



import inspect
import unittest

from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
				import jax
				import jax.numpy as jnp

				from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
				from PIL import Image

				from transformers import AutoImageProcessor



class 	_lowercase							(   unittest.TestCase	):





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_		,       lowerCamelCase_=3		,       lowerCamelCase_=32		,       lowerCamelCase_=3		,       lowerCamelCase_=10		,       lowerCamelCase_=[10, 20, 30, 40]		,       lowerCamelCase_=[1, 1, 2, 1]		,       lowerCamelCase_=True		,       lowerCamelCase_=True		,       lowerCamelCase_="relu"		,       lowerCamelCase_=3		,       lowerCamelCase_=None		,       ):
						"""simple docstring"""

						a					     =   parent
						a					     =   batch_size
						a					     =   image_size
						a					     =   num_channels
						a					     =   embeddings_size
						a					     =   hidden_sizes
						a					     =   depths
						a					     =   is_training
						a					     =   use_labels
						a					     =   hidden_act
						a					     =   num_labels
						a					     =   scope
						a					     =   len(lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

						a					     =   self.get_config()

						return config, pixel_values




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return RegNetConfig(
						    num_channels=self.num_channels		,       embeddings_size=self.embeddings_size		,       hidden_sizes=self.hidden_sizes		,       depths=self.depths		,       hidden_act=self.hidden_act		,       num_labels=self.num_labels		,       image_size=self.image_size		,       )




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   FlaxRegNetModel(config=lowerCamelCase_		)
						a					     =   model(lowerCamelCase_		)

						# Output shape (b, c, h, w)
						self.parent.assertEqual(
						    result.last_hidden_state.shape		,       (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)		,       )




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   self.num_labels
						a					     =   FlaxRegNetForImageClassification(config=lowerCamelCase_		)
						a					     =   model(lowerCamelCase_		)
						self.parent.assertEqual(result.logits.shape		,       (self.batch_size, self.num_labels)		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.prepare_config_and_inputs()
						a     ,				a					     =   config_and_inputs
						a					     =   {"pixel_values": pixel_values}
						return config, inputs_dict




@require_flax
class 	_lowercase							(   lowerCAmelCase,      unittest.TestCase	):





	"""simple docstring"""
	__A							=							(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

	__A							=							False
	__A							=							False
	__A							=							False




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   FlaxRegNetModelTester(self		)
						a					     =   ConfigTester(self		,       config_class=lowerCamelCase_		,       has_text_modality=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						self.create_and_test_config_common_properties()
						self.config_tester.create_and_test_config_to_json_string()
						self.config_tester.create_and_test_config_to_json_file()
						self.config_tester.create_and_test_config_from_and_save_pretrained()
						self.config_tester.create_and_test_config_with_num_labels()
						self.config_tester.check_config_can_be_init_without_params()
						self.config_tester.check_config_arguments_init()




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*lowerCamelCase_		)




	@unittest.skip(reason="RegNet does not use inputs_embeds"		)
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						pass




	@unittest.skip(reason="RegNet does not support input and output embeddings"		)
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						pass




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a     ,				a					     =   self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
											a					     =   model_class(lowerCamelCase_		)
											a					     =   inspect.signature(model.__call__		)
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											a					     =   [*signature.parameters.keys()]

											a					     =   ["pixel_values"]
											self.assertListEqual(arg_names[:1]		,       lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						def check_hidden_states_output(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		):
											a					     =   model_class(lowerCamelCase_		)

											a					     =   model(**self._prepare_for_class(lowerCamelCase_		,       lowerCamelCase_		)		)

											a					     =   outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

											a					     =   self.model_tester.num_stages
											self.assertEqual(len(lowerCamelCase_		)		,       expected_num_stages + 1		)

						a     ,				a					     =   self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
											a					     =   True
											check_hidden_states_output(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		)

