


import os
import tempfile
import unittest

import numpy as np
import torch

from diffusers.models import ModelMixin, UNetaDConditionModel
from diffusers.models.attention_processor import AttnProcessor, LoRAAttnProcessor
from diffusers.utils import (
    floats_tensor,
    logging,
    skip_mps,
    torch_device,
)
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

_UpperCamelCase          =     logging.get_logger(__name__)


def 		_lowercase   (SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE = True       ):




     '''simple docstring'''





     __A  :					str						     =       {}
     for name in model.attn_processors.keys():
          __A  :					str						     =       name.endswith("attn2.processor"       ) and not (
              name.startswith("transformer_in"       ) or "temp_attentions" in name.split("."       )
          )
          __A  :					Optional[Any]						     =       model.config.cross_attention_dim if has_cross_attention else None
          if name.startswith("mid_block"       ):
               __A  :					Any						     =       model.config.block_out_channels[-1]
          elif name.startswith("up_blocks"       ):
               __A  :					List[str]						     =       int(name[len("up_blocks."       )]       )
               __A  :					Any						     =       list(reversed(model.config.block_out_channels       )       )[block_id]
          elif name.startswith("down_blocks"       ):
               __A  :					int						     =       int(name[len("down_blocks."       )]       )
               __A  :					Any						     =       model.config.block_out_channels[block_id]
          elif name.startswith("transformer_in"       ):
               # Note that the `8 * ...` comes from: https://github.com/huggingface/diffusers/blob/7139f0e874f10b2463caa8cbd585762a309d12d6/src/diffusers/models/unet_3d_condition.py#L148
               __A  :					Union[str, Any]						     =       8 * model.config.attention_head_dim

          __A  :					Optional[Any]						     =       LoRAAttnProcessor(hidden_size=SCREAMING_SNAKE_CASE					,				cross_attention_dim=SCREAMING_SNAKE_CASE       )
          __A  :					List[Any]						     =       lora_attn_procs[name].to(model.device       )

          if mock_weights:
               # add 1 to weights to mock trained weights
               with torch.no_grad():
                    lora_attn_procs[name].to_q_lora.up.weight += 1
                    lora_attn_procs[name].to_k_lora.up.weight += 1
                    lora_attn_procs[name].to_v_lora.up.weight += 1
                    lora_attn_procs[name].to_out_lora.up.weight += 1

     return lora_attn_procs







@skip_mps
class        __magic_name__  (					lowerCAmelCase						,		lowerCAmelCase						,		unittest.TestCase   ):

     """simple docstring"""






     lowerCamelCase__						=							UNetaDConditionModel
     lowerCamelCase__						=							'sample'


     @property
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					str						     =       4
          __A  :					Optional[int]						     =       4
          __A  :					Optional[int]						     =       4
          __A  :					List[str]						     =       (32, 32)

          __A  :					Tuple						     =       floats_tensor((batch_size, num_channels, num_frames) + sizes		).to(lowerCamelCase		)
          __A  :					int						     =       torch.tensor([10]		).to(lowerCamelCase		)
          __A  :					Tuple						     =       floats_tensor((batch_size, 4, 32)		).to(lowerCamelCase		)

          return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}


     @property
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          return (4, 4, 32, 32)


     @property
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          return (4, 4, 32, 32)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					Any						     =       {
              "block_out_channels": (32, 64),
              "down_block_types": (
                  "CrossAttnDownBlock3D",
                  "DownBlock3D",
              ),
              "up_block_types": ("UpBlock3D", "CrossAttnUpBlock3D"),
              "cross_attention_dim": 32,
              "attention_head_dim": 8,
              "out_channels": 4,
              "in_channels": 4,
              "layers_per_block": 1,
              "sample_size": 32,
          }
          __A  :					Union[str, Any]						     =       self.dummy_input
          return init_dict, inputs_dict


     @unittest.skipIf(
         torch_device != "cuda" or not is_xformers_available() ,    reason="XFormers attention is only available with CUDA and `xformers` installed" ,    )
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					int						     =       self.prepare_init_args_and_inputs_for_common()
          __A  :					List[str]						     =       self.model_class(**lowerCamelCase		)

          model.enable_xformers_memory_efficient_attention()

          assert (
              model.mid_block.attentions[0].transformer_blocks[0].attna.processor.__class__.__name__
              == "XFormersAttnProcessor"
          ), "xformers is not enabled"


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					Dict						     =       self.prepare_init_args_and_inputs_for_common()

