





'''simple docstring'''


import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, slow, torch_device

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin



class 				__UpperCAmelCase	(							A__		,       A__		,       unittest.TestCase    ):







		'''simple docstring'''







		__lowerCAmelCase								=     UNetaDModel
		__lowerCAmelCase								=     '''sample'''







		@property
		def 							A  (self    :   Union[str, Any]   ):
			A					=   4
			A					=   14
			A					=   16

			A					=   floats_tensor((batch_size, num_features, seq_len)   ).to(_lowerCAmelCase   )
			A					=   torch.tensor([10] * batch_size   ).to(_lowerCAmelCase   )

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







		@property
		def 							A  (self    :   Dict   ):
			return (4, 14, 16)







		@property
		def 							A  (self    :   List[str]   ):
			return (4, 14, 16)







		def 							A  (self    :   str   ):
			pass







		def 							A  (self    :   List[Any]   ):
			pass







		@unittest.skipIf(torch_device == """mps"""   ,   """mish op not supported in MPS"""   )
		def 							A  (self    :   Union[str, Any]   ):
			super().test_determinism()







		@unittest.skipIf(torch_device == """mps"""   ,   """mish op not supported in MPS"""   )
		def 							A  (self    :   Optional[Any]   ):
			super().test_outputs_equivalence()







		@unittest.skipIf(torch_device == """mps"""   ,   """mish op not supported in MPS"""   )
		def 							A  (self    :   int   ):
			super().test_from_save_pretrained()







		@unittest.skipIf(torch_device == """mps"""   ,   """mish op not supported in MPS"""   )
		def 							A  (self    :   List[str]   ):
			super().test_from_save_pretrained_variant()







		@unittest.skipIf(torch_device == """mps"""   ,   """mish op not supported in MPS"""   )
		def 							A  (self    :   Optional[Any]   ):
			super().test_model_from_pretrained()







		@unittest.skipIf(torch_device == """mps"""   ,   """mish op not supported in MPS"""   )
		def 							A  (self    :   str   ):
			super().test_output()







		def 							A  (self    :   List[str]   ):
			A					=   {
			    """block_out_channels""": (32, 64, 128, 256),
			    """in_channels""": 14,
			    """out_channels""": 14,
			    """time_embedding_type""": """positional""",
			    """use_timestep_embedding""": True,
			    """flip_sin_to_cos""": False,
			    """freq_shift""": 1.0,
			    """out_block_type""": """OutConv1DBlock""",
			    """mid_block_type""": """MidResTemporalBlock1D""",
			    """down_block_types""": ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D"""),
			    """up_block_types""": ("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D"""),
			    """act_fn""": """mish""",
			}
			A					=   self.dummy_input
			return init_dict, inputs_dict







		@unittest.skipIf(torch_device == """mps"""   ,   """mish op not supported in MPS"""   )
		def 							A  (self    :   Optional[int]   ):
			A	,     A					=   UNetaDModel.from_pretrained(
			    """bglick13/hopper-medium-v2-value-function-hor32"""   ,   output_loading_info=_lowerCAmelCase   ,   subfolder="""unet"""   )
			self.assertIsNotNone(_lowerCAmelCase   )
			self.assertEqual(len(loading_info["""missing_keys"""]   )   ,   0   )

			model.to(_lowerCAmelCase   )
			A					=   model(**self.dummy_input   )

			assert image is not None, "Make sure output is not None"







		@unittest.skipIf(torch_device == """mps"""   ,   """mish op not supported in MPS"""   )
		def 							A  (self    :   str   ):
			A					=   UNetaDModel.from_pretrained("""bglick13/hopper-medium-v2-value-function-hor32"""   ,   subfolder="""unet"""   )
			torch.manual_seed(0   )
			if torch.cuda.is_available():
				torch.cuda.manual_seed_all(0   )

			A					=   model.config.in_channels
			A					=   16
			A					=   torch.randn((1, seq_len, num_features)   ).permute(
			    0   ,   2   ,   1   )  # match original, we can update values and remove
			A					=   torch.full((num_features,)   ,   0   )

			with torch.no_grad():
				A					=   model(_lowerCAmelCase   ,   _lowerCAmelCase   ).sample.permute(0   ,   2   ,   1   )

