from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class __snake_case    (						lowerCamelCase_		):




   def __init__(   self      :    Optional[Any]	,     _lowercase      :    TransformeraDModel	,     _lowercase      :    AutoencoderKL	,     _lowercase      :    KarrasDiffusionSchedulers	,     _lowercase      :    Optional[Dict[int, str]] = None	,     ):



     """simple docstring"""






     super().__init__()
     self.register_modules(transformer=_lowercase	,     vae=_lowercase	,     scheduler=_lowercase				)

     # create a imagenet -> id dictionary for easier use
     SCREAMING_SNAKE_CASE__      							=				{}
     if idalabel is not None:
       for key, value in idalabel.items():
         for label in value.split(""","""				):
           SCREAMING_SNAKE_CASE__      							=				int(_lowercase				)
       SCREAMING_SNAKE_CASE__      							=				dict(sorted(self.labels.items()				)				)




   def      __a						(   self      :    Optional[int]	,     _lowercase      :    Union[str, List[str]]				):



     """simple docstring"""






     if not isinstance(_lowercase	,     _lowercase				):
       SCREAMING_SNAKE_CASE__      							=				list(_lowercase				)

     for l in label:
       if l not in self.labels:
         raise ValueError(
             f"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."""				)

     return [self.labels[l] for l in label]




   @torch.no_grad()
   def __call__(   self      :    Tuple	,     _lowercase      :    List[int]	,     _lowercase      :    float = 4.0	,     _lowercase      :    Optional[Union[torch.Generator, List[torch.Generator]]] = None	,     _lowercase      :    int = 50	,     _lowercase      :    Optional[str] = "pil"	,     _lowercase      :    bool = True	,     ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				len(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				self.transformer.config.sample_size
     SCREAMING_SNAKE_CASE__      							=				self.transformer.config.in_channels

     SCREAMING_SNAKE_CASE__      							=				randn_tensor(
         shape=(batch_size, latent_channels, latent_size, latent_size)	,     generator=_lowercase	,     device=self.device	,     dtype=self.transformer.dtype	,     )
     SCREAMING_SNAKE_CASE__      							=				torch.cat([latents] * 2				) if guidance_scale > 1 else latents

     SCREAMING_SNAKE_CASE__      							=				torch.tensor(_lowercase	,     device=self.device				).reshape(-1				)
     SCREAMING_SNAKE_CASE__      							=				torch.tensor([10_00] * batch_size	,     device=self.device				)
     SCREAMING_SNAKE_CASE__      							=				torch.cat([class_labels, class_null]	,     0				) if guidance_scale > 1 else class_labels

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

     for t in self.progress_bar(self.scheduler.timesteps				):
       if guidance_scale > 1:
         SCREAMING_SNAKE_CASE__      							=				latent_model_input[: len(_lowercase				) // 2]
         SCREAMING_SNAKE_CASE__      							=				torch.cat([half, half]	,     dim=0				)
       SCREAMING_SNAKE_CASE__      							=				self.scheduler.scale_model_input(_lowercase	,     _lowercase				)

       SCREAMING_SNAKE_CASE__      							=				t
       if not torch.is_tensor(_lowercase				):
         # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
         # This would be a good case for the `match` statement (Python 3.10+)
         SCREAMING_SNAKE_CASE__      							=				latent_model_input.device.type == """mps"""
         if isinstance(_lowercase	,     _lowercase				):
           SCREAMING_SNAKE_CASE__      							=				torch.floataa if is_mps else torch.floataa
         else:
           SCREAMING_SNAKE_CASE__      							=				torch.intaa if is_mps else torch.intaa
         SCREAMING_SNAKE_CASE__      							=				torch.tensor([timesteps]	,     dtype=_lowercase	,     device=latent_model_input.device				)
       elif len(timesteps.shape				) == 0:
         SCREAMING_SNAKE_CASE__      							=				timesteps[None].to(latent_model_input.device				)
       # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
       SCREAMING_SNAKE_CASE__      							=				timesteps.expand(latent_model_input.shape[0]				)
       # predict noise model_output
       SCREAMING_SNAKE_CASE__      							=				self.transformer(
           _lowercase	,     timestep=_lowercase	,     class_labels=_lowercase				).sample

       # perform guidance
       if guidance_scale > 1:
         SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
         SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				torch.split(_lowercase	,     len(_lowercase				) // 2	,     dim=0				)

         SCREAMING_SNAKE_CASE__      							=				uncond_eps + guidance_scale * (cond_eps - uncond_eps)
         SCREAMING_SNAKE_CASE__      							=				torch.cat([half_eps, half_eps]	,     dim=0				)

         SCREAMING_SNAKE_CASE__      							=				torch.cat([eps, rest]	,     dim=1				)