          __A  :					Optional[int]						     =       32

          __A  :					Any						     =       self.model_class(**lowerCamelCase		)
          model.to(lowerCamelCase		)
          model.eval()

          with torch.no_grad():
               __A  :					Dict						     =       model(**lowerCamelCase		)

               if isinstance(lowerCamelCase ,    lowerCamelCase		):
                    __A  :					int						     =       output.sample

          self.assertIsNotNone(lowerCamelCase		)
          __A  :					Union[str, Any]						     =       inputs_dict["sample"].shape
          self.assertEqual(output.shape ,    lowerCamelCase ,    "Input and output shapes do not match"		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					List[Any]						     =       self.prepare_init_args_and_inputs_for_common()
          __A  :					Dict						     =       self.model_class(**lowerCamelCase		)
          model.to(lowerCamelCase		)
          model.eval()

          with torch.no_grad():
               # Warmup pass when using mps (see #372)
               if torch_device == "mps" and isinstance(lowerCamelCase ,    lowerCamelCase		):
                    model(**self.dummy_input		)

               __A  :					Tuple						     =       model(**lowerCamelCase		)
               if isinstance(lowerCamelCase ,    lowerCamelCase		):
                    __A  :					Optional[int]						     =       first.sample

               __A  :					Union[str, Any]						     =       model(**lowerCamelCase		)
               if isinstance(lowerCamelCase ,    lowerCamelCase		):
                    __A  :					Optional[Any]						     =       second.sample

          __A  :					int						     =       first.cpu().numpy()
          __A  :					Optional[Any]						     =       second.cpu().numpy()
          __A  :					Tuple						     =       out_a[~np.isnan(lowerCamelCase		)]
          __A  :					Union[str, Any]						     =       out_a[~np.isnan(lowerCamelCase		)]
          __A  :					Any						     =       np.amax(np.abs(out_a - out_a		)		)
          self.assertLessEqual(lowerCamelCase ,    1E-5		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					Optional[int]						     =       self.prepare_init_args_and_inputs_for_common()

          __A  :					str						     =       8

          __A  :					List[str]						     =       self.model_class(**lowerCamelCase		)
          model.to(lowerCamelCase		)
          model.eval()

          model.set_attention_slice("auto"		)
          with torch.no_grad():
               __A  :					Any						     =       model(**lowerCamelCase		)
          assert output is not None

          model.set_attention_slice("max"		)
          with torch.no_grad():
               __A  :					Union[str, Any]						     =       model(**lowerCamelCase		)
          assert output is not None

          model.set_attention_slice(2		)
          with torch.no_grad():
               __A  :					str						     =       model(**lowerCamelCase		)
          assert output is not None


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					Union[str, Any]						     =       self.prepare_init_args_and_inputs_for_common()

          __A  :					List[Any]						     =       8

          __A  :					List[Any]						     =       self.model_class(**lowerCamelCase		)
          model.to(lowerCamelCase		)

          with torch.no_grad():
               __A  :					Any						     =       model(**lowerCamelCase		).sample

          __A  :					int						     =       create_lora_layers(lowerCamelCase		)

          # make sure we can set a list of attention processors
          model.set_attn_processor(lowerCamelCase		)
          model.to(lowerCamelCase		)

          # test that attn processors can be set to itself
          model.set_attn_processor(model.attn_processors		)

          with torch.no_grad():
               __A  :					Optional[int]						     =       model(**lowerCamelCase ,    cross_attention_kwargs={"scale": 0.0}		).sample
               __A  :					Tuple						     =       model(**lowerCamelCase ,    cross_attention_kwargs={"scale": 0.5}		).sample
               __A  :					List[Any]						     =       model(**lowerCamelCase ,    cross_attention_kwargs={"scale": 0.5}		).sample

          assert (samplea - samplea).abs().max() < 3E-3
          assert (samplea - samplea).abs().max() < 3E-3

          # sample 2 and sample 3 should be different
          assert (samplea - samplea).abs().max() > 3E-3


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					Optional[Any]						     =       self.prepare_init_args_and_inputs_for_common()

          __A  :					Dict						     =       8

          torch.manual_seed(0		)
          __A  :					Optional[int]						     =       self.model_class(**lowerCamelCase		)
          model.to(lowerCamelCase		)

          with torch.no_grad():
               __A  :					int						     =       model(**lowerCamelCase		).sample