			A					=   output[0, -3:, -3:].flatten()
			# fmt: off
			A					=   torch.tensor([-2.137_172, 1.1_426_016, 0.3_688_687, -0.766_922, 0.7_303_146, 0.11_038_864, -0.4_760_633, 0.13_270_172, 0.02_591_348]   )
			# fmt: on
			self.assertTrue(torch.allclose(_lowerCAmelCase   ,   _lowerCAmelCase   ,   rtol=1e-3   )   )







		def 							A  (self    :   Any   ):
			# Not implemented yet for this UNet
			pass







		@slow
		def 							A  (self    :   Any   ):
			A					=   """harmonai/maestro-150k"""
			A					=   UNetaDModel.from_pretrained(_lowerCAmelCase   ,   subfolder="""unet"""   )
			model.to(_lowerCAmelCase   )

			A					=   6_5536
			A					=   torch.sin(torch.arange(_lowerCAmelCase   )[None, None, :].repeat(1   ,   2   ,   1   )   ).to(_lowerCAmelCase   )
			A					=   torch.tensor([1]   ).to(_lowerCAmelCase   )

			with torch.no_grad():
				A					=   model(_lowerCAmelCase   ,   _lowerCAmelCase   ).sample

			A					=   output.abs().sum()
			A					=   output.abs().max()

			assert (output_sum - 224.0_896).abs() < 0.5
			assert (output_max - 0.0_607).abs() < 4e-4






class 				__UpperCAmelCase	(							A__		,       A__		,       unittest.TestCase    ):







		'''simple docstring'''







		__lowerCAmelCase								=     UNetaDModel
		__lowerCAmelCase								=     '''sample'''







		@property
		def 							A  (self    :   Optional[Any]   ):
			A					=   4
			A					=   14
			A					=   16

			A					=   floats_tensor((batch_size, num_features, seq_len)   ).to(_lowerCAmelCase   )
			A					=   torch.tensor([10] * batch_size   ).to(_lowerCAmelCase   )

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







		@property
		def 							A  (self    :   Tuple   ):
			return (4, 14, 16)







		@property
		def 							A  (self    :   List[Any]   ):
			return (4, 14, 1)







		@unittest.skipIf(torch_device == """mps"""   ,   """mish op not supported in MPS"""   )
		def 							A  (self    :   int   ):
			super().test_determinism()







		@unittest.skipIf(torch_device == """mps"""   ,   """mish op not supported in MPS"""   )
		def 							A  (self    :   Optional[int]   ):
			super().test_outputs_equivalence()







		@unittest.skipIf(torch_device == """mps"""   ,   """mish op not supported in MPS"""   )
		def 							A  (self    :   Union[str, Any]   ):
			super().test_from_save_pretrained()







		@unittest.skipIf(torch_device == """mps"""   ,   """mish op not supported in MPS"""   )
		def 							A  (self    :   Union[str, Any]   ):
			super().test_from_save_pretrained_variant()







		@unittest.skipIf(torch_device == """mps"""   ,   """mish op not supported in MPS"""   )
		def 							A  (self    :   List[str]   ):
			super().test_model_from_pretrained()







		@unittest.skipIf(torch_device == """mps"""   ,   """mish op not supported in MPS"""   )
		def 							A  (self    :   Optional[Any]   ):
			# UNetRL is a value-function is different output shape
			A	,     A					=   self.prepare_init_args_and_inputs_for_common()
			A					=   self.model_class(**_lowerCAmelCase   )
			model.to(_lowerCAmelCase   )
			model.eval()

			with torch.no_grad():
				A					=   model(**_lowerCAmelCase   )

				if isinstance(_lowerCAmelCase   ,   _lowerCAmelCase   ):
					A					=   output.sample

			self.assertIsNotNone(_lowerCAmelCase   )
			A					=   torch.Size((inputs_dict["""sample"""].shape[0], 1)   )
			self.assertEqual(output.shape   ,   _lowerCAmelCase   ,   """Input and output shapes do not match"""   )







		def 							A  (self    :   Any   ):
			pass







		def 							A  (self    :   Optional[Any]   ):
			pass







		def 							A  (self    :   str   ):
			A					=   {
			    """in_channels""": 14,
			    """out_channels""": 14,
			    """down_block_types""": ["""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D"""],
			    """up_block_types""": [],
			    """out_block_type""": """ValueFunction""",
			    """mid_block_type""": """ValueFunctionMidBlock1D""",
			    """block_out_channels""": [32, 64, 128, 256],
			    """layers_per_block""": 1,
			    """downsample_each_block""": True,
			    """use_timestep_embedding""": True,
			    """freq_shift""": 1.0,
			    """flip_sin_to_cos""": False,
			    """time_embedding_type""": """positional""",
			    """act_fn""": """mish""",
			}
			A					=   self.dummy_input
			return init_dict, inputs_dict







