
from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


snake_case				:	List[str]							   =						logging.get_logger(__name__)





class    _snake_case      (  snake_case       ):
			UpperCamelCase__					   =					['input_values', 'attention_mask']







			def __init__(       self		,     _a=1		,     _a=16_000		,     _a=0.0		,     _a=False		,     _a=True		,     **_a		,     ):
				super().__init__(feature_size=_a		,     sampling_rate=_a		,     padding_value=_a		,     **_a	)
				__magic_name__	:	Union[str, Any]			     =  return_attention_mask
				__magic_name__	:	List[str]			     =  do_normalize







			@staticmethod
			def     SCREAMING_SNAKE_CASE					(       _a		,     _a		,     _a = 0.0	):
				if attention_mask is not None:
					__magic_name__	:	Dict			     =  np.array(_a		,     np.intaa	)
					__magic_name__	:	str			     =  []

					for vector, length in zip(_a		,     attention_mask.sum(-1	)	):
						__magic_name__	:	Tuple			     =  (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7	)
						if length < normed_slice.shape[0]:
							__magic_name__	:	int			     =  padding_value

						normed_input_values.append(_a	)
				else:
					__magic_name__	:	Optional[Any]			     =  [(x - x.mean()) / np.sqrt(x.var() + 1e-7	) for x in input_values]

				return normed_input_values







			def __call__(       self		,     _a		,     _a = False		,     _a = None		,     _a = False		,     _a = None		,     _a = None		,     _a = None		,     _a = None		,     **_a		,     ):

				if sampling_rate is not None:
					if sampling_rate != self.sampling_rate:
						raise ValueError(
						    f'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
						    f''' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'''
						    f''' {self.sampling_rate} and not {sampling_rate}.'''	)
				else:
					logger.warning(
					    "It is strongly recommended to pass the ``sampling_rate`` argument to this function. "
					    "Failing to do so can result in silent errors that might be hard to debug."	)

				__magic_name__	:	List[str]			     =  isinstance(_a		,     np.ndarray	) and len(raw_speech.shape	) > 1
				if is_batched_numpy and len(raw_speech.shape	) > 2:
					raise ValueError(f'''Only mono-channel audio is supported for input to {self}'''	)
				__magic_name__	:	Union[str, Any]			     =  is_batched_numpy or (
				    isinstance(_a		,     (list, tuple)	) and (isinstance(raw_speech[0]		,     (np.ndarray, tuple, list)	))
				)

				# always return batch
				if not is_batched:
					__magic_name__	:	int			     =  [raw_speech]

				# convert into correct format for padding
				__magic_name__	:	List[str]			     =  BatchFeature({"input_values": raw_speech}	)

				__magic_name__	:	Tuple			     =  self.pad(
				    _a		,     padding=_a		,     max_length=_a		,     truncation=_a		,     pad_to_multiple_of=_a		,     return_attention_mask=_a		,     )

				# convert input values to correct format
				__magic_name__	:	Tuple			     =  padded_inputs["input_values"]
				if not isinstance(input_values[0]		,     np.ndarray	):
					__magic_name__	:	Optional[Any]			     =  [np.asarray(_a		,     dtype=np.floataa	) for array in input_values]
				elif (
				    not isinstance(_a		,     np.ndarray	)
				    and isinstance(input_values[0]		,     np.ndarray	)
				    and input_values[0].dtype is np.dtype(np.floataa	)
				):
					__magic_name__	:	List[str]			     =  [array.astype(np.floataa	) for array in input_values]
				elif isinstance(_a		,     np.ndarray	) and input_values.dtype is np.dtype(np.floataa	):
					__magic_name__	:	Union[str, Any]			     =  input_values.astype(np.floataa	)

				# convert attention_mask to correct format
				__magic_name__	:	int			     =  padded_inputs.get("attention_mask"	)
				if attention_mask is not None:
					__magic_name__	:	Optional[Any]			     =  [np.asarray(_a		,     dtype=np.intaa	) for array in attention_mask]

				# zero-mean and unit-variance normalization
				if self.do_normalize:
					__magic_name__	:	Tuple			     =  (
					    attention_mask
					    if self._get_padding_strategies(_a		,     max_length=_a	) is not PaddingStrategy.DO_NOT_PAD
					    else None
					)
					__magic_name__	:	List[Any]			     =  self.zero_mean_unit_var_norm(
					    padded_inputs["input_values"]		,     attention_mask=_a		,     padding_value=self.padding_value	)

				if return_tensors is not None:
					__magic_name__	:	List[str]			     =  padded_inputs.convert_to_tensors(_a	)

				return padded_inputs


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				:	int							   =						logging.get_logger(__name__)






def 						lowerCAmelCase_		(      _snake_case   :						List[Any]   ,     _snake_case   :						bool = True     )       ->	Tuple:







	'''simple docstring'''


