


'''simple docstring'''


import argparse
import os

import align
import numpy as np
import requests
import tensorflow as tf
import torch
from PIL import Image
from tokenizer import Tokenizer

from transformers import (
    AlignConfig,
    AlignModel,
    AlignProcessor,
    BertConfig,
    BertTokenizer,
    EfficientNetConfig,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase__     						=  logging.get_logger(__name__)






def 			__lowerCAmelCase			(__lowerCAmelCase ):
		_UpperCAmelCase		:							Dict             =   tf.image.resize(__lowerCAmelCase   ,			(346, 346) )
		_UpperCAmelCase		:							Optional[Any]             =   tf.image.crop_to_bounding_box(__lowerCAmelCase   ,			(346 - 289) // 2   ,			(346 - 289) // 2   ,			289   ,			289 )
		return image






def 			__lowerCAmelCase			():
		_UpperCAmelCase		:							Dict             =   EfficientNetConfig.from_pretrained("google/efficientnet-b7" )
		_UpperCAmelCase		:							Dict             =   289
		_UpperCAmelCase		:							List[str]             =   640
		_UpperCAmelCase		:							Dict             =   {"0": "LABEL_0", "1": "LABEL_1"}
		_UpperCAmelCase		:							List[Any]             =   {"LABEL_0": 0, "LABEL_1": 1}
		_UpperCAmelCase		:							Union[str, Any]             =   []

		_UpperCAmelCase		:							Optional[int]             =   BertConfig()
		_UpperCAmelCase		:							List[Any]             =   AlignConfig.from_text_vision_configs(
		    text_config=__lowerCAmelCase   ,			vision_config=__lowerCAmelCase   ,			projection_dim=640 )
		return config






def 			__lowerCAmelCase			():
		_UpperCAmelCase		:							List[Any]             =   "http://images.cocodataset.org/val2017/000000039769.jpg"
		_UpperCAmelCase		:							Dict             =   Image.open(requests.get(__lowerCAmelCase   ,			stream=__lowerCAmelCase ).raw )
		return im






def 			__lowerCAmelCase			():
		_UpperCAmelCase		:							Optional[int]             =   EfficientNetImageProcessor(
		    do_center_crop=__lowerCAmelCase   ,			rescale_factor=1 / 1_2_7.5   ,			rescale_offset=__lowerCAmelCase   ,			do_normalize=__lowerCAmelCase   ,			include_top=__lowerCAmelCase   ,			resample=Image.BILINEAR   ,			)
		_UpperCAmelCase		:							Optional[Any]             =   BertTokenizer.from_pretrained("bert-base-uncased" )
		_UpperCAmelCase		:							Optional[int]             =   64
		_UpperCAmelCase		:							int             =   AlignProcessor(image_processor=__lowerCAmelCase   ,			tokenizer=__lowerCAmelCase )
		return processor






def 			__lowerCAmelCase			(__lowerCAmelCase ):
		# EfficientNet image encoder
		_UpperCAmelCase		:							List[str]             =   [v.split("_" )[0].split("block" )[1] for v in original_param_names if v.startswith("block" )]
		_UpperCAmelCase		:							str             =   list(set(__lowerCAmelCase ) )
		_UpperCAmelCase		:							Dict             =   sorted(__lowerCAmelCase )
		_UpperCAmelCase		:							Optional[int]             =   len(__lowerCAmelCase )
		_UpperCAmelCase		:							Union[str, Any]             =   {b: str(__lowerCAmelCase ) for b, i in zip(__lowerCAmelCase   ,			range(__lowerCAmelCase ) )}