		@unittest.skipIf(torch_device == """mps"""   ,   """mish op not supported in MPS"""   )
		def 							A  (self    :   Union[str, Any]   ):
			A	,     A					=   UNetaDModel.from_pretrained(
			    """bglick13/hopper-medium-v2-value-function-hor32"""   ,   output_loading_info=_lowerCAmelCase   ,   subfolder="""value_function"""   )
			self.assertIsNotNone(_lowerCAmelCase   )
			self.assertEqual(len(vf_loading_info["""missing_keys"""]   )   ,   0   )

			value_function.to(_lowerCAmelCase   )
			A					=   value_function(**self.dummy_input   )

			assert image is not None, "Make sure output is not None"







		@unittest.skipIf(torch_device == """mps"""   ,   """mish op not supported in MPS"""   )
		def 							A  (self    :   str   ):
			A	,     A					=   UNetaDModel.from_pretrained(
			    """bglick13/hopper-medium-v2-value-function-hor32"""   ,   output_loading_info=_lowerCAmelCase   ,   subfolder="""value_function"""   )
			torch.manual_seed(0   )
			if torch.cuda.is_available():
				torch.cuda.manual_seed_all(0   )

			A					=   value_function.config.in_channels
			A					=   14
			A					=   torch.randn((1, seq_len, num_features)   ).permute(
			    0   ,   2   ,   1   )  # match original, we can update values and remove
			A					=   torch.full((num_features,)   ,   0   )

			with torch.no_grad():
				A					=   value_function(_lowerCAmelCase   ,   _lowerCAmelCase   ).sample

			# fmt: off
			A					=   torch.tensor([165.25] * seq_len   )
			# fmt: on
			self.assertTrue(torch.allclose(_lowerCAmelCase   ,   _lowerCAmelCase   ,   rtol=1e-3   )   )







		def 							A  (self    :   Union[str, Any]   ):
			# Not implemented yet for this UNet
			pass








'''simple docstring'''


import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


_lowerCamelCase					:						List[str]					     =							logging.getLogger(__name__)



class 				__UpperCAmelCase	:







		'''simple docstring'''







		def __init__(self    :   Any   ):
			A					=   False







		def 							A  (self    :   Tuple   ,   _lowerCAmelCase    :   Dict   ,   _lowerCAmelCase    :   Any   ,   _lowerCAmelCase    :   Tuple   ,   _lowerCAmelCase    :   Dict   ):
			if not self.initialized:
				A					=   RagRetriever(
				    _lowerCAmelCase   ,   question_encoder_tokenizer=_lowerCAmelCase   ,   generator_tokenizer=_lowerCAmelCase   ,   index=_lowerCAmelCase   ,   init_retrieval=_lowerCAmelCase   ,   )
				A					=   True







		def 							A  (self    :   List[str]   ):
			self.retriever.index.init_index()







		def 							A  (self    :   List[str]   ):
			# delete the old self.retriever object before assigning the new index
			del self.retriever
			A					=   False







		def 							A  (self    :   Any   ,   _lowerCAmelCase    :   List[str]   ,   _lowerCAmelCase    :   Optional[int]   ):
			A	,     A					=   self.retriever._main_retrieve(_lowerCAmelCase   ,   _lowerCAmelCase   )
			A					=   self.retriever.index.get_doc_dicts(_lowerCAmelCase   )
			return doc_ids, retrieved_doc_embeds, doc_dicts






class 				__UpperCAmelCase	(							A__    ):







