


import argparse
import json
from pathlib import Path

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

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase       =							logging.get_logger(__name__)
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE=False     ):


		"""simple docstring"""




		lowercase__            =      []
		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'deit.encoder.layer.{i}.layernorm_before.weight')     )
				rename_keys.append((f'blocks.{i}.norm1.bias', f'deit.encoder.layer.{i}.layernorm_before.bias')     )
				rename_keys.append((f'blocks.{i}.attn.proj.weight', f'deit.encoder.layer.{i}.attention.output.dense.weight')     )
				rename_keys.append((f'blocks.{i}.attn.proj.bias', f'deit.encoder.layer.{i}.attention.output.dense.bias')     )
				rename_keys.append((f'blocks.{i}.norm2.weight', f'deit.encoder.layer.{i}.layernorm_after.weight')     )
				rename_keys.append((f'blocks.{i}.norm2.bias', f'deit.encoder.layer.{i}.layernorm_after.bias')     )
				rename_keys.append((f'blocks.{i}.mlp.fc1.weight', f'deit.encoder.layer.{i}.intermediate.dense.weight')     )
				rename_keys.append((f'blocks.{i}.mlp.fc1.bias', f'deit.encoder.layer.{i}.intermediate.dense.bias')     )
				rename_keys.append((f'blocks.{i}.mlp.fc2.weight', f'deit.encoder.layer.{i}.output.dense.weight')     )
				rename_keys.append((f'blocks.{i}.mlp.fc2.bias', f'deit.encoder.layer.{i}.output.dense.bias')     )

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

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

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

		return rename_keys
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE=False     ):


		"""simple docstring"""




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


		"""simple docstring"""




		lowercase__            =      dct.pop(__UpperCAmelCase     )
		lowercase__            =      val
def 			_a (      ):


		"""simple docstring"""




		lowercase__            =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
		lowercase__            =      Image.open(requests.get(__UpperCAmelCase						,				stream=__UpperCAmelCase     ).raw     )
		return im
@torch.no_grad()
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      DeiTConfig()
		# all deit models have fine-tuned heads
		lowercase__            =      False
		# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
		lowercase__            =      10_00
		lowercase__            =      '''huggingface/label-files'''
		lowercase__            =      '''imagenet-1k-id2label.json'''
		lowercase__            =      json.load(open(hf_hub_download(__UpperCAmelCase						,				__UpperCAmelCase						,				repo_type='''dataset'''     )						,				'''r'''     )     )
		lowercase__            =      {int(__UpperCAmelCase     ): v for k, v in idalabel.items()}
		lowercase__            =      idalabel
		lowercase__            =      {v: k for k, v in idalabel.items()}
		lowercase__            =      int(deit_name[-6:-4]     )
		lowercase__            =      int(deit_name[-3:]     )
		# size of the architecture
		if deit_name[9:].startswith('''tiny'''     ):
				lowercase__            =      1_92
				lowercase__            =      7_68
				lowercase__            =      12
				lowercase__            =      3
		elif deit_name[9:].startswith('''small'''     ):
				lowercase__            =      3_84
				lowercase__            =      15_36
				lowercase__            =      12
				lowercase__            =      6
		if deit_name[9:].startswith('''base'''     ):
				pass
		elif deit_name[4:].startswith('''large'''     ):
				lowercase__            =      10_24
				lowercase__            =      40_96
				lowercase__            =      24
				lowercase__            =      16

		# load original model from timm
		lowercase__            =      timm.create_model(__UpperCAmelCase						,				pretrained=__UpperCAmelCase     )
		timm_model.eval()

		# load state_dict of original model, remove and rename some keys
		lowercase__            =      timm_model.state_dict()
		lowercase__            =      create_rename_keys(__UpperCAmelCase						,				__UpperCAmelCase     )
		for src, dest in rename_keys:
				rename_key(__UpperCAmelCase						,				__UpperCAmelCase						,				__UpperCAmelCase     )
		read_in_q_k_v(__UpperCAmelCase						,				__UpperCAmelCase						,				__UpperCAmelCase     )

