """s""",
                """t""",
                """i""",
                """d""",
                """n""",
                """\u0120""",
                """\u0120l""",
                """\u0120n""",
                """\u0120lo""",
                """\u0120low""",
                """er""",
                """\u0120lowest""",
                """\u0120newer""",
                """\u0120wider""",
                """[UNK]""",
            ]
            _lowerCAmelCase					=				dict(zip(__snake_case     ,     range(len(__snake_case    )    )    )    )
            _lowerCAmelCase					=				["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
            _lowerCAmelCase					=				{"""unk_token""": """[UNK]"""}

            _lowerCAmelCase					=				os.path.join(self.tmpdirname     ,     VOCAB_FILES_NAMES["""vocab_file"""]    )
            _lowerCAmelCase					=				os.path.join(self.tmpdirname     ,     VOCAB_FILES_NAMES["""merges_file"""]    )
            with open(self.vocab_file     ,     """w"""     ,     encoding="""utf-8"""    ) as fp:
                  fp.write(json.dumps(__snake_case    ) + """\n"""    )
            with open(self.merges_file     ,     """w"""     ,     encoding="""utf-8"""    ) as fp:
                  fp.write("""\n""".join(__snake_case    )    )




      def 							lowercase__       (     self     :					List[str]     ,     **__snake_case     :					Any    )							-> Dict:
            kwargs.update(self.special_tokens_map    )
            return self.tokenizer_class.from_pretrained(self.tmpdirname     ,     **__snake_case    )




      def 							lowercase__       (     self     :					Dict     ,     __snake_case     :					Dict    )							-> Optional[Any]:
            _lowerCAmelCase					=				"""lower newer"""
            _lowerCAmelCase					=				"""lower newer"""
            return input_text, output_text




      def 							lowercase__       (     self     :					Union[str, Any]    )							-> List[str]:
            _lowerCAmelCase					=				self.get_tokenizer()
            _lowerCAmelCase					=				"""lower newer"""
            _lowerCAmelCase					=				["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
            _lowerCAmelCase					=				tokenizer.tokenize(__snake_case    )
            self.assertListEqual(__snake_case     ,     __snake_case    )

            _lowerCAmelCase					=				tokens + [tokenizer.unk_token]
            _lowerCAmelCase					=				[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case    )     ,     __snake_case    )




      def 							lowercase__       (     self     :					Union[str, Any]    )							-> Tuple:
            _lowerCAmelCase					=				self.get_tokenizer()
            _lowerCAmelCase					=				tokenizer("""Hello"""     ,     """World"""    )
            _lowerCAmelCase					=				[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
            self.assertListEqual(tokd["""token_type_ids"""]     ,     __snake_case    )




      @slow
      def 							lowercase__       (     self     :					int    )							-> Union[str, Any]:
            _lowerCAmelCase					=				self.tokenizer_class.from_pretrained("""microsoft/deberta-base"""    )

            _lowerCAmelCase					=				tokenizer.encode("""sequence builders"""     ,     add_special_tokens=__snake_case    )
            _lowerCAmelCase					=				tokenizer.encode("""multi-sequence build"""     ,     add_special_tokens=__snake_case    )

            _lowerCAmelCase					=				tokenizer.encode(
                """sequence builders"""     ,     add_special_tokens=__snake_case     ,     add_prefix_space=__snake_case    )
            _lowerCAmelCase					=				tokenizer.encode(
                """sequence builders"""     ,     """multi-sequence build"""     ,     add_special_tokens=__snake_case     ,     add_prefix_space=__snake_case    )

            _lowerCAmelCase					=				tokenizer.build_inputs_with_special_tokens(__snake_case    )
            _lowerCAmelCase					=				tokenizer.build_inputs_with_special_tokens(__snake_case     ,     __snake_case    )

            assert encoded_sentence == encoded_text_from_decode
            assert encoded_pair == encoded_pair_from_decode




      @slow
      def 							lowercase__       (     self     :					Any    )							-> str:
            _lowerCAmelCase					=				[self.tokenizer_class]
            if self.test_rust_tokenizer:
                  tokenizer_classes.append(self.rust_tokenizer_class    )

            for tokenizer_class in tokenizer_classes:
                  _lowerCAmelCase					=				tokenizer_class.from_pretrained("""microsoft/deberta-base"""    )

                  _lowerCAmelCase					=				[
                      """ALBERT: A Lite BERT for Self-supervised Learning of Language Representations""",
                      """ALBERT incorporates two parameter reduction techniques""",
                      """The first one is a factorized embedding parameterization. By decomposing the large vocabulary"""
                      """ embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"""
                      """ vocabulary embedding.""",
                  ]

                  _lowerCAmelCase					=				tokenizer(__snake_case     ,     padding=__snake_case    )
                  _lowerCAmelCase					=				[tokenizer.decode(__snake_case     ,     skip_special_tokens=__snake_case    ) for seq in encoding["""input_ids"""]]

                  # fmt: off
                  _lowerCAmelCase					=				{
                      """input_ids""": [
                          [1, 21_18, 1_11_26, 5_65, 35, 83, 2_51_91, 1_63, 1_88_54, 13, 1_21_56, 12, 1_61_01, 2_53_76, 1_38_07, 9, 2_22_05, 2_78_93, 16_35, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                          [1, 21_18, 1_11_26, 5_65, 2_45_36, 80, 4_37_97, 48_78, 73_73, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                          [1, 1_33, 78, 65, 16, 10, 37_24, 15_38, 3_31_83, 1_13_03, 4_37_97, 19_38, 4, 8_70, 2_41_65, 2_91_05, 5, 7_39, 3_26_44, 3_31_83, 1_13_03, 3_61_73, 88, 80, 6_50, 78_21, 4_59_40, 6, 52, 25_59, 5, 18_36, 9, 5, 73_97, 1_31_71, 31, 5, 18_36, 9, 3_26_44, 3_31_83, 1_13_03, 4, 2]
                      ],
                      """token_type_ids""": [
                          [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                          [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                          [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
                      ],
                      """attention_mask""": [
                          [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                          [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                          [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
                      ]
                  }
                  # fmt: on

                  _lowerCAmelCase					=				[
                      """ALBERT: A Lite BERT for Self-supervised Learning of Language Representations""",
                      """ALBERT incorporates two parameter reduction techniques""",
                      """The first one is a factorized embedding parameterization. By decomposing the large vocabulary"""
                      """ embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"""
                      """ vocabulary embedding.""",
                  ]

                  self.assertDictEqual(encoding.data     ,     __snake_case    )

                  for expected, decoded in zip(__snake_case     ,     __snake_case    ):
                        self.assertEqual(__snake_case     ,     __snake_case    )






