



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()

__snake_case		:Dict 						=	logging.get_logger(__name__)





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

        __a					=							LoRAAttnProcessor(hidden_size=UpperCamelCase_  ,					cross_attention_dim=UpperCamelCase_							)
        __a					=							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       ):
       UpperCamelCase__    :       List[Any]		   =		UNetaDConditionModel
       UpperCamelCase__    :       List[Any]		   =		'sample'



       @property
       def 						_lowerCamelCase     (						self				:	Union[str, Any]):


           '''simple docstring'''

           __a					=							4
           __a					=							4
           __a					=							4
           __a					=							(32, 32)

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

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



       @property
       def 						_lowerCamelCase     (						self				:	str):


           '''simple docstring'''

           return (4, 4, 32, 32)



       @property
       def 						_lowerCamelCase     (						self				:	List[Any]):


           '''simple docstring'''

           return (4, 4, 32, 32)



       def 						_lowerCamelCase     (						self				:	Tuple):


           '''simple docstring'''

           __a					=							{
               '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					=							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				:	str):


           '''simple docstring'''

           __a					=							self.prepare_init_args_and_inputs_for_common()
           __a					=							self.model_class(**lowercase_)

           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				:	str):


           '''simple docstring'''

           __a					=							self.prepare_init_args_and_inputs_for_common()

           __a					=							32

           __a					=							self.model_class(**lowercase_)
           model.to(lowercase_)
           model.eval()

           with torch.no_grad():
               __a					=							model(**lowercase_)

               if isinstance(lowercase_	,						lowercase_):
                   __a					=							output.sample

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



       def 						_lowerCamelCase     (						self				:	str):


           '''simple docstring'''

           __a					=							self.prepare_init_args_and_inputs_for_common()
           __a					=							self.model_class(**lowercase_)
           model.to(lowercase_)
           model.eval()

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

               __a					=							model(**lowercase_)
               if isinstance(lowercase_	,						lowercase_):
                   __a					=							first.sample

               __a					=							model(**lowercase_)
               if isinstance(lowercase_	,						lowercase_):
                   __a					=							second.sample

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



       def 						_lowerCamelCase     (						self				:	int):


           '''simple docstring'''

           __a					=							self.prepare_init_args_and_inputs_for_common()

           __a					=							8

           __a					=							self.model_class(**lowercase_)
           model.to(lowercase_)
           model.eval()

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

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

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



       def 						_lowerCamelCase     (						self				:	Union[str, Any]):


           '''simple docstring'''

           __a					=							self.prepare_init_args_and_inputs_for_common()

           __a					=							8

           __a					=							self.model_class(**lowercase_)
           model.to(lowercase_)

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

           __a					=							create_lora_layers(lowercase_)

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

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

           with torch.no_grad():
               __a					=							model(**lowercase_	,						cross_attention_kwargs={'''scale''': 0.0}).sample
               __a					=							model(**lowercase_	,						cross_attention_kwargs={'''scale''': 0.5}).sample
               __a					=							model(**lowercase_	,						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				:	Optional[int]):


           '''simple docstring'''

           __a					=							self.prepare_init_args_and_inputs_for_common()

           __a					=							8

           torch.manual_seed(0)
           __a					=							self.model_class(**lowercase_)
           model.to(lowercase_)

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

           __a					=							create_lora_layers(lowercase_)
           model.set_attn_processor(lowercase_)

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

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

           with torch.no_grad():
               __a					=							new_model(**lowercase_	,						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				:	str):


           '''simple docstring'''

           __a					=							self.prepare_init_args_and_inputs_for_common()

           __a					=							8

           torch.manual_seed(0)
           __a					=							self.model_class(**lowercase_)
           model.to(lowercase_)

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

           __a					=							create_lora_layers(lowercase_)
           model.set_attn_processor(lowercase_)

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

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

           with torch.no_grad():
               __a					=							new_model(**lowercase_	,						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				:	Tuple):


           '''simple docstring'''

           __a					=							self.prepare_init_args_and_inputs_for_common()

           __a					=							8

           torch.manual_seed(0)
           __a					=							self.model_class(**lowercase_)
           model.to(lowercase_)

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



       def 						_lowerCamelCase     (						self				:	Optional[int]):


           '''simple docstring'''

           __a					=							self.prepare_init_args_and_inputs_for_common()

           __a					=							8

           torch.manual_seed(0)
           __a					=							self.model_class(**lowercase_)
           model.to(lowercase_)

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



       def 						_lowerCamelCase     (						self				:	str):


           '''simple docstring'''

           __a					=							self.prepare_init_args_and_inputs_for_common()

           __a					=							8

           torch.manual_seed(0)
           __a					=							self.model_class(**lowercase_)
           model.to(lowercase_)

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

           __a					=							create_lora_layers(lowercase_)
           model.set_attn_processor(lowercase_)

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

           model.set_attn_processor(AttnProcessor())

           with torch.no_grad():
               __a					=							model(**lowercase_).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				:	Union[str, Any]):


           '''simple docstring'''