          __A  :					Any						     =       create_lora_layers(lowerCamelCase		)
          model.set_attn_processor(lowerCamelCase		)

          with torch.no_grad():
               __A  :					str						     =       model(**lowerCamelCase ,    cross_attention_kwargs={"scale": 0.5}		).sample

          with tempfile.TemporaryDirectory() as tmpdirname:
               model.save_attn_procs(lowerCamelCase		)
               self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase ,    "pytorch_lora_weights.bin"		)		)		)
               torch.manual_seed(0		)
               __A  :					Dict						     =       self.model_class(**lowerCamelCase		)
               new_model.to(lowerCamelCase		)
               new_model.load_attn_procs(lowerCamelCase		)

          with torch.no_grad():
               __A  :					Optional[int]						     =       new_model(**lowerCamelCase ,    cross_attention_kwargs={"scale": 0.5}		).sample

          assert (sample - new_sample).abs().max() < 1E-3

          # LoRA and no LoRA should NOT be the same
          assert (sample - old_sample).abs().max() > 1E-4


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					Any						     =       self.prepare_init_args_and_inputs_for_common()

          __A  :					Union[str, Any]						     =       8

          torch.manual_seed(0		)
          __A  :					Any						     =       self.model_class(**lowerCamelCase		)
          model.to(lowerCamelCase		)

          with torch.no_grad():
               __A  :					Optional[int]						     =       model(**lowerCamelCase		).sample

          __A  :					str						     =       create_lora_layers(lowerCamelCase		)
          model.set_attn_processor(lowerCamelCase		)

          with torch.no_grad():
               __A  :					int						     =       model(**lowerCamelCase ,    cross_attention_kwargs={"scale": 0.5}		).sample

          with tempfile.TemporaryDirectory() as tmpdirname:
               model.save_attn_procs(lowerCamelCase ,    safe_serialization=lowerCamelCase		)
               self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase ,    "pytorch_lora_weights.safetensors"		)		)		)
               torch.manual_seed(0		)
               __A  :					Dict						     =       self.model_class(**lowerCamelCase		)
               new_model.to(lowerCamelCase		)
               new_model.load_attn_procs(lowerCamelCase		)

          with torch.no_grad():
               __A  :					Optional[Any]						     =       new_model(**lowerCamelCase ,    cross_attention_kwargs={"scale": 0.5}		).sample

          assert (sample - new_sample).abs().max() < 3E-3

          # LoRA and no LoRA should NOT be the same
          assert (sample - old_sample).abs().max() > 1E-4


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					Any						     =       self.prepare_init_args_and_inputs_for_common()

          __A  :					str						     =       8

          torch.manual_seed(0		)
          __A  :					int						     =       self.model_class(**lowerCamelCase		)
          model.to(lowerCamelCase		)

          __A  :					str						     =       create_lora_layers(lowerCamelCase ,    mock_weights=lowerCamelCase		)
          model.set_attn_processor(lowerCamelCase		)
          # Saving as torch, properly reloads with directly filename
          with tempfile.TemporaryDirectory() as tmpdirname:
               model.save_attn_procs(lowerCamelCase		)
               self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase ,    "pytorch_lora_weights.bin"		)		)		)
               torch.manual_seed(0		)
               __A  :					str						     =       self.model_class(**lowerCamelCase		)
               new_model.to(lowerCamelCase		)
               new_model.load_attn_procs(lowerCamelCase ,    weight_name="pytorch_lora_weights.bin"		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					Optional[int]						     =       self.prepare_init_args_and_inputs_for_common()

          __A  :					str						     =       8

          torch.manual_seed(0		)
          __A  :					Union[str, Any]						     =       self.model_class(**lowerCamelCase		)
          model.to(lowerCamelCase		)

          __A  :					Optional[Any]						     =       create_lora_layers(lowerCamelCase ,    mock_weights=lowerCamelCase		)
          model.set_attn_processor(lowerCamelCase		)
          # Saving as torch, properly reloads with directly filename
          with tempfile.TemporaryDirectory() as tmpdirname:
               model.save_attn_procs(lowerCamelCase		)
               self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase ,    "pytorch_lora_weights.bin"		)		)		)
               torch.manual_seed(0		)
               __A  :					List[str]						     =       self.model_class(**lowerCamelCase		)
               new_model.to(lowerCamelCase		)
               with self.assertRaises(lowerCamelCase		) as e:
                    new_model.load_attn_procs(lowerCamelCase ,    use_safetensors=lowerCamelCase		)
               self.assertIn("Error no file named pytorch_lora_weights.safetensors" ,    str(e.exception		)		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					Optional[int]						     =       self.prepare_init_args_and_inputs_for_common()

