
import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCAmelCase :			Dict              =	logging.get_logger(__name__)


def 				A	( lowercase   ,	lowercase=False			)       ->  Any:
		'''simple docstring'''

		UpperCamelCase          =					[]
		for i in range(config.num_hidden_layers			):
				# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
				rename_keys.append((f'''blocks.{i}.norm1.weight''', f'''vit.encoder.layer.{i}.layernorm_before.weight''')			)
				rename_keys.append((f'''blocks.{i}.norm1.bias''', f'''vit.encoder.layer.{i}.layernorm_before.bias''')			)
				rename_keys.append((f'''blocks.{i}.attn.proj.weight''', f'''vit.encoder.layer.{i}.attention.output.dense.weight''')			)
				rename_keys.append((f'''blocks.{i}.attn.proj.bias''', f'''vit.encoder.layer.{i}.attention.output.dense.bias''')			)
				rename_keys.append((f'''blocks.{i}.norm2.weight''', f'''vit.encoder.layer.{i}.layernorm_after.weight''')			)
				rename_keys.append((f'''blocks.{i}.norm2.bias''', f'''vit.encoder.layer.{i}.layernorm_after.bias''')			)
				rename_keys.append((f'''blocks.{i}.mlp.fc1.weight''', f'''vit.encoder.layer.{i}.intermediate.dense.weight''')			)
				rename_keys.append((f'''blocks.{i}.mlp.fc1.bias''', f'''vit.encoder.layer.{i}.intermediate.dense.bias''')			)
				rename_keys.append((f'''blocks.{i}.mlp.fc2.weight''', f'''vit.encoder.layer.{i}.output.dense.weight''')			)
				rename_keys.append((f'''blocks.{i}.mlp.fc2.bias''', f'''vit.encoder.layer.{i}.output.dense.bias''')			)

		# projection layer + position embeddings
		rename_keys.extend(
		    [
		        ('cls_token', 'vit.embeddings.cls_token'),
		        ('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight'),
		        ('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias'),
		        ('pos_embed', 'vit.embeddings.position_embeddings'),
		    ]			)

		if base_model:
				# layernorm + pooler
				rename_keys.extend(
				    [
				        ('norm.weight', 'layernorm.weight'),
				        ('norm.bias', 'layernorm.bias'),
				    ]			)

				# if just the base model, we should remove "vit" from all keys that start with "vit"
				UpperCamelCase          =					[(pair[0], pair[1][4:]) if pair[1].startswith('vit'			) else pair for pair in rename_keys]
		else:
				# layernorm + classification head
				rename_keys.extend(
				    [
				        ('norm.weight', 'vit.layernorm.weight'),
				        ('norm.bias', 'vit.layernorm.bias'),
				        ('head.weight', 'classifier.weight'),
				        ('head.bias', 'classifier.bias'),
				    ]			)

		return rename_keys


def 				A	( lowercase   ,	lowercase   ,	lowercase=False			)       ->  List[str]:
		'''simple docstring'''

		for i in range(config.num_hidden_layers			):
				if base_model:
						UpperCamelCase          =					''
				else:
						UpperCamelCase          =					'vit.'
				# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
				UpperCamelCase          =					state_dict.pop(f'''blocks.{i}.attn.qkv.weight'''			)
				UpperCamelCase          =					state_dict.pop(f'''blocks.{i}.attn.qkv.bias'''			)
				# next, add query, keys and values (in that order) to the state dict
				UpperCamelCase          =					in_proj_weight[
				    : config.hidden_size, :
				]
				UpperCamelCase          =					in_proj_bias[: config.hidden_size]
				UpperCamelCase          =					in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				UpperCamelCase          =					in_proj_bias[
				    config.hidden_size : config.hidden_size * 2
				]
				UpperCamelCase          =					in_proj_weight[
				    -config.hidden_size :, :
				]
				UpperCamelCase          =					in_proj_bias[-config.hidden_size :]


def 				A	( lowercase			)       ->  Union[str, Any]:
		'''simple docstring'''

