




'''simple docstring'''







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()

lowercase			:      List[Any]			=					logging.get_logger(__name__)



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase = True							):
     snake_case :			List[Any]					   = {}
     for name in model.attn_processors.keys():
          snake_case :			Tuple					   = name.endswith("""attn2.processor"""							) and not (
              name.startswith("""transformer_in"""							) or """temp_attentions""" in name.split("""."""							)
          )
          snake_case :			Any					   = model.config.cross_attention_dim if has_cross_attention else None
          if name.startswith("""mid_block"""							):
               snake_case :			int					   = model.config.block_out_channels[-1]
          elif name.startswith("""up_blocks"""							):
               snake_case :			Dict					   = int(name[len("""up_blocks."""							)]							)
               snake_case :			Any					   = list(reversed(model.config.block_out_channels							)							)[block_id]
          elif name.startswith("""down_blocks"""							):
               snake_case :			Optional[Any]					   = int(name[len("""down_blocks."""							)]							)
               snake_case :			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
               snake_case :			int					   = 8 * model.config.attention_head_dim

          snake_case :			Any					   = LoRAAttnProcessor(hidden_size=__lowerCamelCase    ,      cross_attention_dim=__lowerCamelCase							)
          snake_case :			Optional[int]					   = 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 				_a   (snake_case_,		snake_case_,		unittest.TestCase  ):





     '''simple docstring'''



     lowerCAmelCase_     :    Dict									=				UNetaDConditionModel
     lowerCAmelCase_     :    Optional[Any]									=				"""sample"""







     @property
     def  snake_case_			(							self       )			->							Dict:
          snake_case :			Tuple					   = 4
          snake_case :			Optional[Any]					   = 4
          snake_case :			Optional[int]					   = 4
          snake_case :			int					   = (32, 32)

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

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







     @property
     def  snake_case_			(							self       )			->							Optional[Any]:
          return (4, 4, 32, 32)







     @property
     def  snake_case_			(							self       )			->							int:
          return (4, 4, 32, 32)







     def  snake_case_			(							self       )			->							Any:
          snake_case :			List[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,
          }
          snake_case :			str					   = 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  snake_case_			(							self       )			->							Tuple:
          snake_case   ,		snake_case :			List[Any]					   = self.prepare_init_args_and_inputs_for_common()
          snake_case :			Union[str, Any]					   = self.model_class(**__a       )

          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  snake_case_			(							self       )			->							Optional[int]:
          snake_case   ,		snake_case :			Union[str, Any]					   = self.prepare_init_args_and_inputs_for_common()

          snake_case :			Optional[int]					   = 32

          snake_case :			List[Any]					   = self.model_class(**__a       )
          model.to(__a       )
          model.eval()

          with torch.no_grad():
               snake_case :			List[Any]					   = model(**__a       )

               if isinstance(__a       ,__a       ):
                    snake_case :			Optional[Any]					   = output.sample

          self.assertIsNotNone(__a       )
          snake_case :			Optional[Any]					   = inputs_dict["""sample"""].shape
          self.assertEqual(output.shape       ,__a       ,"""Input and output shapes do not match"""       )







     def  snake_case_			(							self       )			->							Optional[Any]:
          snake_case   ,		snake_case :			Tuple					   = self.prepare_init_args_and_inputs_for_common()
          snake_case :			Optional[Any]					   = self.model_class(**__a       )
          model.to(__a       )
          model.eval()

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

               snake_case :			Union[str, Any]					   = model(**__a       )
               if isinstance(__a       ,__a       ):
                    snake_case :			str					   = first.sample

               snake_case :			str					   = model(**__a       )
               if isinstance(__a       ,__a       ):
                    snake_case :			List[str]					   = second.sample

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







     def  snake_case_			(							self       )			->							Union[str, Any]:
          snake_case   ,		snake_case :			Dict					   = self.prepare_init_args_and_inputs_for_common()

          snake_case :			List[str]					   = 8

          snake_case :			str					   = self.model_class(**__a       )
          model.to(__a       )
          model.eval()

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

          model.set_attention_slice("""max"""       )
          with torch.no_grad():
               snake_case :			Dict					   = model(**__a       )
          assert output is not None

          model.set_attention_slice(2       )
          with torch.no_grad():
               snake_case :			Optional[Any]					   = model(**__a       )
          assert output is not None







     def  snake_case_			(							self       )			->							Optional[Any]:
          snake_case   ,		snake_case :			List[str]					   = self.prepare_init_args_and_inputs_for_common()

          snake_case :			Any					   = 8

          snake_case :			List[Any]					   = self.model_class(**__a       )
          model.to(__a       )

          with torch.no_grad():
               snake_case :			Tuple					   = model(**__a       ).sample

          snake_case :			List[str]					   = create_lora_layers(__a       )

