
'''simple docstring'''



import argparse
import logging
import os
import sys
import time

import tensorflow as tf
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
from transformers.utils import is_sagemaker_dp_enabled


if os.environ.get("SDP_ENABLED") or is_sagemaker_dp_enabled():
				__A          =    True
				__A          =    "p3dn.24xlarge"
				import smdistributed.dataparallel.tensorflow as sdp
else:
				__A          =    False





def  _A		(							lowercase__		,		lowercase__		,		lowercase__		,		lowercase__		,		lowercase__		,		lowercase__		,		lowercase__=None	):
						lowercase__							 =       tqdm(lowercase__	)
						for i, batch in enumerate(lowercase__	):
												with tf.GradientTape() as tape:
																		lowercase__ ,						lowercase__							 =       batch
																		lowercase__							 =       model(lowercase__	)
																		lowercase__							 =       loss(lowercase__		,		outputs.logits	)

												if SDP_ENABLED:
																		lowercase__							 =       sdp.DistributedGradientTape(lowercase__		,		sparse_as_dense=lowercase__	)

												lowercase__							 =       tape.gradient(lowercase__		,		model.trainable_variables	)
												opt.apply_gradients(zip(lowercase__		,		model.trainable_variables	)	)

												pbar.set_description(f'''Loss: {loss_value:.4f}'''	)

												if SDP_ENABLED and i == 0:
																		sdp.broadcast_variables(model.variables		,		root_rank=0	)
																		sdp.broadcast_variables(opt.variables()		,		root_rank=0	)

												if max_steps and i >= max_steps:
																		break

						lowercase__							 =       {"""loss""": loss_value.numpy()}
						return train_results





def  _A		(							lowercase__		,		lowercase__		,		lowercase__	):
						# Load dataset
						lowercase__ ,						lowercase__							 =       load_dataset("""imdb"""		,		split=["""train""", """test"""]	)

						# Preprocess train dataset
						lowercase__							 =       train_dataset.map(
						    lambda lowercase__	: tokenizer(e["""text"""]		,		truncation=lowercase__		,		padding="""max_length"""	)		,		batched=lowercase__	)
						train_dataset.set_format(type="""tensorflow"""		,		columns=["""input_ids""", """attention_mask""", """label"""]	)

						lowercase__							 =       {
						    x: train_dataset[x].to_tensor(default_value=0		,		shape=[None, tokenizer.model_max_length]	)
						    for x in ["""input_ids""", """attention_mask"""]
						}
						lowercase__							 =       tf.data.Dataset.from_tensor_slices((train_features, train_dataset["""label"""])	)

						# Preprocess test dataset
						lowercase__							 =       test_dataset.map(
						    lambda lowercase__	: tokenizer(e["""text"""]		,		truncation=lowercase__		,		padding="""max_length"""	)		,		batched=lowercase__	)
						test_dataset.set_format(type="""tensorflow"""		,		columns=["""input_ids""", """attention_mask""", """label"""]	)

						lowercase__							 =       {
						    x: test_dataset[x].to_tensor(default_value=0		,		shape=[None, tokenizer.model_max_length]	)
						    for x in ["""input_ids""", """attention_mask"""]
						}
						lowercase__							 =       tf.data.Dataset.from_tensor_slices((test_features, test_dataset["""label"""])	)

						if SDP_ENABLED:
												lowercase__							 =       tf_train_dataset.shard(sdp.size()		,		sdp.rank()	)
												lowercase__							 =       tf_test_dataset.shard(sdp.size()		,		sdp.rank()	)
						lowercase__							 =       tf_train_dataset.batch(lowercase__		,		drop_remainder=lowercase__	)
						lowercase__							 =       tf_test_dataset.batch(lowercase__		,		drop_remainder=lowercase__	)

						return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
				__A          =    argparse.ArgumentParser()

				# Hyperparameters sent by the client are passed as command-line arguments to the script.
				parser.add_argument("--epochs", type=int, default=3)
				parser.add_argument("--per_device_train_batch_size", type=int, default=16)
				parser.add_argument("--per_device_eval_batch_size", type=int, default=8)
				parser.add_argument("--model_name_or_path", type=str)
				parser.add_argument("--learning_rate", type=str, default=5E-5)
				parser.add_argument("--do_train", type=bool, default=True)
				parser.add_argument("--do_eval", type=bool, default=True)
				parser.add_argument("--output_dir", type=str)
				parser.add_argument("--max_steps", type=int, default=None)

				# Data, model, and output directories
				parser.add_argument("--output_data_dir", type=str, default=os.environ["SM_OUTPUT_DATA_DIR"])
				parser.add_argument("--model_dir", type=str, default=os.environ["SM_MODEL_DIR"])
				parser.add_argument("--n_gpus", type=str, default=os.environ["SM_NUM_GPUS"])

				__A    ,       __A          =    parser.parse_known_args()

				# Set up logging
				__A          =    logging.getLogger(__name__)

				logging.basicConfig(
				    level=logging.getLevelName("INFO"),
				    handlers=[logging.StreamHandler(sys.stdout)],
				    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
				)

				if SDP_ENABLED:
								sdp.init()

