

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


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

UpperCamelCase__  :      str			=    "main"
# Default branch name
UpperCamelCase__  :      str			=    "f2c752cfc5c0ab6f4bdec59acea69eefbee381c2"
# One particular commit (not the top of `main`)
UpperCamelCase__  :      Any			=    "aaaaaaa"
# This commit does not exist, so we should 404.

UpperCamelCase__  :      Any			=    "d9e9f15bc825e4b2c9249e9578f884bbcb5e3684"
# Sha-1 of config.json on the top of `main`, for checking purposes
UpperCamelCase__  :      int			=    "4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3"


@contextlib.contextmanager
def 					_UpperCAmelCase       (  ):





    """simple docstring"""


    print('Welcome!'       )
    yield
    print('Bye!'       )


@contextlib.contextmanager
def 					_UpperCAmelCase       (  ):





    """simple docstring"""


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



class       __snake_case  (    unittest.TestCase       ):

  def 						lowerCAmelCase__     (					self):
      # 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       __snake_case  (    unittest.TestCase       ):

  @unittest.mock.patch('sys.stdout'						,		new_callable=io.StringIO)
  def 						lowerCAmelCase__     (					self						,		_A):
      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 						lowerCAmelCase__     (					self						,		_A):
      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 						lowerCAmelCase__     (					self						,		_A):
      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 						lowerCAmelCase__     (					self):
      self.assertEqual(find_labels(_A)						,		['labels'])
      self.assertEqual(find_labels(_A)						,		['labels', 'next_sentence_label'])
      self.assertEqual(find_labels(_A)						,		['start_positions', 'end_positions'])



      class       __snake_case  (    lowerCAmelCase__       ):
        pass






      self.assertEqual(find_labels(_A)						,		['labels'])

  @require_tf
  def 						lowerCAmelCase__     (					self):
      self.assertEqual(find_labels(_A)						,		['labels'])
      self.assertEqual(find_labels(_A)						,		['labels', 'next_sentence_label'])
      self.assertEqual(find_labels(_A)						,		['start_positions', 'end_positions'])



      class       __snake_case  (    lowerCAmelCase__       ):
        pass






      self.assertEqual(find_labels(_A)						,		['labels'])


  @require_flax
  def 						lowerCAmelCase__     (					self):
      # Flax models don't have labels
      self.assertEqual(find_labels(_A)						,		[])
      self.assertEqual(find_labels(_A)						,		[])
      self.assertEqual(find_labels(_A)						,		[])



      class       __snake_case  (    lowerCAmelCase__       ):
        pass






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





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						,		_A						,		_A						,		_A						,		_A = None						,		):
      super().__init__()
      self.register_modules(transformer=_A						,		vae=_A						,		scheduler=_A)

      # 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(_A)
          SCREAMING_SNAKE_CASE_									=						dict(sorted(self.labels.items()))

  def 						lowerCAmelCase__     (					self						,		_A):

      if not isinstance(_A						,		_A):
          SCREAMING_SNAKE_CASE_									=						list(_A)

      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						,		_A						,		_A = 4.0						,		_A = None						,		_A = 50						,		_A = "pil"						,		_A = True						,		):
      SCREAMING_SNAKE_CASE_									=						len(_A)
      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=_A						,		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(_A						,		device=self.device).reshape(-1)
      SCREAMING_SNAKE_CASE_									=						torch.tensor([1000] * 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(_A)

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

          SCREAMING_SNAKE_CASE_									=						t
          if not torch.is_tensor(_A):
              # 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(_A						,		_A):
                  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=_A						,		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(
              _A						,		timestep=_A						,		class_labels=_A).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(_A						,		len(_A) // 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(_A						,		_A						,		dim=1)
          else:
              SCREAMING_SNAKE_CASE_									=						noise_pred

          # compute previous image: x_t -> x_t-1
          SCREAMING_SNAKE_CASE_									=						self.scheduler.step(_A						,		_A						,		_A).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(_A).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(_A)

      if not return_dict:
          return (samples,)

      return ImagePipelineOutput(images=_A)


