






'''simple docstring'''



import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


def 							lowercase_				(    __A :       int			)       ->      Union[str, Any]:



 """simple docstring"""




 lowercase					:   str				  =[tensor.shape for tensor in tensor_list]
 return all(shape == shapes[0] for shape in shapes[1:]			)
class UpperCAmelCase_					(    __A				,       __A				,       __A				,       unittest.TestCase				):




      """simple docstring"""




      UpperCamelCase_								=			StableDiffusionLatentUpscalePipeline
      UpperCamelCase_								=			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
          '''height''',
          '''width''',
          '''cross_attention_kwargs''',
          '''negative_prompt_embeds''',
          '''prompt_embeds''',
      }
      UpperCamelCase_								=			PipelineTesterMixin.required_optional_params - {'''num_images_per_prompt'''}
      UpperCamelCase_								=			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
      UpperCamelCase_								=			frozenset(
          []				)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
      UpperCamelCase_								=			frozenset([]				)

      UpperCamelCase_								=			True






      @property
      def 				A__      ( self			:		Optional[Any]		)  ->  Optional[Any]:


       '''simple docstring'''

       lowercase					:   str				  =1
       lowercase					:   List[str]				  =4
       lowercase					:   Union[str, Any]				  =(16, 16)

       lowercase					:   Dict				  =floats_tensor((batch_size, num_channels) + sizes     ,      rng=random.Random(0		)		).to(UpperCAmelCase		)
       return image






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


       '''simple docstring'''

       torch.manual_seed(0		)
       lowercase					:   Optional[int]				  =UNetaDConditionModel(
           act_fn='''gelu'''     ,      attention_head_dim=8     ,      norm_num_groups=UpperCAmelCase     ,      block_out_channels=[32, 32, 64, 64]     ,      time_cond_proj_dim=160     ,      conv_in_kernel=1     ,      conv_out_kernel=1     ,      cross_attention_dim=32     ,      down_block_types=(
               '''KDownBlock2D''',
               '''KCrossAttnDownBlock2D''',
               '''KCrossAttnDownBlock2D''',
               '''KCrossAttnDownBlock2D''',
           )     ,      in_channels=8     ,      mid_block_type=UpperCAmelCase     ,      only_cross_attention=UpperCAmelCase     ,      out_channels=5     ,      resnet_time_scale_shift='''scale_shift'''     ,      time_embedding_type='''fourier'''     ,      timestep_post_act='''gelu'''     ,      up_block_types=('''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KUpBlock2D''')     ,      )
       lowercase					:   List[str]				  =AutoencoderKL(
           block_out_channels=[32, 32, 64, 64]     ,      in_channels=3     ,      out_channels=3     ,      down_block_types=[
               '''DownEncoderBlock2D''',
               '''DownEncoderBlock2D''',
               '''DownEncoderBlock2D''',
               '''DownEncoderBlock2D''',
           ]     ,      up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''']     ,      latent_channels=4     ,      )
       lowercase					:   Union[str, Any]				  =EulerDiscreteScheduler(prediction_type='''sample'''		)
       lowercase					:   int				  =CLIPTextConfig(
           bos_token_id=0     ,      eos_token_id=2     ,      hidden_size=32     ,      intermediate_size=37     ,      layer_norm_eps=1e-05     ,      num_attention_heads=4     ,      num_hidden_layers=5     ,      pad_token_id=1     ,      vocab_size=1000     ,      hidden_act='''quick_gelu'''     ,      projection_dim=512     ,      )
       lowercase					:   Dict				  =CLIPTextModel(UpperCAmelCase		)
       lowercase					:   int				  =CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

       lowercase					:   List[Any]				  ={
           '''unet''': model.eval(),
           '''vae''': vae.eval(),
           '''scheduler''': scheduler,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
       }

       return components






      def 				A__      ( self			:		Union[str, Any]     ,      UpperCAmelCase			:		Optional[int]     ,      UpperCAmelCase			:		Optional[int]=0		)  ->  List[str]:


       '''simple docstring'''

       if str(UpperCAmelCase		).startswith('''mps'''		):
        lowercase					:   int				  =torch.manual_seed(UpperCAmelCase		)
       else:
        lowercase					:   Union[str, Any]				  =torch.Generator(device=UpperCAmelCase		).manual_seed(UpperCAmelCase		)
       lowercase					:   List[str]				  ={
           '''prompt''': '''A painting of a squirrel eating a burger''',
           '''image''': self.dummy_image.cpu(),
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''output_type''': '''numpy''',
       }
       return inputs






      def 				A__      ( self			:		str		)  ->  List[Any]:


