





import gc
import random
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
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


enable_full_determinism()

class 		SCREAMING_SNAKE_CASE__				(       unittest.TestCase  ):


      def    a (self       :      Tuple      ):





         """simple docstring"""



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


      @property
      def    a (self       :      Optional[Any]      ):





         """simple docstring"""



         __snake_case							       =   1
         __snake_case							       =   3
         __snake_case							       =   (32, 32)

         __snake_case							       =   floats_tensor((batch_size, num_channels) + sizes				,			rng=random.Random(0      )      ).to(a__      )
         return image


      @property
      def    a (self       :      List[str]      ):





         """simple docstring"""



         torch.manual_seed(0      )
         __snake_case							       =   UNetaDConditionModel(
             block_out_channels=(32, 32, 64)				,			layers_per_block=2				,			sample_size=32				,			in_channels=7				,			out_channels=4				,			down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''', '''CrossAttnDownBlock2D''')				,			up_block_types=('''CrossAttnUpBlock2D''', '''CrossAttnUpBlock2D''', '''UpBlock2D''')				,			cross_attention_dim=32				,			attention_head_dim=8				,			use_linear_projection=a__				,			only_cross_attention=(True, True, False)				,			num_class_embeds=100				,			)
         return model


      @property
      def    a (self       :      List[str]      ):





         """simple docstring"""



         torch.manual_seed(0      )
         __snake_case							       =   AutoencoderKL(
             block_out_channels=[32, 32, 64]				,			in_channels=3				,			out_channels=3				,			down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''', '''DownEncoderBlock2D''']				,			up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''']				,			latent_channels=4				,			)
         return model


      @property
      def    a (self       :      int      ):





         """simple docstring"""



         torch.manual_seed(0      )
         __snake_case							       =   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='''gelu'''				,			projection_dim=512				,			)
         return CLIPTextModel(a__      )


      def    a (self       :      Tuple      ):





         """simple docstring"""



         __snake_case							       =   '''cpu'''  # ensure determinism for the device-dependent torch.Generator
         __snake_case							       =   self.dummy_cond_unet_upscale
         __snake_case							       =   DDPMScheduler()
         __snake_case							       =   DDIMScheduler(prediction_type='''v_prediction'''      )
         __snake_case							       =   self.dummy_vae
         __snake_case							       =   self.dummy_text_encoder
         __snake_case							       =   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )

         __snake_case							       =   self.dummy_image.cpu().permute(0				,			2				,			3				,			1      )[0]
         __snake_case							       =   Image.fromarray(np.uinta(a__      )      ).convert('''RGB'''      ).resize((64, 64)      )

         # make sure here that pndm scheduler skips prk
         __snake_case							       =   StableDiffusionUpscalePipeline(
             unet=a__				,			low_res_scheduler=a__				,			scheduler=a__				,			vae=a__				,			text_encoder=a__				,			tokenizer=a__				,			max_noise_level=350				,			)
         __snake_case							       =   sd_pipe.to(a__      )
         sd_pipe.set_progress_bar_config(disable=a__      )

         __snake_case							       =   '''A painting of a squirrel eating a burger'''
         __snake_case							       =   torch.Generator(device=a__      ).manual_seed(0      )
         __snake_case							       =   sd_pipe(
             [prompt]				,			image=a__				,			generator=a__				,			guidance_scale=6.0				,			noise_level=20				,			num_inference_steps=2				,			output_type='''np'''				,			)

         __snake_case							       =   output.images

         __snake_case							       =   torch.Generator(device=a__      ).manual_seed(0      )
         __snake_case							       =   sd_pipe(
             [prompt]				,			image=a__				,			generator=a__				,			guidance_scale=6.0				,			noise_level=20				,			num_inference_steps=2				,			output_type='''np'''				,			return_dict=a__				,			)[0]

         __snake_case							       =   image[0, -3:, -3:, -1]
         __snake_case							       =   image_from_tuple[0, -3:, -3:, -1]

         __snake_case							       =   low_res_image.size[0] * 4
         assert image.shape == (1, expected_height_width, expected_height_width, 3)
         __snake_case							       =   np.array([0.3_1_1_3, 0.3_9_1_0, 0.4_2_7_2, 0.4_8_5_9, 0.5_0_6_1, 0.4_6_5_2, 0.5_3_6_2, 0.5_7_1_5, 0.5_6_6_1]      )

         assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1E-2


      def    a (self       :      str      ):





