




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()

a_ 		=  logging.get_logger(__name__)




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_ = True   )							->						Dict:

    """simple docstring"""


    __UpperCamelCase			=    {}
    for name in model.attn_processors.keys():
        __UpperCamelCase			=    name.endswith('''attn2.processor'''   ) and not (
            name.startswith('''transformer_in'''   ) or '''temp_attentions''' in name.split('''.'''   )
        )
        __UpperCamelCase			=    model.config.cross_attention_dim if has_cross_attention else None
        if name.startswith('''mid_block'''   ):
            __UpperCamelCase			=    model.config.block_out_channels[-1]
        elif name.startswith('''up_blocks'''   ):
            __UpperCamelCase			=    int(name[len('''up_blocks.'''   )]   )
            __UpperCamelCase			=    list(reversed(model.config.block_out_channels   )   )[block_id]
        elif name.startswith('''down_blocks'''   ):
            __UpperCamelCase			=    int(name[len('''down_blocks.'''   )]   )
            __UpperCamelCase			=    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
            __UpperCamelCase			=    8 * model.config.attention_head_dim

        __UpperCamelCase			=    LoRAAttnProcessor(hidden_size=lowercase_       ,  cross_attention_dim=lowercase_   )
        __UpperCamelCase			=    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 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE				,      _SCREAMING_SNAKE_CASE				,      unittest.TestCase ):

      """simple docstring"""

      lowerCAmelCase__      :			Dict     =       UNetaDConditionModel
      lowerCAmelCase__      :			Any     =       "sample"







      @property
      def    snake_case						(  self       :			Any					):
          __UpperCamelCase			=    4
          __UpperCamelCase			=    4
          __UpperCamelCase			=    4
          __UpperCamelCase			=    (32, 32)

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

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







      @property
      def    snake_case						(  self       :			Tuple					):
          return (4, 4, 32, 32)







      @property
      def    snake_case						(  self       :			Optional[int]					):
          return (4, 4, 32, 32)







      def    snake_case						(  self       :			int					):
          __UpperCamelCase			=    {
              '''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,
          }
          __UpperCamelCase			=    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       :			Any					):
          __UpperCamelCase		,   __UpperCamelCase			=    self.prepare_init_args_and_inputs_for_common()
          __UpperCamelCase			=    self.model_class(**snake_case					)

          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       :			str					):
          __UpperCamelCase		,   __UpperCamelCase			=    self.prepare_init_args_and_inputs_for_common()

          __UpperCamelCase			=    32

          __UpperCamelCase			=    self.model_class(**snake_case					)
          model.to(snake_case					)
          model.eval()

          with torch.no_grad():
              __UpperCamelCase			=    model(**snake_case					)

              if isinstance(snake_case			,							snake_case					):
                  __UpperCamelCase			=    output.sample

          self.assertIsNotNone(snake_case					)
          __UpperCamelCase			=    inputs_dict['''sample'''].shape
          self.assertEqual(output.shape			,							snake_case			,							'''Input and output shapes do not match'''					)







      def    snake_case						(  self       :			Union[str, Any]					):
          __UpperCamelCase		,   __UpperCamelCase			=    self.prepare_init_args_and_inputs_for_common()
          __UpperCamelCase			=    self.model_class(**snake_case					)
          model.to(snake_case					)
          model.eval()

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

              __UpperCamelCase			=    model(**snake_case					)
              if isinstance(snake_case			,							snake_case					):
                  __UpperCamelCase			=    first.sample

              __UpperCamelCase			=    model(**snake_case					)
              if isinstance(snake_case			,							snake_case					):
                  __UpperCamelCase			=    second.sample

          __UpperCamelCase			=    first.cpu().numpy()
          __UpperCamelCase			=    second.cpu().numpy()
          __UpperCamelCase			=    out_a[~np.isnan(snake_case					)]
          __UpperCamelCase			=    out_a[~np.isnan(snake_case					)]
          __UpperCamelCase			=    np.amax(np.abs(out_a - out_a					)					)
          self.assertLessEqual(snake_case			,							1E-5					)







      def    snake_case						(  self       :			int					):
          __UpperCamelCase		,   __UpperCamelCase			=    self.prepare_init_args_and_inputs_for_common()