											# check that output_hidden_states also work using config
											del inputs_dict["output_hidden_states"]
											a					     =   True

											check_hidden_states_output(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a     ,				a					     =   self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__		):
																a					     =   self._prepare_for_class(lowerCamelCase_		,       lowerCamelCase_		)
																a					     =   model_class(lowerCamelCase_		)

																@jax.jit
																def model_jitted(lowerCamelCase_		,       **lowerCamelCase_		):
																					return model(pixel_values=lowerCamelCase_		,       **lowerCamelCase_		)

																with self.subTest("JIT Enabled"		):
																					a					     =   model_jitted(**lowerCamelCase_		).to_tuple()

																with self.subTest("JIT Disabled"		):
																					with jax.disable_jit():
																										a					     =   model_jitted(**lowerCamelCase_		).to_tuple()

																self.assertEqual(len(lowerCamelCase_		)		,       len(lowerCamelCase_		)		)
																for jitted_output, output in zip(lowerCamelCase_		,       lowerCamelCase_		):
																					self.assertEqual(jitted_output.shape		,       output.shape		)






def     a(      )		->       Optional[int]:
					"""simple docstring"""





					a					     =   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
					return image







@require_flax
class 	_lowercase							(   unittest.TestCase	):





	"""simple docstring"""




	@cached_property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return AutoImageProcessor.from_pretrained("facebook/regnet-y-040"		) if is_vision_available() else None




	@slow
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   FlaxRegNetForImageClassification.from_pretrained("facebook/regnet-y-040"		)

						a					     =   self.default_image_processor
						a					     =   prepare_img()
						a					     =   image_processor(images=lowerCamelCase_		,       return_tensors="np"		)

						a					     =   model(**lowerCamelCase_		)

						# verify the logits
						a					     =   (1, 1000)
						self.assertEqual(outputs.logits.shape		,       lowerCamelCase_		)

						a					     =   jnp.array([-0.4180, -1.5051, -3.4836]		)

						self.assertTrue(jnp.allclose(outputs.logits[0, :3]		,       lowerCamelCase_		,       atol=1E-4		)		)





import gc
import importlib.metadata
import tempfile
import unittest

from packaging import version

from transformers import (
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    BitsAndBytesConfig,
    pipeline,
)
from transformers.testing_utils import (
    is_torch_available,
    require_accelerate,
    require_bitsandbytes,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)






def     a(      A       :    List[str]       )		->       List[str]:
					"""simple docstring"""





					if model.config.model_type == "gpt2":
										return model.transformer.h[0].mlp.c_fc
					return model.transformer.h[0].mlp.dense_ah_to_h


if is_torch_available():
				import torch
				import torch.nn as nn



				class 	_lowercase							(   nn.Module	):





					"""simple docstring"""




					def __init__(self		,       lowerCamelCase_		,       lowerCamelCase_		):
										"""simple docstring"""

										super().__init__()
										a					     =   module
										a					     =   nn.Sequential(
										    nn.Linear(module.in_features		,       lowerCamelCase_		,       bias=lowerCamelCase_		)		,       nn.Linear(lowerCamelCase_		,       module.out_features		,       bias=lowerCamelCase_		)		,       )
										a					     =   (2.0 / (5 * min(module.in_features		,       module.out_features		))) ** 0.5
										nn.init.normal_(self.adapter[0].weight		,       std=lowerCamelCase_		)
										nn.init.zeros_(self.adapter[1].weight		)
										self.adapter.to(module.weight.device		)




					def      UpperCamelCase_       (self		,       lowerCamelCase_		,       *lowerCamelCase_		,       **lowerCamelCase_		):
										"""simple docstring"""

										return self.module(lowerCamelCase_		,       *lowerCamelCase_		,       **lowerCamelCase_		) + self.adapter(lowerCamelCase_		)



@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class 	_lowercase							(   unittest.TestCase	):





	"""simple docstring"""
	# We keep the constants inside the init function and model loading inside setUp function