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

		__magic_name__	:	Union[str, Any]			     =  LoRAAttnProcessor(hidden_size=_snake_case   ,     cross_attention_dim=_snake_case     )
		__magic_name__	:	str			     =  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    _snake_case      (  snake_case       ,		snake_case       ,		unittest.TestCase       ):
			UpperCamelCase__					   =					UNetaDConditionModel
			UpperCamelCase__					   =					'sample'







			@property
			def     SCREAMING_SNAKE_CASE					(       self	):
				__magic_name__	:	Optional[Any]			     =  4
				__magic_name__	:	str			     =  4
				__magic_name__	:	List[str]			     =  4
				__magic_name__	:	Optional[int]			     =  (32, 32)

				__magic_name__	:	Optional[Any]			     =  floats_tensor((batch_size, num_channels, num_frames) + sizes	).to(_a	)
				__magic_name__	:	Union[str, Any]			     =  torch.tensor([10]	).to(_a	)
				__magic_name__	:	List[str]			     =  floats_tensor((batch_size, 4, 32)	).to(_a	)

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







			@property
			def     SCREAMING_SNAKE_CASE					(       self	):
				return (4, 4, 32, 32)







			@property
			def     SCREAMING_SNAKE_CASE					(       self	):
				return (4, 4, 32, 32)







			def     SCREAMING_SNAKE_CASE					(       self	):
				__magic_name__	:	Optional[Any]			     =  {
				    "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,
				}
				__magic_name__	:	List[str]			     =  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     SCREAMING_SNAKE_CASE					(       self	):
				__magic_name__     ,					__magic_name__	:	Any			     =  self.prepare_init_args_and_inputs_for_common()
				__magic_name__	:	Union[str, Any]			     =  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     SCREAMING_SNAKE_CASE					(       self	):
				__magic_name__     ,					__magic_name__	:	Tuple			     =  self.prepare_init_args_and_inputs_for_common()

				__magic_name__	:	Any			     =  32

				__magic_name__	:	Dict			     =  self.model_class(**_a	)
				model.to(_a	)
				model.eval()

				with torch.no_grad():
					__magic_name__	:	Any			     =  model(**_a	)

					if isinstance(_a		,     _a	):
						__magic_name__	:	Any			     =  output.sample

				self.assertIsNotNone(_a	)
				__magic_name__	:	Union[str, Any]			     =  inputs_dict["sample"].shape
				self.assertEqual(output.shape		,     _a		,     "Input and output shapes do not match"	)







			def     SCREAMING_SNAKE_CASE					(       self	):
				__magic_name__     ,					__magic_name__	:	Optional[int]			     =  self.prepare_init_args_and_inputs_for_common()
				__magic_name__	:	int			     =  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	)

					__magic_name__	:	str			     =  model(**_a	)
					if isinstance(_a		,     _a	):
						__magic_name__	:	str			     =  first.sample

					__magic_name__	:	Tuple			     =  model(**_a	)
					if isinstance(_a		,     _a	):
						__magic_name__	:	str			     =  second.sample

				__magic_name__	:	Optional[int]			     =  first.cpu().numpy()
				__magic_name__	:	Any			     =  second.cpu().numpy()
				__magic_name__	:	List[Any]			     =  out_a[~np.isnan(_a	)]
				__magic_name__	:	Any			     =  out_a[~np.isnan(_a	)]
				__magic_name__	:	Tuple			     =  np.amax(np.abs(out_a - out_a	)	)
				self.assertLessEqual(_a		,     1e-5	)







			def     SCREAMING_SNAKE_CASE					(       self	):
				__magic_name__     ,					__magic_name__	:	Optional[int]			     =  self.prepare_init_args_and_inputs_for_common()

				__magic_name__	:	Union[str, Any]			     =  8

				__magic_name__	:	List[str]			     =  self.model_class(**_a	)
				model.to(_a	)
				model.eval()

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

				model.set_attention_slice("max"	)
				with torch.no_grad():
					__magic_name__	:	Tuple			     =  model(**_a	)
				assert output is not None

				model.set_attention_slice(2	)
				with torch.no_grad():
					__magic_name__	:	Union[str, Any]			     =  model(**_a	)
				assert output is not None







			def     SCREAMING_SNAKE_CASE					(       self	):
				__magic_name__     ,					__magic_name__	:	str			     =  self.prepare_init_args_and_inputs_for_common()

				__magic_name__	:	List[Any]			     =  8

				__magic_name__	:	Dict			     =  self.model_class(**_a	)
				model.to(_a	)

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

				__magic_name__	:	int			     =  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():
					__magic_name__	:	int			     =  model(**_a		,     cross_attention_kwargs={"scale": 0.0}	).sample
					__magic_name__	:	Optional[Any]			     =  model(**_a		,     cross_attention_kwargs={"scale": 0.5}	).sample
					__magic_name__	:	Union[str, Any]			     =  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     SCREAMING_SNAKE_CASE					(       self	):
				__magic_name__     ,					__magic_name__	:	Union[str, Any]			     =  self.prepare_init_args_and_inputs_for_common()