         """simple docstring"""



         __snake_case							       =   '''cpu'''  # ensure determinism for the device-dependent torch.Generator
         __snake_case							       =   self.dummy_cond_unet_upscale
         __snake_case							       =   DDPMScheduler()
         __snake_case							       =   DDIMScheduler(prediction_type='''v_prediction'''      )
         __snake_case							       =   self.dummy_vae
         __snake_case							       =   self.dummy_text_encoder
         __snake_case							       =   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )

         __snake_case							       =   self.dummy_image.cpu().permute(0				,			2				,			3				,			1      )[0]
         __snake_case							       =   Image.fromarray(np.uinta(a__      )      ).convert('''RGB'''      ).resize((64, 64)      )

         # make sure here that pndm scheduler skips prk
         __snake_case							       =   StableDiffusionUpscalePipeline(
             unet=a__				,			low_res_scheduler=a__				,			scheduler=a__				,			vae=a__				,			text_encoder=a__				,			tokenizer=a__				,			max_noise_level=350				,			)
         __snake_case							       =   sd_pipe.to(a__      )
         sd_pipe.set_progress_bar_config(disable=a__      )

         __snake_case							       =   '''A painting of a squirrel eating a burger'''
         __snake_case							       =   sd_pipe(
             2 * [prompt]				,			image=2 * [low_res_image]				,			guidance_scale=6.0				,			noise_level=20				,			num_inference_steps=2				,			output_type='''np'''				,			)
         __snake_case							       =   output.images
         assert image.shape[0] == 2

         __snake_case							       =   torch.Generator(device=a__      ).manual_seed(0      )
         __snake_case							       =   sd_pipe(
             [prompt]				,			image=a__				,			generator=a__				,			num_images_per_prompt=2				,			guidance_scale=6.0				,			noise_level=20				,			num_inference_steps=2				,			output_type='''np'''				,			)
         __snake_case							       =   output.images
         assert image.shape[0] == 2


      @unittest.skipIf(torch_device != '''cuda'''				,			'''This test requires a GPU'''      )
      def    a (self       :      List[Any]      ):





         """simple docstring"""



         __snake_case							       =   self.dummy_cond_unet_upscale
         __snake_case							       =   DDPMScheduler()
         __snake_case							       =   DDIMScheduler(prediction_type='''v_prediction'''      )
         __snake_case							       =   self.dummy_vae
         __snake_case							       =   self.dummy_text_encoder
         __snake_case							       =   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )

         __snake_case							       =   self.dummy_image.cpu().permute(0				,			2				,			3				,			1      )[0]
         __snake_case							       =   Image.fromarray(np.uinta(a__      )      ).convert('''RGB'''      ).resize((64, 64)      )

         # put models in fp16, except vae as it overflows in fp16
         __snake_case							       =   unet.half()
         __snake_case							       =   text_encoder.half()

         # make sure here that pndm scheduler skips prk
         __snake_case							       =   StableDiffusionUpscalePipeline(
             unet=a__				,			low_res_scheduler=a__				,			scheduler=a__				,			vae=a__				,			text_encoder=a__				,			tokenizer=a__				,			max_noise_level=350				,			)
         __snake_case							       =   sd_pipe.to(a__      )
         sd_pipe.set_progress_bar_config(disable=a__      )

         __snake_case							       =   '''A painting of a squirrel eating a burger'''
         __snake_case							       =   torch.manual_seed(0      )
         __snake_case							       =   sd_pipe(
             [prompt]				,			image=a__				,			generator=a__				,			num_inference_steps=2				,			output_type='''np'''				,			).images

         __snake_case							       =   low_res_image.size[0] * 4
         assert image.shape == (1, expected_height_width, expected_height_width, 3)






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


      def    a (self       :      Optional[int]      ):





         """simple docstring"""



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


      def    a (self       :      List[Any]      ):





         """simple docstring"""



         __snake_case							       =   load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/sd2-upscale/low_res_cat.png'''      )
         __snake_case							       =   load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
             '''/upsampled_cat.npy'''      )

         __snake_case							       =   '''stabilityai/stable-diffusion-x4-upscaler'''
         __snake_case							       =   StableDiffusionUpscalePipeline.from_pretrained(a__      )
         pipe.to(a__      )
         pipe.set_progress_bar_config(disable=a__      )
         pipe.enable_attention_slicing()