		# load HuggingFace model
		lowercase__            =      DeiTForImageClassificationWithTeacher(__UpperCAmelCase     ).eval()
		model.load_state_dict(__UpperCAmelCase     )

		# Check outputs on an image, prepared by DeiTImageProcessor
		lowercase__            =      int(
		    (2_56 / 2_24) * config.image_size     )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
		lowercase__            =      DeiTImageProcessor(size=__UpperCAmelCase						,				crop_size=config.image_size     )
		lowercase__            =      image_processor(images=prepare_img()						,				return_tensors='''pt'''     )
		lowercase__            =      encoding['''pixel_values''']
		lowercase__            =      model(__UpperCAmelCase     )

		lowercase__            =      timm_model(__UpperCAmelCase     )
		assert timm_logits.shape == outputs.logits.shape
		assert torch.allclose(__UpperCAmelCase						,				outputs.logits						,				atol=1E-3     )

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


if __name__ == "__main__":
			lowerCAmelCase       =							argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--deit_name',
			    default='vit_deit_base_distilled_patch16_224',
			    type=str,
			    help='Name of the DeiT timm model you\'d like to convert.',
			)
			parser.add_argument(
			    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
			)

			lowerCAmelCase       =							parser.parse_args()
			convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)








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        lowerCamelCase_							:


 '''simple docstring'''







 def __init__(							self  :		Optional[Any]				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		Tuple=13				,				_lowerCAmelCase  :		List[str]=7				,				_lowerCAmelCase  :		Dict=True				,				_lowerCAmelCase  :		Dict=True				,				_lowerCAmelCase  :		Optional[int]=False				,				_lowerCAmelCase  :		Any=True				,				_lowerCAmelCase  :		str=99				,				_lowerCAmelCase  :		List[str]=32				,				_lowerCAmelCase  :		Tuple=5				,				_lowerCAmelCase  :		List[str]=4				,				_lowerCAmelCase  :		str=37				,				_lowerCAmelCase  :		Any="gelu"				,				_lowerCAmelCase  :		str=0.1				,				_lowerCAmelCase  :		Union[str, Any]=0.1				,				_lowerCAmelCase  :		Optional[int]=512				,				_lowerCAmelCase  :		Union[str, Any]=16				,				_lowerCAmelCase  :		Tuple=2				,				_lowerCAmelCase  :		Tuple=0.02				,				_lowerCAmelCase  :		Tuple=3				,				_lowerCAmelCase  :		Dict=4				,				_lowerCAmelCase  :		Union[str, Any]=None				,				):
       SCREAMING_SNAKE_CASE_							=			parent
       SCREAMING_SNAKE_CASE_							=			batch_size
       SCREAMING_SNAKE_CASE_							=			seq_length
       SCREAMING_SNAKE_CASE_							=			is_training
       SCREAMING_SNAKE_CASE_							=			use_input_mask
       SCREAMING_SNAKE_CASE_							=			use_token_type_ids
       SCREAMING_SNAKE_CASE_							=			use_labels
       SCREAMING_SNAKE_CASE_							=			vocab_size
       SCREAMING_SNAKE_CASE_							=			hidden_size
       SCREAMING_SNAKE_CASE_							=			num_hidden_layers
       SCREAMING_SNAKE_CASE_							=			num_attention_heads
       SCREAMING_SNAKE_CASE_							=			intermediate_size
       SCREAMING_SNAKE_CASE_							=			hidden_act
       SCREAMING_SNAKE_CASE_							=			hidden_dropout_prob
       SCREAMING_SNAKE_CASE_							=			attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE_							=			max_position_embeddings
       SCREAMING_SNAKE_CASE_							=			type_vocab_size
       SCREAMING_SNAKE_CASE_							=			type_sequence_label_size
       SCREAMING_SNAKE_CASE_							=			initializer_range
       SCREAMING_SNAKE_CASE_							=			num_labels
       SCREAMING_SNAKE_CASE_							=			num_choices
       SCREAMING_SNAKE_CASE_							=			scope



 def       lowerCAmelCase_  (							self  :		List[str]						):
       SCREAMING_SNAKE_CASE_							=			ids_tensor([self.batch_size, self.seq_length]				,				self.vocab_size						)

