
'''simple docstring'''
import os
import sys
import unittest


lowercase__						       =      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
lowercase__						       =      os.path.join(git_repo_path, "src", "transformers")

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

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


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






class 	snake_case__       (      unittest.TestCase					):







		"""simple docstring"""

		def       lowerCAmelCase				(      self				:			Union[str, Any]      )						->			int:






				"""simple docstring"""







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

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

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

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

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

				snake_case  :       Optional[Any]     			=						find_backend(
				    '''    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):'''      )
				self.assertEqual(UpperCamelCase__  ,	'''sentencepiece_and_tokenizers_and_vision'''      )



		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			int:






				"""simple docstring"""







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

				# 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       lowerCAmelCase				(      self				:			Tuple      )						->			Optional[int]:






				"""simple docstring"""







				snake_case  :       List[str]     			=						create_dummy_object('''CONSTANT'''  ,	'''\'torch\''''      )
				self.assertEqual(UpperCamelCase__  ,	'''\nCONSTANT = None\n'''      )

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

				snake_case  :       Tuple     			=						'''\nclass FakeClass(metaclass=DummyObject):\n    _backends = \'torch\'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, \'torch\')\n'''
				snake_case  :       List[Any]     			=						create_dummy_object('''FakeClass'''  ,	'''\'torch\''''      )
				self.assertEqual(UpperCamelCase__  ,	UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			str:






				"""simple docstring"""







				snake_case  :       List[Any]     			=						'''# 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  :       Union[str, Any]     			=						create_dummy_files({'''torch''': ['''CONSTANT''', '''function''', '''FakeClass''']}      )
				self.assertEqual(dummy_files['''torch''']  ,	UpperCamelCase__      )










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_							):



	'''simple docstring'''




	def __init__(			self			,     a			,     a   )			->       Optional[Any]:
								super().__init__()

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

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






	@torch.no_grad()
	def __call__(			self			,     a = 1			,     a = None			,     a = 0.0			,     a = 50			,     a = None			,     a = "pil"			,     a = True			,     )			->       Union[ImagePipelineOutput, Tuple]:

								# Sample gaussian noise to begin loop
								if isinstance(self.unet.config.sample_size			,     a   ):
															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(a			,     a   ) and len(a   ) != batch_size:
															raise ValueError(
															    F'''You have passed a list of generators of length {len(a   )}, 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(a			,     generator=a			,     device=self.device			,     dtype=self.unet.dtype   )

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

								for t in self.progress_bar(self.scheduler.timesteps   ):
															# 1. predict noise model_output
															snake_case_           =				self.unet(a			,     a   ).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(
															    a			,     a			,     a			,     eta=a			,     use_clipped_model_output=a			,     generator=a   ).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(a   )

								if not return_dict:
															return (image,)

								return ImagePipelineOutput(images=a   )