          __UpperCamelCase			=    8

          __UpperCamelCase			=    self.model_class(**snake_case					)
          model.to(snake_case					)
          model.eval()

          model.set_attention_slice('''auto'''					)
          with torch.no_grad():
              __UpperCamelCase			=    model(**snake_case					)
          assert output is not None

          model.set_attention_slice('''max'''					)
          with torch.no_grad():
              __UpperCamelCase			=    model(**snake_case					)
          assert output is not None

          model.set_attention_slice(2					)
          with torch.no_grad():
              __UpperCamelCase			=    model(**snake_case					)
          assert output is not None







      def    snake_case						(  self       :			int					):
          __UpperCamelCase		,   __UpperCamelCase			=    self.prepare_init_args_and_inputs_for_common()

          __UpperCamelCase			=    8

          __UpperCamelCase			=    self.model_class(**snake_case					)
          model.to(snake_case					)

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

          __UpperCamelCase			=    create_lora_layers(snake_case					)

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

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

          with torch.no_grad():
              __UpperCamelCase			=    model(**snake_case			,							cross_attention_kwargs={'''scale''': 0.0}					).sample
              __UpperCamelCase			=    model(**snake_case			,							cross_attention_kwargs={'''scale''': 0.5}					).sample
              __UpperCamelCase			=    model(**snake_case			,							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       :			List[str]					):
          __UpperCamelCase		,   __UpperCamelCase			=    self.prepare_init_args_and_inputs_for_common()

          __UpperCamelCase			=    8

          torch.manual_seed(0					)
          __UpperCamelCase			=    self.model_class(**snake_case					)
          model.to(snake_case					)

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

          __UpperCamelCase			=    create_lora_layers(snake_case					)
          model.set_attn_processor(snake_case					)

          with torch.no_grad():
              __UpperCamelCase			=    model(**snake_case			,							cross_attention_kwargs={'''scale''': 0.5}					).sample

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

          with torch.no_grad():
              __UpperCamelCase			=    new_model(**snake_case			,							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       :			Optional[Any]					):
          __UpperCamelCase		,   __UpperCamelCase			=    self.prepare_init_args_and_inputs_for_common()

          __UpperCamelCase			=    8

          torch.manual_seed(0					)
          __UpperCamelCase			=    self.model_class(**snake_case					)
          model.to(snake_case					)

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

          __UpperCamelCase			=    create_lora_layers(snake_case					)
          model.set_attn_processor(snake_case					)

          with torch.no_grad():
              __UpperCamelCase			=    model(**snake_case			,							cross_attention_kwargs={'''scale''': 0.5}					).sample

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

          with torch.no_grad():
              __UpperCamelCase			=    new_model(**snake_case			,							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       :			Optional[int]					):
          # enable deterministic behavior for gradient checkpointing
          __UpperCamelCase		,   __UpperCamelCase			=    self.prepare_init_args_and_inputs_for_common()

          __UpperCamelCase			=    8

          torch.manual_seed(0					)
          __UpperCamelCase			=    self.model_class(**snake_case					)
          model.to(snake_case					)

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







      def    snake_case						(  self       :			List[str]					):
          __UpperCamelCase		,   __UpperCamelCase			=    self.prepare_init_args_and_inputs_for_common()

          __UpperCamelCase			=    8

          torch.manual_seed(0					)
          __UpperCamelCase			=    self.model_class(**snake_case					)
          model.to(snake_case					)

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







      def    snake_case						(  self       :			Optional[Any]					):
          __UpperCamelCase		,   __UpperCamelCase			=    self.prepare_init_args_and_inputs_for_common()

          __UpperCamelCase			=    8

          torch.manual_seed(0					)
          __UpperCamelCase			=    self.model_class(**snake_case					)
          model.to(snake_case					)

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

          __UpperCamelCase			=    create_lora_layers(snake_case					)
          model.set_attn_processor(snake_case					)

          with torch.no_grad():
              __UpperCamelCase			=    model(**snake_case			,							cross_attention_kwargs={'''scale''': 0.0}					).sample

          model.set_attn_processor(AttnProcessor()					)

          with torch.no_grad():
              __UpperCamelCase			=    model(**snake_case					).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       :			Union[str, Any]					):
          # enable deterministic behavior for gradient checkpointing
          __UpperCamelCase		,   __UpperCamelCase			=    self.prepare_init_args_and_inputs_for_common()