								__A          =    tf.config.experimental.list_physical_devices("GPU")
								for gpu in gpus:
												tf.config.experimental.set_memory_growth(gpu, True)
								if gpus:
												tf.config.experimental.set_visible_devices(gpus[sdp.local_rank()], "GPU")

    # Load model and tokenizer
				__A          =    TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
				__A          =    AutoTokenizer.from_pretrained(args.model_name_or_path)

				# get datasets
				__A    ,       __A          =    get_datasets(
				    tokenizer=tokenizer,
				    train_batch_size=args.per_device_train_batch_size,
				    eval_batch_size=args.per_device_eval_batch_size,
				)

				# fine optimizer and loss
				__A          =    tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
				__A          =    tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
				__A          =    [tf.keras.metrics.SparseCategoricalAccuracy()]
				model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

				# Training
				if args.do_train:
								# train_results = model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.train_batch_size)
								__A          =    time.time()
								__A          =    fit(
								    model,
								    loss,
								    optimizer,
								    tf_train_dataset,
								    args.epochs,
								    args.per_device_train_batch_size,
								    max_steps=args.max_steps,
								)
								__A          =    time.time() - start_train_time
								logger.info("*** Train ***")
								logger.info(F'''train_runtime = {end_train_time}''')

								__A          =    os.path.join(args.output_dir, "train_results.txt")

								if not SDP_ENABLED or sdp.rank() == 0:
												with open(output_eval_file, "w") as writer:
																logger.info("***** Train results *****")
																logger.info(train_results)
																for key, value in train_results.items():
																				logger.info(F'''  {key} = {value}''')
																				writer.write(F'''{key} = {value}\n''')

    # Evaluation
				if args.do_eval and (not SDP_ENABLED or sdp.rank() == 0):
								__A          =    model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
								logger.info("*** Evaluate ***")

								__A          =    os.path.join(args.output_dir, "eval_results.txt")

								with open(output_eval_file, "w") as writer:
												logger.info("***** Eval results *****")
												logger.info(result)
												for key, value in result.items():
																logger.info(F'''  {key} = {value}''')
																writer.write(F'''{key} = {value}\n''')

    # Save result
				if SDP_ENABLED:
								if sdp.rank() == 0:
												model.save_pretrained(args.output_dir)
												tokenizer.save_pretrained(args.output_dir)
				else:
								model.save_pretrained(args.output_dir)
								tokenizer.save_pretrained(args.output_dir)








'''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()

__A          =    logging.get_logger(__name__)





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

												lowercase__							 =       LoRAAttnProcessor(hidden_size=lowercase__		,		cross_attention_dim=lowercase__	)
												lowercase__							 =       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							(       __UpperCAmelCase		,						__UpperCAmelCase		,						unittest.TestCase				):
			lowerCamelCase							:							List[Any]       		=  UNetaDConditionModel
			lowerCamelCase							:							int       		=  """sample"""



			@property
			def A__ (					self       )			->		Optional[Any]:




									'''simple docstring'''



									lowercase__							 =       4
									lowercase__							 =       4
									lowercase__							 =       4
									lowercase__							 =       (32, 32)

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

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



			@property
			def A__ (					self       )			->		Any:




									'''simple docstring'''



									return (4, 4, 32, 32)



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




									'''simple docstring'''



									return (4, 4, 32, 32)



			def A__ (					self       )			->		Optional[int]:




									'''simple docstring'''



									lowercase__							 =       {
									    """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,
									}
									lowercase__							 =       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 A__ (					self       )			->		str:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
									lowercase__							 =       self.model_class(**lowerCamelCase__       )

									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 A__ (					self       )			->		Dict:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()

									lowercase__							 =       32

									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )
									model.eval()

									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       )

															if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
																					lowercase__							 =       output.sample

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



			def A__ (					self       )			->		List[Any]:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )
									model.eval()

									with torch.no_grad():
															# Warmup pass when using mps (see #372)
															if torch_device == "mps" and isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
																					model(**self.dummy_input       )

															lowercase__							 =       model(**lowerCamelCase__       )
															if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
																					lowercase__							 =       first.sample

															lowercase__							 =       model(**lowerCamelCase__       )
															if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
																					lowercase__							 =       second.sample

									lowercase__							 =       first.cpu().numpy()
									lowercase__							 =       second.cpu().numpy()
									lowercase__							 =       out_a[~np.isnan(lowerCamelCase__       )]
									lowercase__							 =       out_a[~np.isnan(lowerCamelCase__       )]
									lowercase__							 =       np.amax(np.abs(out_a - out_a       )       )
									self.assertLessEqual(lowerCamelCase__       ,     1e-5       )



			def A__ (					self       )			->		Any:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()

									lowercase__							 =       8

									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )
									model.eval()

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

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

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



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




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()

									lowercase__							 =       8

									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )

									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       ).sample

									lowercase__							 =       create_lora_layers(lowerCamelCase__       )