		_UpperCAmelCase		:							int             =   []
		rename_keys.append(("stem_conv/kernel:0", "embeddings.convolution.weight") )
		rename_keys.append(("stem_bn/gamma:0", "embeddings.batchnorm.weight") )
		rename_keys.append(("stem_bn/beta:0", "embeddings.batchnorm.bias") )
		rename_keys.append(("stem_bn/moving_mean:0", "embeddings.batchnorm.running_mean") )
		rename_keys.append(("stem_bn/moving_variance:0", "embeddings.batchnorm.running_var") )

		for b in block_names:
				_UpperCAmelCase		:							List[Any]             =   block_name_mapping[b]
				rename_keys.append((F"""block{b}_expand_conv/kernel:0""", F"""encoder.blocks.{hf_b}.expansion.expand_conv.weight""") )
				rename_keys.append((F"""block{b}_expand_bn/gamma:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.weight""") )
				rename_keys.append((F"""block{b}_expand_bn/beta:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.bias""") )
				rename_keys.append(
				    (F"""block{b}_expand_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.running_mean""") )
				rename_keys.append(
				    (F"""block{b}_expand_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.running_var""") )
				rename_keys.append(
				    (F"""block{b}_dwconv/depthwise_kernel:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight""") )
				rename_keys.append((F"""block{b}_bn/gamma:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight""") )
				rename_keys.append((F"""block{b}_bn/beta:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias""") )
				rename_keys.append(
				    (F"""block{b}_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean""") )
				rename_keys.append(
				    (F"""block{b}_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var""") )

				rename_keys.append((F"""block{b}_se_reduce/kernel:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.reduce.weight""") )
				rename_keys.append((F"""block{b}_se_reduce/bias:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.reduce.bias""") )
				rename_keys.append((F"""block{b}_se_expand/kernel:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.expand.weight""") )
				rename_keys.append((F"""block{b}_se_expand/bias:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.expand.bias""") )
				rename_keys.append(
				    (F"""block{b}_project_conv/kernel:0""", F"""encoder.blocks.{hf_b}.projection.project_conv.weight""") )
				rename_keys.append((F"""block{b}_project_bn/gamma:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.weight""") )
				rename_keys.append((F"""block{b}_project_bn/beta:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.bias""") )
				rename_keys.append(
				    (F"""block{b}_project_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.running_mean""") )
				rename_keys.append(
				    (F"""block{b}_project_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.running_var""") )

		_UpperCAmelCase		:							Dict             =   {}
		for item in rename_keys:
				if item[0] in original_param_names:
						_UpperCAmelCase		:							List[Any]             =   "vision_model." + item[1]

    # BERT text encoder
		_UpperCAmelCase		:							Any             =   []
		_UpperCAmelCase		:							Union[str, Any]             =   "tf_bert_model/bert"
		_UpperCAmelCase		:							int             =   "text_model"
		for i in range(12 ):
				rename_keys.append(
				    (
				        F"""{old}/encoder/layer_._{i}/attention/self/query/kernel:0""",
				        F"""{new}.encoder.layer.{i}.attention.self.query.weight""",
				    ) )
				rename_keys.append(
				    (
				        F"""{old}/encoder/layer_._{i}/attention/self/query/bias:0""",
				        F"""{new}.encoder.layer.{i}.attention.self.query.bias""",
				    ) )
				rename_keys.append(
				    (
				        F"""{old}/encoder/layer_._{i}/attention/self/key/kernel:0""",
				        F"""{new}.encoder.layer.{i}.attention.self.key.weight""",
				    ) )
				rename_keys.append(
				    (
				        F"""{old}/encoder/layer_._{i}/attention/self/key/bias:0""",
				        F"""{new}.encoder.layer.{i}.attention.self.key.bias""",
				    ) )
				rename_keys.append(
				    (
				        F"""{old}/encoder/layer_._{i}/attention/self/value/kernel:0""",
				        F"""{new}.encoder.layer.{i}.attention.self.value.weight""",
				    ) )
				rename_keys.append(
				    (
				        F"""{old}/encoder/layer_._{i}/attention/self/value/bias:0""",
				        F"""{new}.encoder.layer.{i}.attention.self.value.bias""",
				    ) )
				rename_keys.append(
				    (
				        F"""{old}/encoder/layer_._{i}/attention/output/dense/kernel:0""",
				        F"""{new}.encoder.layer.{i}.attention.output.dense.weight""",
				    ) )
				rename_keys.append(
				    (
				        F"""{old}/encoder/layer_._{i}/attention/output/dense/bias:0""",
				        F"""{new}.encoder.layer.{i}.attention.output.dense.bias""",
				    ) )
				rename_keys.append(
				    (
				        F"""{old}/encoder/layer_._{i}/attention/output/LayerNorm/gamma:0""",
				        F"""{new}.encoder.layer.{i}.attention.output.LayerNorm.weight""",
				    ) )
				rename_keys.append(
				    (
				        F"""{old}/encoder/layer_._{i}/attention/output/LayerNorm/beta:0""",
				        F"""{new}.encoder.layer.{i}.attention.output.LayerNorm.bias""",
				    ) )
				rename_keys.append(
				    (
				        F"""{old}/encoder/layer_._{i}/intermediate/dense/kernel:0""",
				        F"""{new}.encoder.layer.{i}.intermediate.dense.weight""",
				    ) )
				rename_keys.append(
				    (
				        F"""{old}/encoder/layer_._{i}/intermediate/dense/bias:0""",
				        F"""{new}.encoder.layer.{i}.intermediate.dense.bias""",
				    ) )
				rename_keys.append(
				    (F"""{old}/encoder/layer_._{i}/output/dense/kernel:0""", F"""{new}.encoder.layer.{i}.output.dense.weight""") )
				rename_keys.append(
				    (F"""{old}/encoder/layer_._{i}/output/dense/bias:0""", F"""{new}.encoder.layer.{i}.output.dense.bias""") )
				rename_keys.append(
				    (F"""{old}/encoder/layer_._{i}/output/LayerNorm/gamma:0""", F"""{new}.encoder.layer.{i}.output.LayerNorm.weight""") )
				rename_keys.append(
				    (F"""{old}/encoder/layer_._{i}/output/LayerNorm/beta:0""", F"""{new}.encoder.layer.{i}.output.LayerNorm.bias""") )