          __UpperCamelCase			=    4

          torch.manual_seed(0					)
          __UpperCamelCase			=    self.model_class(**snake_case					)
          model.to(snake_case					)
          __UpperCamelCase			=    create_lora_layers(snake_case					)
          model.set_attn_processor(snake_case					)

          # default
          with torch.no_grad():
              __UpperCamelCase			=    model(**snake_case					).sample

              model.enable_xformers_memory_efficient_attention()
              __UpperCamelCase			=    model(**snake_case					).sample

              model.disable_xformers_memory_efficient_attention()
              __UpperCamelCase			=    model(**snake_case					).sample

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







      def    snake_case						(  self       :			Dict					):
          __UpperCamelCase		,   __UpperCamelCase			=    self.prepare_init_args_and_inputs_for_common()
          __UpperCamelCase			=    32

          __UpperCamelCase			=    self.model_class(**snake_case					)
          model.to(snake_case					)
          model.eval()

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

          model.enable_forward_chunking()
          with torch.no_grad():
              __UpperCamelCase			=    model(**snake_case					)[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.














from typing import Any

import numpy as np
class 							__snake_case:



 def __init__(						self : str			,		UpperCAmelCase__ : np.ndarray			,		UpperCAmelCase__ : int						):

    """simple docstring"""






    if np.any(tableau[:, -1]			,		where=tableau[:, -1] < 0						):
       raise ValueError("""RHS must be > 0"""						)

    lowercase__    :						List[str]       =			tableau
    lowercase__  ,       lowercase__    :						Optional[Any]       =			tableau.shape

    # Number of decision variables x1, x2, x3...
    lowercase__    :						List[str]       =			n_vars

    # Number of artificial variables to be minimised
    lowercase__    :						str       =			len(np.where(tableau[self.n_vars : -1] == -1						)[0]						)

    # 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
    lowercase__    :						Optional[Any]       =			(self.n_art_vars > 0) + 1

    # Number of slack variables added to make inequalities into equalities
    lowercase__    :						Optional[int]       =			self.n_rows - self.n_stages

    # Objectives for each stage
    lowercase__    :						Any       =			["""max"""]

    # In two stage simplex, first minimise then maximise
    if self.n_art_vars:
       self.objectives.append("""min"""						)

    lowercase__    :						Union[str, Any]       =			[""""""]

    # Index of current pivot row and column
    lowercase__    :						List[str]       =			None
    lowercase__    :						List[str]       =			None

    # Does objective row only contain (non)-negative values?
    lowercase__    :						str       =			False



 @staticmethod
 def     UpperCAmelCase     (						*UpperCAmelCase__ : int						):

    """simple docstring"""






    if len(UpperCAmelCase__						) != 3:
       raise ValueError("""Must provide n_vars, n_slack, and n_art_vars"""						)

    if not all(x >= 0 and isinstance(UpperCAmelCase__			,		UpperCAmelCase__						) for x in args						):
       raise ValueError("""All arguments must be non-negative integers"""						)

    # decision | slack | artificial
    lowercase__    :						Optional[int]       =			["""x""", """s""", """a"""]
    lowercase__    :						int       =			[]
    for i in range(3						):
       for j in range(args[i]						):
          titles.append(string_starts[i] + str(j + 1						)						)
    titles.append("""RHS"""						)
    return titles



 def     UpperCAmelCase     (						self : List[str]			,		UpperCAmelCase__ : np.ndarray						):

    """simple docstring"""






    lowercase__    :						Optional[Any]       =			self.objectives[-1]

    # Find entries of highest magnitude in objective rows
    lowercase__    :						Union[str, Any]       =			(objective == """min""") - (objective == """max""")
    lowercase__    :						Optional[int]       =			np.argmax(sign * tableau[0, : self.n_vars]						)

    # Choice is only valid if below 0 for maximise, and above for minimise
    if sign * self.tableau[0, col_idx] <= 0:
       lowercase__    :						Optional[int]       =			True
       return 0, 0

    # Pivot row is chosen as having the lowest quotient when elements of
    # the pivot column divide the right-hand side

    # Slice excluding the objective rows
    lowercase__    :						Any       =			slice(self.n_stages			,		self.n_rows						)

    # RHS
    lowercase__    :						Union[str, Any]       =			tableau[s, -1]

    # Elements of pivot column within slice
    lowercase__    :						Optional[int]       =			tableau[s, col_idx]

    # Array filled with nans
    lowercase__    :						Optional[int]       =			np.full(self.n_rows - self.n_stages			,		np.nan						)