         __snake_case							       =   '''a cat sitting on a park bench'''

         __snake_case							       =   torch.manual_seed(0      )
         __snake_case							       =   pipe(
             prompt=a__				,			image=a__				,			generator=a__				,			output_type='''np'''				,			)
         __snake_case							       =   output.images[0]

         assert image.shape == (512, 512, 3)
         assert np.abs(expected_image - image      ).max() < 1E-3


      def    a (self       :      int      ):





         """simple docstring"""



         __snake_case							       =   load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/sd2-upscale/low_res_cat.png'''      )
         __snake_case							       =   load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
             '''/upsampled_cat_fp16.npy'''      )

         __snake_case							       =   '''stabilityai/stable-diffusion-x4-upscaler'''
         __snake_case							       =   StableDiffusionUpscalePipeline.from_pretrained(
             a__				,			torch_dtype=torch.floataa				,			)
         pipe.to(a__      )
         pipe.set_progress_bar_config(disable=a__      )
         pipe.enable_attention_slicing()

         __snake_case							       =   '''a cat sitting on a park bench'''

         __snake_case							       =   torch.manual_seed(0      )
         __snake_case							       =   pipe(
             prompt=a__				,			image=a__				,			generator=a__				,			output_type='''np'''				,			)
         __snake_case							       =   output.images[0]

         assert image.shape == (512, 512, 3)
         assert np.abs(expected_image - image      ).max() < 5E-1


      def    a (self       :      str      ):





         """simple docstring"""



         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()

         __snake_case							       =   load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/sd2-upscale/low_res_cat.png'''      )

         __snake_case							       =   '''stabilityai/stable-diffusion-x4-upscaler'''
         __snake_case							       =   StableDiffusionUpscalePipeline.from_pretrained(
             a__				,			torch_dtype=torch.floataa				,			)
         pipe.to(a__      )
         pipe.set_progress_bar_config(disable=a__      )
         pipe.enable_attention_slicing(1      )
         pipe.enable_sequential_cpu_offload()

         __snake_case							       =   '''a cat sitting on a park bench'''

         __snake_case							       =   torch.manual_seed(0      )
         __snake_case							       =   pipe(
             prompt=a__				,			image=a__				,			generator=a__				,			num_inference_steps=5				,			output_type='''np'''				,			)

         __snake_case							       =   torch.cuda.max_memory_allocated()
         # make sure that less than 2.9 GB is allocated
         assert mem_bytes < 2.9 * 10**9


import collections
import importlib.util
import os
import re
from pathlib import Path


_SCREAMING_SNAKE_CASE										=						'src/transformers'


# Matches is_xxx_available()
_SCREAMING_SNAKE_CASE										=						re.compile(R'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {xxx}
_SCREAMING_SNAKE_CASE										=						re.compile(R'^_import_structure\s+=\s+\{([^\}]+)\}')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
_SCREAMING_SNAKE_CASE										=						re.compile(R'\s+"\S*":\s+\[([^\]]*)\]')
# Catches a line if not is_foo_available
_SCREAMING_SNAKE_CASE										=						re.compile(R'^\s*if\s+not\s+is\_[a-z_]*\_available\(\)')
# Catches a line _import_struct["bla"].append("foo")
_SCREAMING_SNAKE_CASE										=						re.compile(R'^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
_SCREAMING_SNAKE_CASE										=						re.compile(R'^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]')
# Catches a line with an object between quotes and a comma:     "MyModel",
_SCREAMING_SNAKE_CASE										=						re.compile('^\s+"([^"]+)",')
# Catches a line with objects between brackets only:    ["foo", "bar"],
_SCREAMING_SNAKE_CASE										=						re.compile('^\s+\[([^\]]+)\]')
# Catches a line with from foo import bar, bla, boo
_SCREAMING_SNAKE_CASE										=						re.compile(R'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')
# Catches a line with try:
_SCREAMING_SNAKE_CASE										=						re.compile(R'^\s*try:')
# Catches a line with else:
_SCREAMING_SNAKE_CASE										=						re.compile(R'^\s*else:')






def  snake_case  (       snake_case__			:Optional[Any])						->   List[str]:
       if _re_test_backend.search(snake_case__) is None:
              return None
       _A        =  [b[0] for b in _re_backend.findall(snake_case__)]
       backends.sort()
       return "_and_".join(snake_case__)






def  snake_case  (       snake_case__			:Any)						->   Any:
       with open(snake_case__   ,	"""r"""   ,	encoding="""utf-8"""   ,	newline="""\n""") as f:
              _A        =  f.readlines()

