
'''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__					:						Optional[int]											=     logging.get_logger(__name__)
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_ = True  )   ->   Tuple:




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

      _SCREAMING_SNAKE_CASE          = LoRAAttnProcessor(hidden_size=SCREAMING_SNAKE_CASE_			,					cross_attention_dim=SCREAMING_SNAKE_CASE_  )
      _SCREAMING_SNAKE_CASE          = 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):





       """simple docstring"""




       SCREAMING_SNAKE_CASE					=     UNetaDConditionModel
       SCREAMING_SNAKE_CASE					=     'sample'






       @property
       def 				UpperCamelCase (							self					)			->	List[Any]:
          _SCREAMING_SNAKE_CASE          = 4
          _SCREAMING_SNAKE_CASE          = 4
          _SCREAMING_SNAKE_CASE          = 4
          _SCREAMING_SNAKE_CASE          = (32, 32)

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

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






       @property
       def 				UpperCamelCase (							self					)			->	Union[str, Any]:
          return (4, 4, 32, 32)






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






       def 				UpperCamelCase (							self					)			->	str:
          _SCREAMING_SNAKE_CASE          = {
              """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,
          }
          _SCREAMING_SNAKE_CASE          = 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					)			->	Union[str, Any]:
          _SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE          = self.prepare_init_args_and_inputs_for_common()
          _SCREAMING_SNAKE_CASE          = 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					)			->	List[Any]:
          _SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE          = self.prepare_init_args_and_inputs_for_common()

          _SCREAMING_SNAKE_CASE          = 32

          _SCREAMING_SNAKE_CASE          = self.model_class(**A__					)
          model.to(A__					)
          model.eval()

          with torch.no_grad():
             _SCREAMING_SNAKE_CASE          = model(**A__					)

             if isinstance(A__				,							A__					):
                _SCREAMING_SNAKE_CASE          = output.sample

          self.assertIsNotNone(A__					)
          _SCREAMING_SNAKE_CASE          = inputs_dict["""sample"""].shape
          self.assertEqual(output.shape				,							A__				,							"""Input and output shapes do not match"""					)






       def 				UpperCamelCase (							self					)			->	Any:
          _SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE          = self.prepare_init_args_and_inputs_for_common()
          _SCREAMING_SNAKE_CASE          = 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					)

             _SCREAMING_SNAKE_CASE          = model(**A__					)
             if isinstance(A__				,							A__					):
                _SCREAMING_SNAKE_CASE          = first.sample

             _SCREAMING_SNAKE_CASE          = model(**A__					)
             if isinstance(A__				,							A__					):
                _SCREAMING_SNAKE_CASE          = second.sample

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






       def 				UpperCamelCase (							self					)			->	Union[str, Any]:
          _SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE          = self.prepare_init_args_and_inputs_for_common()

          _SCREAMING_SNAKE_CASE          = 8

          _SCREAMING_SNAKE_CASE          = self.model_class(**A__					)
          model.to(A__					)
          model.eval()

          model.set_attention_slice("""auto"""					)
          with torch.no_grad():
             _SCREAMING_SNAKE_CASE          = model(**A__					)
          assert output is not None

          model.set_attention_slice("""max"""					)
          with torch.no_grad():
             _SCREAMING_SNAKE_CASE          = model(**A__					)
          assert output is not None

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






       def 				UpperCamelCase (							self					)			->	str:
          _SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE          = self.prepare_init_args_and_inputs_for_common()

          _SCREAMING_SNAKE_CASE          = 8

          _SCREAMING_SNAKE_CASE          = self.model_class(**A__					)
          model.to(A__					)

          with torch.no_grad():
             _SCREAMING_SNAKE_CASE          = model(**A__					).sample

          _SCREAMING_SNAKE_CASE          = 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():
             _SCREAMING_SNAKE_CASE          = model(**A__				,							cross_attention_kwargs={"""scale""": 0.0}					).sample
             _SCREAMING_SNAKE_CASE          = model(**A__				,							cross_attention_kwargs={"""scale""": 0.5}					).sample
             _SCREAMING_SNAKE_CASE          = 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					)			->	Optional[Any]:
          _SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE          = self.prepare_init_args_and_inputs_for_common()

          _SCREAMING_SNAKE_CASE          = 8

          torch.manual_seed(0					)
          _SCREAMING_SNAKE_CASE          = self.model_class(**A__					)
          model.to(A__					)

          with torch.no_grad():
             _SCREAMING_SNAKE_CASE          = model(**A__					).sample