		'''simple docstring'''







		def __init__(self    :   Any   ,   _lowerCAmelCase    :   Dict   ,   _lowerCAmelCase    :   Dict   ,   _lowerCAmelCase    :   Union[str, Any]   ,   _lowerCAmelCase    :   Dict   ,   _lowerCAmelCase    :   List[Any]=None   ):
			if index is not None and index.is_initialized() and len(_lowerCAmelCase   ) > 0:
				raise ValueError(
				    """When using Ray for distributed fine-tuning, """
				    """you'll need to provide the paths instead, """
				    """as the dataset and the index are loaded """
				    """separately. More info in examples/rag/use_own_knowledge_dataset.py """   )

			super().__init__(
			    _lowerCAmelCase   ,   question_encoder_tokenizer=_lowerCAmelCase   ,   generator_tokenizer=_lowerCAmelCase   ,   index=_lowerCAmelCase   ,   init_retrieval=_lowerCAmelCase   ,   )

			A					=   retrieval_workers
			A					=   question_encoder_tokenizer
			A					=   generator_tokenizer
			if len(self.retrieval_workers   ) > 0:
				ray.get(
				    [
				        worker.create_rag_retriever.remote(_lowerCAmelCase   ,   _lowerCAmelCase   ,   _lowerCAmelCase   ,   _lowerCAmelCase   )
				        for worker in self.retrieval_workers
				    ]   )







		def 							A  (self    :   Optional[Any]   ):
			logger.info("""initializing retrieval"""   )

			if len(self.retrieval_workers   ) > 0:
				ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]   )
			else:
				# Non-distributed training. Load index into this same process.
				self.index.init_index()







		def 							A  (self    :   int   ,   _lowerCAmelCase    :   Dict   ,   _lowerCAmelCase    :   List[Any]   ):
			if len(self.retrieval_workers   ) > 0:
				# Select a random retrieval actor.
				A					=   self.retrieval_workers[random.randint(0   ,   len(self.retrieval_workers   ) - 1   )]
				A	,     A	,     A					=   ray.get(
				    random_worker.retrieve.remote(_lowerCAmelCase   ,   _lowerCAmelCase   )   )
			else:
				A	,     A					=   self._main_retrieve(_lowerCAmelCase   ,   _lowerCAmelCase   )
				A					=   self.index.get_doc_dicts(_lowerCAmelCase   )
			return retrieved_doc_embeds, doc_ids, doc_dicts







		@classmethod
		def 							A  (cls    :   Dict   ,   _lowerCAmelCase    :   Tuple   ,   _lowerCAmelCase    :   str=None   ,   **_lowerCAmelCase    :   List[Any]   ):
			return super(_lowerCAmelCase   ,   cls   ).get_tokenizers(_lowerCAmelCase   ,   _lowerCAmelCase   ,   **_lowerCAmelCase   )







		@classmethod
		def 							A  (cls    :   List[str]   ,   _lowerCAmelCase    :   List[str]   ,   _lowerCAmelCase    :   List[Any]   ,   _lowerCAmelCase    :   Optional[int]=None   ,   **_lowerCAmelCase    :   Optional[int]   ):
			A					=   kwargs.pop("""config"""   ,   _lowerCAmelCase   ) or RagConfig.from_pretrained(_lowerCAmelCase   ,   **_lowerCAmelCase   )
			A					=   RagTokenizer.from_pretrained(_lowerCAmelCase   ,   config=_lowerCAmelCase   )
			A					=   rag_tokenizer.question_encoder
			A					=   rag_tokenizer.generator

			if indexed_dataset is not None:
				A					=   """custom"""
				A					=   CustomHFIndex(config.retrieval_vector_size   ,   _lowerCAmelCase   )
			else:
				A					=   cls._build_index(_lowerCAmelCase   )

			return cls(
			    _lowerCAmelCase   ,   question_encoder_tokenizer=_lowerCAmelCase   ,   generator_tokenizer=_lowerCAmelCase   ,   retrieval_workers=_lowerCAmelCase   ,   index=_lowerCAmelCase   ,   )







		def 							A  (self    :   str   ):
			logger.info("""re-loading the new dataset with embeddings"""   )
			# access from the training loop

			ray.get([worker.clear_object.remote() for worker in self.retrieval_workers]   )

			# build the index object again
			A					=   self._build_index(self.config   )

			ray.get(
			    [
			        worker.create_rag_retriever.remote(
			            self.config   ,   self.question_encoder_tokenizer   ,   self.generator_tokenizer   ,   _lowerCAmelCase   )
			        for worker in self.retrieval_workers
			    ]   )