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

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

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

       SCREAMING_SNAKE_CASE_							=			self.get_config()

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



 def       lowerCAmelCase_  (							self  :		Optional[int]						):
       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=_lowerCAmelCase				,				initializer_range=self.initializer_range				,				)



 def       lowerCAmelCase_  (							self  :		Optional[Any]				,				_lowerCAmelCase  :		Tuple				,				_lowerCAmelCase  :		List[Any]				,				_lowerCAmelCase  :		List[Any]				,				_lowerCAmelCase  :		Union[str, Any]				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		Optional[Any]						):
       SCREAMING_SNAKE_CASE_							=			LlamaModel(config=_lowerCAmelCase						)
       model.to(_lowerCAmelCase						)
       model.eval()
       SCREAMING_SNAKE_CASE_							=			model(_lowerCAmelCase				,				attention_mask=_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			model(_lowerCAmelCase						)
       self.parent.assertEqual(result.last_hidden_state.shape				,				(self.batch_size, self.seq_length, self.hidden_size)						)



 def       lowerCAmelCase_  (							self  :		Any				,				_lowerCAmelCase  :		Dict				,				_lowerCAmelCase  :		Optional[Any]				,				_lowerCAmelCase  :		Any				,				_lowerCAmelCase  :		Dict				,				_lowerCAmelCase  :		Any				,				_lowerCAmelCase  :		Tuple				,				_lowerCAmelCase  :		Optional[int]				,				_lowerCAmelCase  :		Optional[Any]				,				_lowerCAmelCase  :		List[str]				,				):
       SCREAMING_SNAKE_CASE_							=			True
       SCREAMING_SNAKE_CASE_							=			LlamaModel(_lowerCAmelCase						)
       model.to(_lowerCAmelCase						)
       model.eval()
       SCREAMING_SNAKE_CASE_							=			model(
           _lowerCAmelCase				,				attention_mask=_lowerCAmelCase				,				encoder_hidden_states=_lowerCAmelCase				,				encoder_attention_mask=_lowerCAmelCase				,				)
       SCREAMING_SNAKE_CASE_							=			model(
           _lowerCAmelCase				,				attention_mask=_lowerCAmelCase				,				encoder_hidden_states=_lowerCAmelCase				,				)
       SCREAMING_SNAKE_CASE_							=			model(_lowerCAmelCase				,				attention_mask=_lowerCAmelCase						)
       self.parent.assertEqual(result.last_hidden_state.shape				,				(self.batch_size, self.seq_length, self.hidden_size)						)



 def       lowerCAmelCase_  (							self  :		int				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		List[str]				,				_lowerCAmelCase  :		Any				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		List[Any]				,				_lowerCAmelCase  :		Any				,				_lowerCAmelCase  :		Union[str, Any]				,				_lowerCAmelCase  :		str				,				):
       SCREAMING_SNAKE_CASE_							=			LlamaForCausalLM(config=_lowerCAmelCase						)
       model.to(_lowerCAmelCase						)
       model.eval()
       SCREAMING_SNAKE_CASE_							=			model(_lowerCAmelCase				,				attention_mask=_lowerCAmelCase				,				labels=_lowerCAmelCase						)
       self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.seq_length, self.vocab_size)						)



 def       lowerCAmelCase_  (							self  :		List[str]				,				_lowerCAmelCase  :		List[str]				,				_lowerCAmelCase  :		Union[str, Any]				,				_lowerCAmelCase  :		List[Any]				,				_lowerCAmelCase  :		Dict				,				_lowerCAmelCase  :		List[Any]				,				_lowerCAmelCase  :		List[Any]				,				_lowerCAmelCase  :		Any				,				_lowerCAmelCase  :		List[str]				,				_lowerCAmelCase  :		List[str]				,				):
       SCREAMING_SNAKE_CASE_							=			True
       SCREAMING_SNAKE_CASE_							=			True
       SCREAMING_SNAKE_CASE_							=			LlamaForCausalLM(config=_lowerCAmelCase						)
       model.to(_lowerCAmelCase						)
       model.eval()