       _A        =  0
       while line_index < len(snake_case__) and not lines[line_index].startswith("""_import_structure = {"""):
              line_index += 1

       # If this is a traditional init, just return.
       if line_index >= len(snake_case__):
              return None

       # First grab the objects without a specific backend in _import_structure
       _A        =  []
       while not lines[line_index].startswith("""if TYPE_CHECKING""") and find_backend(lines[line_index]) is None:
              _A        =  lines[line_index]
              # If we have everything on a single line, let's deal with it.
              if _re_one_line_import_struct.search(snake_case__):
                     _A        =  _re_one_line_import_struct.search(snake_case__).groups()[0]
                     _A        =  re.findall("""\[([^\]]+)\]"""   ,	snake_case__)
                     for imp in imports:
                            objects.extend([obj[1:-1] for obj in imp.split(""", """)])
                     line_index += 1
                     continue
              _A        =  _re_import_struct_key_value.search(snake_case__)
              if single_line_import_search is not None:
                     _A        =  [obj[1:-1] for obj in single_line_import_search.groups()[0].split(""", """) if len(snake_case__) > 0]
                     objects.extend(snake_case__)
              elif line.startswith(""" """ * 8 + """\""""):
                     objects.append(line[9:-3])
              line_index += 1

       _A        =  {"""none""": objects}
       # Let's continue with backend-specific objects in _import_structure
       while not lines[line_index].startswith("""if TYPE_CHECKING"""):
              # If the line is an if not is_backend_available, we grab all objects associated.
              _A        =  find_backend(lines[line_index])
              # Check if the backend declaration is inside a try block:
              if _re_try.search(lines[line_index - 1]) is None:
                     _A        =  None

              if backend is not None:
                     line_index += 1

                     # Scroll until we hit the else block of try-except-else
                     while _re_else.search(lines[line_index]) is None:
                            line_index += 1

                     line_index += 1

                     _A        =  []
                     # Until we unindent, add backend objects to the list
                     while len(lines[line_index]) <= 1 or lines[line_index].startswith(""" """ * 4):
                            _A        =  lines[line_index]
                            if _re_import_struct_add_one.search(snake_case__) is not None:
                                   objects.append(_re_import_struct_add_one.search(snake_case__).groups()[0])
                            elif _re_import_struct_add_many.search(snake_case__) is not None:
                                   _A        =  _re_import_struct_add_many.search(snake_case__).groups()[0].split(""", """)
                                   _A        =  [obj[1:-1] for obj in imports if len(snake_case__) > 0]
                                   objects.extend(snake_case__)
                            elif _re_between_brackets.search(snake_case__) is not None:
                                   _A        =  _re_between_brackets.search(snake_case__).groups()[0].split(""", """)
                                   _A        =  [obj[1:-1] for obj in imports if len(snake_case__) > 0]
                                   objects.extend(snake_case__)
                            elif _re_quote_object.search(snake_case__) is not None:
                                   objects.append(_re_quote_object.search(snake_case__).groups()[0])
                            elif line.startswith(""" """ * 8 + """\""""):
                                   objects.append(line[9:-3])
                            elif line.startswith(""" """ * 12 + """\""""):
                                   objects.append(line[13:-3])
                            line_index += 1

                     _A        =  objects
              else:
                     line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
       _A        =  []
       while (
           line_index < len(snake_case__)
           and find_backend(lines[line_index]) is None
           and not lines[line_index].startswith("""else""")
       ):
              _A        =  lines[line_index]
              _A        =  _re_import.search(snake_case__)
              if single_line_import_search is not None:
                     objects.extend(single_line_import_search.groups()[0].split(""", """))
              elif line.startswith(""" """ * 8):
                     objects.append(line[8:-2])
              line_index += 1

       _A        =  {"""none""": objects}
       # Let's continue with backend-specific objects
       while line_index < len(snake_case__):
              # If the line is an if is_backend_available, we grab all objects associated.
              _A        =  find_backend(lines[line_index])
              # Check if the backend declaration is inside a try block:
              if _re_try.search(lines[line_index - 1]) is None:
                     _A        =  None

              if backend is not None:
                     line_index += 1

                     # Scroll until we hit the else block of try-except-else
                     while _re_else.search(lines[line_index]) is None:
                            line_index += 1

                     line_index += 1

                     _A        =  []
                     # Until we unindent, add backend objects to the list
                     while len(lines[line_index]) <= 1 or lines[line_index].startswith(""" """ * 8):
                            _A        =  lines[line_index]
                            _A        =  _re_import.search(snake_case__)
                            if single_line_import_search is not None:
                                   objects.extend(single_line_import_search.groups()[0].split(""", """))
                            elif line.startswith(""" """ * 12):
                                   objects.append(line[12:-2])
                            line_index += 1