          _SCREAMING_SNAKE_CASE          = create_lora_layers(A__					)
          model.set_attn_processor(A__					)

          with torch.no_grad():
             _SCREAMING_SNAKE_CASE          = 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					)
             _SCREAMING_SNAKE_CASE          = self.model_class(**A__					)
             new_model.to(A__					)
             new_model.load_attn_procs(A__					)

          with torch.no_grad():
             _SCREAMING_SNAKE_CASE          = 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					)			->	Optional[int]:
          _SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE          = self.prepare_init_args_and_inputs_for_common()

          _SCREAMING_SNAKE_CASE          = 8

          torch.manual_seed(0					)
          _SCREAMING_SNAKE_CASE          = self.model_class(**A__					)
          model.to(A__					)

          with torch.no_grad():
             _SCREAMING_SNAKE_CASE          = model(**A__					).sample

          _SCREAMING_SNAKE_CASE          = create_lora_layers(A__					)
          model.set_attn_processor(A__					)

          with torch.no_grad():
             _SCREAMING_SNAKE_CASE          = 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					)
             _SCREAMING_SNAKE_CASE          = self.model_class(**A__					)
             new_model.to(A__					)
             new_model.load_attn_procs(A__					)

          with torch.no_grad():
             _SCREAMING_SNAKE_CASE          = 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					)			->	List[str]:
          # enable deterministic behavior for gradient checkpointing
          _SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE          = self.prepare_init_args_and_inputs_for_common()

          _SCREAMING_SNAKE_CASE          = 8

          torch.manual_seed(0					)
          _SCREAMING_SNAKE_CASE          = self.model_class(**A__					)
          model.to(A__					)

          _SCREAMING_SNAKE_CASE          = 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					)
             _SCREAMING_SNAKE_CASE          = self.model_class(**A__					)
             new_model.to(A__					)
             new_model.load_attn_procs(A__				,							weight_name="""pytorch_lora_weights.bin"""					)






       def 				UpperCamelCase (							self					)			->	Dict:
          _SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE          = self.prepare_init_args_and_inputs_for_common()

          _SCREAMING_SNAKE_CASE          = 8

          torch.manual_seed(0					)
          _SCREAMING_SNAKE_CASE          = self.model_class(**A__					)
          model.to(A__					)

          _SCREAMING_SNAKE_CASE          = 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					)
             _SCREAMING_SNAKE_CASE          = 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					)			->	Dict:
          _SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE          = self.prepare_init_args_and_inputs_for_common()

          _SCREAMING_SNAKE_CASE          = 8

          torch.manual_seed(0					)
          _SCREAMING_SNAKE_CASE          = self.model_class(**A__					)
          model.to(A__					)

          with torch.no_grad():
             _SCREAMING_SNAKE_CASE          = model(**A__					).sample

          _SCREAMING_SNAKE_CASE          = create_lora_layers(A__					)
          model.set_attn_processor(A__					)

          with torch.no_grad():
             _SCREAMING_SNAKE_CASE          = model(**A__				,							cross_attention_kwargs={"""scale""": 0.0}					).sample

          model.set_attn_processor(AttnProcessor()					)

          with torch.no_grad():
             _SCREAMING_SNAKE_CASE          = 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					)			->	Optional[int]:
          # enable deterministic behavior for gradient checkpointing
          _SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE          = self.prepare_init_args_and_inputs_for_common()

          _SCREAMING_SNAKE_CASE          = 4

          torch.manual_seed(0					)
          _SCREAMING_SNAKE_CASE          = self.model_class(**A__					)
          model.to(A__					)
          _SCREAMING_SNAKE_CASE          = create_lora_layers(A__					)
          model.set_attn_processor(A__					)

          # default
          with torch.no_grad():
             _SCREAMING_SNAKE_CASE          = model(**A__					).sample

             model.enable_xformers_memory_efficient_attention()
             _SCREAMING_SNAKE_CASE          = model(**A__					).sample

             model.disable_xformers_memory_efficient_attention()
             _SCREAMING_SNAKE_CASE          = model(**A__					).sample

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






       def 				UpperCamelCase (							self					)			->	Union[str, Any]:
          _SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE          = self.prepare_init_args_and_inputs_for_common()
          _SCREAMING_SNAKE_CASE          = 32