          __A  :					str						     =       8

          torch.manual_seed(0		)
          __A  :					Any						     =       self.model_class(**lowerCamelCase		)
          model.to(lowerCamelCase		)

          with torch.no_grad():
               __A  :					int						     =       model(**lowerCamelCase		).sample

          __A  :					Optional[int]						     =       create_lora_layers(lowerCamelCase		)
          model.set_attn_processor(lowerCamelCase		)

          with torch.no_grad():
               __A  :					Tuple						     =       model(**lowerCamelCase ,    cross_attention_kwargs={"scale": 0.0}		).sample

          model.set_attn_processor(AttnProcessor()		)

          with torch.no_grad():
               __A  :					Optional[Any]						     =       model(**lowerCamelCase		).sample

          assert (sample - new_sample).abs().max() < 1E-4
          assert (sample - old_sample).abs().max() < 3E-3


     @unittest.skipIf(
         torch_device != "cuda" or not is_xformers_available() ,    reason="XFormers attention is only available with CUDA and `xformers` installed" ,    )
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					str						     =       self.prepare_init_args_and_inputs_for_common()

          __A  :					List[Any]						     =       4

          torch.manual_seed(0		)
          __A  :					Optional[int]						     =       self.model_class(**lowerCamelCase		)
          model.to(lowerCamelCase		)
          __A  :					List[str]						     =       create_lora_layers(lowerCamelCase		)
          model.set_attn_processor(lowerCamelCase		)

          # default
          with torch.no_grad():
               __A  :					List[Any]						     =       model(**lowerCamelCase		).sample

               model.enable_xformers_memory_efficient_attention()
               __A  :					Tuple						     =       model(**lowerCamelCase		).sample

               model.disable_xformers_memory_efficient_attention()
               __A  :					Optional[Any]						     =       model(**lowerCamelCase		).sample

          assert (sample - on_sample).abs().max() < 1E-4
          assert (sample - off_sample).abs().max() < 1E-4


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					List[str]						     =       self.prepare_init_args_and_inputs_for_common()
          __A  :					List[Any]						     =       32

          __A  :					List[Any]						     =       self.model_class(**lowerCamelCase		)
          model.to(lowerCamelCase		)
          model.eval()

          with torch.no_grad():
               __A  :					Optional[Any]						     =       model(**lowerCamelCase		)[0]

          model.enable_forward_chunking()
          with torch.no_grad():
               __A  :					Optional[int]						     =       model(**lowerCamelCase		)[0]

          self.assertEqual(output.shape ,    output_a.shape ,    "Shape doesn't match"		)
          assert np.abs(output.cpu() - output_a.cpu()		).max() < 1E-2


# (todo: sayakpaul) implement SLOW tests.





'''simple docstring'''


import os
from string import ascii_letters

_UpperCamelCase          =     ascii_letters + """ \t\n"""


def 		_lowercase   ():




     '''simple docstring'''





     __A  :					Any						     =       os.path.split(os.path.realpath(SCREAMING_SNAKE_CASE       )       )
     __A  :					dict[str, None]						     =       {}
     with open(path[0] + "/dictionary.txt"       ) as dictionary_file:
          for word in dictionary_file.read().split("\n"       ):
               __A  :					int						     =       None
     return english_words


_UpperCamelCase          =     load_dictionary()


def 		_lowercase   (SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A  :					int						     =       message.upper()
     __A  :					int						     =       remove_non_letters(SCREAMING_SNAKE_CASE       )
     __A  :					str						     =       message.split()
     __A  :					Tuple						     =       len([word for word in possible_words if word in ENGLISH_WORDS]       )
     return float(SCREAMING_SNAKE_CASE       ) / len(SCREAMING_SNAKE_CASE       )


def 		_lowercase   (SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE       )


def 		_lowercase   (SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE = 20					,				SCREAMING_SNAKE_CASE = 85       ):




     '''simple docstring'''





     __A  :					Optional[Any]						     =       get_english_count(SCREAMING_SNAKE_CASE       ) * 100 >= word_percentage
     __A  :					Optional[int]						     =       len(remove_non_letters(SCREAMING_SNAKE_CASE       )       )
     __A  :					Any						     =       (float(SCREAMING_SNAKE_CASE       ) / len(SCREAMING_SNAKE_CASE       )) * 100
     __A  :					int						     =       message_letters_percentage >= letter_percentage
     return words_match and letters_match


if __name__ == "__main__":
      import doctest

      doctest.testmod()

