
import argparse
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging


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




def 				a						(							snake_case__:					List[Any]					):


				'''simple docstring'''
				lowercase_       =					OrderedDict()
				for key, value in state_dict.items():
								if key.startswith('''module.encoder'''					):
												lowercase_       =					key.replace('''module.encoder'''						,   '''glpn.encoder'''					)
								if key.startswith('''module.decoder'''					):
												lowercase_       =					key.replace('''module.decoder'''						,   '''decoder.stages'''					)
								if "patch_embed" in key:
												# replace for example patch_embed1 by patch_embeddings.0
												lowercase_       =					key[key.find('''patch_embed'''					) + len('''patch_embed'''					)]
												lowercase_       =					key.replace(F'''patch_embed{idx}'''						,   F'''patch_embeddings.{int(_lowercase					)-1}'''					)
								if "norm" in key:
												lowercase_       =					key.replace('''norm'''						,   '''layer_norm'''					)
								if "glpn.encoder.layer_norm" in key:
												# replace for example layer_norm1 by layer_norm.0
												lowercase_       =					key[key.find('''glpn.encoder.layer_norm'''					) + len('''glpn.encoder.layer_norm'''					)]
												lowercase_       =					key.replace(F'''layer_norm{idx}'''						,   F'''layer_norm.{int(_lowercase					)-1}'''					)
								if "layer_norm1" in key:
												lowercase_       =					key.replace('''layer_norm1'''						,   '''layer_norm_1'''					)
								if "layer_norm2" in key:
												lowercase_       =					key.replace('''layer_norm2'''						,   '''layer_norm_2'''					)
								if "block" in key:
												# replace for example block1 by block.0
												lowercase_       =					key[key.find('''block'''					) + len('''block'''					)]
												lowercase_       =					key.replace(F'''block{idx}'''						,   F'''block.{int(_lowercase					)-1}'''					)
								if "attn.q" in key:
												lowercase_       =					key.replace('''attn.q'''						,   '''attention.self.query'''					)
								if "attn.proj" in key:
												lowercase_       =					key.replace('''attn.proj'''						,   '''attention.output.dense'''					)
								if "attn" in key:
												lowercase_       =					key.replace('''attn'''						,   '''attention.self'''					)
								if "fc1" in key:
												lowercase_       =					key.replace('''fc1'''						,   '''dense1'''					)
								if "fc2" in key:
												lowercase_       =					key.replace('''fc2'''						,   '''dense2'''					)
								if "linear_pred" in key:
												lowercase_       =					key.replace('''linear_pred'''						,   '''classifier'''					)
								if "linear_fuse" in key:
												lowercase_       =					key.replace('''linear_fuse.conv'''						,   '''linear_fuse'''					)
												lowercase_       =					key.replace('''linear_fuse.bn'''						,   '''batch_norm'''					)
								if "linear_c" in key:
												# replace for example linear_c4 by linear_c.3
												lowercase_       =					key[key.find('''linear_c'''					) + len('''linear_c'''					)]
												lowercase_       =					key.replace(F'''linear_c{idx}'''						,   F'''linear_c.{int(_lowercase					)-1}'''					)
								if "bot_conv" in key:
												lowercase_       =					key.replace('''bot_conv'''						,   '''0.convolution'''					)
								if "skip_conv1" in key:
												lowercase_       =					key.replace('''skip_conv1'''						,   '''1.convolution'''					)
								if "skip_conv2" in key:
												lowercase_       =					key.replace('''skip_conv2'''						,   '''2.convolution'''					)
								if "fusion1" in key:
												lowercase_       =					key.replace('''fusion1'''						,   '''1.fusion'''					)
								if "fusion2" in key:
												lowercase_       =					key.replace('''fusion2'''						,   '''2.fusion'''					)
								if "fusion3" in key:
												lowercase_       =					key.replace('''fusion3'''						,   '''3.fusion'''					)
								if "fusion" in key and "conv" in key:
												lowercase_       =					key.replace('''conv'''						,   '''convolutional_layer'''					)
								if key.startswith('''module.last_layer_depth'''					):
												lowercase_       =					key.replace('''module.last_layer_depth'''						,   '''head.head'''					)
								lowercase_       =					value

				return new_state_dict




def 				a						(							snake_case__:					Optional[int]						,   snake_case__:					Tuple					):