    # If element in pivot column is greater than zeron_stages, return
    # quotient or nan otherwise
    lowercase__    :						Optional[int]       =			np.divide(UpperCAmelCase__			,		UpperCAmelCase__			,		out=UpperCAmelCase__			,		where=divisor > 0						)

    # Arg of minimum quotient excluding the nan values. n_stages is added
    # to compensate for earlier exclusion of objective columns
    lowercase__    :						Dict       =			np.nanargmin(UpperCAmelCase__						) + self.n_stages
    return row_idx, col_idx



 def     UpperCAmelCase     (						self : Union[str, Any]			,		UpperCAmelCase__ : np.ndarray			,		UpperCAmelCase__ : int			,		UpperCAmelCase__ : int						):

    """simple docstring"""






    lowercase__    :						int       =			tableau[row_idx].copy()

    lowercase__    :						Optional[Any]       =			piv_row[col_idx]

    # Entry becomes 1
    piv_row *= 1 / piv_val

    # Variable in pivot column becomes basic, ie the only non-zero entry
    for idx, coeff in enumerate(tableau[:, col_idx]						):
       tableau[idx] += -coeff * piv_row
    lowercase__    :						Any       =			piv_row
    return tableau



 def     UpperCAmelCase     (						self : Optional[Any]			,		UpperCAmelCase__ : np.ndarray						):

    """simple docstring"""






    self.objectives.pop()

    if not self.objectives:
       return tableau

    # Slice containing ids for artificial columns
    lowercase__    :						Optional[int]       =			slice(-self.n_art_vars - 1			,		-1						)

    # Delete the artificial variable columns
    lowercase__    :						Dict       =			np.delete(UpperCAmelCase__			,		UpperCAmelCase__			,		axis=1						)

    # Delete the objective row of the first stage
    lowercase__    :						Optional[Any]       =			np.delete(UpperCAmelCase__			,		0			,		axis=0						)

    lowercase__    :						str       =			1
    self.n_rows -= 1
    lowercase__    :						List[str]       =			0
    lowercase__    :						List[Any]       =			False
    return tableau



 def     UpperCAmelCase     (						self : Optional[Any]						):

    """simple docstring"""






    for _ in range(100						):
       # Completion of each stage removes an objective. If both stages
       # are complete, then no objectives are left
       if not self.objectives:
          lowercase__    :						List[Any]       =			self.generate_col_titles(
              self.n_vars			,		self.n_slack			,		self.n_art_vars						)

          # Find the values of each variable at optimal solution
          return self.interpret_tableau(self.tableau			,		self.col_titles						)

       lowercase__  ,       lowercase__    :						Optional[Any]       =			self.find_pivot(self.tableau						)

       # If there are no more negative values in objective row
       if self.stop_iter:
          # Delete artificial variable columns and rows. Update attributes
          lowercase__    :						Any       =			self.change_stage(self.tableau						)
       else:
          lowercase__    :						str       =			self.pivot(self.tableau			,		UpperCAmelCase__			,		UpperCAmelCase__						)
    return {}







 def     UpperCAmelCase     (						self : str			,		UpperCAmelCase__ : np.ndarray			,		UpperCAmelCase__ : list[str]						):

    """simple docstring"""






    lowercase__    :						List[str]       =			{"""P""": abs(tableau[0, -1]						)}

    for i in range(self.n_vars						):
       # Gives ids of nonzero entries in the ith column
       lowercase__    :						int       =			np.nonzero(tableau[:, i]						)
       lowercase__    :						Tuple       =			len(nonzero[0]						)

       # First entry in the nonzero ids
       lowercase__    :						int       =			nonzero[0][0]
       lowercase__    :						Tuple       =			tableau[nonzero_rowidx, i]

       # If there is only one nonzero value in column, which is one
       if n_nonzero == nonzero_val == 1:
          lowercase__    :						Tuple       =			tableau[nonzero_rowidx, -1]
          lowercase__    :						Union[str, Any]       =			rhs_val

        # Check for basic variables
    for title in col_titles:
       # Don't add RHS or slack variables to output dict
       if title[0] not in "R-s-a":
          output_dict.setdefault(UpperCAmelCase__			,		0						)
    return output_dict


if __name__ == "__main__":
 import doctest

 doctest.testmod()