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

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

          with torch.no_grad():
               snake_case :			List[Any]					   = model(**__a       ,cross_attention_kwargs={"""scale""": 0.0}       ).sample
               snake_case :			Tuple					   = model(**__a       ,cross_attention_kwargs={"""scale""": 0.5}       ).sample
               snake_case :			Optional[Any]					   = model(**__a       ,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  snake_case_			(							self       )			->							Dict:
          snake_case   ,		snake_case :			List[str]					   = self.prepare_init_args_and_inputs_for_common()

          snake_case :			Any					   = 8

          torch.manual_seed(0       )
          snake_case :			int					   = self.model_class(**__a       )
          model.to(__a       )

          with torch.no_grad():
               snake_case :			int					   = model(**__a       ).sample

          snake_case :			int					   = create_lora_layers(__a       )
          model.set_attn_processor(__a       )

          with torch.no_grad():
               snake_case :			Dict					   = model(**__a       ,cross_attention_kwargs={"""scale""": 0.5}       ).sample

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

          with torch.no_grad():
               snake_case :			List[Any]					   = new_model(**__a       ,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  snake_case_			(							self       )			->							List[str]:
          snake_case   ,		snake_case :			Union[str, Any]					   = self.prepare_init_args_and_inputs_for_common()

          snake_case :			Union[str, Any]					   = 8

          torch.manual_seed(0       )
          snake_case :			int					   = self.model_class(**__a       )
          model.to(__a       )

          with torch.no_grad():
               snake_case :			Optional[Any]					   = model(**__a       ).sample

          snake_case :			int					   = create_lora_layers(__a       )
          model.set_attn_processor(__a       )

          with torch.no_grad():
               snake_case :			List[str]					   = model(**__a       ,cross_attention_kwargs={"""scale""": 0.5}       ).sample

          with tempfile.TemporaryDirectory() as tmpdirname:
               model.save_attn_procs(__a       ,safe_serialization=__a       )
               self.assertTrue(os.path.isfile(os.path.join(__a       ,"""pytorch_lora_weights.safetensors"""       )       )       )
               torch.manual_seed(0       )
               snake_case :			List[Any]					   = self.model_class(**__a       )
               new_model.to(__a       )
               new_model.load_attn_procs(__a       )

          with torch.no_grad():
               snake_case :			Tuple					   = new_model(**__a       ,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  snake_case_			(							self       )			->							List[str]:
          # enable deterministic behavior for gradient checkpointing
          snake_case   ,		snake_case :			Union[str, Any]					   = self.prepare_init_args_and_inputs_for_common()

          snake_case :			List[str]					   = 8

          torch.manual_seed(0       )
          snake_case :			List[Any]					   = self.model_class(**__a       )
          model.to(__a       )

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







     def  snake_case_			(							self       )			->							Any:
          snake_case   ,		snake_case :			List[Any]					   = self.prepare_init_args_and_inputs_for_common()

          snake_case :			str					   = 8

          torch.manual_seed(0       )
          snake_case :			Dict					   = self.model_class(**__a       )
          model.to(__a       )

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







     def  snake_case_			(							self       )			->							Union[str, Any]:
          snake_case   ,		snake_case :			Optional[Any]					   = self.prepare_init_args_and_inputs_for_common()

          snake_case :			str					   = 8

          torch.manual_seed(0       )
          snake_case :			str					   = self.model_class(**__a       )
          model.to(__a       )

          with torch.no_grad():
               snake_case :			Optional[int]					   = model(**__a       ).sample

          snake_case :			List[Any]					   = create_lora_layers(__a       )
          model.set_attn_processor(__a       )

          with torch.no_grad():
               snake_case :			Union[str, Any]					   = model(**__a       ,cross_attention_kwargs={"""scale""": 0.0}       ).sample

          model.set_attn_processor(AttnProcessor()       )

          with torch.no_grad():
               snake_case :			Dict					   = model(**__a       ).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  snake_case_			(							self       )			->							Optional[Any]:
          # enable deterministic behavior for gradient checkpointing
          snake_case   ,		snake_case :			str					   = self.prepare_init_args_and_inputs_for_common()

          snake_case :			Optional[Any]					   = 4

          torch.manual_seed(0       )
          snake_case :			List[str]					   = self.model_class(**__a       )
          model.to(__a       )
          snake_case :			Union[str, Any]					   = create_lora_layers(__a       )
          model.set_attn_processor(__a       )