		rename_keys.append((F"""{old}/embeddings/word_embeddings/weight:0""", F"""{new}.embeddings.word_embeddings.weight""") )
		rename_keys.append(
		    (F"""{old}/embeddings/position_embeddings/embeddings:0""", F"""{new}.embeddings.position_embeddings.weight""") )
		rename_keys.append(
		    (F"""{old}/embeddings/token_type_embeddings/embeddings:0""", F"""{new}.embeddings.token_type_embeddings.weight""") )
		rename_keys.append((F"""{old}/embeddings/LayerNorm/gamma:0""", F"""{new}.embeddings.LayerNorm.weight""") )
		rename_keys.append((F"""{old}/embeddings/LayerNorm/beta:0""", F"""{new}.embeddings.LayerNorm.bias""") )

		rename_keys.append((F"""{old}/pooler/dense/kernel:0""", F"""{new}.pooler.dense.weight""") )
		rename_keys.append((F"""{old}/pooler/dense/bias:0""", F"""{new}.pooler.dense.bias""") )
		rename_keys.append(("dense/kernel:0", "text_projection.weight") )
		rename_keys.append(("dense/bias:0", "text_projection.bias") )
		rename_keys.append(("dense/bias:0", "text_projection.bias") )
		rename_keys.append(("temperature:0", "temperature") )

		for item in rename_keys:
				if item[0] in original_param_names:
						_UpperCAmelCase		:							int             =   item[1]
		return key_mapping






def 			__lowerCAmelCase			(__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase ):
		list(hf_params.keys() )

		for key, value in tf_params.items():
				if key not in key_mapping:
						continue

				_UpperCAmelCase		:							str             =   key_mapping[key]
				if "_conv" in key and "kernel" in key:
						_UpperCAmelCase		:							Dict             =   torch.from_numpy(__lowerCAmelCase ).permute(3   ,			2   ,			0   ,			1 )
				elif "embeddings" in key:
						_UpperCAmelCase		:							List[Any]             =   torch.from_numpy(__lowerCAmelCase )
				elif "depthwise_kernel" in key:
						_UpperCAmelCase		:							Dict             =   torch.from_numpy(__lowerCAmelCase ).permute(2   ,			3   ,			0   ,			1 )
				elif "kernel" in key:
						_UpperCAmelCase		:							Optional[Any]             =   torch.from_numpy(np.transpose(__lowerCAmelCase ) )
				elif "temperature" in key:
						_UpperCAmelCase		:							str             =   value
				elif "bn/gamma" or "bn/beta" in key:
						_UpperCAmelCase		:							Union[str, Any]             =   torch.from_numpy(np.transpose(__lowerCAmelCase ) ).squeeze()
				else:
						_UpperCAmelCase		:							Dict             =   torch.from_numpy(__lowerCAmelCase )

