"""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 					lowerCAmelCase_      (     __SCREAMING_SNAKE_CASE   ):



    """simple docstring"""

    def __init__(			self						,      lowerCAmelCase						,      lowerCAmelCase      ):






          """simple docstring"""

          super().__init__()

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

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

    @torch.no_grad()
    def __call__(			self						,      lowerCAmelCase = 1						,      lowerCAmelCase = None						,      lowerCAmelCase = 0.0						,      lowerCAmelCase = 50						,      lowerCAmelCase = None						,      lowerCAmelCase = "pil"						,      lowerCAmelCase = True						,      ):






          """simple docstring"""

          if isinstance(self.unet.config.sample_size						,      _snake_case      ):
                snake_case        =     (
                    batch_size,
                    self.unet.config.in_channels,
                    self.unet.config.sample_size,
                    self.unet.config.sample_size,
                )
          else:
                snake_case        =     (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."""      )

          snake_case        =     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
                snake_case        =     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
                snake_case        =     self.scheduler.step(
                    _snake_case						,      _snake_case						,      _snake_case						,      eta=_snake_case						,      use_clipped_model_output=_snake_case						,      generator=_snake_case      ).prev_sample

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

          if not return_dict:
                return (image,)

          return ImagePipelineOutput(images=_snake_case      )



"""simple docstring"""
import os
import sys
import unittest


SCREAMING_SNAKE_CASE__   =					os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
SCREAMING_SNAKE_CASE__   =					os.path.join(git_repo_path, "src", "transformers")

SCREAMING_SNAKE_CASE__   =					"\n{0} = None\n"

SCREAMING_SNAKE_CASE__   =					"\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n"


SCREAMING_SNAKE_CASE__   =					"\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"



class 					lowerCAmelCase_      (     unittest.TestCase   ):



				"""simple docstring"""

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     find_backend('    _import_structure["models.albert"].append("AlbertTokenizerFast")'      )
										self.assertIsNone(lowerCAmelCase      )

										snake_case        =     find_backend('    if not is_tokenizers_available():'      )
										self.assertEqual(lowerCAmelCase						,      'tokenizers'      )

										snake_case        =     find_backend('    if not is_tensorflow_text_available():'      )
										self.assertEqual(lowerCAmelCase						,      'tensorflow_text'      )

										snake_case        =     find_backend('    if not (is_sentencepiece_available() and is_tokenizers_available()):'      )
										self.assertEqual(lowerCAmelCase						,      'sentencepiece_and_tokenizers'      )

										snake_case        =     find_backend(
										    '    if not (is_sentencepiece_available() and is_tensorflow_text_available()):'      )
										self.assertEqual(lowerCAmelCase						,      'sentencepiece_and_tensorflow_text'      )

										snake_case        =     find_backend(
										    '    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):'      )
										self.assertEqual(lowerCAmelCase						,      'sentencepiece_and_tokenizers_and_vision'      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     read_init()
										# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
										self.assertIn('torch'						,      lowerCAmelCase      )
										self.assertIn('tensorflow_text'						,      lowerCAmelCase      )
										self.assertIn('sentencepiece_and_tokenizers'						,      lowerCAmelCase      )

										# Likewise, we can't assert on the exact content of a key
										self.assertIn('BertModel'						,      objects['torch']      )
										self.assertIn('TFBertModel'						,      objects['tf']      )
										self.assertIn('FlaxBertModel'						,      objects['flax']      )
										self.assertIn('BertModel'						,      objects['torch']      )
										self.assertIn('TFBertTokenizer'						,      objects['tensorflow_text']      )
										self.assertIn('convert_slow_tokenizer'						,      objects['sentencepiece_and_tokenizers']      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     create_dummy_object('CONSTANT'						,      '\'torch\''      )
										self.assertEqual(lowerCAmelCase						,      '\nCONSTANT = None\n'      )

										snake_case        =     create_dummy_object('function'						,      '\'torch\''      )
										self.assertEqual(
										    lowerCAmelCase						,      '\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'      )

										snake_case        =     '\nclass FakeClass(metaclass=DummyObject):\n    _backends = \'torch\'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, \'torch\')\n'
										snake_case        =     create_dummy_object('FakeClass'						,      '\'torch\''      )
										self.assertEqual(lowerCAmelCase						,      lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     '# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = ["torch"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, ["torch"])\n'
										snake_case        =     create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']}      )
										self.assertEqual(dummy_files['torch']						,      lowerCAmelCase      )


