
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 		_lowerCamelCase       (   _a			,   _a = True  ):
  """simple docstring"""




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

    _lowerCamelCase      =						LoRAAttnProcessor(hidden_size=_a			,   cross_attention_dim=_a  )
    _lowerCamelCase      =						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__				( lowercase_						,lowercase_						,unittest.TestCase					):


  """simple docstring"""





  _UpperCamelCase						 = UNetaDConditionModel
  _UpperCamelCase						 = "sample"


  @property
  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						4
    _lowerCamelCase      =						4
    _lowerCamelCase      =						4
    _lowerCamelCase      =						(32, 32)

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

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


  @property
  def     _UpperCAmelCase  (					self		):
    return (4, 4, 32, 32)


  @property
  def     _UpperCAmelCase  (					self		):
    return (4, 4, 32, 32)


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						{
        '''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,
    }
    _lowerCamelCase      =						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     _UpperCAmelCase  (					self		):
    _lowerCamelCase    ,    _lowerCamelCase      =						self.prepare_init_args_and_inputs_for_common()
    _lowerCamelCase      =						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     _UpperCAmelCase  (					self		):
    _lowerCamelCase    ,    _lowerCamelCase      =						self.prepare_init_args_and_inputs_for_common()

    _lowerCamelCase      =						32

    _lowerCamelCase      =						self.model_class(**a__		)
    model.to(a__		)
    model.eval()

    with torch.no_grad():
      _lowerCamelCase      =						model(**a__		)

      if isinstance(a__	,		a__		):
        _lowerCamelCase      =						output.sample

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


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase    ,    _lowerCamelCase      =						self.prepare_init_args_and_inputs_for_common()
    _lowerCamelCase      =						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		)

      _lowerCamelCase      =						model(**a__		)
      if isinstance(a__	,		a__		):
        _lowerCamelCase      =						first.sample

      _lowerCamelCase      =						model(**a__		)
      if isinstance(a__	,		a__		):
        _lowerCamelCase      =						second.sample

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


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase    ,    _lowerCamelCase      =						self.prepare_init_args_and_inputs_for_common()

    _lowerCamelCase      =						8

    _lowerCamelCase      =						self.model_class(**a__		)
    model.to(a__		)
    model.eval()

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

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

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


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase    ,    _lowerCamelCase      =						self.prepare_init_args_and_inputs_for_common()

    _lowerCamelCase      =						8

    _lowerCamelCase      =						self.model_class(**a__		)
    model.to(a__		)

    with torch.no_grad():
      _lowerCamelCase      =						model(**a__		).sample

    _lowerCamelCase      =						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():
      _lowerCamelCase      =						model(**a__	,		cross_attention_kwargs={'''scale''': 0.0}		).sample
      _lowerCamelCase      =						model(**a__	,		cross_attention_kwargs={'''scale''': 0.5}		).sample
      _lowerCamelCase      =						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     _UpperCAmelCase  (					self		):
    _lowerCamelCase    ,    _lowerCamelCase      =						self.prepare_init_args_and_inputs_for_common()

    _lowerCamelCase      =						8

    torch.manual_seed(0		)
    _lowerCamelCase      =						self.model_class(**a__		)
    model.to(a__		)

    with torch.no_grad():
      _lowerCamelCase      =						model(**a__		).sample

    _lowerCamelCase      =						create_lora_layers(a__		)
    model.set_attn_processor(a__		)

    with torch.no_grad():
      _lowerCamelCase      =						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		)
      _lowerCamelCase      =						self.model_class(**a__		)
      new_model.to(a__		)
      new_model.load_attn_procs(a__		)

    with torch.no_grad():
      _lowerCamelCase      =						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     _UpperCAmelCase  (					self		):
    _lowerCamelCase    ,    _lowerCamelCase      =						self.prepare_init_args_and_inputs_for_common()

