
'''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()

UpperCamelCase_            =    logging.get_logger(__name__)


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

    _lowerCAmelCase    :					List[str]							=   LoRAAttnProcessor(hidden_size=_SCREAMING_SNAKE_CASE					, cross_attention_dim=_SCREAMING_SNAKE_CASE						)
    _lowerCAmelCase    :					Union[str, 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 					a_					(lowerCAmelCase__							,     lowerCAmelCase__							,     unittest.TestCase      ):
       __lowerCAmelCase      :			Union[str, Any]      = UNetaDConditionModel
       __lowerCAmelCase      :			Optional[int]      = """sample"""




       @property
       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					str							=   4
         _lowerCAmelCase    :					Dict							=   4
         _lowerCAmelCase    :					List[Any]							=   4
         _lowerCAmelCase    :					List[Any]							=   (3_2, 3_2)

         _lowerCAmelCase    :					Union[str, Any]							=   floats_tensor((batch_size, num_channels, num_frames) + sizes					).to(UpperCamelCase__					)
         _lowerCAmelCase    :					str							=   torch.tensor([1_0]					).to(UpperCamelCase__					)
         _lowerCAmelCase    :					int							=   floats_tensor((batch_size, 4, 3_2)					).to(UpperCamelCase__					)

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




       @property
       def 							__UpperCamelCase    (       self					):
         return (4, 4, 3_2, 3_2)




       @property
       def 							__UpperCamelCase    (       self					):
         return (4, 4, 3_2, 3_2)




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					int							=   {
             "block_out_channels": (3_2, 6_4),
             "down_block_types": (
                 "CrossAttnDownBlock3D",
                 "DownBlock3D",
             ),
             "up_block_types": ("UpBlock3D", "CrossAttnUpBlock3D"),
             "cross_attention_dim": 3_2,
             "attention_head_dim": 8,
             "out_channels": 4,
             "in_channels": 4,
             "layers_per_block": 1,
             "sample_size": 3_2,
         }
         _lowerCAmelCase    :					Dict							=   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    :					Optional[Any]							=   self.prepare_init_args_and_inputs_for_common()
         _lowerCAmelCase    :					str							=   self.model_class(**UpperCamelCase__					)

         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    :					Optional[Any]							=   self.prepare_init_args_and_inputs_for_common()

         _lowerCAmelCase    :					Tuple							=   3_2

         _lowerCAmelCase    :					Optional[int]							=   self.model_class(**UpperCamelCase__					)
         model.to(UpperCamelCase__					)
         model.eval()

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

           if isinstance(UpperCamelCase__				,				UpperCamelCase__					):
             _lowerCAmelCase    :					Union[str, Any]							=   output.sample

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




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					str							=   self.prepare_init_args_and_inputs_for_common()
         _lowerCAmelCase    :					Tuple							=   self.model_class(**UpperCamelCase__					)
         model.to(UpperCamelCase__					)
         model.eval()

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

           _lowerCAmelCase    :					Optional[Any]							=   model(**UpperCamelCase__					)
           if isinstance(UpperCamelCase__				,				UpperCamelCase__					):
             _lowerCAmelCase    :					str							=   first.sample

           _lowerCAmelCase    :					Tuple							=   model(**UpperCamelCase__					)
           if isinstance(UpperCamelCase__				,				UpperCamelCase__					):
             _lowerCAmelCase    :					Optional[Any]							=   second.sample

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




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					str							=   self.prepare_init_args_and_inputs_for_common()

         _lowerCAmelCase    :					Union[str, Any]							=   8

         _lowerCAmelCase    :					Union[str, Any]							=   self.model_class(**UpperCamelCase__					)
         model.to(UpperCamelCase__					)
         model.eval()

         model.set_attention_slice("""auto"""					)
         with torch.no_grad():
           _lowerCAmelCase    :					List[Any]							=   model(**UpperCamelCase__					)
         assert output is not None

         model.set_attention_slice("""max"""					)
         with torch.no_grad():
           _lowerCAmelCase    :					Optional[Any]							=   model(**UpperCamelCase__					)
         assert output is not None

         model.set_attention_slice(2					)
         with torch.no_grad():
           _lowerCAmelCase    :					List[Any]							=   model(**UpperCamelCase__					)
         assert output is not None




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Tuple							=   self.prepare_init_args_and_inputs_for_common()