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

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

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

       SCREAMING_SNAKE_CASE_							=			model(
           _lowerCAmelCase				,				attention_mask=_lowerCAmelCase				,				encoder_hidden_states=_lowerCAmelCase				,				encoder_attention_mask=_lowerCAmelCase				,				output_hidden_states=_lowerCAmelCase				,				)['hidden_states'][0]
       SCREAMING_SNAKE_CASE_							=			model(
           _lowerCAmelCase				,				attention_mask=_lowerCAmelCase				,				encoder_hidden_states=_lowerCAmelCase				,				encoder_attention_mask=_lowerCAmelCase				,				past_key_values=_lowerCAmelCase				,				output_hidden_states=_lowerCAmelCase				,				)['hidden_states'][0]

       # select random slice
       SCREAMING_SNAKE_CASE_							=			ids_tensor((1,)				,				output_from_past.shape[-1]						).item()
       SCREAMING_SNAKE_CASE_							=			output_from_no_past[:, -3:, random_slice_idx].detach()
       SCREAMING_SNAKE_CASE_							=			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(_lowerCAmelCase				,				_lowerCAmelCase				,				atol=1E-3						)						)




 def       lowerCAmelCase_  (							self  :		List[str]						):
       SCREAMING_SNAKE_CASE_							=			self.prepare_config_and_inputs()
       (
           (
           SCREAMING_SNAKE_CASE_
       )						, (
           SCREAMING_SNAKE_CASE_
       )						, (
           SCREAMING_SNAKE_CASE_
       )						, (
           SCREAMING_SNAKE_CASE_
       )						, (
           SCREAMING_SNAKE_CASE_
       )						, (
           SCREAMING_SNAKE_CASE_
       )						, (
           SCREAMING_SNAKE_CASE_
       )						, 
       )							=			config_and_inputs
       SCREAMING_SNAKE_CASE_							=			{'input_ids': input_ids, 'attention_mask': input_mask}
       return config, inputs_dict





@require_torch
class        lowerCamelCase_							(      _SCREAMING_SNAKE_CASE       ,   _SCREAMING_SNAKE_CASE       ,   _SCREAMING_SNAKE_CASE       ,   unittest.TestCase					):


 '''simple docstring'''







 lowercase_				    =    (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
 lowercase_				    =    (LlamaForCausalLM,) if is_torch_available() else ()
 lowercase_				    =    (
     {
         "feature-extraction": LlamaModel,
         "text-classification": LlamaForSequenceClassification,
         "text-generation": LlamaForCausalLM,
         "zero-shot": LlamaForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 lowercase_				    =    False
 lowercase_				    =    False



 def       lowerCAmelCase_  (							self  :		Dict						):
       SCREAMING_SNAKE_CASE_							=			LlamaModelTester(self						)
       SCREAMING_SNAKE_CASE_							=			ConfigTester(self				,				config_class=_lowerCAmelCase				,				hidden_size=37						)



 def       lowerCAmelCase_  (							self  :		Any						):
       self.config_tester.run_common_tests()



 def       lowerCAmelCase_  (							self  :		List[Any]						):
       SCREAMING_SNAKE_CASE_							=			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_lowerCAmelCase						)



 def       lowerCAmelCase_  (							self  :		Union[str, Any]						):
       SCREAMING_SNAKE_CASE_							=			self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
             SCREAMING_SNAKE_CASE_							=			type
             self.model_tester.create_and_check_model(*_lowerCAmelCase						)