    _lowerCamelCase      =						8

    torch.manual_seed(0		)
    _lowerCamelCase      =						self.model_class(**a__		)
    model.to(a__		)

    with torch.no_grad():
      _lowerCamelCase      =						model(**a__		).sample

    _lowerCamelCase      =						create_lora_layers(a__		)
    model.set_attn_processor(a__		)

    with torch.no_grad():
      _lowerCamelCase      =						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		)
      _lowerCamelCase      =						self.model_class(**a__		)
      new_model.to(a__		)
      new_model.load_attn_procs(a__		)

    with torch.no_grad():
      _lowerCamelCase      =						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     _UpperCAmelCase  (					self		):
    # enable deterministic behavior for gradient checkpointing
    _lowerCamelCase    ,    _lowerCamelCase      =						self.prepare_init_args_and_inputs_for_common()

    _lowerCamelCase      =						8

    torch.manual_seed(0		)
    _lowerCamelCase      =						self.model_class(**a__		)
    model.to(a__		)

    _lowerCamelCase      =						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		)
      _lowerCamelCase      =						self.model_class(**a__		)
      new_model.to(a__		)
      new_model.load_attn_procs(a__	,		weight_name='''pytorch_lora_weights.bin'''		)


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase    ,    _lowerCamelCase      =						self.prepare_init_args_and_inputs_for_common()

    _lowerCamelCase      =						8

    torch.manual_seed(0		)
    _lowerCamelCase      =						self.model_class(**a__		)
    model.to(a__		)

    _lowerCamelCase      =						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		)
      _lowerCamelCase      =						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     _UpperCAmelCase  (					self		):
    _lowerCamelCase    ,    _lowerCamelCase      =						self.prepare_init_args_and_inputs_for_common()

    _lowerCamelCase      =						8

    torch.manual_seed(0		)
    _lowerCamelCase      =						self.model_class(**a__		)
    model.to(a__		)

    with torch.no_grad():
      _lowerCamelCase      =						model(**a__		).sample

    _lowerCamelCase      =						create_lora_layers(a__		)
    model.set_attn_processor(a__		)

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

    model.set_attn_processor(AttnProcessor()		)

    with torch.no_grad():
      _lowerCamelCase      =						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     _UpperCAmelCase  (					self		):
    # enable deterministic behavior for gradient checkpointing
    _lowerCamelCase    ,    _lowerCamelCase      =						self.prepare_init_args_and_inputs_for_common()

    _lowerCamelCase      =						4

    torch.manual_seed(0		)
    _lowerCamelCase      =						self.model_class(**a__		)
    model.to(a__		)
    _lowerCamelCase      =						create_lora_layers(a__		)
    model.set_attn_processor(a__		)

    # default
    with torch.no_grad():
      _lowerCamelCase      =						model(**a__		).sample

      model.enable_xformers_memory_efficient_attention()
      _lowerCamelCase      =						model(**a__		).sample

      model.disable_xformers_memory_efficient_attention()
      _lowerCamelCase      =						model(**a__		).sample

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


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase    ,    _lowerCamelCase      =						self.prepare_init_args_and_inputs_for_common()
    _lowerCamelCase      =						32

    _lowerCamelCase      =						self.model_class(**a__		)
    model.to(a__		)
    model.eval()

    with torch.no_grad():
      _lowerCamelCase      =						model(**a__		)[0]

    model.enable_forward_chunking()
    with torch.no_grad():
      _lowerCamelCase      =						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.






import warnings

from ...utils import logging
from .image_processing_conditional_detr import ConditionalDetrImageProcessor


_UpperCAmelCase    			=					logging.get_logger(__name__)

class 		__magic_name__				( lowercase_					):


  """simple docstring"""





  def __init__(					self	,		*a__	,		**a__		):
    warnings.warn(
        '''The class ConditionalDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
        ''' Please use ConditionalDetrImageProcessor instead.'''	,		a__	,		)
    super().__init__(*a__	,		**a__		)