          _SCREAMING_SNAKE_CASE          = self.model_class(**A__					)
          model.to(A__					)
          model.eval()

          with torch.no_grad():
             _SCREAMING_SNAKE_CASE          = model(**A__					)[0]

          model.enable_forward_chunking()
          with torch.no_grad():
             _SCREAMING_SNAKE_CASE          = 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 typing import Mapping

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


UpperCamelCase__					:						Any											=     logging.get_logger(__name__)

class       _a (_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE					=     'mt5'
       SCREAMING_SNAKE_CASE					=     ['past_key_values']






       def __init__(							self				,							A__=25_01_12				,							A__=5_12				,							A__=64				,							A__=10_24				,							A__=8				,							A__=None				,							A__=6				,							A__=32				,							A__=1_28				,							A__=0.1				,							A__=1E-6				,							A__=1.0				,							A__="gated-gelu"				,							A__=True				,							A__=True				,							A__="T5Tokenizer"				,							A__=False				,							A__=0				,							A__=1				,							A__=0				,							**A__				,							)			->	Union[str, Any]:
          super().__init__(
              is_encoder_decoder=A__				,							tokenizer_class=A__				,							tie_word_embeddings=A__				,							pad_token_id=A__				,							eos_token_id=A__				,							decoder_start_token_id=A__				,							**A__				,							)
          _SCREAMING_SNAKE_CASE          = vocab_size
          _SCREAMING_SNAKE_CASE          = d_model
          _SCREAMING_SNAKE_CASE          = d_kv
          _SCREAMING_SNAKE_CASE          = d_ff
          _SCREAMING_SNAKE_CASE          = num_layers
          _SCREAMING_SNAKE_CASE          = (
              num_decoder_layers if num_decoder_layers is not None else self.num_layers
          )  # default = symmetry
          _SCREAMING_SNAKE_CASE          = num_heads
          _SCREAMING_SNAKE_CASE          = relative_attention_num_buckets
          _SCREAMING_SNAKE_CASE          = relative_attention_max_distance
          _SCREAMING_SNAKE_CASE          = dropout_rate
          _SCREAMING_SNAKE_CASE          = layer_norm_epsilon
          _SCREAMING_SNAKE_CASE          = initializer_factor
          _SCREAMING_SNAKE_CASE          = feed_forward_proj
          _SCREAMING_SNAKE_CASE          = use_cache

          _SCREAMING_SNAKE_CASE          = self.feed_forward_proj.split("""-"""					)
          _SCREAMING_SNAKE_CASE          = act_info[-1]
          _SCREAMING_SNAKE_CASE          = act_info[0] == """gated"""

          if len(A__					) > 1 and act_info[0] != "gated" or len(A__					) > 2:
             raise ValueError(
                 F"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
                 """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
                 """'gated-gelu' or 'relu'"""					)

          # for backwards compatibility
          if feed_forward_proj == "gated-gelu":
             _SCREAMING_SNAKE_CASE          = """gelu_new"""






       @property
       def 				UpperCamelCase (							self					)			->	int:
          return self.d_model






       @property
       def 				UpperCamelCase (							self					)			->	Optional[Any]:
          return self.num_heads






       @property
       def 				UpperCamelCase (							self					)			->	Dict:
          return self.num_layers






class       _a (_lowerCamelCase):





       """simple docstring"""




       @property
       # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
       def 				UpperCamelCase (							self					)			->	Mapping[str, Mapping[int, str]]:
          _SCREAMING_SNAKE_CASE          = {
              """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
              """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
          }
          if self.use_past:
             _SCREAMING_SNAKE_CASE          = """past_encoder_sequence + sequence"""
             _SCREAMING_SNAKE_CASE          = {0: """batch"""}
             _SCREAMING_SNAKE_CASE          = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
          else:
             _SCREAMING_SNAKE_CASE          = {0: """batch""", 1: """decoder_sequence"""}
             _SCREAMING_SNAKE_CASE          = {0: """batch""", 1: """decoder_sequence"""}

          if self.use_past:
             self.fill_with_past_key_values_(A__				,							direction="""inputs"""					)

          return common_inputs






       @property
       # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
       def 				UpperCamelCase (							self					)			->	int:
          return 13






       @property
       def 				UpperCamelCase (							self					)			->	float:
          return 5E-4