									# make sure we can set a list of attention processors
									model.set_attn_processor(lowerCamelCase__       )
									model.to(lowerCamelCase__       )

									# test that attn processors can be set to itself
									model.set_attn_processor(model.attn_processors       )

									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.0}       ).sample
															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.5}       ).sample
															lowercase__							 =       model(**lowerCamelCase__       ,     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 A__ (					self       )			->		Optional[int]:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()

									lowercase__							 =       8

									torch.manual_seed(0       )
									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )

									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       ).sample

									lowercase__							 =       create_lora_layers(lowerCamelCase__       )
									model.set_attn_processor(lowerCamelCase__       )

									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.5}       ).sample

									with tempfile.TemporaryDirectory() as tmpdirname:
															model.save_attn_procs(lowerCamelCase__       )
															self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase__       ,     """pytorch_lora_weights.bin"""       )       )       )
															torch.manual_seed(0       )
															lowercase__							 =       self.model_class(**lowerCamelCase__       )
															new_model.to(lowerCamelCase__       )
															new_model.load_attn_procs(lowerCamelCase__       )

									with torch.no_grad():
															lowercase__							 =       new_model(**lowerCamelCase__       ,     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 A__ (					self       )			->		Any:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()

									lowercase__							 =       8

									torch.manual_seed(0       )
									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )

									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       ).sample

									lowercase__							 =       create_lora_layers(lowerCamelCase__       )
									model.set_attn_processor(lowerCamelCase__       )

									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.5}       ).sample

									with tempfile.TemporaryDirectory() as tmpdirname:
															model.save_attn_procs(lowerCamelCase__       ,     safe_serialization=lowerCamelCase__       )
															self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase__       ,     """pytorch_lora_weights.safetensors"""       )       )       )
															torch.manual_seed(0       )
															lowercase__							 =       self.model_class(**lowerCamelCase__       )
															new_model.to(lowerCamelCase__       )
															new_model.load_attn_procs(lowerCamelCase__       )

									with torch.no_grad():
															lowercase__							 =       new_model(**lowerCamelCase__       ,     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 A__ (					self       )			->		Dict:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()

									lowercase__							 =       8

									torch.manual_seed(0       )
									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )

									lowercase__							 =       create_lora_layers(lowerCamelCase__       ,     mock_weights=lowerCamelCase__       )
									model.set_attn_processor(lowerCamelCase__       )
									# Saving as torch, properly reloads with directly filename
									with tempfile.TemporaryDirectory() as tmpdirname:
															model.save_attn_procs(lowerCamelCase__       )
															self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase__       ,     """pytorch_lora_weights.bin"""       )       )       )
															torch.manual_seed(0       )
															lowercase__							 =       self.model_class(**lowerCamelCase__       )
															new_model.to(lowerCamelCase__       )
															new_model.load_attn_procs(lowerCamelCase__       ,     weight_name="""pytorch_lora_weights.bin"""       )



			def A__ (					self       )			->		List[Any]:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()

									lowercase__							 =       8

									torch.manual_seed(0       )
									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )

									lowercase__							 =       create_lora_layers(lowerCamelCase__       ,     mock_weights=lowerCamelCase__       )
									model.set_attn_processor(lowerCamelCase__       )
									# Saving as torch, properly reloads with directly filename
									with tempfile.TemporaryDirectory() as tmpdirname:
															model.save_attn_procs(lowerCamelCase__       )
															self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase__       ,     """pytorch_lora_weights.bin"""       )       )       )
															torch.manual_seed(0       )
															lowercase__							 =       self.model_class(**lowerCamelCase__       )
															new_model.to(lowerCamelCase__       )
															with self.assertRaises(lowerCamelCase__       ) as e:
																					new_model.load_attn_procs(lowerCamelCase__       ,     use_safetensors=lowerCamelCase__       )
															self.assertIn("""Error no file named pytorch_lora_weights.safetensors"""       ,     str(e.exception       )       )



			def A__ (					self       )			->		Any:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()

									lowercase__							 =       8

									torch.manual_seed(0       )
									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )

									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       ).sample

									lowercase__							 =       create_lora_layers(lowerCamelCase__       )
									model.set_attn_processor(lowerCamelCase__       )

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

									model.set_attn_processor(AttnProcessor()       )

									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       ).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 A__ (					self       )			->		Optional[int]:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()

									lowercase__							 =       4

									torch.manual_seed(0       )
									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )
									lowercase__							 =       create_lora_layers(lowerCamelCase__       )
									model.set_attn_processor(lowerCamelCase__       )

									# default
									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       ).sample

															model.enable_xformers_memory_efficient_attention()
															lowercase__							 =       model(**lowerCamelCase__       ).sample

															model.disable_xformers_memory_efficient_attention()
															lowercase__							 =       model(**lowerCamelCase__       ).sample

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



			def A__ (					self       )			->		List[Any]:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
									lowercase__							 =       32

									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )
									model.eval()

									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       )[0]

									model.enable_forward_chunking()
									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       )[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.