		UpperCamelCase          =					['head.weight', 'head.bias']
		for k in ignore_keys:
				state_dict.pop(lowercase   ,	lowercase			)


def 				A	( lowercase   ,	lowercase   ,	lowercase			)       ->  List[Any]:
		'''simple docstring'''

		UpperCamelCase          =					dct.pop(lowercase			)
		UpperCamelCase          =					val


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

		UpperCamelCase          =					'http://images.cocodataset.org/val2017/000000039769.jpg'
		UpperCamelCase          =					Image.open(requests.get(lowercase   ,	stream=lowercase			).raw			)
		return im


@torch.no_grad()
def 				A	( lowercase   ,	lowercase   ,	lowercase=True			)       ->  List[Any]:
		'''simple docstring'''

		UpperCamelCase          =					ViTConfig()
		# patch_size
		if model_name[-1] == "8":
				UpperCamelCase          =					8
		# set labels if required
		if not base_model:
				UpperCamelCase          =					1_000
				UpperCamelCase          =					'huggingface/label-files'
				UpperCamelCase          =					'imagenet-1k-id2label.json'
				UpperCamelCase          =					json.load(open(hf_hub_download(lowercase   ,	lowercase   ,	repo_type='dataset'			)   ,	'r'			)			)
				UpperCamelCase          =					{int(lowercase			): v for k, v in idalabel.items()}
				UpperCamelCase          =					idalabel
				UpperCamelCase          =					{v: k for k, v in idalabel.items()}
		# size of the architecture
		if model_name in ["dino_vits8", "dino_vits16"]:
				UpperCamelCase          =					384
				UpperCamelCase          =					1_536
				UpperCamelCase          =					12
				UpperCamelCase          =					6

		# load original model from torch hub
		UpperCamelCase          =					torch.hub.load('facebookresearch/dino:main'   ,	lowercase			)
		original_model.eval()

		# load state_dict of original model, remove and rename some keys
		UpperCamelCase          =					original_model.state_dict()
		if base_model:
				remove_classification_head_(lowercase			)
		UpperCamelCase          =					create_rename_keys(lowercase   ,	base_model=lowercase			)
		for src, dest in rename_keys:
				rename_key(lowercase   ,	lowercase   ,	lowercase			)
		read_in_q_k_v(lowercase   ,	lowercase   ,	lowercase			)

		# load HuggingFace model
		if base_model:
				UpperCamelCase          =					ViTModel(lowercase   ,	add_pooling_layer=lowercase			).eval()
		else:
				UpperCamelCase          =					ViTForImageClassification(lowercase			).eval()
		model.load_state_dict(lowercase			)

		# Check outputs on an image, prepared by ViTImageProcessor
		UpperCamelCase          =					ViTImageProcessor()
		UpperCamelCase          =					image_processor(images=prepare_img()   ,	return_tensors='pt'			)
		UpperCamelCase          =					encoding['pixel_values']
		UpperCamelCase          =					model(lowercase			)

		if base_model:
				UpperCamelCase          =					original_model(lowercase			)
				assert torch.allclose(lowercase   ,	outputs.last_hidden_state[:, 0, :]   ,	atol=1e-1			)
		else:
				UpperCamelCase          =					original_model(lowercase			)
				assert logits.shape == outputs.logits.shape
				assert torch.allclose(lowercase   ,	outputs.logits   ,	atol=1e-3			)

		Path(lowercase			).mkdir(exist_ok=lowercase			)
		print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''			)
		model.save_pretrained(lowercase			)
		print(f'''Saving image processor to {pytorch_dump_folder_path}'''			)
		image_processor.save_pretrained(lowercase			)


if __name__ == "__main__":
		_UpperCAmelCase :			Optional[int]              =	argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--model_name",
		    default="dino_vitb16",
		    type=str,
		    help="Name of the model trained with DINO you'd like to convert.",
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
		)
		parser.add_argument(
		    "--base_model",
		    action="store_true",
		    help="Whether to only convert the base model (no projection head weights).",
		)

		parser.set_defaults(base_model=True)
		_UpperCAmelCase :			Optional[int]              =	parser.parse_args()
		convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)


import unittest

from parameterized import parameterized

from transformers import LlamaConfig, 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 LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer


class     lowercase :



		def __init__(			self    ,				A_    ,				A_=13    ,				A_=7    ,				A_=True    ,				A_=True    ,				A_=False    ,				A_=True    ,				A_=99    ,				A_=32    ,				A_=5    ,				A_=4    ,				A_=37    ,				A_="gelu"    ,				A_=0.1    ,				A_=0.1    ,				A_=512    ,				A_=16    ,				A_=2    ,				A_=0.02    ,				A_=3    ,				A_=4    ,				A_=None    ,				)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					parent
				UpperCamelCase          =					batch_size
				UpperCamelCase          =					seq_length
				UpperCamelCase          =					is_training
				UpperCamelCase          =					use_input_mask
				UpperCamelCase          =					use_token_type_ids
				UpperCamelCase          =					use_labels
				UpperCamelCase          =					vocab_size
				UpperCamelCase          =					hidden_size
				UpperCamelCase          =					num_hidden_layers
				UpperCamelCase          =					num_attention_heads
				UpperCamelCase          =					intermediate_size
				UpperCamelCase          =					hidden_act
				UpperCamelCase          =					hidden_dropout_prob
				UpperCamelCase          =					attention_probs_dropout_prob
				UpperCamelCase          =					max_position_embeddings
				UpperCamelCase          =					type_vocab_size
				UpperCamelCase          =					type_sequence_label_size
				UpperCamelCase          =					initializer_range
				UpperCamelCase          =					num_labels
				UpperCamelCase          =					num_choices
				UpperCamelCase          =					scope



		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				UpperCamelCase          =					ids_tensor([self.batch_size, self.seq_length]    ,				self.vocab_size	)

				UpperCamelCase          =					None
				if self.use_input_mask:
						UpperCamelCase          =					random_attention_mask([self.batch_size, self.seq_length]	)

				UpperCamelCase          =					None
				if self.use_token_type_ids:
						UpperCamelCase          =					ids_tensor([self.batch_size, self.seq_length]    ,				self.type_vocab_size	)

				UpperCamelCase          =					None
				UpperCamelCase          =					None
				UpperCamelCase          =					None
				if self.use_labels:
						UpperCamelCase          =					ids_tensor([self.batch_size]    ,				self.type_sequence_label_size	)
						UpperCamelCase          =					ids_tensor([self.batch_size, self.seq_length]    ,				self.num_labels	)
						UpperCamelCase          =					ids_tensor([self.batch_size]    ,				self.num_choices	)

				UpperCamelCase          =					self.get_config()

				return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				return LlamaConfig(
				    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    ,				)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					LlamaModel(config=A_	)
				model.to(A_	)
				model.eval()
				UpperCamelCase          =					model(A_    ,				attention_mask=A_	)
				UpperCamelCase          =					model(A_	)
				self.parent.assertEqual(result.last_hidden_state.shape    ,				(self.batch_size, self.seq_length, self.hidden_size)	)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					True
				UpperCamelCase          =					LlamaModel(A_	)
				model.to(A_	)
				model.eval()
				UpperCamelCase          =					model(
				    A_    ,				attention_mask=A_    ,				encoder_hidden_states=A_    ,				encoder_attention_mask=A_    ,				)
				UpperCamelCase          =					model(
				    A_    ,				attention_mask=A_    ,				encoder_hidden_states=A_    ,				)
				UpperCamelCase          =					model(A_    ,				attention_mask=A_	)
				self.parent.assertEqual(result.last_hidden_state.shape    ,				(self.batch_size, self.seq_length, self.hidden_size)	)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				)  -> str:
				"""simple docstring"""
				UpperCamelCase          =					LlamaForCausalLM(config=A_	)
				model.to(A_	)
				model.eval()
				UpperCamelCase          =					model(A_    ,				attention_mask=A_    ,				labels=A_	)
				self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.seq_length, self.vocab_size)	)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					True
				UpperCamelCase          =					True
				UpperCamelCase          =					LlamaForCausalLM(config=A_	)
				model.to(A_	)
				model.eval()

