






import unittest

from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device

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 GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseModel





class     SCREAMING_SNAKE_CASE					:





							"""simple docstring"""

							def __init__(	self: Union[str, Any]		,  __A: Optional[Any]		,  __A: List[Any]=13		,  __A: Optional[Any]=7		,  __A: Dict=True		,  __A: Union[str, Any]=True		,  __A: Union[str, Any]=True		,  __A: int=True		,  __A: Optional[int]=99		,  __A: str=32		,  __A: Any=5		,  __A: Any=4		,  __A: List[Any]=4		,  __A: Optional[int]="gelu"		,  __A: Any=0.0		,  __A: List[Any]=0.1		,  __A: int=True		,  __A: List[Any]=5_12		,  __A: str=16		,  __A: Optional[Any]=2		,  __A: Optional[Any]=0.02		,  __A: List[Any]=3		,  __A: Optional[int]=4		,  __A: Any=None		,  )     ->					int:
												_A										=			parent
												_A										=			batch_size
												_A										=			seq_length
												_A										=			is_training
												_A										=			use_input_mask
												_A										=			use_token_type_ids
												_A										=			use_labels
												_A										=			vocab_size
												_A										=			hidden_size
												_A										=			num_hidden_layers
												_A										=			num_attention_heads
												_A										=			intermediate_multiple_size
												_A										=			hidden_act
												_A										=			hidden_dropout
												_A										=			attention_dropout
												_A										=			weight_tying
												_A										=			max_position_embeddings
												_A										=			type_vocab_size
												_A										=			type_sequence_label_size
												_A										=			initializer_range
												_A										=			num_labels
												_A										=			num_choices
												_A										=			scope

							def    __A			(	self: Optional[int] )     ->					int:
												_A										=			ids_tensor([self.batch_size, self.seq_length]		,  self.vocab_size )

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

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

												_A										=			self.get_config()

												return config, input_ids, input_mask, token_labels

							def    __A			(	self: Optional[int] )     ->					Any:
												return GPTNeoXJapaneseConfig(
												    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_multiple_size=self.intermediate_multiple_size		,  hidden_act=self.hidden_act		,  hidden_dropout=self.hidden_dropout		,  attention_dropout=self.attention_dropout		,  weight_tying=self.weight_tying		,  max_position_embeddings=self.max_position_embeddings		,  type_vocab_size=self.type_vocab_size		,  is_decoder=__A		,  initializer_range=self.initializer_range		,  )

							def    __A			(	self: Tuple )     ->					Optional[Any]:
												_A										=			self.prepare_config_and_inputs()

												_A										=			True

												return config, input_ids, input_mask, token_labels

							def    __A			(	self: Union[str, Any]		,  __A: Any		,  __A: Any		,  __A: Any )     ->					Tuple:
												_A										=			GPTNeoXJapaneseModel(config=__A )
												model.to(__A )
												model.eval()
												_A										=			model(__A		,  attention_mask=__A )
												_A										=			model(__A )
												self.parent.assertEqual(result.last_hidden_state.shape		,  (self.batch_size, self.seq_length, self.hidden_size) )

							def    __A			(	self: int		,  __A: Tuple		,  __A: List[Any]		,  __A: Any )     ->					str:
												_A										=			True
												_A										=			GPTNeoXJapaneseModel(__A )
												model.to(__A )
												model.eval()
												_A										=			model(__A		,  attention_mask=__A )
												self.parent.assertEqual(result.last_hidden_state.shape		,  (self.batch_size, self.seq_length, self.hidden_size) )

							def    __A			(	self: Union[str, Any]		,  __A: Optional[Any]		,  __A: List[str]		,  __A: int		,  __A: Tuple )     ->					str:
												_A										=			GPTNeoXJapaneseForCausalLM(config=__A )
												model.to(__A )
												model.eval()
												_A										=			model(__A		,  attention_mask=__A		,  labels=__A )
												self.parent.assertEqual(result.logits.shape		,  (self.batch_size, self.seq_length, self.vocab_size) )

							def    __A			(	self: Optional[int]		,  __A: Dict		,  __A: Union[str, Any]		,  __A: Tuple )     ->					Optional[int]:
												_A										=			True
												_A										=			GPTNeoXJapaneseForCausalLM(config=__A )
												model.to(__A )
												model.eval()

												# first forward pass
												_A										=			model(__A		,  attention_mask=__A		,  use_cache=__A )
												_A										=			outputs.past_key_values

