
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 		lowercase_     (  A__					,						A__ = True						)				->				Any:
    """simple docstring"""

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

        snake_case						=   LoRAAttnProcessor(hidden_size=A__					,						cross_attention_dim=A__						)
        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 	lowerCamelCase						(    A_				,					A_				,					unittest.TestCase    ):
     UpperCAmelCase__							:				int 	=						UNetaDConditionModel
     UpperCAmelCase__							:				Dict 	=						"sample"





     @property
     def 						UpperCAmelCase(self :       str  )		->		Union[str, Any]:
         snake_case						=   4
         snake_case						=   4
         snake_case						=   4
         snake_case						=   (3_2, 3_2)

         snake_case						=   floats_tensor((batch_size, num_channels, num_frames) + sizes  ).to(_A  )
         snake_case						=   torch.tensor([1_0]  ).to(_A  )
         snake_case						=   floats_tensor((batch_size, 4, 3_2)  ).to(_A  )

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





     @property
     def 						UpperCAmelCase(self :       Optional[int]  )		->		List[Any]:
         return (4, 4, 3_2, 3_2)





     @property
     def 						UpperCAmelCase(self :       Any  )		->		Tuple:
         return (4, 4, 3_2, 3_2)





     def 						UpperCAmelCase(self :       Any  )		->		Optional[int]:
         snake_case						=   {
             "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,
         }
         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 :       List[str]  )		->		List[Any]:
         snake_case	,				snake_case						=   self.prepare_init_args_and_inputs_for_common()
         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 :       Union[str, Any]  )		->		Union[str, Any]:
         snake_case	,				snake_case						=   self.prepare_init_args_and_inputs_for_common()

         snake_case						=   3_2

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

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

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

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





     def 						UpperCAmelCase(self :       int  )		->		Union[str, Any]:
         snake_case	,				snake_case						=   self.prepare_init_args_and_inputs_for_common()
         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  )

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

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

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





     def 						UpperCAmelCase(self :       int  )		->		Union[str, Any]:
         snake_case	,				snake_case						=   self.prepare_init_args_and_inputs_for_common()

         snake_case						=   8

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

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

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

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





     def 						UpperCAmelCase(self :       int  )		->		Optional[int]:
         snake_case	,				snake_case						=   self.prepare_init_args_and_inputs_for_common()

         snake_case						=   8

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

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

         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():
             snake_case						=   model(**_A							,       cross_attention_kwargs={"scale": 0.0}  ).sample
             snake_case						=   model(**_A							,       cross_attention_kwargs={"scale": 0.5}  ).sample
             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 :       int  )		->		Any:
         snake_case	,				snake_case						=   self.prepare_init_args_and_inputs_for_common()

         snake_case						=   8

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

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

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

         with torch.no_grad():
             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  )
             snake_case						=   self.model_class(**_A  )
             new_model.to(_A  )
             new_model.load_attn_procs(_A  )

         with torch.no_grad():
             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[Any]  )		->		str:
         snake_case	,				snake_case						=   self.prepare_init_args_and_inputs_for_common()

         snake_case						=   8

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

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

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

         with torch.no_grad():
             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  )
             snake_case						=   self.model_class(**_A  )
             new_model.to(_A  )
             new_model.load_attn_procs(_A  )

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

         snake_case						=   8

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

         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  )
             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 :       Optional[Any]  )		->		Union[str, Any]:
         snake_case	,				snake_case						=   self.prepare_init_args_and_inputs_for_common()

         snake_case						=   8

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

         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  )
             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 :       Any  )		->		Optional[int]:
         snake_case	,				snake_case						=   self.prepare_init_args_and_inputs_for_common()

         snake_case						=   8

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

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

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

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

         model.set_attn_processor(AttnProcessor()  )

         with torch.no_grad():
             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 :       str  )		->		Dict:
         # enable deterministic behavior for gradient checkpointing
         snake_case	,				snake_case						=   self.prepare_init_args_and_inputs_for_common()

         snake_case						=   4

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

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

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

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

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





     def 						UpperCAmelCase(self :       Tuple  )		->		Dict:
         snake_case	,				snake_case						=   self.prepare_init_args_and_inputs_for_common()
         snake_case						=   3_2

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

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

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



import unittest

from transformers import load_tool
from transformers.tools.agent_types import AGENT_TYPE_MAPPING

from .test_tools_common import ToolTesterMixin, output_types







class 	lowerCamelCase						(    unittest.TestCase				,					A_    ):





     def 						UpperCAmelCase(self :       Any  )		->		int:
         snake_case						=   load_tool("translation"  )
         self.tool.setup()
         snake_case						=   load_tool("translation"							,       remote=_A  )





     def 						UpperCAmelCase(self :       Optional[Any]  )		->		Optional[int]:
         snake_case						=   self.tool("Hey, what's up?"							,       src_lang="English"							,       tgt_lang="French"  )
         self.assertEqual(_A							,       "- Hé, comment ça va?"  )





     def 						UpperCAmelCase(self :       str  )		->		int:
         snake_case						=   self.remote_tool("Hey, what's up?"							,       src_lang="English"							,       tgt_lang="French"  )
         self.assertEqual(_A							,       "- Hé, comment ça va?"  )





     def 						UpperCAmelCase(self :       str  )		->		List[str]:
         snake_case						=   self.tool(text="Hey, what's up?"							,       src_lang="English"							,       tgt_lang="French"  )
         self.assertEqual(_A							,       "- Hé, comment ça va?"  )





     def 						UpperCAmelCase(self :       Optional[Any]  )		->		str:
         snake_case						=   self.remote_tool(text="Hey, what's up?"							,       src_lang="English"							,       tgt_lang="French"  )
         self.assertEqual(_A							,       "- Hé, comment ça va?"  )





     def 						UpperCAmelCase(self :       Any  )		->		Tuple:
         snake_case						=   ["Hey, what's up?", "English", "Spanish"]
         snake_case						=   self.tool(*_A  )

         # There is a single output
         if len(self.tool.outputs  ) == 1:
             snake_case						=   [outputs]

         self.assertListEqual(output_types(_A  )							,       self.tool.outputs  )





     def 						UpperCAmelCase(self :       List[str]  )		->		List[Any]:
         snake_case						=   ["Hey, what's up?", "English", "Spanish"]
         snake_case						=   self.tool(*_A  )

         if not isinstance(_A							,       _A  ):
             snake_case						=   [outputs]

         self.assertEqual(len(_A  )							,       len(self.tool.outputs  )  )

         for output, output_type in zip(_A							,       self.tool.outputs  ):
             snake_case						=   AGENT_TYPE_MAPPING[output_type]
             self.assertTrue(isinstance(_A							,       _A  )  )





     def 						UpperCAmelCase(self :       int  )		->		Any:
         snake_case						=   ["Hey, what's up?", "English", "Spanish"]

         snake_case						=   []

         for _input, input_type in zip(_A							,       self.tool.inputs  ):
             if isinstance(_A							,       _A  ):
                 _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input  ) for _input_type in input_type]  )
             else:
                 _inputs.append(AGENT_TYPE_MAPPING[input_type](_input  )  )

        # Should not raise an error
         snake_case						=   self.tool(*_A  )

         if not isinstance(_A							,       _A  ):
             snake_case						=   [outputs]

         self.assertEqual(len(_A  )							,       len(self.tool.outputs  )  )