				'''simple docstring'''
				# for each of the encoder blocks:
				for i in range(config.num_encoder_blocks					):
								for j in range(config.depths[i]					):
												# read in weights + bias of keys and values (which is a single matrix in the original implementation)
												lowercase_       =					state_dict.pop(F'''glpn.encoder.block.{i}.{j}.attention.self.kv.weight'''					)
												lowercase_       =					state_dict.pop(F'''glpn.encoder.block.{i}.{j}.attention.self.kv.bias'''					)
												# next, add keys and values (in that order) to the state dict
												lowercase_       =					kv_weight[
												    : config.hidden_sizes[i], :
												]
												lowercase_       =					kv_bias[: config.hidden_sizes[i]]
												lowercase_       =					kv_weight[
												    config.hidden_sizes[i] :, :
												]
												lowercase_       =					kv_bias[config.hidden_sizes[i] :]




def 				a						(							):


				'''simple docstring'''
				lowercase_       =					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
				lowercase_       =					Image.open(requests.get(_lowercase						,   stream=_lowercase					).raw					)

				return image




@torch.no_grad()
def 				a						(							snake_case__:					str						,   snake_case__:					Optional[int]						,   snake_case__:					Union[str, Any]=False						,   snake_case__:					Optional[Any]=None					):


				'''simple docstring'''
				lowercase_       =					GLPNConfig(hidden_sizes=[64, 128, 320, 512]						,   decoder_hidden_size=64						,   depths=[3, 8, 27, 3]					)

				# load image processor (only resize + rescale)
				lowercase_       =					GLPNImageProcessor()

				# prepare image
				lowercase_       =					prepare_img()
				lowercase_       =					image_processor(images=_lowercase						,   return_tensors='''pt'''					).pixel_values

				logger.info('''Converting model...'''					)

				# load original state dict
				lowercase_       =					torch.load(_lowercase						,   map_location=torch.device('''cpu'''					)					)

				# rename keys
				lowercase_       =					rename_keys(_lowercase					)

				# key and value matrices need special treatment
				read_in_k_v(_lowercase						,   _lowercase					)

				# create HuggingFace model and load state dict
				lowercase_       =					GLPNForDepthEstimation(_lowercase					)
				model.load_state_dict(_lowercase					)
				model.eval()

				# forward pass
				lowercase_       =					model(_lowercase					)
				lowercase_       =					outputs.predicted_depth

				# verify output
				if model_name is not None:
								if "nyu" in model_name:
												lowercase_       =					torch.tensor(
												    [[4.4_1_4_7, 4.0_8_7_3, 4.0_6_7_3], [3.7_8_9_0, 3.2_8_8_1, 3.1_5_2_5], [3.7_6_7_4, 3.5_4_2_3, 3.4_9_1_3]]					)
								elif "kitti" in model_name:
												lowercase_       =					torch.tensor(
												    [[3.4_2_9_1, 2.7_8_6_5, 2.5_1_5_1], [3.2_8_4_1, 2.7_0_2_1, 2.3_5_0_2], [3.1_1_4_7, 2.4_6_2_5, 2.2_4_8_1]]					)
								else:
												raise ValueError(F'''Unknown model name: {model_name}'''					)

								lowercase_       =					torch.Size([1, 480, 640]					)

								assert predicted_depth.shape == expected_shape
								assert torch.allclose(predicted_depth[0, :3, :3]						,   _lowercase						,   atol=1e-4					)
								print('''Looks ok!'''					)

				# finally, push to hub if required
				if push_to_hub:
								logger.info('''Pushing model and image processor to the hub...'''					)
								model.push_to_hub(
								    repo_path_or_name=Path(_lowercase						,   _lowercase					)						,   organization='''nielsr'''						,   commit_message='''Add model'''						,   use_temp_dir=_lowercase						,   )
								image_processor.push_to_hub(
								    repo_path_or_name=Path(_lowercase						,   _lowercase					)						,   organization='''nielsr'''						,   commit_message='''Add image processor'''						,   use_temp_dir=_lowercase						,   )