				# first forward pass
				UpperCamelCase          =					model(
				    A_    ,				attention_mask=A_    ,				encoder_hidden_states=A_    ,				encoder_attention_mask=A_    ,				use_cache=A_    ,				)
				UpperCamelCase          =					outputs.past_key_values

				# create hypothetical multiple next token and extent to next_input_ids
				UpperCamelCase          =					ids_tensor((self.batch_size, 3)    ,				config.vocab_size	)
				UpperCamelCase          =					ids_tensor((self.batch_size, 3)    ,				vocab_size=2	)

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

				UpperCamelCase          =					model(
				    A_    ,				attention_mask=A_    ,				encoder_hidden_states=A_    ,				encoder_attention_mask=A_    ,				output_hidden_states=A_    ,				)['hidden_states'][0]
				UpperCamelCase          =					model(
				    A_    ,				attention_mask=A_    ,				encoder_hidden_states=A_    ,				encoder_attention_mask=A_    ,				past_key_values=A_    ,				output_hidden_states=A_    ,				)['hidden_states'][0]

				# select random slice
				UpperCamelCase          =					ids_tensor((1,)    ,				output_from_past.shape[-1]	).item()
				UpperCamelCase          =					output_from_no_past[:, -3:, random_slice_idx].detach()
				UpperCamelCase          =					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 __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					self.prepare_config_and_inputs()
				(
				    (
				    UpperCamelCase
				)     ,  (
				    UpperCamelCase
				)     ,  (
				    UpperCamelCase
				)     ,  (
				    UpperCamelCase
				)     ,  (
				    UpperCamelCase
				)     ,  (
				    UpperCamelCase
				)     ,  (
				    UpperCamelCase
				)     ,  
				)          =					config_and_inputs
				UpperCamelCase          =					{'input_ids': input_ids, 'attention_mask': input_mask}
				return config, inputs_dict






@require_torch
class     lowercase (		_SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE  ,       unittest.TestCase       ):
		__lowercase	:	str     					=    (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
		__lowercase	:	str     					=    (LlamaForCausalLM,) if is_torch_available() else ()
		__lowercase	:	Any     					=    (
		    {
		        "feature-extraction": LlamaModel,
		        "text-classification": LlamaForSequenceClassification,
		        "text-generation": LlamaForCausalLM,
		        "zero-shot": LlamaForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		__lowercase	:	int     					=    False
		__lowercase	:	Optional[int]     					=    False



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					LlamaModelTester(self	)
				UpperCamelCase          =					ConfigTester(self    ,				config_class=A_    ,				hidden_size=37	)



		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				self.config_tester.run_common_tests()



		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*A_	)



		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					self.model_tester.prepare_config_and_inputs()
				for type in ["absolute", "relative_key", "relative_key_query"]:
						UpperCamelCase          =					type
						self.model_tester.create_and_check_model(*A_	)



		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase     ,  UpperCamelCase          =					self.model_tester.prepare_config_and_inputs_for_common()
				UpperCamelCase          =					3
				UpperCamelCase          =					input_dict['input_ids']
				UpperCamelCase          =					input_ids.ne(1	).to(A_	)
				UpperCamelCase          =					ids_tensor([self.model_tester.batch_size]    ,				self.model_tester.type_sequence_label_size	)
				UpperCamelCase          =					LlamaForSequenceClassification(A_	)
				model.to(A_	)
				model.eval()
				UpperCamelCase          =					model(A_    ,				attention_mask=A_    ,				labels=A_	)
				self.assertEqual(result.logits.shape    ,				(self.model_tester.batch_size, self.model_tester.num_labels)	)



		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase     ,  UpperCamelCase          =					self.model_tester.prepare_config_and_inputs_for_common()
				UpperCamelCase          =					3
				UpperCamelCase          =					'single_label_classification'
				UpperCamelCase          =					input_dict['input_ids']
				UpperCamelCase          =					input_ids.ne(1	).to(A_	)
				UpperCamelCase          =					ids_tensor([self.model_tester.batch_size]    ,				self.model_tester.type_sequence_label_size	)
				UpperCamelCase          =					LlamaForSequenceClassification(A_	)
				model.to(A_	)
				model.eval()
				UpperCamelCase          =					model(A_    ,				attention_mask=A_    ,				labels=A_	)
				self.assertEqual(result.logits.shape    ,				(self.model_tester.batch_size, self.model_tester.num_labels)	)



