






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


UpperCamelCase__     =	{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     __SCREAMING_SNAKE_CASE   (       nn.Module    ):






							def __init__(  self					,	__lowerCAmelCase					):
														super().__init__()
														UpperCamelCase__			       =     torchvision.models.resnetaaa(pretrained=snake_case__					)
														UpperCamelCase__			       =     list(model.children()					)[:-2]
														UpperCamelCase__			       =     nn.Sequential(*snake_case__					)
														UpperCamelCase__			       =     nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]					)







							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					):
														UpperCamelCase__			       =     self.pool(self.model(snake_case__					)					)
														UpperCamelCase__			       =     torch.flatten(snake_case__					,	start_dim=2					)
														UpperCamelCase__			       =     out.transpose(1					,	2					).contiguous()
														return out  # BxNx2048






class     __SCREAMING_SNAKE_CASE   (       A_    ):






							def __init__(  self					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					):
														UpperCamelCase__			       =     [json.loads(snake_case__					) for l in open(snake_case__					)]
														UpperCamelCase__			       =     os.path.dirname(snake_case__					)
														UpperCamelCase__			       =     tokenizer
														UpperCamelCase__			       =     labels
														UpperCamelCase__			       =     len(snake_case__					)
														UpperCamelCase__			       =     max_seq_length

														UpperCamelCase__			       =     transforms






							def __len__(  self					):
														return len(self.data					)






							def __getitem__(  self					,	__lowerCAmelCase					):
														UpperCamelCase__			       =     torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]					,	add_special_tokens=snake_case__					)					)
														UpperCamelCase__			       =     sentence[0], sentence[1:-1], sentence[-1]
														UpperCamelCase__			       =     sentence[: self.max_seq_length]

														UpperCamelCase__			       =     torch.zeros(self.n_classes					)
														UpperCamelCase__			       =     1

														UpperCamelCase__			       =     Image.open(os.path.join(self.data_dir					,	self.data[index]["""img"""]					)					).convert("""RGB"""					)
														UpperCamelCase__			       =     self.transforms(snake_case__					)

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







							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     Counter()
														for row in self.data:
																					label_freqs.update(row["""label"""]					)
														return label_freqs






def       _UpperCamelCase       (a__				:Optional[Any]    ):






							"""simple docstring"""





							UpperCamelCase__			       =     [len(row["""sentence"""]    ) for row in batch]
							UpperCamelCase__			       =     len(__lowerCamelCase    ), max(__lowerCamelCase    )

							UpperCamelCase__			       =     torch.zeros(__lowerCamelCase				,     __lowerCamelCase				,     dtype=torch.long    )
							UpperCamelCase__			       =     torch.zeros(__lowerCamelCase				,     __lowerCamelCase				,     dtype=torch.long    )

							for i_batch, (input_row, length) in enumerate(zip(__lowerCamelCase				,     __lowerCamelCase    )    ):
														UpperCamelCase__			       =     input_row["sentence"]
														UpperCamelCase__			       =     1

							UpperCamelCase__			       =     torch.stack([row["""image"""] for row in batch]    )
							UpperCamelCase__			       =     torch.stack([row["""label"""] for row in batch]    )
							UpperCamelCase__			       =     torch.stack([row["""image_start_token"""] for row in batch]    )
							UpperCamelCase__			       =     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       _UpperCamelCase       ():






							"""simple docstring"""





							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       _UpperCamelCase       ():






							"""simple docstring"""





							return transforms.Compose(
							    [
							        transforms.Resize(256    ),
							        transforms.CenterCrop(224    ),
							        transforms.ToTensor(),
							        transforms.Normalize(
							            mean=[0.4677_7044, 0.4453_1429, 0.4066_1017]				,     std=[0.1222_1994, 0.1214_5835, 0.1438_0469]				,     ),
							    ]    )