       '''simple docstring'''

       lowercase					:   str				  ='''cpu'''

       lowercase					:   List[str]				  =self.get_dummy_components()
       lowercase					:   str				  =self.pipeline_class(**UpperCAmelCase		)
       pipe.to(UpperCAmelCase		)
       pipe.set_progress_bar_config(disable=UpperCAmelCase		)

       lowercase					:   List[str]				  =self.get_dummy_inputs(UpperCAmelCase		)
       lowercase					:   int				  =pipe(**UpperCAmelCase		).images
       lowercase					:   str				  =image[0, -3:, -3:, -1]

       self.assertEqual(image.shape     ,      (1, 256, 256, 3)		)
       lowercase					:   Dict				  =np.array(
           [0.4_7_2_2_2_4_1_2, 0.4_1_9_2_1_6_3_3, 0.4_4_7_1_7_4_3_4, 0.4_6_8_7_4_1_9_2, 0.4_2_5_8_8_2_5_8, 0.4_6_1_5_0_7_2_6, 0.4_6_7_7_5_3_4, 0.4_5_5_8_3_8_3_2, 0.4_8_5_7_9_0_5_5]		)
       lowercase					:   int				  =np.abs(image_slice.flatten() - expected_slice		).max()
       self.assertLessEqual(UpperCAmelCase     ,      1e-3		)






      def 				A__      ( self			:		List[Any]		)  ->  List[str]:


       '''simple docstring'''

       super().test_attention_slicing_forward_pass(expected_max_diff=7e-3		)






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


       '''simple docstring'''

       super().test_cpu_offload_forward_pass(expected_max_diff=3e-3		)






      def 				A__      ( self			:		str		)  ->  int:


       '''simple docstring'''

       super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3		)






      def 				A__      ( self			:		str		)  ->  List[str]:


       '''simple docstring'''

       super().test_inference_batch_single_identical(expected_max_diff=7e-3		)






      def 				A__      ( self			:		Dict		)  ->  int:


       '''simple docstring'''

       super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3		)






      def 				A__      ( self			:		int		)  ->  Tuple:


       '''simple docstring'''

       super().test_save_load_local(expected_max_difference=3e-3		)






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


       '''simple docstring'''

       super().test_save_load_optional_components(expected_max_difference=3e-3		)






      def 				A__      ( self			:		Dict		)  ->  Dict:


       '''simple docstring'''

       lowercase					:   Dict				  =[
           '''DDIMScheduler''',
           '''DDPMScheduler''',
           '''PNDMScheduler''',
           '''HeunDiscreteScheduler''',
           '''EulerAncestralDiscreteScheduler''',
           '''KDPM2DiscreteScheduler''',
           '''KDPM2AncestralDiscreteScheduler''',
           '''DPMSolverSDEScheduler''',
       ]
       lowercase					:   int				  =self.get_dummy_components()
       lowercase					:   Dict				  =self.pipeline_class(**UpperCAmelCase		)

       # make sure that PNDM does not need warm-up
       pipe.scheduler.register_to_config(skip_prk_steps=UpperCAmelCase		)

       pipe.to(UpperCAmelCase		)
       pipe.set_progress_bar_config(disable=UpperCAmelCase		)
       lowercase					:   int				  =self.get_dummy_inputs(UpperCAmelCase		)
       lowercase					:   int				  =2

       lowercase					:   Optional[Any]				  =[]
       for scheduler_enum in KarrasDiffusionSchedulers:
        if scheduler_enum.name in skip_schedulers:
         # no sigma schedulers are not supported
         # no schedulers
         continue

        lowercase					:   Dict				  =getattr(UpperCAmelCase     ,      scheduler_enum.name		)
        lowercase					:   Tuple				  =scheduler_cls.from_config(pipe.scheduler.config		)
        lowercase					:   Dict				  =pipe(**UpperCAmelCase		)[0]
        outputs.append(UpperCAmelCase		)

       assert check_same_shape(UpperCAmelCase		)





@require_torch_gpu
@slow
class UpperCAmelCase_					(    unittest.TestCase				):




      """simple docstring"""




      def 				A__      ( self			:		Tuple		)  ->  Tuple:


       '''simple docstring'''

       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






      def 				A__      ( self			:		Optional[Any]		)  ->  List[Any]:


       '''simple docstring'''

       lowercase					:   Tuple				  =torch.manual_seed(33		)

       lowercase					:   Dict				  =StableDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''     ,      torch_dtype=torch.floataa		)
       pipe.to('''cuda'''		)

       lowercase					:   List[str]				  =StableDiffusionLatentUpscalePipeline.from_pretrained(
           '''stabilityai/sd-x2-latent-upscaler'''     ,      torch_dtype=torch.floataa		)
       upscaler.to('''cuda'''		)

       lowercase					:   str				  ='''a photo of an astronaut high resolution, unreal engine, ultra realistic'''

       lowercase					:   Dict				  =pipe(UpperCAmelCase     ,      generator=UpperCAmelCase     ,      output_type='''latent'''		).images

       lowercase					:   Union[str, Any]				  =upscaler(
           prompt=UpperCAmelCase     ,      image=UpperCAmelCase     ,      num_inference_steps=20     ,      guidance_scale=0     ,      generator=UpperCAmelCase     ,      output_type='''np'''     ,      ).images[0]

       lowercase					:   str				  =load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy'''		)
       assert np.abs((expected_image - image).mean()		) < 5e-2






      def 				A__      ( self			:		Union[str, Any]		)  ->  List[Any]:


       '''simple docstring'''

       lowercase					:   str				  =torch.manual_seed(33		)

       lowercase					:   Optional[int]				  =StableDiffusionLatentUpscalePipeline.from_pretrained(
           '''stabilityai/sd-x2-latent-upscaler'''     ,      torch_dtype=torch.floataa		)
       upscaler.to('''cuda'''		)

       lowercase					:   Union[str, Any]				  ='''the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'''

       lowercase					:   Union[str, Any]				  =load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png'''		)

       lowercase					:   Optional[Any]				  =upscaler(
           prompt=UpperCAmelCase     ,      image=UpperCAmelCase     ,      num_inference_steps=20     ,      guidance_scale=0     ,      generator=UpperCAmelCase     ,      output_type='''np'''     ,      ).images[0]

       lowercase					:   str				  =load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy'''		)
       assert np.abs((expected_image - image).max()		) < 5e-2











'''simple docstring'''



import subprocess
import sys

from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class UpperCAmelCase_					(    __A				):




      """simple docstring"""




      @require_torch
      def 				A__      ( self			:		Tuple		)  ->  Optional[int]:


       '''simple docstring'''

       lowercase					:   Any				  ='''
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
        '''

       lowercase					:   Optional[int]				  ='''
mname = "hf-internal-testing/tiny-random-bert"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
BertTokenizer.from_pretrained(mname)
pipe = pipeline(task="fill-mask", model=mname)
print("success")
        '''

       lowercase					:   Any				  ='''
import socket
def offline_socket(*args, **kwargs): raise RuntimeError("Offline mode is enabled, we shouldn\'t access internet")
socket.socket = offline_socket
        '''

       # Force fetching the files so that we can use the cache
       lowercase					:   Tuple				  ='''hf-internal-testing/tiny-random-bert'''
       BertConfig.from_pretrained(UpperCAmelCase		)
       BertModel.from_pretrained(UpperCAmelCase		)
       BertTokenizer.from_pretrained(UpperCAmelCase		)
       pipeline(task='''fill-mask'''     ,      model=UpperCAmelCase		)

       # baseline - just load from_pretrained with normal network
       lowercase					:   List[str]				  =[sys.executable, '''-c''', '''\n'''.join([load, run, mock]		)]

       # should succeed
       lowercase					:   Tuple				  =self.get_env()
       # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
       lowercase					:   Optional[Any]				  ='''1'''
       lowercase					:   Any				  =subprocess.run(UpperCAmelCase     ,      env=UpperCAmelCase     ,      check=UpperCAmelCase     ,      capture_output=UpperCAmelCase		)
       self.assertEqual(result.returncode     ,      0     ,      result.stderr		)
       self.assertIn('''success'''     ,      result.stdout.decode()		)






      @require_torch
      def 				A__      ( self			:		str		)  ->  List[str]:


       '''simple docstring'''

       lowercase					:   str				  ='''
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
        '''

       lowercase					:   Optional[Any]				  ='''
mname = "hf-internal-testing/tiny-random-bert"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
BertTokenizer.from_pretrained(mname)
pipe = pipeline(task="fill-mask", model=mname)
print("success")
        '''

       lowercase					:   Optional[int]				  ='''
import socket
def offline_socket(*args, **kwargs): raise socket.error("Faking flaky internet")
socket.socket = offline_socket
        '''

       # Force fetching the files so that we can use the cache
       lowercase					:   Optional[Any]				  ='''hf-internal-testing/tiny-random-bert'''
       BertConfig.from_pretrained(UpperCAmelCase		)
       BertModel.from_pretrained(UpperCAmelCase		)
       BertTokenizer.from_pretrained(UpperCAmelCase		)
       pipeline(task='''fill-mask'''     ,      model=UpperCAmelCase		)

       # baseline - just load from_pretrained with normal network
       lowercase					:   Optional[Any]				  =[sys.executable, '''-c''', '''\n'''.join([load, run, mock]		)]

       # should succeed
       lowercase					:   str				  =self.get_env()
       lowercase					:   Any				  =subprocess.run(UpperCAmelCase     ,      env=UpperCAmelCase     ,      check=UpperCAmelCase     ,      capture_output=UpperCAmelCase		)
       self.assertEqual(result.returncode     ,      0     ,      result.stderr		)
       self.assertIn('''success'''     ,      result.stdout.decode()		)