         _lowerCAmelCase    :					List[str]							=   8

         _lowerCAmelCase    :					Any							=   self.model_class(**UpperCamelCase__					)
         model.to(UpperCamelCase__					)

         with torch.no_grad():
           _lowerCAmelCase    :					int							=   model(**UpperCamelCase__					).sample

         _lowerCAmelCase    :					str							=   create_lora_layers(UpperCamelCase__					)

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

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

         with torch.no_grad():
           _lowerCAmelCase    :					Union[str, Any]							=   model(**UpperCamelCase__				,				cross_attention_kwargs={"""scale""": 0.0}					).sample
           _lowerCAmelCase    :					Tuple							=   model(**UpperCamelCase__				,				cross_attention_kwargs={"""scale""": 0.5}					).sample
           _lowerCAmelCase    :					Optional[int]							=   model(**UpperCamelCase__				,				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    :					Optional[int]							=   self.prepare_init_args_and_inputs_for_common()

         _lowerCAmelCase    :					List[Any]							=   8

         torch.manual_seed(0					)
         _lowerCAmelCase    :					Tuple							=   self.model_class(**UpperCamelCase__					)
         model.to(UpperCamelCase__					)

         with torch.no_grad():
           _lowerCAmelCase    :					Union[str, Any]							=   model(**UpperCamelCase__					).sample

         _lowerCAmelCase    :					Dict							=   create_lora_layers(UpperCamelCase__					)
         model.set_attn_processor(UpperCamelCase__					)

         with torch.no_grad():
           _lowerCAmelCase    :					Union[str, Any]							=   model(**UpperCamelCase__				,				cross_attention_kwargs={"""scale""": 0.5}					).sample

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

         with torch.no_grad():
           _lowerCAmelCase    :					str							=   new_model(**UpperCamelCase__				,				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    :					int							=   self.prepare_init_args_and_inputs_for_common()

         _lowerCAmelCase    :					Optional[Any]							=   8

         torch.manual_seed(0					)
         _lowerCAmelCase    :					str							=   self.model_class(**UpperCamelCase__					)
         model.to(UpperCamelCase__					)

         with torch.no_grad():
           _lowerCAmelCase    :					Optional[Any]							=   model(**UpperCamelCase__					).sample

         _lowerCAmelCase    :					Any							=   create_lora_layers(UpperCamelCase__					)
         model.set_attn_processor(UpperCamelCase__					)

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

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

         with torch.no_grad():
           _lowerCAmelCase    :					List[Any]							=   new_model(**UpperCamelCase__				,				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    :					str							=   self.prepare_init_args_and_inputs_for_common()

         _lowerCAmelCase    :					Dict							=   8

         torch.manual_seed(0					)
         _lowerCAmelCase    :					Optional[Any]							=   self.model_class(**UpperCamelCase__					)
         model.to(UpperCamelCase__					)

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




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Union[str, Any]							=   self.prepare_init_args_and_inputs_for_common()

         _lowerCAmelCase    :					Dict							=   8

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

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




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					List[Any]							=   self.prepare_init_args_and_inputs_for_common()

         _lowerCAmelCase    :					Optional[int]							=   8

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

         with torch.no_grad():
           _lowerCAmelCase    :					Any							=   model(**UpperCamelCase__					).sample

         _lowerCAmelCase    :					int							=   create_lora_layers(UpperCamelCase__					)
         model.set_attn_processor(UpperCamelCase__					)

         with torch.no_grad():
           _lowerCAmelCase    :					Optional[int]							=   model(**UpperCamelCase__				,				cross_attention_kwargs={"""scale""": 0.0}					).sample

         model.set_attn_processor(AttnProcessor()					)

         with torch.no_grad():
           _lowerCAmelCase    :					Optional[int]							=   model(**UpperCamelCase__					).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    :					Dict							=   self.prepare_init_args_and_inputs_for_common()

         _lowerCAmelCase    :					List[Any]							=   4

         torch.manual_seed(0					)
         _lowerCAmelCase    :					Any							=   self.model_class(**UpperCamelCase__					)
         model.to(UpperCamelCase__					)
         _lowerCAmelCase    :					str							=   create_lora_layers(UpperCamelCase__					)
         model.set_attn_processor(UpperCamelCase__					)

         # default
         with torch.no_grad():
           _lowerCAmelCase    :					int							=   model(**UpperCamelCase__					).sample

           model.enable_xformers_memory_efficient_attention()
           _lowerCAmelCase    :					Any							=   model(**UpperCamelCase__					).sample

           model.disable_xformers_memory_efficient_attention()
           _lowerCAmelCase    :					Any							=   model(**UpperCamelCase__					).sample