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

						parser.add_argument(
						    '--checkpoint_path',
						    default=None,
						    type=str,
						    help='Path to the original PyTorch checkpoint (.pth file).',
						)
						parser.add_argument(
						    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
						)
						parser.add_argument(
						    '--push_to_hub', action='store_true', help='Whether to upload the model to the HuggingFace hub.'
						)
						parser.add_argument(
						    '--model_name',
						    default='glpn-kitti',
						    type=str,
						    help='Name of the model in case you\'re pushing to the hub.',
						)
						__a  			=					parser.parse_args()
						convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)









'''simple docstring'''




import unittest

from parameterized import parameterized

from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    GPTNeoXForCausalLM,
							    GPTNeoXForQuestionAnswering,
							    GPTNeoXForSequenceClassification,
							    GPTNeoXForTokenClassification,
							    GPTNeoXModel,
							)

class 			UpperCamelCase_ :





	def __init__(  self		,		A		,		A=13		,		A=7		,		A=True		,		A=True		,		A=True		,		A=True		,		A=99		,		A=64		,		A=5		,		A=4		,		A=37		,		A="gelu"		,		A=0.1		,		A=0.1		,		A=512		,		A=16		,		A=2		,		A=0.0_2		,		A=3		,		A=4		,		A=None		,		)			->			Optional[int]:
								UpperCAmelCase     :		List[Any]							     =		parent
								UpperCAmelCase     :		Optional[int]							     =		batch_size
								UpperCAmelCase     :		Union[str, Any]							     =		seq_length
								UpperCAmelCase     :		Optional[Any]							     =		is_training
								UpperCAmelCase     :		Dict							     =		use_input_mask
								UpperCAmelCase     :		str							     =		use_token_type_ids
								UpperCAmelCase     :		List[Any]							     =		use_labels
								UpperCAmelCase     :		List[Any]							     =		vocab_size
								UpperCAmelCase     :		Dict							     =		hidden_size
								UpperCAmelCase     :		Dict							     =		num_hidden_layers
								UpperCAmelCase     :		Optional[int]							     =		num_attention_heads
								UpperCAmelCase     :		int							     =		intermediate_size
								UpperCAmelCase     :		List[str]							     =		hidden_act
								UpperCAmelCase     :		List[str]							     =		hidden_dropout_prob
								UpperCAmelCase     :		int							     =		attention_probs_dropout_prob
								UpperCAmelCase     :		str							     =		max_position_embeddings
								UpperCAmelCase     :		Optional[Any]							     =		type_vocab_size
								UpperCAmelCase     :		List[str]							     =		type_sequence_label_size
								UpperCAmelCase     :		int							     =		initializer_range
								UpperCAmelCase     :		str							     =		num_labels
								UpperCAmelCase     :		Optional[int]							     =		num_choices
								UpperCAmelCase     :		Dict							     =		scope
								UpperCAmelCase     :		Union[str, Any]							     =		vocab_size - 1





	def  _lowercase(  self    )			->			Union[str, Any]:
								UpperCAmelCase     :		List[str]							     =		ids_tensor([self.batch_size, self.seq_length]		,		self.vocab_size    )

								UpperCAmelCase     :		Any							     =		None
								if self.use_input_mask:
															UpperCAmelCase     :		int							     =		random_attention_mask([self.batch_size, self.seq_length]    )

								UpperCAmelCase     :		List[str]							     =		None
								if self.use_labels:
															UpperCAmelCase     :		Optional[int]							     =		ids_tensor([self.batch_size, self.seq_length]		,		self.num_labels    )

								UpperCAmelCase     :		Optional[int]							     =		self.get_config()

								return config, input_ids, input_mask, token_labels





	def  _lowercase(  self    )			->			Optional[Any]:
								return GPTNeoXConfig(
								    vocab_size=self.vocab_size		,		hidden_size=self.hidden_size		,		num_hidden_layers=self.num_hidden_layers		,		num_attention_heads=self.num_attention_heads		,		intermediate_size=self.intermediate_size		,		hidden_act=self.hidden_act		,		hidden_dropout_prob=self.hidden_dropout_prob		,		attention_probs_dropout_prob=self.attention_probs_dropout_prob		,		max_position_embeddings=self.max_position_embeddings		,		type_vocab_size=self.type_vocab_size		,		is_decoder=A		,		initializer_range=self.initializer_range		,		pad_token_id=self.pad_token_id		,		)





	def  _lowercase(  self    )			->			Optional[Any]:
								UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     :		List[str]							     =		self.prepare_config_and_inputs()