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

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

												_A										=			model(__A		,  attention_mask=__A		,  output_hidden_states=__A )
												_A										=			output_from_no_past['hidden_states'][0]
												_A										=			model(
												    __A		,  attention_mask=__A		,  past_key_values=__A		,  output_hidden_states=__A		,  )['hidden_states'][0]

												# select random slice
												_A										=			ids_tensor((1,)		,  output_from_past.shape[-1] ).item()
												_A										=			output_from_no_past[:, -3:, random_slice_idx].detach()
												_A										=			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    __A			(	self: Optional[Any] )     ->					str:
												_A										=			self.prepare_config_and_inputs()
												_A										=			config_and_inputs
												_A										=			{'input_ids': input_ids, 'attention_mask': input_mask}
												return config, inputs_dict





@require_torch
class     SCREAMING_SNAKE_CASE					(			SCREAMING_SNAKE_CASE_	,						SCREAMING_SNAKE_CASE_	,						unittest.TestCase       ):





							"""simple docstring"""

							A_    	=       (GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
							A_    	=       (GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
							A_    	=       (
							    {'feature-extraction': GPTNeoXJapaneseModel, 'text-generation': GPTNeoXJapaneseForCausalLM}
							    if is_torch_available()
							    else {}
							)
							A_    	=       False
							A_    	=       False
							A_    	=       False
							A_    	=       False

							def    __A			(	self: List[str] )     ->					List[str]:
												_A										=			GPTNeoXJapaneseModelTester(self )
												_A										=			ConfigTester(self		,  config_class=__A		,  hidden_size=37 )

							def    __A			(	self: List[str] )     ->					List[Any]:
												self.config_tester.run_common_tests()

							def    __A			(	self: Optional[int] )     ->					Optional[Any]:
												_A										=			self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(__A		,  __A		,  __A )

							def    __A			(	self: Tuple )     ->					int:
												_A										=			self.model_tester.prepare_config_and_inputs_for_decoder()
												self.model_tester.create_and_check_model_as_decoder(__A		,  __A		,  __A )

							def    __A			(	self: Tuple )     ->					Any:
												_A										=			self.model_tester.prepare_config_and_inputs_for_decoder()

												_A										=			None

												self.model_tester.create_and_check_model_as_decoder(__A		,  __A		,  __A )

							def    __A			(	self: Optional[Any] )     ->					int:
												_A										=			self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_decoder_model_past_large_inputs(__A		,  __A		,  __A )

							def    __A			(	self: int )     ->					str:
												_A										=			self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_causal_lm(*__A )



							@slow
							def    __A			(	self: str )     ->					int:
												_A										=			'abeja/gpt-neox-japanese-2.7b'

												_A										=			['データサイエンティストとは、', '100年後に必要とされる会社は、', 'フルリモートの環境で働くために必要なことは、', '国境の長いトンネルを抜けると', '美味しい日本食といえば、']

												_A										=			[
												    'データサイエンティストとは、データを分析し、ビジネスに役立つ知見を導き出す専門家のことです。',
												    '100年後に必要とされる会社は、「人」が中心の会社です。',
												    'フルリモートの環境で働くために必要なことは、「自分の時間をコントロールする」ことです。',
												    '国境の長いトンネルを抜けると、そこは雪国だった。',
												    '美味しい日本食といえば、やっぱりお寿司ですよね。',
												]

												_A										=			GPTNeoXJapaneseTokenizer.from_pretrained(__A )
												_A										=			GPTNeoXJapaneseForCausalLM.from_pretrained(__A )

												_A										=			[]
												for prompt in prompts:
																	_A										=			tokenizer(__A		,  return_tensors='''pt''' ).input_ids
																	_A										=			model.generate(__A		,  max_length=50 )
																	_A										=			tokenizer.batch_decode(__A		,  skip_special_tokens=__A )
																	predicted_outputs += generated_string
												self.assertListEqual(__A		,  __A )








import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


_lowerCamelCase    :		Dict		      =    {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}


class 						lowercase    (							nn.Module):
						'''simple docstring'''


						def __init__(  self    :				Optional[Any]       ,	snake_case    :				int	):