	# We need to test on relatively large models (aka >1b parameters otherwise the quantiztion may not work as expected)
	# Therefore here we use only bloom-1b3 to test our module
	__A							=							"bigscience/bloom-1b7"

	# Constant values
	__A							=							2.109_659_552_692_574

	__A							=							"Hello my name is"
	__A							=							set()
	EXPECTED_OUTPUTS.add("Hello my name is John and I am a professional photographer. I"	)
	EXPECTED_OUTPUTS.add("Hello my name is John.\nI am a friend of your father.\n"	)
	EXPECTED_OUTPUTS.add("Hello my name is John Doe, I am a student at the University"	)
	__A							=							10




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   AutoTokenizer.from_pretrained(self.model_name		)




class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						super().setUp()

						# Models and tokenizer
						a					     =   AutoModelForCausalLM.from_pretrained(
						    self.model_name		,       torch_dtype=torch.floataa		,       device_map="auto"		)
						a					     =   AutoModelForCausalLM.from_pretrained(self.model_name		,       load_in_abit=lowerCamelCase_		,       device_map="auto"		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						del self.model_fpaa
						del self.model_abit

						gc.collect()
						torch.cuda.empty_cache()




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.model_abit.config

						self.assertTrue(hasattr(lowerCamelCase_		,       "quantization_config"		)		)

						a					     =   config.to_dict()
						a					     =   config.to_diff_dict()

						a					     =   config.to_json_string()




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						from bitsandbytes.nn import Paramsabit

						a					     =   self.model_fpaa.get_memory_footprint()
						a					     =   self.model_abit.get_memory_footprint()

						self.assertAlmostEqual(mem_fpaa / mem_abit		,       self.EXPECTED_RELATIVE_DIFFERENCE		)
						a					     =   get_some_linear_layer(self.model_abit		)
						self.assertTrue(linear.weight.__class__ == Paramsabit		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						from transformers import TaPreTrainedModel

						self.model_fpaa.get_memory_footprint()
						self.model_abit.get_memory_footprint()

						for name, module in self.model_abit.named_modules():
											if isinstance(lowerCamelCase_		,       torch.nn.Linear		):
																if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
																					# 4-bit parameters are packed in uint8 variables
																					self.assertTrue(module.weight.dtype == torch.uinta		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.tokenizer(self.input_text		,       return_tensors="pt"		)
						a					     =   self.model_abit.generate(input_ids=encoded_input["input_ids"].to(0		)		,       max_new_tokens=10		)

						self.assertIn(self.tokenizer.decode(output_sequences[0]		,       skip_special_tokens=lowerCamelCase_		)		,       self.EXPECTED_OUTPUTS		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   BitsAndBytesConfig()
						a					     =   True

						a					     =   AutoModelForCausalLM.from_pretrained(
						    self.model_name		,       quantization_config=lowerCamelCase_		,       device_map="auto"		)

						a					     =   self.tokenizer(self.input_text		,       return_tensors="pt"		)
						a					     =   model_abit_from_config.generate(
						    input_ids=encoded_input["input_ids"].to(0		)		,       max_new_tokens=10		)

						self.assertIn(self.tokenizer.decode(output_sequences[0]		,       skip_special_tokens=lowerCamelCase_		)		,       self.EXPECTED_OUTPUTS		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						with self.assertRaises(lowerCamelCase_		), tempfile.TemporaryDirectory() as tmpdirname:
											self.model_abit.save_pretrained(lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   BitsAndBytesConfig()

						with self.assertRaises(lowerCamelCase_		):
											a					     =   AutoModelForCausalLM.from_pretrained(
											    self.model_name		,       quantization_config=lowerCamelCase_		,       load_in_abit=lowerCamelCase_		,       device_map="auto"		,       bnb_abit_quant_type="nf4"		,       )