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     __SCREAMING_SNAKE_CASE   :






							def __init__(  self					,	__lowerCAmelCase					,	__lowerCAmelCase=13					,	__lowerCAmelCase=7					,	__lowerCAmelCase=True					,	__lowerCAmelCase=True					,	__lowerCAmelCase=True					,	__lowerCAmelCase=True					,	__lowerCAmelCase=99					,	__lowerCAmelCase=64					,	__lowerCAmelCase=5					,	__lowerCAmelCase=4					,	__lowerCAmelCase=37					,	__lowerCAmelCase="gelu"					,	__lowerCAmelCase=0.1					,	__lowerCAmelCase=0.1					,	__lowerCAmelCase=512					,	__lowerCAmelCase=16					,	__lowerCAmelCase=2					,	__lowerCAmelCase=0.02					,	__lowerCAmelCase=3					,	__lowerCAmelCase=4					,	__lowerCAmelCase=None					,	):
														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
														UpperCamelCase__			       =     vocab_size - 1






							def 			_lowerCamelCase						(  self					):
														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_labels:
																					UpperCamelCase__			       =     ids_tensor([self.batch_size, self.seq_length]					,	self.num_labels					)

														UpperCamelCase__			       =     self.get_config()

														return config, input_ids, input_mask, token_labels






							def 			_lowerCamelCase						(  self					):
														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=__lowerCAmelCase					,	initializer_range=self.initializer_range					,	pad_token_id=self.pad_token_id					,	)






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__			       =     self.prepare_config_and_inputs()

														UpperCamelCase__			       =     True

														return config, input_ids, input_mask, token_labels






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					):
														UpperCamelCase__			       =     GPTNeoXModel(config=__lowerCAmelCase					)
														model.to(__lowerCAmelCase					)
														model.eval()
														UpperCamelCase__			       =     model(__lowerCAmelCase					,	attention_mask=__lowerCAmelCase					)
														UpperCamelCase__			       =     model(__lowerCAmelCase					)
														self.parent.assertEqual(result.last_hidden_state.shape					,	(self.batch_size, self.seq_length, self.hidden_size)					)






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					):
														UpperCamelCase__			       =     True
														UpperCamelCase__			       =     GPTNeoXModel(__lowerCAmelCase					)
														model.to(__lowerCAmelCase					)
														model.eval()
														UpperCamelCase__			       =     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					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					):
														UpperCamelCase__			       =     GPTNeoXForCausalLM(config=__lowerCAmelCase					)
														model.to(__lowerCAmelCase					)
														model.eval()
														UpperCamelCase__			       =     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					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					):
														UpperCamelCase__			       =     self.num_labels
														UpperCamelCase__			       =     GPTNeoXForQuestionAnswering(__lowerCAmelCase					)
														model.to(__lowerCAmelCase					)
														model.eval()
														UpperCamelCase__			       =     model(__lowerCAmelCase					,	attention_mask=__lowerCAmelCase					)
														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 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					):
														UpperCamelCase__			       =     self.num_labels
														UpperCamelCase__			       =     GPTNeoXForSequenceClassification(__lowerCAmelCase					)
														model.to(__lowerCAmelCase					)
														model.eval()
														UpperCamelCase__			       =     ids_tensor([self.batch_size]					,	self.type_sequence_label_size					)
														UpperCamelCase__			       =     model(__lowerCAmelCase					,	attention_mask=__lowerCAmelCase					,	labels=__lowerCAmelCase					)
														self.parent.assertEqual(result.logits.shape					,	(self.batch_size, self.num_labels)					)






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					):
														UpperCamelCase__			       =     self.num_labels
														UpperCamelCase__			       =     GPTNeoXForTokenClassification(__lowerCAmelCase					)
														model.to(__lowerCAmelCase					)
														model.eval()
														UpperCamelCase__			       =     model(__lowerCAmelCase					,	attention_mask=__lowerCAmelCase					,	labels=__lowerCAmelCase					)
														self.parent.assertEqual(result.logits.shape					,	(self.batch_size, self.seq_length, self.num_labels)					)






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					):
														UpperCamelCase__			       =     True
														UpperCamelCase__			       =     GPTNeoXForCausalLM(config=__lowerCAmelCase					)
														model.to(__lowerCAmelCase					)
														model.eval()

														# first forward pass
														UpperCamelCase__			       =     model(__lowerCAmelCase					,	attention_mask=__lowerCAmelCase					,	use_cache=__lowerCAmelCase					)
														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(__lowerCAmelCase					,	attention_mask=__lowerCAmelCase					,	output_hidden_states=__lowerCAmelCase					)
														UpperCamelCase__			       =     output_from_no_past["""hidden_states"""][0]
														UpperCamelCase__			       =     model(
														    __lowerCAmelCase					,	attention_mask=__lowerCAmelCase					,	past_key_values=__lowerCAmelCase					,	output_hidden_states=__lowerCAmelCase					,	)["""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(__lowerCAmelCase					,	__lowerCAmelCase					,	atol=1E-3					)					)







