






"""simple docstring"""







import contextlib
import importlib
import io
import unittest

import transformers

# Try to import everything from transformers to ensure every object can be loaded.
from transformers import *  # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from transformers.utils import ContextManagers, find_labels, is_flax_available, is_tf_available, is_torch_available


if is_torch_available():
				from transformers import BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification

if is_tf_available():
				from transformers import TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification

if is_flax_available():
				from transformers import FlaxBertForPreTraining, FlaxBertForQuestionAnswering, FlaxBertForSequenceClassification


_lowerCAmelCase            =	DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co

_lowerCAmelCase            =	'main'
# Default branch name
_lowerCAmelCase            =	'f2c752cfc5c0ab6f4bdec59acea69eefbee381c2'
# One particular commit (not the top of `main`)
_lowerCAmelCase            =	'aaaaaaa'
# This commit does not exist, so we should 404.

_lowerCAmelCase            =	'd9e9f15bc825e4b2c9249e9578f884bbcb5e3684'
# Sha-1 of config.json on the top of `main`, for checking purposes
_lowerCAmelCase            =	'4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3'


@contextlib.contextmanager
def      UpperCamelCase       ( )     ->       Optional[int]:
				print("""Welcome!""" )
				yield
				print("""Bye!""" )


@contextlib.contextmanager
def      UpperCamelCase       ( )     ->       List[Any]:
				print("""Bonjour!""" )
				yield
				print("""Au revoir!""" )


class      UpperCamelCase		(unittest.TestCase     ):






						def      __snake_case      (		self  :Any				)      ->Optional[int]:
										# If the spec is missing, importlib would not be able to import the module dynamically.
										assert transformers.__spec__ is not None
										assert importlib.util.find_spec("""transformers"""				) is not None


class      UpperCamelCase		(unittest.TestCase     ):






						@unittest.mock.patch("""sys.stdout"""			, new_callable=io.StringIO				)
						def      __snake_case      (		self  :Any			, __magic_name__  :List[Any]				)      ->Optional[int]:
										with ContextManagers([]				):
														print("""Transformers are awesome!"""				)
										# The print statement adds a new line at the end of the output
										self.assertEqual(mock_stdout.getvalue()			, """Transformers are awesome!\n"""				)






						@unittest.mock.patch("""sys.stdout"""			, new_callable=io.StringIO				)
						def      __snake_case      (		self  :Union[str, Any]			, __magic_name__  :Optional[Any]				)      ->Union[str, Any]:
										with ContextManagers([context_en()]				):
														print("""Transformers are awesome!"""				)
										# The output should be wrapped with an English welcome and goodbye
										self.assertEqual(mock_stdout.getvalue()			, """Welcome!\nTransformers are awesome!\nBye!\n"""				)






						@unittest.mock.patch("""sys.stdout"""			, new_callable=io.StringIO				)
						def      __snake_case      (		self  :List[Any]			, __magic_name__  :List[Any]				)      ->Optional[int]:
										with ContextManagers([context_fr(), context_en()]				):
														print("""Transformers are awesome!"""				)
										# The output should be wrapped with an English and French welcome and goodbye
										self.assertEqual(mock_stdout.getvalue()			, """Bonjour!\nWelcome!\nTransformers are awesome!\nBye!\nAu revoir!\n"""				)






						@require_torch
						def      __snake_case      (		self  :Union[str, Any]				)      ->List[Any]:
										self.assertEqual(find_labels(__magic_name__				)			, ["""labels"""]				)
										self.assertEqual(find_labels(__magic_name__				)			, ["""labels""", """next_sentence_label"""]				)
										self.assertEqual(find_labels(__magic_name__				)			, ["""start_positions""", """end_positions"""]				)


										class      UpperCamelCase		(__snake_case     ):
																pass
										self.assertEqual(find_labels(__magic_name__				)			, ["""labels"""]				)






						@require_tf
						def      __snake_case      (		self  :List[Any]				)      ->Optional[int]:
										self.assertEqual(find_labels(__magic_name__				)			, ["""labels"""]				)
										self.assertEqual(find_labels(__magic_name__				)			, ["""labels""", """next_sentence_label"""]				)
										self.assertEqual(find_labels(__magic_name__				)			, ["""start_positions""", """end_positions"""]				)


										class      UpperCamelCase		(__snake_case     ):
																pass
										self.assertEqual(find_labels(__magic_name__				)			, ["""labels"""]				)






						@require_flax
						def      __snake_case      (		self  :Any				)      ->Optional[int]:
										# Flax models don't have labels
										self.assertEqual(find_labels(__magic_name__				)			, []				)
										self.assertEqual(find_labels(__magic_name__				)			, []				)
										self.assertEqual(find_labels(__magic_name__				)			, []				)


										class      UpperCamelCase		(__snake_case     ):
																pass
										self.assertEqual(find_labels(__magic_name__				)			, []				)








"""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  :Any			, __magic_name__  :Union[str, Any]			, __magic_name__  :Optional[Any]				)      ->Dict:
										super().__init__()

										# make sure scheduler can always be converted to DDIM
										lowercase    :      Optional[Any]       = DDIMScheduler.from_config(scheduler.config				)

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






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

										# Sample gaussian noise to begin loop
										if isinstance(self.unet.config.sample_size			, __magic_name__				):
														lowercase    :      int       = (
														    batch_size,
														    self.unet.config.in_channels,
														    self.unet.config.sample_size,
														    self.unet.config.sample_size,
														)
										else:
														lowercase    :      Tuple       = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

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

										lowercase    :      Dict       = randn_tensor(__magic_name__			, generator=__magic_name__			, device=self.device			, dtype=self.unet.dtype				)

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

										for t in self.progress_bar(self.scheduler.timesteps				):
														# 1. predict noise model_output
														lowercase    :      Union[str, Any]       = self.unet(__magic_name__			, __magic_name__				).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    :      int       = self.scheduler.step(
														    __magic_name__			, __magic_name__			, __magic_name__			, eta=__magic_name__			, use_clipped_model_output=__magic_name__			, generator=__magic_name__				).prev_sample

										lowercase    :      Optional[Any]       = (image / 2 + 0.5).clamp(0			, 1				)
										lowercase    :      Tuple       = image.cpu().permute(0			, 2			, 3			, 1				).numpy()
										if output_type == "pil":
														lowercase    :      Any       = self.numpy_to_pil(__magic_name__				)

										if not return_dict:
														return (image,)

										return ImagePipelineOutput(images=__magic_name__				)