		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase     ,  UpperCamelCase          =					self.model_tester.prepare_config_and_inputs_for_common()
				UpperCamelCase          =					3
				UpperCamelCase          =					'multi_label_classification'
				UpperCamelCase          =					input_dict['input_ids']
				UpperCamelCase          =					input_ids.ne(1	).to(A_	)
				UpperCamelCase          =					ids_tensor(
				    [self.model_tester.batch_size, config.num_labels]    ,				self.model_tester.type_sequence_label_size	).to(torch.float	)
				UpperCamelCase          =					LlamaForSequenceClassification(A_	)
				model.to(A_	)
				model.eval()
				UpperCamelCase          =					model(A_    ,				attention_mask=A_    ,				labels=A_	)
				self.assertEqual(result.logits.shape    ,				(self.model_tester.batch_size, self.model_tester.num_labels)	)



		@unittest.skip('LLaMA buffers include complex numbers, which breaks this test'	)
		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				pass



		@parameterized.expand([('linear',), ('dynamic',)]	)
		def __UpperCamelCase			(			self    ,				A_	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase     ,  UpperCamelCase          =					self.model_tester.prepare_config_and_inputs_for_common()
				UpperCamelCase          =					ids_tensor([1, 10]    ,				config.vocab_size	)
				UpperCamelCase          =					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          =					LlamaModel(A_	)
				original_model.to(A_	)
				original_model.eval()
				UpperCamelCase          =					original_model(A_	).last_hidden_state
				UpperCamelCase          =					original_model(A_	).last_hidden_state

				set_seed(42	)  # Fixed seed at init time so the two models get the same random weights
				UpperCamelCase          =					{'type': scaling_type, 'factor': 10.0}
				UpperCamelCase          =					LlamaModel(A_	)
				scaled_model.to(A_	)
				scaled_model.eval()
				UpperCamelCase          =					scaled_model(A_	).last_hidden_state
				UpperCamelCase          =					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     lowercase (		unittest.TestCase       ):



		@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'	)
		@slow
		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
				UpperCamelCase          =					LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf'    ,				device_map='auto'	)
				UpperCamelCase          =					model(torch.tensor([input_ids]	)	)
				# Expected mean on dim = -1
				UpperCamelCase          =					torch.tensor([[-6.6550, -4.1227, -4.9859, -3.2406, 0.8262, -3.0033, 1.2964, -3.3699]]	)
				torch.testing.assert_close(out.mean(-1	)    ,				A_    ,				atol=1e-2    ,				rtol=1e-2	)
				# slicing logits[0, 0, 0:30]
				# fmt: off
				UpperCamelCase          =					torch.tensor([-12.8281, -7.4453, -0.4639, -8.0625, -7.2500, -8.0000, -6.4883, -7.7695, -7.8438, -7.0312, -6.2188, -7.1328, -1.8496, 1.9961, -8.6250, -6.7227, -12.8281, -6.9492, -7.0742, -7.7852, -7.5820, -7.9062, -6.9375, -7.9805, -8.3438, -8.1562, -8.0469, -7.6250, -7.7422, -7.3398,]	)
				# fmt: on
				torch.testing.assert_close(out[0, 0, :30]    ,				A_    ,				atol=1e-5    ,				rtol=1e-5	)



		@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'	)
		@slow
		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
				UpperCamelCase          =					LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf'    ,				device_map='auto'	)
				UpperCamelCase          =					model(torch.tensor(A_	)	)
				# Expected mean on dim = -1
				UpperCamelCase          =					torch.tensor([[-2.0622, -1.2794, -1.1638, -0.9788, -1.4603, -1.0238, -1.7893, -1.4411]]	)
				torch.testing.assert_close(out.mean(-1	)    ,				A_    ,				atol=1e-2    ,				rtol=1e-2	)
				# slicing logits[0, 0, 0:30]
				# fmt: off
				UpperCamelCase          =					torch.tensor([-8.1406, -8.0547, 2.7461, -1.2344, -0.1448, -1.8262, -1.0020, -1.8154, -1.6895, -1.8516, -2.3574, -0.9277, 3.7598, 6.5742, -1.2998, -0.1177, -8.1406, -2.9688, -2.9199, -3.1699, -3.5254, -2.3555, -2.7988, -3.4141, -2.8262, -4.5195, -3.3379, -3.3164, -2.7832, -3.0273]	)
				# fmt: on
				torch.testing.assert_close(out[0, 0, :30]    ,				A_    ,				atol=1e-5    ,				rtol=1e-5	)