								UpperCAmelCase     :		Any							     =		True

								return config, input_ids, input_mask, token_labels





	def  _lowercase(  self		,		A		,		A		,		A    )			->			int:
								UpperCAmelCase     :		str							     =		GPTNeoXModel(config=A    )
								model.to(A    )
								model.eval()
								UpperCAmelCase     :		List[str]							     =		model(A		,		attention_mask=A    )
								UpperCAmelCase     :		List[str]							     =		model(A    )
								self.parent.assertEqual(result.last_hidden_state.shape		,		(self.batch_size, self.seq_length, self.hidden_size)    )





	def  _lowercase(  self		,		A		,		A		,		A    )			->			Optional[int]:
								UpperCAmelCase     :		str							     =		True
								UpperCAmelCase     :		Optional[Any]							     =		GPTNeoXModel(A    )
								model.to(A    )
								model.eval()
								UpperCAmelCase     :		List[Any]							     =		model(A		,		attention_mask=A    )
								self.parent.assertEqual(result.last_hidden_state.shape		,		(self.batch_size, self.seq_length, self.hidden_size)    )





	def  _lowercase(  self		,		A		,		A		,		A		,		A    )			->			List[str]:
								UpperCAmelCase     :		Tuple							     =		GPTNeoXForCausalLM(config=A    )
								model.to(A    )
								model.eval()
								UpperCAmelCase     :		str							     =		model(A		,		attention_mask=A		,		labels=A    )
								self.parent.assertEqual(result.logits.shape		,		(self.batch_size, self.seq_length, self.vocab_size)    )





	def  _lowercase(  self		,		A		,		A		,		A		,		A    )			->			Tuple:
								UpperCAmelCase     :		List[str]							     =		self.num_labels
								UpperCAmelCase     :		Any							     =		GPTNeoXForQuestionAnswering(A    )
								model.to(A    )
								model.eval()
								UpperCAmelCase     :		str							     =		model(A		,		attention_mask=A    )
								self.parent.assertEqual(result.start_logits.shape		,		(self.batch_size, self.seq_length)    )
								self.parent.assertEqual(result.end_logits.shape		,		(self.batch_size, self.seq_length)    )





	def  _lowercase(  self		,		A		,		A		,		A		,		A    )			->			int:
								UpperCAmelCase     :		Tuple							     =		self.num_labels
								UpperCAmelCase     :		List[str]							     =		GPTNeoXForSequenceClassification(A    )
								model.to(A    )
								model.eval()
								UpperCAmelCase     :		List[Any]							     =		ids_tensor([self.batch_size]		,		self.type_sequence_label_size    )
								UpperCAmelCase     :		Tuple							     =		model(A		,		attention_mask=A		,		labels=A    )
								self.parent.assertEqual(result.logits.shape		,		(self.batch_size, self.num_labels)    )





	def  _lowercase(  self		,		A		,		A		,		A		,		A    )			->			str:
								UpperCAmelCase     :		List[Any]							     =		self.num_labels
								UpperCAmelCase     :		Tuple							     =		GPTNeoXForTokenClassification(A    )
								model.to(A    )
								model.eval()
								UpperCAmelCase     :		int							     =		model(A		,		attention_mask=A		,		labels=A    )
								self.parent.assertEqual(result.logits.shape		,		(self.batch_size, self.seq_length, self.num_labels)    )





	def  _lowercase(  self		,		A		,		A		,		A    )			->			Union[str, Any]:
								UpperCAmelCase     :		Optional[int]							     =		True
								UpperCAmelCase     :		str							     =		GPTNeoXForCausalLM(config=A    )
								model.to(A    )
								model.eval()