          # default
          with torch.no_grad():
               snake_case :			List[str]					   = model(**__a       ).sample

               model.enable_xformers_memory_efficient_attention()
               snake_case :			Any					   = model(**__a       ).sample

               model.disable_xformers_memory_efficient_attention()
               snake_case :			Dict					   = model(**__a       ).sample

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







     def  snake_case_			(							self       )			->							Dict:
          snake_case   ,		snake_case :			str					   = self.prepare_init_args_and_inputs_for_common()
          snake_case :			Optional[Any]					   = 32

          snake_case :			Tuple					   = self.model_class(**__a       )
          model.to(__a       )
          model.eval()

          with torch.no_grad():
               snake_case :			str					   = model(**__a       )[0]

          model.enable_forward_chunking()
          with torch.no_grad():
               snake_case :			Tuple					   = model(**__a       )[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'''







from dataclasses import dataclass

lowercase			:      Tuple			=					"""0.1"""
lowercase			:      Optional[Any]			=					"""Lucia Harcekova"""







@dataclass
class 				_a   :





     '''simple docstring'''



     lowerCAmelCase_     :    int
     lowerCAmelCase_     :    int
     lowerCAmelCase_     :    str







     def __repr__(							self       )			->							str:
          return F'''({self.offset}, {self.length}, {self.indicator})'''







class 				_a   :





     '''simple docstring'''



     def __init__(							self       ,__a = 13       ,__a = 6       )			->							None:
          snake_case :			Dict					   = window_size
          snake_case :			Any					   = lookahead_buffer_size
          snake_case :			str					   = self.window_size - self.lookahead_buffer_size







     def  snake_case_			(							self       ,__a       )			->							list[Token]:
          snake_case :			Tuple					   = []
          snake_case :			Optional[int]					   = """"""

          # while there are still characters in text to compress
          while text:
               # find the next encoding phrase
               # - triplet with offset, length, indicator (the next encoding character)
               snake_case :			str					   = self._find_encoding_token(__a       ,__a       )

               # update the search buffer:
               # - add new characters from text into it
               # - check if size exceed the max search buffer size, if so, drop the
               #   oldest elements
               search_buffer += text[: token.length + 1]
               if len(__a       ) > self.search_buffer_size:
                    snake_case :			List[Any]					   = search_buffer[-self.search_buffer_size :]

               # update the text
               snake_case :			str					   = text[token.length + 1 :]

               # append the token to output
               output.append(__a       )

          return output







     def  snake_case_			(							self       ,__a       )			->							str:
          snake_case :			Union[str, Any]					   = """"""

          for token in tokens:
               for _ in range(token.length       ):
                    output += output[-token.offset]
               output += token.indicator

          return output







     def  snake_case_			(							self       ,__a       ,__a       )			->							Token:

          if not text:
               raise ValueError("""We need some text to work with."""       )

          # Initialise result parameters to default values
          snake_case   ,		snake_case :			int					   = 0, 0

          if not search_buffer:
               return Token(__a       ,__a       ,text[length]       )

          for i, character in enumerate(__a       ):
               snake_case :			Tuple					   = len(__a       ) - i
               if character == text[0]:
                    snake_case :			List[Any]					   = self._match_length_from_index(__a       ,__a       ,0       ,__a       )
                    # if the found length is bigger than the current or if it's equal,
                    # which means it's offset is smaller: update offset and length
                    if found_length >= length:
                         snake_case   ,		snake_case :			Optional[Any]					   = found_offset, found_length

          return Token(__a       ,__a       ,text[length]       )







     def  snake_case_			(							self       ,__a       ,__a       ,__a       ,__a       )			->							int:
          if not text or text[text_index] != window[window_index]:
               return 0
          return 1 + self._match_length_from_index(
              __a       ,window + text[text_index]       ,text_index + 1       ,window_index + 1       )


if __name__ == "__main__":
      from doctest import testmod

      testmod()
      # Initialize compressor class
      lowercase			:      int			=					LZaaCompressor(window_size=13, lookahead_buffer_size=6)

      # Example
      lowercase			:      int			=					"""cabracadabrarrarrad"""
      lowercase			:      str			=					lzaa_compressor.compress(TEXT)
      print(lzaa_compressor.compress("""ababcbababaa"""))
      lowercase			:      List[Any]			=					lzaa_compressor.decompress(compressed_text)
      assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."