                     _A        =  objects
              else:
                     line_index += 1

       return import_dict_objects, type_hint_objects






def  snake_case  (       snake_case__			:Dict   ,	snake_case__			:int)						->   List[Any]:

       def find_duplicates(snake_case__			:Union[str, Any]):
              return [k for k, v in collections.Counter(snake_case__).items() if v > 1]

       if list(import_dict_objects.keys()) != list(type_hint_objects.keys()):
              return ["Both sides of the init do not have the same backends!"]

       _A        =  []
       for key in import_dict_objects.keys():
              _A        =  find_duplicates(import_dict_objects[key])
              if duplicate_imports:
                     errors.append(F'''Duplicate _import_structure definitions for: {duplicate_imports}''')
              _A        =  find_duplicates(type_hint_objects[key])
              if duplicate_type_hints:
                     errors.append(F'''Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}''')

              if sorted(set(import_dict_objects[key])) != sorted(set(type_hint_objects[key])):
                     _A        =  """base imports""" if key == """none""" else F'''{key} backend'''
                     errors.append(F'''Differences for {name}:''')
                     for a in type_hint_objects[key]:
                            if a not in import_dict_objects[key]:
                                   errors.append(F'''  {a} in TYPE_HINT but not in _import_structure.''')
                     for a in import_dict_objects[key]:
                            if a not in type_hint_objects[key]:
                                   errors.append(F'''  {a} in _import_structure but not in TYPE_HINT.''')
       return errors






def  snake_case  (       )						->   int:
       _A        =  []
       for root, _, files in os.walk(snake_case__):
              if "__init__.py" in files:
                     _A        =  os.path.join(snake_case__   ,	"""__init__.py""")
                     _A        =  parse_init(snake_case__)
                     if objects is not None:
                            _A        =  analyze_results(*snake_case__)
                            if len(snake_case__) > 0:
                                   _A        =  F'''Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'''
                                   failures.append("""\n""".join(snake_case__))
       if len(snake_case__) > 0:
              raise ValueError("""\n\n""".join(snake_case__))






def  snake_case  (       )						->   Optional[Any]:
       _A        =  []
       for path, directories, files in os.walk(snake_case__):
              for folder in directories:
                     # Ignore private modules
                     if folder.startswith("""_"""):
                            directories.remove(snake_case__)
                            continue
                     # Ignore leftovers from branches (empty folders apart from pycache)
                     if len(list((Path(snake_case__) / folder).glob("""*.py"""))) == 0:
                            continue
                     _A        =  str((Path(snake_case__) / folder).relative_to(snake_case__))
                     _A        =  short_path.replace(os.path.sep   ,	""".""")
                     submodules.append(snake_case__)
              for fname in files:
                     if fname == "__init__.py":
                            continue
                     _A        =  str((Path(snake_case__) / fname).relative_to(snake_case__))
                     _A        =  short_path.replace(""".py"""   ,	"""""").replace(os.path.sep   ,	""".""")
                     if len(submodule.split(""".""")) == 1:
                            submodules.append(snake_case__)
       return submodules


_SCREAMING_SNAKE_CASE										=						[
    'convert_pytorch_checkpoint_to_tf2',
    'modeling_flax_pytorch_utils',
]






def  snake_case  (       )						->   Union[str, Any]:
       # This is to make sure the transformers module imported is the one in the repo.
       _A        =  importlib.util.spec_from_file_location(
           """transformers"""   ,	os.path.join(snake_case__   ,	"""__init__.py""")   ,	submodule_search_locations=[PATH_TO_TRANSFORMERS]   ,	)
       _A        =  spec.loader.load_module()

       _A        =  [
           module
           for module in get_transformers_submodules()
           if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
       ]
       if len(snake_case__) > 0:
              _A        =  """\n""".join(F'''- {module}''' for module in module_not_registered)
              raise ValueError(
                  """The following submodules are not properly registered in the main init of Transformers:\n"""
                  F'''{list_of_modules}\n'''
                  """Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.""")


if __name__ == "__main__":
    check_all_inits()
    check_submodules()