								# first forward pass
								UpperCAmelCase     :		List[str]							     =		model(A		,		attention_mask=A		,		use_cache=A    )
								UpperCAmelCase     :		List[Any]							     =		outputs.past_key_values

								# create hypothetical multiple next token and extent to next_input_ids
								UpperCAmelCase     :		Optional[int]							     =		ids_tensor((self.batch_size, 3)		,		config.vocab_size    )
								UpperCAmelCase     :		Any							     =		ids_tensor((self.batch_size, 3)		,		vocab_size=2    )

								# append to next input_ids and
								UpperCAmelCase     :		str							     =		torch.cat([input_ids, next_tokens]		,		dim=-1    )
								UpperCAmelCase     :		Any							     =		torch.cat([input_mask, next_mask]		,		dim=-1    )

								UpperCAmelCase     :		Dict							     =		model(A		,		attention_mask=A		,		output_hidden_states=A    )
								UpperCAmelCase     :		Any							     =		output_from_no_past["""hidden_states"""][0]
								UpperCAmelCase     :		List[str]							     =		model(
								    A		,		attention_mask=A		,		past_key_values=A		,		output_hidden_states=A		,		)["""hidden_states"""][0]

								# select random slice
								UpperCAmelCase     :		Tuple							     =		ids_tensor((1,)		,		output_from_past.shape[-1]    ).item()
								UpperCAmelCase     :		List[Any]							     =		output_from_no_past[:, -3:, random_slice_idx].detach()
								UpperCAmelCase     :		List[str]							     =		output_from_past[:, :, random_slice_idx].detach()

								self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]    )

								# test that outputs are equal for slice
								self.parent.assertTrue(torch.allclose(A		,		A		,		atol=1e-3    )    )






	def  _lowercase(  self    )			->			int:
								UpperCAmelCase     :		Tuple							     =		self.prepare_config_and_inputs()
								UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     :		Optional[Any]							     =		config_and_inputs
								UpperCAmelCase     :		Union[str, Any]							     =		{"""input_ids""": input_ids, """attention_mask""": input_mask}
								return config, inputs_dict




@require_torch
class 			UpperCamelCase_ (							__magic_name__      ,     __magic_name__      ,     __magic_name__      ,     unittest.TestCase			):
	lowercase          = (
	    (
	        GPTNeoXModel,
	        GPTNeoXForCausalLM,
	        GPTNeoXForQuestionAnswering,
	        GPTNeoXForSequenceClassification,
	        GPTNeoXForTokenClassification,
	    )
	    if is_torch_available()
	    else ()
	)
	lowercase          = (GPTNeoXForCausalLM,) if is_torch_available() else ()
	lowercase          = (
	    {
	        'feature-extraction': GPTNeoXModel,
	        'question-answering': GPTNeoXForQuestionAnswering,
	        'text-classification': GPTNeoXForSequenceClassification,
	        'text-generation': GPTNeoXForCausalLM,
	        'token-classification': GPTNeoXForTokenClassification,
	        'zero-shot': GPTNeoXForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	lowercase          = False
	lowercase          = False
	lowercase          = False
	lowercase          = False





	def  _lowercase(  self    )			->			Union[str, Any]:
								UpperCAmelCase     :		str							     =		GPTNeoXModelTester(self    )
								UpperCAmelCase     :		Optional[Any]							     =		ConfigTester(self		,		config_class=A		,		hidden_size=64		,		num_attention_heads=8    )





	def  _lowercase(  self    )			->			Optional[Any]:
								self.config_tester.run_common_tests()





	def  _lowercase(  self    )			->			str:
								UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     :		Any							     =		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(A		,		A		,		A    )





	def  _lowercase(  self    )			->			str:
								UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     :		Union[str, Any]							     =		self.model_tester.prepare_config_and_inputs_for_decoder()
								self.model_tester.create_and_check_model_as_decoder(A		,		A		,		A    )





	def  _lowercase(  self    )			->			Optional[Any]:
								# This regression test was failing with PyTorch < 1.3
								UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     :		Optional[Any]							     =		self.model_tester.prepare_config_and_inputs_for_decoder()