				# Replace HF parameters with original TF model parameters
				hf_params[hf_key].copy_(__lowerCAmelCase )






@torch.no_grad()
def 			__lowerCAmelCase			(__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase ):
		_UpperCAmelCase		:							Optional[int]             =   64
		_UpperCAmelCase		:							Union[str, Any]             =   Tokenizer(__lowerCAmelCase )
		_UpperCAmelCase		:							Optional[int]             =   align.Align("efficientnet-b7"   ,			"bert-base"   ,			640   ,			__lowerCAmelCase   ,			tok.get_vocab_size() )
		original_model.compile()
		original_model.load_weights(__lowerCAmelCase )

		_UpperCAmelCase		:							int             =   original_model.trainable_variables
		_UpperCAmelCase		:							Optional[Any]             =   original_model.non_trainable_variables
		_UpperCAmelCase		:							str             =   {param.name: param.numpy() for param in tf_params}
		for param in tf_non_train_params:
				_UpperCAmelCase		:							Optional[int]             =   param.numpy()
		_UpperCAmelCase		:							Optional[Any]             =   list(tf_params.keys() )

		# Load HuggingFace model
		_UpperCAmelCase		:							Optional[Any]             =   get_align_config()
		_UpperCAmelCase		:							List[Any]             =   AlignModel(__lowerCAmelCase ).eval()
		_UpperCAmelCase		:							Union[str, Any]             =   hf_model.state_dict()

		# Create src-to-dst parameter name mapping dictionary
		print("Converting parameters..." )
		_UpperCAmelCase		:							List[Any]             =   rename_keys(__lowerCAmelCase )
		replace_params(__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase )

		# Initialize processor
		_UpperCAmelCase		:							List[str]             =   get_processor()
		_UpperCAmelCase		:							Optional[Any]             =   processor(
		    images=prepare_img()   ,			text="A picture of a cat"   ,			padding="max_length"   ,			max_length=64   ,			return_tensors="pt" )

		# HF model inference
		hf_model.eval()
		with torch.no_grad():
				_UpperCAmelCase		:							Optional[Any]             =   hf_model(**__lowerCAmelCase )

		_UpperCAmelCase		:							Union[str, Any]             =   outputs.image_embeds.detach().numpy()
		_UpperCAmelCase		:							List[str]             =   outputs.text_embeds.detach().numpy()

		# Original model inference
		_UpperCAmelCase		:							str             =   False
		_UpperCAmelCase		:							Union[str, Any]             =   EfficientNetImageProcessor(
		    do_center_crop=__lowerCAmelCase   ,			do_rescale=__lowerCAmelCase   ,			do_normalize=__lowerCAmelCase   ,			include_top=__lowerCAmelCase   ,			resample=Image.BILINEAR   ,			)
		_UpperCAmelCase		:							List[str]             =   tf_image_processor(images=prepare_img()   ,			return_tensors="tf"   ,			data_format="channels_last" )["pixel_values"]
		_UpperCAmelCase		:							int             =   tok(tf.constant(["A picture of a cat"] ) )

		_UpperCAmelCase		:							int             =   original_model.image_encoder(__lowerCAmelCase   ,			training=__lowerCAmelCase )
		_UpperCAmelCase		:							Any             =   original_model.text_encoder(__lowerCAmelCase   ,			training=__lowerCAmelCase )

		_UpperCAmelCase		:							Union[str, Any]             =   tf.nn.la_normalize(__lowerCAmelCase   ,			axis=-1 )
		_UpperCAmelCase		:							Tuple             =   tf.nn.la_normalize(__lowerCAmelCase   ,			axis=-1 )