      @require_torch
      def 				A__      ( self			:		Any		)  ->  Optional[Any]:


       '''simple docstring'''

       lowercase					:   Optional[Any]				  ='''
from transformers import BertConfig, BertModel, BertTokenizer
        '''

       lowercase					:   List[Any]				  ='''
mname = "hf-internal-testing/tiny-random-bert-sharded"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
print("success")
        '''

       lowercase					:   int				  ='''
import socket
def offline_socket(*args, **kwargs): raise ValueError("Offline mode is enabled")
socket.socket = offline_socket
        '''

       # baseline - just load from_pretrained with normal network
       lowercase					:   Tuple				  =[sys.executable, '''-c''', '''\n'''.join([load, run]		)]

       # should succeed
       lowercase					:   Optional[Any]				  =self.get_env()
       lowercase					:   List[Any]				  =subprocess.run(UpperCAmelCase     ,      env=UpperCAmelCase     ,      check=UpperCAmelCase     ,      capture_output=UpperCAmelCase		)
       self.assertEqual(result.returncode     ,      0     ,      result.stderr		)
       self.assertIn('''success'''     ,      result.stdout.decode()		)

       # next emulate no network
       lowercase					:   Tuple				  =[sys.executable, '''-c''', '''\n'''.join([load, mock, run]		)]

       # Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
       # env["TRANSFORMERS_OFFLINE"] = "0"
       # result = subprocess.run(cmd, env=env, check=False, capture_output=True)
       # self.assertEqual(result.returncode, 1, result.stderr)

       # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
       lowercase					:   Any				  ='''1'''
       lowercase					:   Optional[Any]				  =subprocess.run(UpperCAmelCase     ,      env=UpperCAmelCase     ,      check=UpperCAmelCase     ,      capture_output=UpperCAmelCase		)
       self.assertEqual(result.returncode     ,      0     ,      result.stderr		)
       self.assertIn('''success'''     ,      result.stdout.decode()		)






      @require_torch
      def 				A__      ( self			:		Tuple		)  ->  Optional[int]:


       '''simple docstring'''

       lowercase					:   Optional[int]				  ='''
from transformers import pipeline
        '''
       lowercase					:   List[Any]				  ='''
mname = "hf-internal-testing/tiny-random-bert"
pipe = pipeline(model=mname)
        '''

       lowercase					:   Tuple				  ='''
import socket
def offline_socket(*args, **kwargs): raise socket.error("Offline mode is enabled")
socket.socket = offline_socket
        '''
       lowercase					:   Tuple				  =self.get_env()
       lowercase					:   Optional[int]				  ='''1'''
       lowercase					:   Union[str, Any]				  =[sys.executable, '''-c''', '''\n'''.join([load, mock, run]		)]
       lowercase					:   Dict				  =subprocess.run(UpperCAmelCase     ,      env=UpperCAmelCase     ,      check=UpperCAmelCase     ,      capture_output=UpperCAmelCase		)
       self.assertEqual(result.returncode     ,      1     ,      result.stderr		)
       self.assertIn(
           '''You cannot infer task automatically within `pipeline` when using offline mode'''     ,      result.stderr.decode().replace('''\n'''     ,      ''''''		)     ,      )






      @require_torch
      def 				A__      ( self			:		int		)  ->  Optional[int]:


       '''simple docstring'''

       lowercase					:   List[str]				  ='''
from transformers import AutoModel
        '''
       lowercase					:   Dict				  ='''
mname = "hf-internal-testing/test_dynamic_model"
AutoModel.from_pretrained(mname, trust_remote_code=True)
print("success")
        '''

       # baseline - just load from_pretrained with normal network
       lowercase					:   Dict				  =[sys.executable, '''-c''', '''\n'''.join([load, run]		)]

       # should succeed
       lowercase					:   Optional[Any]				  =self.get_env()
       lowercase					:   int				  =subprocess.run(UpperCAmelCase     ,      env=UpperCAmelCase     ,      check=UpperCAmelCase     ,      capture_output=UpperCAmelCase		)
       self.assertEqual(result.returncode     ,      0     ,      result.stderr		)
       self.assertIn('''success'''     ,      result.stdout.decode()		)

       # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
       lowercase					:   List[str]				  ='''1'''
       lowercase					:   List[Any]				  =subprocess.run(UpperCAmelCase     ,      env=UpperCAmelCase     ,      check=UpperCAmelCase     ,      capture_output=UpperCAmelCase		)
       self.assertEqual(result.returncode     ,      0     ,      result.stderr		)
       self.assertIn('''success'''     ,      result.stdout.decode()		)