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







       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Dict							=   self.prepare_init_args_and_inputs_for_common()
         _lowerCAmelCase    :					List[Any]							=   3_2

         _lowerCAmelCase    :					int							=   self.model_class(**UpperCamelCase__					)
         model.to(UpperCamelCase__					)
         model.eval()

         with torch.no_grad():
           _lowerCAmelCase    :					Union[str, Any]							=   model(**UpperCamelCase__					)[0]

         model.enable_forward_chunking()
         with torch.no_grad():
           _lowerCAmelCase    :					List[Any]							=   model(**UpperCamelCase__					)[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 collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase_            =    logging.get_logger(__name__)

UpperCamelCase_            =    {
    """apple/mobilevit-small""": """https://huggingface.co/apple/mobilevit-small/resolve/main/config.json""",
    """apple/mobilevit-x-small""": """https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json""",
    """apple/mobilevit-xx-small""": """https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json""",
    """apple/deeplabv3-mobilevit-small""": (
        """https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json"""
    ),
    """apple/deeplabv3-mobilevit-x-small""": (
        """https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json"""
    ),
    """apple/deeplabv3-mobilevit-xx-small""": (
        """https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json"""
    ),
    # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
}


class 					a_					(_a      ):
       __lowerCAmelCase      :			List[str]      = """mobilevit"""




       def __init__(       self				,				snake_case_=3				,				snake_case_=2_5_6				,				snake_case_=2				,				snake_case_=[1_4_4, 1_9_2, 2_4_0]				,				snake_case_=[1_6, 3_2, 6_4, 9_6, 1_2_8, 1_6_0, 6_4_0]				,				snake_case_=4				,				snake_case_=2.0				,				snake_case_=4.0				,				snake_case_="silu"				,				snake_case_=3				,				snake_case_=3_2				,				snake_case_=0.1				,				snake_case_=0.0				,				snake_case_=0.1				,				snake_case_=0.02				,				snake_case_=1E-5				,				snake_case_=True				,				snake_case_=2_5_6				,				snake_case_=[6, 1_2, 1_8]				,				snake_case_=0.1				,				snake_case_=2_5_5				,				**snake_case_				,				):
         super().__init__(**snake_case_					)

         _lowerCAmelCase    :					Optional[Any]							=   num_channels
         _lowerCAmelCase    :					Optional[int]							=   image_size
         _lowerCAmelCase    :					Union[str, Any]							=   patch_size
         _lowerCAmelCase    :					Any							=   hidden_sizes
         _lowerCAmelCase    :					List[Any]							=   neck_hidden_sizes
         _lowerCAmelCase    :					Any							=   num_attention_heads
         _lowerCAmelCase    :					List[Any]							=   mlp_ratio
         _lowerCAmelCase    :					Optional[Any]							=   expand_ratio
         _lowerCAmelCase    :					List[Any]							=   hidden_act
         _lowerCAmelCase    :					Dict							=   conv_kernel_size
         _lowerCAmelCase    :					Tuple							=   output_stride
         _lowerCAmelCase    :					Dict							=   hidden_dropout_prob
         _lowerCAmelCase    :					Tuple							=   attention_probs_dropout_prob
         _lowerCAmelCase    :					Optional[int]							=   classifier_dropout_prob
         _lowerCAmelCase    :					int							=   initializer_range
         _lowerCAmelCase    :					List[Any]							=   layer_norm_eps
         _lowerCAmelCase    :					List[Any]							=   qkv_bias

         # decode head attributes for semantic segmentation
         _lowerCAmelCase    :					str							=   aspp_out_channels
         _lowerCAmelCase    :					Optional[Any]							=   atrous_rates
         _lowerCAmelCase    :					Any							=   aspp_dropout_prob
         _lowerCAmelCase    :					Union[str, Any]							=   semantic_loss_ignore_index



class 					a_					(_a      ):
       __lowerCAmelCase      :			Optional[int]      = version.parse("""1.11"""      )




       @property
       def 							__UpperCamelCase    (       self					):
         return OrderedDict([("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""})]					)




       @property
       def 							__UpperCamelCase    (       self					):
         if self.task == "image-classification":
           return OrderedDict([("""logits""", {0: """batch"""})]					)
         else:
           return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})]					)







       @property
       def 							__UpperCamelCase    (       self					):
         return 1E-4