 def       lowerCAmelCase_  (							self  :		str						):
       SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE_							=			3
       SCREAMING_SNAKE_CASE_							=			input_dict['input_ids']
       SCREAMING_SNAKE_CASE_							=			input_ids.ne(1						).to(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			ids_tensor([self.model_tester.batch_size]				,				self.model_tester.type_sequence_label_size						)
       SCREAMING_SNAKE_CASE_							=			LlamaForSequenceClassification(_lowerCAmelCase						)
       model.to(_lowerCAmelCase						)
       model.eval()
       SCREAMING_SNAKE_CASE_							=			model(_lowerCAmelCase				,				attention_mask=_lowerCAmelCase				,				labels=_lowerCAmelCase						)
       self.assertEqual(result.logits.shape				,				(self.model_tester.batch_size, self.model_tester.num_labels)						)



 def       lowerCAmelCase_  (							self  :		Union[str, Any]						):
       SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE_							=			3
       SCREAMING_SNAKE_CASE_							=			'single_label_classification'
       SCREAMING_SNAKE_CASE_							=			input_dict['input_ids']
       SCREAMING_SNAKE_CASE_							=			input_ids.ne(1						).to(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			ids_tensor([self.model_tester.batch_size]				,				self.model_tester.type_sequence_label_size						)
       SCREAMING_SNAKE_CASE_							=			LlamaForSequenceClassification(_lowerCAmelCase						)
       model.to(_lowerCAmelCase						)
       model.eval()
       SCREAMING_SNAKE_CASE_							=			model(_lowerCAmelCase				,				attention_mask=_lowerCAmelCase				,				labels=_lowerCAmelCase						)
       self.assertEqual(result.logits.shape				,				(self.model_tester.batch_size, self.model_tester.num_labels)						)



 def       lowerCAmelCase_  (							self  :		List[Any]						):
       SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE_							=			3
       SCREAMING_SNAKE_CASE_							=			'multi_label_classification'
       SCREAMING_SNAKE_CASE_							=			input_dict['input_ids']
       SCREAMING_SNAKE_CASE_							=			input_ids.ne(1						).to(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			ids_tensor(
           [self.model_tester.batch_size, config.num_labels]				,				self.model_tester.type_sequence_label_size						).to(torch.float						)
       SCREAMING_SNAKE_CASE_							=			LlamaForSequenceClassification(_lowerCAmelCase						)
       model.to(_lowerCAmelCase						)
       model.eval()
       SCREAMING_SNAKE_CASE_							=			model(_lowerCAmelCase				,				attention_mask=_lowerCAmelCase				,				labels=_lowerCAmelCase						)
       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       lowerCAmelCase_  (							self  :		int						):
       pass




 @parameterized.expand([('linear',), ('dynamic',)]						)
 def       lowerCAmelCase_  (							self  :		str				,				_lowerCAmelCase  :		Tuple						):
       SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE_							=			ids_tensor([1, 10]				,				config.vocab_size						)
       SCREAMING_SNAKE_CASE_							=			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
       SCREAMING_SNAKE_CASE_							=			LlamaModel(_lowerCAmelCase						)
       original_model.to(_lowerCAmelCase						)
       original_model.eval()
       SCREAMING_SNAKE_CASE_							=			original_model(_lowerCAmelCase						).last_hidden_state
       SCREAMING_SNAKE_CASE_							=			original_model(_lowerCAmelCase						).last_hidden_state

       set_seed(42						)  # Fixed seed at init time so the two models get the same random weights
       SCREAMING_SNAKE_CASE_							=			{'type': scaling_type, 'factor': 10.0}
       SCREAMING_SNAKE_CASE_							=			LlamaModel(_lowerCAmelCase						)
       scaled_model.to(_lowerCAmelCase						)
       scaled_model.eval()
       SCREAMING_SNAKE_CASE_							=			scaled_model(_lowerCAmelCase						).last_hidden_state
       SCREAMING_SNAKE_CASE_							=			scaled_model(_lowerCAmelCase						).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(_lowerCAmelCase				,				_lowerCAmelCase				,				atol=1E-5						)						)
       else:
             self.assertFalse(torch.allclose(_lowerCAmelCase				,				_lowerCAmelCase				,				atol=1E-5						)						)