		@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'	)
		@slow
		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
				UpperCamelCase          =					LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf'    ,				device_map='auto'	)
				UpperCamelCase          =					model(torch.tensor(A_	)	)
				# Expected mean on dim = -1
				UpperCamelCase          =					torch.tensor([[-0.8562, -1.8520, -0.7551, -0.4162, -1.5161, -1.2038, -2.4823, -2.3254]]	)
				torch.testing.assert_close(out.mean(-1	)    ,				A_    ,				atol=1e-2    ,				rtol=1e-2	)
				# slicing logits[0, 0, 0:30]
				# fmt: off
				UpperCamelCase          =					torch.tensor([-2.2227, 4.8828, 0.9023, -0.4578, -0.7871, -0.1033, -0.6221, -0.5786, -0.7803, -1.0674, -1.2920, -0.1570, 0.8008, 2.0723, -0.9497, 0.2771, -2.2227, -0.7612, -1.4346, -1.2061, -1.6426, -0.3000, -0.7139, -1.1934, -1.8691, -1.6973, -1.5947, -1.2705, -0.3523, -0.5513]	)
				# fmt: on
				torch.testing.assert_close(out.mean(-1	)    ,				A_    ,				atol=1e-2    ,				rtol=1e-2	)



		@unittest.skip(
		    'Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test'	)
		@slow
		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
				UpperCamelCase          =					LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf'    ,				device_map='auto'	)
				UpperCamelCase          =					model(torch.tensor(A_	)	)

				UpperCamelCase          =					torch.tensor(
				    [[-4.2327, -3.3360, -4.6665, -4.7631, -1.8180, -3.4170, -1.4211, -3.1810]]    ,				dtype=torch.floataa	)
				torch.testing.assert_close(out.mean(-1	)    ,				A_    ,				atol=1e-2    ,				rtol=1e-2	)
				# fmt: off
				UpperCamelCase          =					torch.tensor([-9.4922, -3.9551, 1.7998, -5.6758, -5.1055, -5.8984, -4.8320, -6.8086, -6.5391, -5.6172, -5.5820, -5.5352, 1.7881, 3.6289, -6.5117, -3.4785, -9.5000, -6.0352, -6.8125, -6.0195, -6.6836, -5.4727, -6.2812, -6.0391, -7.3398, -7.4297, -7.4844, -6.5820, -5.8789, -5.5312]	)
				# fmt: on
				torch.testing.assert_close(out[0, 0, :30]    ,				A_    ,				atol=1e-5    ,				rtol=1e-5	)



		@unittest.skip('Model is curently gated'	)
		@slow
		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					'Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the "princi'
				UpperCamelCase          =					'Simply put, the theory of relativity states that '
				UpperCamelCase          =					LlamaTokenizer.from_pretrained('meta-llama/Llama-2-13b-chat-hf'	)
				UpperCamelCase          =					tokenizer.encode(A_    ,				return_tensors='pt'	)
				UpperCamelCase          =					LlamaForCausalLM.from_pretrained(
				    'meta-llama/Llama-2-13b-chat-hf'    ,				device_map='sequential'    ,				use_safetensors=A_	)

				# greedy generation outputs
				UpperCamelCase          =					model.generate(A_    ,				max_new_tokens=64    ,				top_p=A_    ,				temperature=1    ,				do_sample=A_	)
				UpperCamelCase          =					tokenizer.decode(generated_ids[0]    ,				skip_special_tokens=A_	)
				self.assertEqual(A_    ,				A_	)