       # learned sigma
       if self.transformer.config.out_channels // 2 == latent_channels:
         SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				torch.split(_lowercase	,     _lowercase	,     dim=1				)
       else:
         SCREAMING_SNAKE_CASE__      							=				noise_pred

       # compute previous image: x_t -> x_t-1
       SCREAMING_SNAKE_CASE__      							=				self.scheduler.step(_lowercase	,     _lowercase	,     _lowercase				).prev_sample

     if guidance_scale > 1:
       SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				latent_model_input.chunk(2	,     dim=0				)
     else:
       SCREAMING_SNAKE_CASE__      							=				latent_model_input

     SCREAMING_SNAKE_CASE__      							=				1 / self.vae.config.scaling_factor * latents
     SCREAMING_SNAKE_CASE__      							=				self.vae.decode(_lowercase				).sample

     SCREAMING_SNAKE_CASE__      							=				(samples / 2 + 0.5).clamp(0	,     1				)

     # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
     SCREAMING_SNAKE_CASE__      							=				samples.cpu().permute(0	,     2	,     3	,     1				).float().numpy()

     if output_type == "pil":
       SCREAMING_SNAKE_CASE__      							=				self.numpy_to_pil(_lowercase				)

     if not return_dict:
       return (samples,)

     return ImagePipelineOutput(images=_lowercase				)

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      :			Tuple							       =		DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co

__lowerCamelCase      :			Optional[Any]							       =		'''main'''
# Default branch name
__lowerCamelCase      :			Optional[int]							       =		'''f2c752cfc5c0ab6f4bdec59acea69eefbee381c2'''
# One particular commit (not the top of `main`)
__lowerCamelCase      :			Any							       =		'''aaaaaaa'''
# This commit does not exist, so we should 404.

__lowerCamelCase      :			List[str]							       =		'''d9e9f15bc825e4b2c9249e9578f884bbcb5e3684'''
# Sha-1 of config.json on the top of `main`, for checking purposes
__lowerCamelCase      :			List[Any]							       =		'''4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3'''



@contextlib.contextmanager
def      __SCREAMING_SNAKE_CASE  ( )							-> str:







  """simple docstring"""





  print("""Welcome!"""     )
  yield
  print("""Bye!"""     )



@contextlib.contextmanager
def      __SCREAMING_SNAKE_CASE  ( )							-> Any:







  """simple docstring"""





  print("""Bonjour!"""     )
  yield
  print("""Au revoir!"""     )







class __snake_case    (						unittest.TestCase		):




   def      __a						(   self      :    List[Any]				):



     """simple docstring"""






     assert transformers.__spec__ is not None
     assert importlib.util.find_spec("""transformers"""				) is not None







class __snake_case    (						unittest.TestCase		):




   @unittest.mock.patch("""sys.stdout"""	,     new_callable=io.StringIO				)
   def      __a						(   self      :    List[str]	,     _lowercase      :    str				):



     """simple docstring"""






     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      __a						(   self      :    Optional[Any]	,     _lowercase      :    str				):



     """simple docstring"""






     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      __a						(   self      :    Tuple	,     _lowercase      :    Dict				):



     """simple docstring"""






     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      __a						(   self      :    Union[str, Any]				):



     """simple docstring"""






     self.assertEqual(find_labels(_lowercase				)	,     ["""labels"""]				)
     self.assertEqual(find_labels(_lowercase				)	,     ["""labels""", """next_sentence_label"""]				)
     self.assertEqual(find_labels(_lowercase				)	,     ["""start_positions""", """end_positions"""]				)







     class __snake_case    (						lowerCamelCase_		):
        pass





     self.assertEqual(find_labels(_lowercase				)	,     ["""labels"""]				)




   @require_tf
   def      __a						(   self      :    Any				):



     """simple docstring"""






     self.assertEqual(find_labels(_lowercase				)	,     ["""labels"""]				)
     self.assertEqual(find_labels(_lowercase				)	,     ["""labels""", """next_sentence_label"""]				)
     self.assertEqual(find_labels(_lowercase				)	,     ["""start_positions""", """end_positions"""]				)







     class __snake_case    (						lowerCamelCase_		):
        pass





     self.assertEqual(find_labels(_lowercase				)	,     ["""labels"""]				)




   @require_flax
   def      __a						(   self      :    Union[str, Any]				):



     """simple docstring"""






     self.assertEqual(find_labels(_lowercase				)	,     []				)
     self.assertEqual(find_labels(_lowercase				)	,     []				)
     self.assertEqual(find_labels(_lowercase				)	,     []				)







     class __snake_case    (						lowerCamelCase_		):
        pass





     self.assertEqual(find_labels(_lowercase				)	,     []				)