											'''simple docstring'''


											super().__init__()
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				torchvision.models.resnetaaa(pretrained=snake_case	)
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				list(model.children()	)[:-2]
											SCREAMING_SNAKE_CASE							:     List[str]						=				nn.Sequential(*snake_case	)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]	)







						def       lowerCamelCase_				(  self    :				Any       ,	snake_case    :				Optional[Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[int]						=				self.pool(self.model(snake_case	)	)
											SCREAMING_SNAKE_CASE							:     Any						=				torch.flatten(snake_case       ,	start_dim=2	)
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				out.transpose(1       ,	2	).contiguous()
											return out  # BxNx2048


class 						lowercase    (							SCREAMING_SNAKE_CASE_):
						'''simple docstring'''


						def __init__(  self    :				str       ,	snake_case    :				Optional[Any]       ,	snake_case    :				Union[str, Any]       ,	snake_case    :				Optional[int]       ,	snake_case    :				int       ,	snake_case    :				Optional[Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				[json.loads(snake_case	) for l in open(snake_case	)]
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				os.path.dirname(snake_case	)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				tokenizer
											SCREAMING_SNAKE_CASE							:     Dict						=				labels
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				len(snake_case	)
											SCREAMING_SNAKE_CASE							:     int						=				max_seq_length

											SCREAMING_SNAKE_CASE							:     List[Any]						=				transforms







						def __len__(  self    :				str	):



											'''simple docstring'''


											return len(self.data	)







						def __getitem__(  self    :				Dict       ,	snake_case    :				int	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[int]						=				torch.LongTensor(self.tokenizer.encode(self.data[index]['text']       ,	add_special_tokens=snake_case	)	)
											SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Optional[Any]						=				sentence[0], sentence[1:-1], sentence[-1]
											SCREAMING_SNAKE_CASE							:     str						=				sentence[: self.max_seq_length]

											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				torch.zeros(self.n_classes	)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				1

											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				Image.open(os.path.join(self.data_dir       ,	self.data[index]['img']	)	).convert('RGB'	)
											SCREAMING_SNAKE_CASE							:     Tuple						=				self.transforms(snake_case	)

											return {
											    "image_start_token": start_token,
											    "image_end_token": end_token,
											    "sentence": sentence,
											    "image": image,
											    "label": label,
											}







						def       lowerCamelCase_				(  self    :				Any	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     int						=				Counter()
											for row in self.data:
																label_freqs.update(row['label']	)
											return label_freqs


def        __a			(   __lowerCAmelCase   )     ->     Union[str, Any]:
					SCREAMING_SNAKE_CASE							:     str						=				[len(row['sentence']   ) for row in batch]
					SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Dict						=				len(__lowerCAmelCase   ), max(__lowerCAmelCase   )

					SCREAMING_SNAKE_CASE							:     List[Any]						=				torch.zeros(__lowerCAmelCase      ,	__lowerCAmelCase      ,	dtype=torch.long   )
					SCREAMING_SNAKE_CASE							:     List[str]						=				torch.zeros(__lowerCAmelCase      ,	__lowerCAmelCase      ,	dtype=torch.long   )

					for i_batch, (input_row, length) in enumerate(zip(__lowerCAmelCase      ,	__lowerCAmelCase   )   ):
										SCREAMING_SNAKE_CASE							:     Optional[Any]						=				input_row['sentence']
										SCREAMING_SNAKE_CASE							:     Optional[Any]						=				1

					SCREAMING_SNAKE_CASE							:     List[str]						=				torch.stack([row['image'] for row in batch]   )
					SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				torch.stack([row['label'] for row in batch]   )
					SCREAMING_SNAKE_CASE							:     Tuple						=				torch.stack([row['image_start_token'] for row in batch]   )
					SCREAMING_SNAKE_CASE							:     Any						=				torch.stack([row['image_end_token'] for row in batch]   )

					return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor

def        __a			(   )     ->     str:
					return [
					    "Crime",
					    "Drama",
					    "Thriller",
					    "Action",
					    "Comedy",
					    "Romance",
					    "Documentary",
					    "Short",
					    "Mystery",
					    "History",
					    "Family",
					    "Adventure",
					    "Fantasy",
					    "Sci-Fi",
					    "Western",
					    "Horror",
					    "Sport",
					    "War",
					    "Music",
					    "Musical",
					    "Animation",
					    "Biography",
					    "Film-Noir",
					]



def        __a			(   )     ->     Union[str, Any]:
					return transforms.Compose(
					    [
					        transforms.Resize(256   ),
					        transforms.CenterCrop(224   ),
					        transforms.ToTensor(),
					        transforms.Normalize(
					            mean=[0.46_777_044, 0.44_531_429, 0.40_661_017]      ,	std=[0.12_221_994, 0.12_145_835, 0.14_380_469]      ,	),
					    ]   )