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						with self.assertRaises(lowerCamelCase_		):
											# Tries with `str`
											self.model_abit.to("cpu"		)

						with self.assertRaises(lowerCamelCase_		):
											# Tries with a `dtype``
											self.model_abit.to(torch.floataa		)

						with self.assertRaises(lowerCamelCase_		):
											# Tries with a `device`
											self.model_abit.to(torch.device("cuda:0"		)		)

						with self.assertRaises(lowerCamelCase_		):
											# Tries with a `device`
											self.model_abit.float()

						with self.assertRaises(lowerCamelCase_		):
											# Tries with a `device`
											self.model_abit.half()

						# Test if we did not break anything
						a					     =   self.tokenizer(self.input_text		,       return_tensors="pt"		)

						a					     =   self.model_fpaa.to(torch.floataa		)
						a					     =   self.model_fpaa.generate(input_ids=encoded_input["input_ids"].to(0		)		,       max_new_tokens=10		)

						# Check this does not throw an error
						a					     =   self.model_fpaa.to("cpu"		)

						# Check this does not throw an error
						a					     =   self.model_fpaa.half()

						# Check this does not throw an error
						a					     =   self.model_fpaa.float()




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   AutoModelForSeqaSeqLM.from_pretrained("t5-small"		,       load_in_abit=lowerCamelCase_		,       device_map="auto"		)
						self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa		)




@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class 	_lowercase							(   unittest.TestCase	):





	"""simple docstring"""




	@classmethod
	def      UpperCamelCase_       (cls		):
						"""simple docstring"""

						a					     =   "t5-small"
						a					     =   "google/flan-t5-small"  # flan-t5 uses dense-act instead of dense-relu-dense
						a					     =   AutoTokenizer.from_pretrained(cls.model_name		)
						a					     =   "Translate in German: Hello, my dog is cute"




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						gc.collect()
						torch.cuda.empty_cache()




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						from transformers import TaForConditionalGeneration

						a					     =   TaForConditionalGeneration._keep_in_fpaa_modules
						a					     =   None

						# test with `t5-small`
						a					     =   TaForConditionalGeneration.from_pretrained(self.model_name		,       load_in_abit=lowerCamelCase_		,       device_map="auto"		)
						a					     =   self.tokenizer(self.input_text		,       return_tensors="pt"		).to(0		)
						a					     =   model.generate(**lowerCamelCase_		)

						# test with `flan-t5-small`
						a					     =   TaForConditionalGeneration.from_pretrained(
						    self.dense_act_model_name		,       load_in_abit=lowerCamelCase_		,       device_map="auto"		)
						a					     =   self.tokenizer(self.input_text		,       return_tensors="pt"		).to(0		)
						a					     =   model.generate(**lowerCamelCase_		)
						a					     =   modules




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						import bitsandbytes as bnb

						from transformers import TaForConditionalGeneration

						# test with `t5-small`
						a					     =   TaForConditionalGeneration.from_pretrained(self.model_name		,       load_in_abit=lowerCamelCase_		,       device_map="auto"		)

						# there was a bug with decoders - this test checks that it is fixed
						self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q		,       bnb.nn.Linearabit		)		)

						a					     =   self.tokenizer(self.input_text		,       return_tensors="pt"		).to(0		)
						a					     =   model.generate(**lowerCamelCase_		)

						# test with `flan-t5-small`
						a					     =   TaForConditionalGeneration.from_pretrained(
						    self.dense_act_model_name		,       load_in_abit=lowerCamelCase_		,       device_map="auto"		)
						a					     =   self.tokenizer(self.input_text		,       return_tensors="pt"		).to(0		)
						a					     =   model.generate(**lowerCamelCase_		)




class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						super().setUp()
						# model_name
						a					     =   "bigscience/bloom-560m"
						a					     =   "t5-small"