           __a					=							self.prepare_init_args_and_inputs_for_common()

           __a					=							4

           torch.manual_seed(0)
           __a					=							self.model_class(**lowercase_)
           model.to(lowercase_)
           __a					=							create_lora_layers(lowercase_)
           model.set_attn_processor(lowercase_)

           # default
           with torch.no_grad():
               __a					=							model(**lowercase_).sample

               model.enable_xformers_memory_efficient_attention()
               __a					=							model(**lowercase_).sample

               model.disable_xformers_memory_efficient_attention()
               __a					=							model(**lowercase_).sample

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





       def 						_lowerCamelCase     (						self				:	Union[str, Any]):


           '''simple docstring'''

           __a					=							self.prepare_init_args_and_inputs_for_common()
           __a					=							32

           __a					=							self.model_class(**lowercase_)
           model.to(lowercase_)
           model.eval()

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

           model.enable_forward_chunking()
           with torch.no_grad():
               __a					=							model(**lowercase_)[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 math




def 					a			(  UpperCamelCase_				:					int		)	->		str:
 if number == 0:
  __A						:							Dict      =						'Zero'
  return words
 else:
  __A						:							List[str]      =						math.logaa(UpperCamelCase_		)
  __A						:							Union[str, Any]      =						digits + 1
  __A						:							List[str]      =						{}
  __A						:							int      =						''
  __A						:							Any      =						'One'
  __A						:							Tuple      =						'Two'
  __A						:							Any      =						'Three'
  __A						:							Any      =						'Four'
  __A						:							Optional[Any]      =						'Five'
  __A						:							List[Any]      =						'Six'
  __A						:							List[Any]      =						'Seven'
  __A						:							Dict      =						'Eight'
  __A						:							Union[str, Any]      =						'Nine'

  __A						:							List[Any]      =						{}
  __A						:							Tuple      =						''
  __A						:							List[Any]      =						'Twenty'
  __A						:							Optional[Any]      =						'Thirty'
  __A						:							Any      =						'Forty'
  __A						:							Optional[int]      =						'Fifty'
  __A						:							Dict      =						'Sixty'
  __A						:							Dict      =						'Seventy'
  __A						:							Optional[Any]      =						'Eighty'
  __A						:							Any      =						'Ninety'

  __A						:							int      =						{}
  __A						:							List[str]      =						'Ten'
  __A						:							Optional[Any]      =						'Eleven'
  __A						:							Dict      =						'Twelve'
  __A						:							Union[str, Any]      =						'Thirteen'
  __A						:							Union[str, Any]      =						'Fourteen'
  __A						:							Dict      =						'Fifteen'
  __A						:							str      =						'Sixteen'
  __A						:							str      =						'Seventeen'
  __A						:							Union[str, Any]      =						'Eighteen'
  __A						:							Union[str, Any]      =						'Nineteen'

  __A						:							Optional[Any]      =						{}
  __A						:							Optional[int]      =						'Hundred,'
  __A						:							Union[str, Any]      =						'Thousand,'
  __A						:							Union[str, Any]      =						'Lakh,'
  __A						:							Union[str, Any]      =						'Crore,'

  __A						:							Optional[Any]      =						number
  __A						:							Optional[Any]      =						''
  __A						:							List[str]      =						0
  __A						:							List[str]      =						int(UpperCamelCase_		)
  while counter < digits:
   __A						:							Any      =						temp_num % 1_0
   if counter % 2 == 0:
    __A						:							Optional[Any]      =						''
    if counter in placevalue and current != 0:
     __A						:							Union[str, Any]      =						placevalue[counter]
    if counter == 2:
     __A						:							Dict      =						singles[current] + addition + words
    elif counter == 0:
     if ((temp_num % 1_0_0) // 1_0) == 1:
      __A						:							Any      =						teens[current] + addition + words
      __A						:							int      =						temp_num // 1_0
      counter += 1
     else:
      __A						:							Optional[Any]      =						singles[current] + addition + words

    else:
     __A						:							str      =						doubles[current] + addition + words

   else:
    if counter == 1:
     if current == 1:
      __A						:							List[Any]      =						teens[number % 1_0] + words
     else:
      __A						:							Union[str, Any]      =						''
      if counter in placevalue:
       __A						:							int      =						placevalue[counter]
      __A						:							Optional[Any]      =						doubles[current] + addition + words
    else:
     __A						:							Any      =						''
     if counter in placevalue:
      if current == 0 and ((temp_num % 1_0_0) // 1_0) == 0:
       __A						:							Optional[int]      =						''
      else:
       __A						:							Optional[int]      =						placevalue[counter]
     if ((temp_num % 1_0_0) // 1_0) == 1:
      __A						:							Dict      =						teens[current] + addition + words
      __A						:							Any      =						temp_num // 1_0
      counter += 1
     else:
      __A						:							List[str]      =						singles[current] + addition + words
   counter += 1
   __A						:							Dict      =						temp_num // 1_0
 return words


if __name__ == "__main__":
   import doctest

   doctest.testmod()