		# Check whether original and HF model outputs match  -> np.allclose
		if not np.allclose(__lowerCAmelCase   ,			__lowerCAmelCase   ,			atol=1e-3 ):
				raise ValueError("The predicted image features are not the same." )
		if not np.allclose(__lowerCAmelCase   ,			__lowerCAmelCase   ,			atol=1e-3 ):
				raise ValueError("The predicted text features are not the same." )
		print("Model outputs match!" )

		if save_model:
				# Create folder to save model
				if not os.path.isdir(__lowerCAmelCase ):
						os.mkdir(__lowerCAmelCase )
				# Save converted model and image processor
				hf_model.save_pretrained(__lowerCAmelCase )
				processor.save_pretrained(__lowerCAmelCase )

		if push_to_hub:
				# Push model and image processor to hub
				print("Pushing converted ALIGN to the hub..." )
				processor.push_to_hub("align-base" )
				hf_model.push_to_hub("align-base" )


if __name__ == "__main__":
						lowerCamelCase__     						=  argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--checkpoint_path',
						    default='./weights/model-weights',
						    type=str,
						    help='Path to the pretrained TF ALIGN checkpoint.',
						)
						parser.add_argument(
						    '--pytorch_dump_folder_path',
						    default='hf_model',
						    type=str,
						    help='Path to the output PyTorch model directory.',
						)
						parser.add_argument('--save_model', action='store_true', help='Save model to local')
						parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')

						lowerCamelCase__     						=  parser.parse_args()
						convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)







'''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 						lowerCAmelCase__      (      UpperCAmelCase__     ,							UpperCAmelCase__     ,							unittest.TestCase						):
			lowerCAmelCase			:						Any							 =  UNetaDModel
			lowerCAmelCase			:						List[Any]							 =  "sample"




			@property
			def 				lowerCAmelCase__     (		self	:  Optional[Any]			) ->Union[str, Any]:



					'''simple docstring'''







					_UpperCAmelCase		:							List[str]             =   4
					_UpperCAmelCase		:							Any             =   14
					_UpperCAmelCase		:							Any             =   16

					_UpperCAmelCase		:							Optional[int]             =   floats_tensor((batch_size, num_features, seq_len)			).to(lowerCamelCase__			)
					_UpperCAmelCase		:							Dict             =   torch.tensor([10] * batch_size			).to(lowerCamelCase__			)

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




			@property
			def 				lowerCAmelCase__     (		self	:  Tuple			) ->List[Any]:



					'''simple docstring'''







					return (4, 14, 16)




			@property
			def 				lowerCAmelCase__     (		self	:  Optional[int]			) ->Tuple:



					'''simple docstring'''







					return (4, 14, 16)




			def 				lowerCAmelCase__     (		self	:  int			) ->Tuple:



					'''simple docstring'''







					pass




			def 				lowerCAmelCase__     (		self	:  Dict			) ->Tuple:



					'''simple docstring'''







					pass




			@unittest.skipIf(torch_device == "mps"						,	"mish op not supported in MPS"			)
			def 				lowerCAmelCase__     (		self	:  Any			) ->List[str]:



					'''simple docstring'''







					super().test_determinism()




			@unittest.skipIf(torch_device == "mps"						,	"mish op not supported in MPS"			)
			def 				lowerCAmelCase__     (		self	:  Optional[int]			) ->Dict:



					'''simple docstring'''







					super().test_outputs_equivalence()




			@unittest.skipIf(torch_device == "mps"						,	"mish op not supported in MPS"			)
			def 				lowerCAmelCase__     (		self	:  int			) ->List[str]:



					'''simple docstring'''







					super().test_from_save_pretrained()




			@unittest.skipIf(torch_device == "mps"						,	"mish op not supported in MPS"			)
			def 				lowerCAmelCase__     (		self	:  Union[str, Any]			) ->Optional[int]:



					'''simple docstring'''







					super().test_from_save_pretrained_variant()




			@unittest.skipIf(torch_device == "mps"						,	"mish op not supported in MPS"			)
			def 				lowerCAmelCase__     (		self	:  str			) ->int:



					'''simple docstring'''







					super().test_model_from_pretrained()




			@unittest.skipIf(torch_device == "mps"						,	"mish op not supported in MPS"			)
			def 				lowerCAmelCase__     (		self	:  int			) ->Union[str, Any]:



					'''simple docstring'''







					super().test_output()




			def 				lowerCAmelCase__     (		self	:  int			) ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase		:							Any             =   {
					    "block_out_channels": (32, 64, 1_28, 2_56),
					    "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",
					}
					_UpperCAmelCase		:							List[Any]             =   self.dummy_input
					return init_dict, inputs_dict




			@unittest.skipIf(torch_device == "mps"						,	"mish op not supported in MPS"			)
			def 				lowerCAmelCase__     (		self	:  List[Any]			) ->Union[str, Any]:



					'''simple docstring'''







					_UpperCAmelCase       ,     _UpperCAmelCase		:							int             =   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__			)
					_UpperCAmelCase		:							Dict             =   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 				lowerCAmelCase__     (		self	:  List[str]			) ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase		:							str             =   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			)

					_UpperCAmelCase		:							str             =   model.config.in_channels
					_UpperCAmelCase		:							Optional[int]             =   16
					_UpperCAmelCase		:							List[str]             =   torch.randn((1, seq_len, num_features)			).permute(
					    0						,	2						,	1			)  # match original, we can update values and remove
					_UpperCAmelCase		:							Tuple             =   torch.full((num_features,)						,	0			)

					with torch.no_grad():
							_UpperCAmelCase		:							List[Any]             =   model(lowerCamelCase__						,	lowerCamelCase__			).sample.permute(0						,	2						,	1			)

					_UpperCAmelCase		:							Optional[int]             =   output[0, -3:, -3:].flatten()
					# fmt: off
					_UpperCAmelCase		:							Dict             =   torch.tensor([-2.1_3_7_1_7_2, 1.1_4_2_6_0_1_6, 0.3_6_8_8_6_8_7, -0.7_6_6_9_2_2, 0.7_3_0_3_1_4_6, 0.1_1_0_3_8_8_6_4, -0.4_7_6_0_6_3_3, 0.1_3_2_7_0_1_7_2, 0.0_2_5_9_1_3_4_8]			)
					# fmt: on
					self.assertTrue(torch.allclose(lowerCamelCase__						,	lowerCamelCase__						,	rtol=1E-3			)			)




			def 				lowerCAmelCase__     (		self	:  Any			) ->List[str]:



					'''simple docstring'''







					pass






			@slow
			def 				lowerCAmelCase__     (		self	:  Union[str, Any]			) ->Optional[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:							int             =   "harmonai/maestro-150k"
					_UpperCAmelCase		:							Tuple             =   UNetaDModel.from_pretrained(lowerCamelCase__						,	subfolder="unet"			)
					model.to(lowerCamelCase__			)

					_UpperCAmelCase		:							List[str]             =   6_55_36
					_UpperCAmelCase		:							Any             =   torch.sin(torch.arange(lowerCamelCase__			)[None, None, :].repeat(1						,	2						,	1			)			).to(lowerCamelCase__			)
					_UpperCAmelCase		:							List[Any]             =   torch.tensor([1]			).to(lowerCamelCase__			)

					with torch.no_grad():
							_UpperCAmelCase		:							List[str]             =   model(lowerCamelCase__						,	lowerCamelCase__			).sample

					_UpperCAmelCase		:							Union[str, Any]             =   output.abs().sum()
					_UpperCAmelCase		:							Any             =   output.abs().max()

					assert (output_sum - 2_2_4.0_8_9_6).abs() < 0.5
					assert (output_max - 0.0_6_0_7).abs() < 4E-4






class 						lowerCAmelCase__      (      UpperCAmelCase__     ,							UpperCAmelCase__     ,							unittest.TestCase						):
			lowerCAmelCase			:						Union[str, Any]							 =  UNetaDModel
			lowerCAmelCase			:						Union[str, Any]							 =  "sample"




			@property
			def 				lowerCAmelCase__     (		self	:  List[Any]			) ->str:



					'''simple docstring'''







					_UpperCAmelCase		:							Tuple             =   4
					_UpperCAmelCase		:							Optional[int]             =   14
					_UpperCAmelCase		:							Optional[int]             =   16