							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     self.prepare_config_and_inputs()
														UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__			       =     config_and_inputs
														UpperCamelCase__			       =     {"""input_ids""": input_ids, """attention_mask""": input_mask}
														return config, inputs_dict






@require_torch
class     __SCREAMING_SNAKE_CASE   (       _a				,						_a				,						_a				,						unittest.TestCase    ):
							snake_case       : Optional[Any]         =    (
							    (
							        GPTNeoXModel,
							        GPTNeoXForCausalLM,
							        GPTNeoXForQuestionAnswering,
							        GPTNeoXForSequenceClassification,
							        GPTNeoXForTokenClassification,
							    )
							    if is_torch_available()
							    else ()
							)
							snake_case       : Union[str, Any]         =    (GPTNeoXForCausalLM,) if is_torch_available() else ()
							snake_case       : Dict         =    (
							    {
							        """feature-extraction""": GPTNeoXModel,
							        """question-answering""": GPTNeoXForQuestionAnswering,
							        """text-classification""": GPTNeoXForSequenceClassification,
							        """text-generation""": GPTNeoXForCausalLM,
							        """token-classification""": GPTNeoXForTokenClassification,
							        """zero-shot""": GPTNeoXForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							snake_case       : Tuple         =    False
							snake_case       : Dict         =    False
							snake_case       : Tuple         =    False
							snake_case       : Any         =    False






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     GPTNeoXModelTester(self					)
														UpperCamelCase__			       =     ConfigTester(self					,	config_class=__lowerCAmelCase					,	hidden_size=64					,	num_attention_heads=8					)






							def 			_lowerCamelCase						(  self					):
														self.config_tester.run_common_tests()






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__			       =     self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					)






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__			       =     self.model_tester.prepare_config_and_inputs_for_decoder()
														self.model_tester.create_and_check_model_as_decoder(__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					)






							def 			_lowerCamelCase						(  self					):
														# This regression test was failing with PyTorch < 1.3
														UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__			       =     self.model_tester.prepare_config_and_inputs_for_decoder()

														UpperCamelCase__			       =     None

														self.model_tester.create_and_check_model_as_decoder(__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					)






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__			       =     self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_decoder_model_past_large_inputs(__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					)






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_causal_lm(*__lowerCAmelCase					)






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_question_answering(*__lowerCAmelCase					)






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_sequence_classification(*__lowerCAmelCase					)






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_token_classification(*__lowerCAmelCase					)






							@unittest.skip(reason="""Feed forward chunking is not implemented"""					)
							def 			_lowerCamelCase						(  self					):
														pass







							@parameterized.expand([("""linear""",), ("""dynamic""",)]					)
							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					):
														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__			       =     GPTNeoXModel(__lowerCAmelCase					)
														original_model.to(__lowerCAmelCase					)
														original_model.eval()
														UpperCamelCase__			       =     original_model(__lowerCAmelCase					).last_hidden_state
														UpperCamelCase__			       =     original_model(__lowerCAmelCase					).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__			       =     GPTNeoXModel(__lowerCAmelCase					)
														scaled_model.to(__lowerCAmelCase					)
														scaled_model.eval()
														UpperCamelCase__			       =     scaled_model(__lowerCAmelCase					).last_hidden_state
														UpperCamelCase__			       =     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     __SCREAMING_SNAKE_CASE   (       unittest.TestCase    ):






							@slow
							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     AutoTokenizer.from_pretrained("""EleutherAI/pythia-410m-deduped"""					)
														for checkpointing in [True, False]:
																					UpperCamelCase__			       =     GPTNeoXForCausalLM.from_pretrained("""EleutherAI/pythia-410m-deduped"""					)

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

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

																					UpperCamelCase__			       =     model.generate(**__lowerCAmelCase					,	do_sample=__lowerCAmelCase					,	max_new_tokens=20					)
																					UpperCamelCase__			       =     tokenizer.batch_decode(__lowerCAmelCase					)[0]

																					self.assertEqual(__lowerCAmelCase					,	__lowerCAmelCase					)