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





@require_torch
class        lowerCamelCase_							(      unittest.TestCase					):


 '''simple docstring'''







 @unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'						)
 @slow
 def       lowerCAmelCase_  (							self  :		List[str]						):
       SCREAMING_SNAKE_CASE_							=			[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
       SCREAMING_SNAKE_CASE_							=			LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf'				,				device_map='auto'						)
       SCREAMING_SNAKE_CASE_							=			model(torch.tensor([input_ids]						)						)
       # Expected mean on dim = -1
       SCREAMING_SNAKE_CASE_							=			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						)				,				_lowerCAmelCase				,				atol=1E-2				,				rtol=1E-2						)
       # slicing logits[0, 0, 0:30]
       # fmt: off
       SCREAMING_SNAKE_CASE_							=			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]				,				_lowerCAmelCase				,				atol=1E-5				,				rtol=1E-5						)



 @unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'						)
 @slow
 def       lowerCAmelCase_  (							self  :		Optional[int]						):
       SCREAMING_SNAKE_CASE_							=			[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
       SCREAMING_SNAKE_CASE_							=			LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf'				,				device_map='auto'						)
       SCREAMING_SNAKE_CASE_							=			model(torch.tensor(_lowerCAmelCase						)						)
       # Expected mean on dim = -1
       SCREAMING_SNAKE_CASE_							=			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						)				,				_lowerCAmelCase				,				atol=1E-2				,				rtol=1E-2						)
       # slicing logits[0, 0, 0:30]
       # fmt: off
       SCREAMING_SNAKE_CASE_							=			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]				,				_lowerCAmelCase				,				atol=1E-5				,				rtol=1E-5						)



 @unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'						)
 @slow
 def       lowerCAmelCase_  (							self  :		int						):
       SCREAMING_SNAKE_CASE_							=			[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
       SCREAMING_SNAKE_CASE_							=			LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf'				,				device_map='auto'						)
       SCREAMING_SNAKE_CASE_							=			model(torch.tensor(_lowerCAmelCase						)						)
       # Expected mean on dim = -1
       SCREAMING_SNAKE_CASE_							=			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						)				,				_lowerCAmelCase				,				atol=1E-2				,				rtol=1E-2						)
       # slicing logits[0, 0, 0:30]
       # fmt: off
       SCREAMING_SNAKE_CASE_							=			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						)				,				_lowerCAmelCase				,				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       lowerCAmelCase_  (							self  :		int						):
       SCREAMING_SNAKE_CASE_							=			[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
       SCREAMING_SNAKE_CASE_							=			LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf'				,				device_map='auto'						)
       SCREAMING_SNAKE_CASE_							=			model(torch.tensor(_lowerCAmelCase						)						)

       SCREAMING_SNAKE_CASE_							=			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						)				,				_lowerCAmelCase				,				atol=1E-2				,				rtol=1E-2						)
       # fmt: off
       SCREAMING_SNAKE_CASE_							=			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]				,				_lowerCAmelCase				,				atol=1E-5				,				rtol=1E-5						)




 @unittest.skip('Model is curently gated'						)
 @slow
 def       lowerCAmelCase_  (							self  :		Optional[Any]						):
       SCREAMING_SNAKE_CASE_							=			'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'
       SCREAMING_SNAKE_CASE_							=			'Simply put, the theory of relativity states that '
       SCREAMING_SNAKE_CASE_							=			LlamaTokenizer.from_pretrained('meta-llama/Llama-2-13b-chat-hf'						)
       SCREAMING_SNAKE_CASE_							=			tokenizer.encode(_lowerCAmelCase				,				return_tensors='pt'						)
       SCREAMING_SNAKE_CASE_							=			LlamaForCausalLM.from_pretrained(
           'meta-llama/Llama-2-13b-chat-hf'				,				device_map='sequential'				,				use_safetensors=_lowerCAmelCase						)

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