						# Different types of model

						a					     =   AutoModel.from_pretrained(self.model_name		,       load_in_abit=lowerCamelCase_		,       device_map="auto"		)
						# Sequence classification model
						a					     =   AutoModelForSequenceClassification.from_pretrained(
						    self.model_name		,       load_in_abit=lowerCamelCase_		,       device_map="auto"		)
						# CausalLM model
						a					     =   AutoModelForCausalLM.from_pretrained(self.model_name		,       load_in_abit=lowerCamelCase_		,       device_map="auto"		)
						# Seq2seq model
						a					     =   AutoModelForSeqaSeqLM.from_pretrained(
						    self.seq_to_seq_name		,       load_in_abit=lowerCamelCase_		,       device_map="auto"		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						del self.base_model
						del self.sequence_model
						del self.model_abit
						del self.seq_to_seq_model

						gc.collect()
						torch.cuda.empty_cache()




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						from bitsandbytes.nn import Paramsabit

						self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit		)

						# Other heads should be nn.Parameter
						self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter		)
						self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter		)
						self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter		)




class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						super().setUp()




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						del self.pipe

						gc.collect()
						torch.cuda.empty_cache()




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   pipeline(
						    "text-generation"		,       model=self.model_name		,       model_kwargs={"device_map": "auto", "load_in_4bit": True, "torch_dtype": torch.floataa}		,       max_new_tokens=self.MAX_NEW_TOKENS		,       )

						# Real second forward pass
						a					     =   self.pipe(self.input_text		)
						self.assertIn(pipeline_output[0]["generated_text"]		,       self.EXPECTED_OUTPUTS		)




@require_torch_multi_gpu
class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						super().setUp()




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   AutoModelForCausalLM.from_pretrained(
						    self.model_name		,       load_in_abit=lowerCamelCase_		,       device_map="balanced"		)

						# Check correct device map
						self.assertEqual(set(model_parallel.hf_device_map.values()		)		,       {0, 1}		)

						# Check that inference pass works on the model
						a					     =   self.tokenizer(self.input_text		,       return_tensors="pt"		)

						# Second real batch
						a					     =   model_parallel.generate(input_ids=encoded_input["input_ids"].to(0		)		,       max_new_tokens=10		)
						self.assertIn(self.tokenizer.decode(output_parallel[0]		,       skip_special_tokens=lowerCamelCase_		)		,       self.EXPECTED_OUTPUTS		)




class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   "facebook/opt-350m"
						super().setUp()




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						if version.parse(importlib.metadata.version("bitsandbytes"		)		) < version.parse("0.37.0"		):
											return

						# Step 1: freeze all parameters
						a					     =   AutoModelForCausalLM.from_pretrained(self.model_name		,       load_in_abit=lowerCamelCase_		)

						self.assertEqual(set(model.hf_device_map.values()		)		,       {torch.cuda.current_device()}		)

						for param in model.parameters():
											a					     =   False  # freeze the model - train adapters later
											if param.ndim == 1:
																# cast the small parameters (e.g. layernorm) to fp32 for stability
																a					     =   param.data.to(torch.floataa		)

        # Step 2: add adapters
						for _, module in model.named_modules():
											if "OPTAttention" in repr(type(lowerCamelCase_		)		):
																a					     =   LoRALayer(module.q_proj		,       rank=16		)
																a					     =   LoRALayer(module.k_proj		,       rank=16		)
																a					     =   LoRALayer(module.v_proj		,       rank=16		)

        # Step 3: dummy batch
						a					     =   self.tokenizer("Test batch "		,       return_tensors="pt"		).to(0		)

						# Step 4: Check if the gradient is not None
						with torch.cuda.amp.autocast():
											a					     =   model.forward(**lowerCamelCase_		)
											out.logits.norm().backward()

						for module in model.modules():
											if isinstance(lowerCamelCase_		,       lowerCamelCase_		):
																self.assertTrue(module.adapter[1].weight.grad is not None		)
																self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0		)
											elif isinstance(lowerCamelCase_		,       nn.Embedding		):
																self.assertTrue(module.weight.grad is None		)




class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""
	__A							=							"gpt2-xl"
	__A							=							3.3_191_854_854_152_187