								UpperCAmelCase     :		Optional[Any]							     =		None

								self.model_tester.create_and_check_model_as_decoder(A		,		A		,		A    )





	def  _lowercase(  self    )			->			str:
								UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     :		List[str]							     =		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_decoder_model_past_large_inputs(A		,		A		,		A    )





	def  _lowercase(  self    )			->			int:
								UpperCAmelCase     :		Any							     =		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_causal_lm(*A    )





	def  _lowercase(  self    )			->			Optional[int]:
								UpperCAmelCase     :		Tuple							     =		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*A    )





	def  _lowercase(  self    )			->			Any:
								UpperCAmelCase     :		Tuple							     =		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*A    )





	def  _lowercase(  self    )			->			Optional[Any]:
								UpperCAmelCase     :		Optional[Any]							     =		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*A    )





	@unittest.skip(reason="""Feed forward chunking is not implemented"""    )
	def  _lowercase(  self    )			->			Optional[int]:
								pass






	@parameterized.expand([("""linear""",), ("""dynamic""",)]    )
	def  _lowercase(  self		,		A    )			->			str:
								UpperCAmelCase     ,     UpperCAmelCase     :		List[Any]							     =		self.model_tester.prepare_config_and_inputs_for_common()
								UpperCAmelCase     :		int							     =		ids_tensor([1, 10]		,		config.vocab_size    )
								UpperCAmelCase     :		Optional[Any]							     =		ids_tensor([1, int(config.max_position_embeddings * 1.5    )]		,		config.vocab_size    )

								set_seed(42    )  # Fixed seed at init time so the two models get the same random weights
								UpperCAmelCase     :		Dict							     =		GPTNeoXModel(A    )
								original_model.to(A    )
								original_model.eval()
								UpperCAmelCase     :		List[str]							     =		original_model(A    ).last_hidden_state
								UpperCAmelCase     :		Any							     =		original_model(A    ).last_hidden_state

								set_seed(42    )  # Fixed seed at init time so the two models get the same random weights
								UpperCAmelCase     :		Any							     =		{"""type""": scaling_type, """factor""": 1_0.0}
								UpperCAmelCase     :		str							     =		GPTNeoXModel(A    )
								scaled_model.to(A    )
								scaled_model.eval()
								UpperCAmelCase     :		Optional[Any]							     =		scaled_model(A    ).last_hidden_state
								UpperCAmelCase     :		Optional[Any]							     =		scaled_model(A    ).last_hidden_state

								# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
								# maximum sequence length, so the outputs for the short input should match.
								if scaling_type == "dynamic":
															self.assertTrue(torch.allclose(A		,		A		,		atol=1e-5    )    )
								else:
															self.assertFalse(torch.allclose(A		,		A		,		atol=1e-5    )    )

								# The output should be different for long inputs
								self.assertFalse(torch.allclose(A		,		A		,		atol=1e-5    )    )




@require_torch
class 			UpperCamelCase_ (							unittest.TestCase			):





	@slow
	def  _lowercase(  self    )			->			List[Any]:
								UpperCAmelCase     :		str							     =		AutoTokenizer.from_pretrained("""EleutherAI/pythia-410m-deduped"""    )
								for checkpointing in [True, False]:
															UpperCAmelCase     :		int							     =		GPTNeoXForCausalLM.from_pretrained("""EleutherAI/pythia-410m-deduped"""    )

															if checkpointing:
																						model.gradient_checkpointing_enable()
															else:
																						model.gradient_checkpointing_disable()
															model.to(A    )

															UpperCAmelCase     :		List[Any]							     =		tokenizer("""My favorite food is"""		,		return_tensors="""pt"""    ).to(A    )
															# The hub repo. is updated on 2023-04-04, resulting in poor outputs.
															# See: https://github.com/huggingface/transformers/pull/24193
															UpperCAmelCase     :		List[str]							     =		"""My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI'm not sure"""

															UpperCAmelCase     :		Union[str, Any]							     =		model.generate(**A		,		do_sample=A		,		max_new_tokens=20    )
															UpperCAmelCase     :		Tuple							     =		tokenizer.batch_decode(A    )[0]

															self.assertEqual(A		,		A    )