				__magic_name__	:	Optional[int]			     =  8

				torch.manual_seed(0	)
				__magic_name__	:	Tuple			     =  self.model_class(**_a	)
				model.to(_a	)

				with torch.no_grad():
					__magic_name__	:	Tuple			     =  model(**_a	).sample

				__magic_name__	:	Union[str, Any]			     =  create_lora_layers(_a	)
				model.set_attn_processor(_a	)

				with torch.no_grad():
					__magic_name__	:	Optional[Any]			     =  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	)
					__magic_name__	:	Union[str, Any]			     =  self.model_class(**_a	)
					new_model.to(_a	)
					new_model.load_attn_procs(_a	)

				with torch.no_grad():
					__magic_name__	:	int			     =  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     SCREAMING_SNAKE_CASE					(       self	):
				__magic_name__     ,					__magic_name__	:	Optional[Any]			     =  self.prepare_init_args_and_inputs_for_common()

				__magic_name__	:	Optional[int]			     =  8

				torch.manual_seed(0	)
				__magic_name__	:	Optional[int]			     =  self.model_class(**_a	)
				model.to(_a	)

				with torch.no_grad():
					__magic_name__	:	str			     =  model(**_a	).sample

				__magic_name__	:	Union[str, Any]			     =  create_lora_layers(_a	)
				model.set_attn_processor(_a	)

				with torch.no_grad():
					__magic_name__	:	Dict			     =  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	)
					__magic_name__	:	List[str]			     =  self.model_class(**_a	)
					new_model.to(_a	)
					new_model.load_attn_procs(_a	)

				with torch.no_grad():
					__magic_name__	:	List[Any]			     =  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     SCREAMING_SNAKE_CASE					(       self	):
				# enable deterministic behavior for gradient checkpointing
				__magic_name__     ,					__magic_name__	:	Union[str, Any]			     =  self.prepare_init_args_and_inputs_for_common()

				__magic_name__	:	List[str]			     =  8

				torch.manual_seed(0	)
				__magic_name__	:	Tuple			     =  self.model_class(**_a	)
				model.to(_a	)

				__magic_name__	:	Any			     =  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	)
					__magic_name__	:	List[Any]			     =  self.model_class(**_a	)
					new_model.to(_a	)
					new_model.load_attn_procs(_a		,     weight_name="pytorch_lora_weights.bin"	)







			def     SCREAMING_SNAKE_CASE					(       self	):
				__magic_name__     ,					__magic_name__	:	str			     =  self.prepare_init_args_and_inputs_for_common()

				__magic_name__	:	List[str]			     =  8

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

				__magic_name__	:	str			     =  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	)
					__magic_name__	:	List[Any]			     =  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     SCREAMING_SNAKE_CASE					(       self	):
				__magic_name__     ,					__magic_name__	:	Optional[Any]			     =  self.prepare_init_args_and_inputs_for_common()

				__magic_name__	:	Any			     =  8

				torch.manual_seed(0	)
				__magic_name__	:	List[str]			     =  self.model_class(**_a	)
				model.to(_a	)

				with torch.no_grad():
					__magic_name__	:	Any			     =  model(**_a	).sample

				__magic_name__	:	int			     =  create_lora_layers(_a	)
				model.set_attn_processor(_a	)

				with torch.no_grad():
					__magic_name__	:	Dict			     =  model(**_a		,     cross_attention_kwargs={"scale": 0.0}	).sample

				model.set_attn_processor(AttnProcessor()	)

				with torch.no_grad():
					__magic_name__	:	Optional[Any]			     =  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     SCREAMING_SNAKE_CASE					(       self	):
				# enable deterministic behavior for gradient checkpointing
				__magic_name__     ,					__magic_name__	:	int			     =  self.prepare_init_args_and_inputs_for_common()

				__magic_name__	:	Optional[int]			     =  4

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

				# default
				with torch.no_grad():
					__magic_name__	:	int			     =  model(**_a	).sample

					model.enable_xformers_memory_efficient_attention()
					__magic_name__	:	str			     =  model(**_a	).sample

					model.disable_xformers_memory_efficient_attention()
					__magic_name__	:	Optional[Any]			     =  model(**_a	).sample

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







			def     SCREAMING_SNAKE_CASE					(       self	):
				__magic_name__     ,					__magic_name__	:	int			     =  self.prepare_init_args_and_inputs_for_common()
				__magic_name__	:	str			     =  32

				__magic_name__	:	int			     =  self.model_class(**_a	)
				model.to(_a	)
				model.eval()

				with torch.no_grad():
					__magic_name__	:	Optional[Any]			     =  model(**_a	)[0]

				model.enable_forward_chunking()
				with torch.no_grad():
					__magic_name__	:	int			     =  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.