					_UpperCAmelCase		:							Optional[int]             =   floats_tensor((batch_size, num_features, seq_len)			).to(lowerCamelCase__			)
					_UpperCAmelCase		:							Union[str, Any]             =   torch.tensor([10] * batch_size			).to(lowerCamelCase__			)

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




			@property
			def 				lowerCAmelCase__     (		self	:  Dict			) ->Optional[int]:



					'''simple docstring'''







					return (4, 14, 16)




			@property
			def 				lowerCAmelCase__     (		self	:  Union[str, Any]			) ->Optional[int]:



					'''simple docstring'''







					return (4, 14, 1)




			@unittest.skipIf(torch_device == "mps"						,	"mish op not supported in MPS"			)
			def 				lowerCAmelCase__     (		self	:  List[str]			) ->Dict:



					'''simple docstring'''







					super().test_determinism()




			@unittest.skipIf(torch_device == "mps"						,	"mish op not supported in MPS"			)
			def 				lowerCAmelCase__     (		self	:  int			) ->Optional[Any]:



					'''simple docstring'''







					super().test_outputs_equivalence()




			@unittest.skipIf(torch_device == "mps"						,	"mish op not supported in MPS"			)
			def 				lowerCAmelCase__     (		self	:  str			) ->Optional[int]:



					'''simple docstring'''







					super().test_from_save_pretrained()




			@unittest.skipIf(torch_device == "mps"						,	"mish op not supported in MPS"			)
			def 				lowerCAmelCase__     (		self	:  int			) ->Dict:



					'''simple docstring'''







					super().test_from_save_pretrained_variant()




			@unittest.skipIf(torch_device == "mps"						,	"mish op not supported in MPS"			)
			def 				lowerCAmelCase__     (		self	:  int			) ->Dict:



					'''simple docstring'''







					super().test_model_from_pretrained()




			@unittest.skipIf(torch_device == "mps"						,	"mish op not supported in MPS"			)
			def 				lowerCAmelCase__     (		self	:  Tuple			) ->Dict:



					'''simple docstring'''







					_UpperCAmelCase       ,     _UpperCAmelCase		:							str             =   self.prepare_init_args_and_inputs_for_common()
					_UpperCAmelCase		:							Optional[int]             =   self.model_class(**lowerCamelCase__			)
					model.to(lowerCamelCase__			)
					model.eval()

					with torch.no_grad():
							_UpperCAmelCase		:							int             =   model(**lowerCamelCase__			)

							if isinstance(lowerCamelCase__						,	lowerCamelCase__			):
									_UpperCAmelCase		:							List[Any]             =   output.sample

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




			def 				lowerCAmelCase__     (		self	:  Tuple			) ->Dict:



					'''simple docstring'''







					pass




			def 				lowerCAmelCase__     (		self	:  List[str]			) ->List[str]:



					'''simple docstring'''







					pass




			def 				lowerCAmelCase__     (		self	:  Dict			) ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase		:							List[str]             =   {
					    "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, 1_28, 2_56],
					    "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",
					}
					_UpperCAmelCase		:							List[Any]             =   self.dummy_input
					return init_dict, inputs_dict




			@unittest.skipIf(torch_device == "mps"						,	"mish op not supported in MPS"			)
			def 				lowerCAmelCase__     (		self	:  List[str]			) ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase       ,     _UpperCAmelCase		:							Any             =   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__			)
					_UpperCAmelCase		:							Optional[int]             =   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 				lowerCAmelCase__     (		self	:  Union[str, Any]			) ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase       ,     _UpperCAmelCase		:							List[str]             =   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			)

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

					with torch.no_grad():
							_UpperCAmelCase		:							int             =   value_function(lowerCamelCase__						,	lowerCamelCase__			).sample

					# fmt: off
					_UpperCAmelCase		:							Union[str, Any]             =   torch.tensor([1_6_5.2_5] * seq_len			)
					# fmt: on
					self.assertTrue(torch.allclose(lowerCamelCase__						,	lowerCamelCase__						,	rtol=1E-3			)			)






			def 				lowerCAmelCase__     (		self	:  Dict			) ->Union[str, Any]:



					'''simple docstring'''







					pass



