
'''simple docstring'''





import math
import unittest

from transformers import BioGptConfig, is_torch_available
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 (
			    BioGptForCausalLM,
			    BioGptForSequenceClassification,
			    BioGptForTokenClassification,
			    BioGptModel,
			    BioGptTokenizer,
			)
			from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST





class 			_lowerCAmelCase      :




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




				def   __a		(      self  )       ->   str:
							lowerCAmelCase_      			=			ids_tensor([self.batch_size, self.seq_length]       ,       self.vocab_size  )

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

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

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

							lowerCAmelCase_      			=			self.get_config()

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




				def   __a		(      self  )       ->   Dict:
							return BioGptConfig(
							    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   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   str:
							lowerCAmelCase_      			=			BioGptModel(config=lowerCamelCase_  )
							model.to(lowerCamelCase_  )
							model.eval()
							lowerCAmelCase_      			=			model(lowerCamelCase_       ,       attention_mask=lowerCamelCase_  )
							lowerCAmelCase_      			=			model(lowerCamelCase_  )
							self.parent.assertEqual(result.last_hidden_state.shape       ,       (self.batch_size, self.seq_length, self.hidden_size)  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       )       ->   List[Any]:
							lowerCAmelCase_      			=			BioGptForCausalLM(config=lowerCamelCase_  )
							model.to(lowerCamelCase_  )
							model.eval()
							lowerCAmelCase_      			=			model(lowerCamelCase_       ,       attention_mask=lowerCamelCase_       ,       token_type_ids=lowerCamelCase_       ,       labels=lowerCamelCase_  )
							self.parent.assertEqual(result.logits.shape       ,       (self.batch_size, self.seq_length, self.vocab_size)  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       *_UpperCamelCase  )       ->   Dict:
							lowerCAmelCase_      			=			BioGptModel(config=lowerCamelCase_  )
							model.to(lowerCamelCase_  )
							model.eval()

							# create attention mask
							lowerCAmelCase_      			=			torch.ones(input_ids.shape       ,       dtype=torch.long       ,       device=lowerCamelCase_  )
							lowerCAmelCase_      			=			self.seq_length // 2
							lowerCAmelCase_      			=			0

							# first forward pass
							lowerCAmelCase_      			=			model(lowerCamelCase_       ,       attention_mask=lowerCamelCase_  ).to_tuple()

							# create hypothetical next token and extent to next_input_ids
							lowerCAmelCase_      			=			ids_tensor((self.batch_size, 1)       ,       config.vocab_size  )

							# change a random masked slice from input_ids
							lowerCAmelCase_      			=			ids_tensor((1,)       ,       lowerCamelCase_  ).item() + 1
							lowerCAmelCase_      			=			ids_tensor((self.batch_size, 1)       ,       config.vocab_size  ).squeeze(-1  )
							lowerCAmelCase_      			=			random_other_next_tokens

							# append to next input_ids and attn_mask
							lowerCAmelCase_      			=			torch.cat([input_ids, next_tokens]       ,       dim=-1  )
							lowerCAmelCase_      			=			torch.cat(
							    [attn_mask, torch.ones((attn_mask.shape[0], 1)       ,       dtype=torch.long       ,       device=lowerCamelCase_  )]       ,       dim=1       ,       )

							# get two different outputs
							lowerCAmelCase_      			=			model(lowerCamelCase_       ,       attention_mask=lowerCamelCase_  )["""last_hidden_state"""]
							lowerCAmelCase_      			=			model(lowerCamelCase_       ,       past_key_values=lowerCamelCase_       ,       attention_mask=lowerCamelCase_  )["""last_hidden_state"""]

							# select random slice
							lowerCAmelCase_      			=			ids_tensor((1,)       ,       output_from_past.shape[-1]  ).item()
							lowerCAmelCase_      			=			output_from_no_past[:, -1, random_slice_idx].detach()
							lowerCAmelCase_      			=			output_from_past[:, 0, random_slice_idx].detach()

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




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       *_UpperCamelCase  )       ->   Any:
							lowerCAmelCase_      			=			BioGptModel(config=lowerCamelCase_  ).to(lowerCamelCase_  ).eval()

							lowerCAmelCase_      			=			torch.ones(input_ids.shape       ,       dtype=torch.long       ,       device=lowerCamelCase_  )

							# first forward pass
							lowerCAmelCase_      			=			model(lowerCamelCase_       ,       attention_mask=lowerCamelCase_       ,       use_cache=lowerCamelCase_  )

							lowerCAmelCase_      			=			outputs.to_tuple()

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

							# append to next input_ids and
							lowerCAmelCase_      			=			torch.cat([input_ids, next_tokens]       ,       dim=-1  )
							lowerCAmelCase_      			=			torch.cat([attention_mask, next_attn_mask]       ,       dim=-1  )

							lowerCAmelCase_      			=			model(lowerCamelCase_       ,       attention_mask=lowerCamelCase_  )["""last_hidden_state"""]
							lowerCAmelCase_      			=			model(lowerCamelCase_       ,       attention_mask=lowerCamelCase_       ,       past_key_values=lowerCamelCase_  )[
							    """last_hidden_state"""
							]

							# select random slice
							lowerCAmelCase_      			=			ids_tensor((1,)       ,       output_from_past.shape[-1]  ).item()
							lowerCAmelCase_      			=			output_from_no_past[:, -3:, random_slice_idx].detach()
							lowerCAmelCase_      			=			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   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       *_UpperCamelCase       ,       _UpperCamelCase=False  )       ->   str:
							lowerCAmelCase_      			=			BioGptForCausalLM(lowerCamelCase_  )
							model.to(lowerCamelCase_  )
							if gradient_checkpointing:
										model.gradient_checkpointing_enable()

							lowerCAmelCase_      			=			model(lowerCamelCase_       ,       labels=lowerCamelCase_  )
							self.parent.assertEqual(result.loss.shape       ,       ()  )
							self.parent.assertEqual(result.logits.shape       ,       (self.batch_size, self.seq_length, self.vocab_size)  )
							result.loss.backward()




				def   __a		(      self       ,       _UpperCamelCase       ,       *_UpperCamelCase  )       ->   Optional[Any]:
							lowerCAmelCase_      			=			BioGptModel(lowerCamelCase_  )
							lowerCAmelCase_      			=			model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers  )
							for key in model.state_dict().keys():
										if "c_proj" in key and "weight" in key:
													self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]  ) - model_std  )       ,       0.001  )
													self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]  ) - 0.0  )       ,       0.01  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       *_UpperCamelCase  )       ->   Optional[Any]:
							lowerCAmelCase_      			=			self.num_labels
							lowerCAmelCase_      			=			BioGptForTokenClassification(lowerCamelCase_  )
							model.to(lowerCamelCase_  )
							model.eval()
							lowerCAmelCase_      			=			model(lowerCamelCase_       ,       attention_mask=lowerCamelCase_       ,       token_type_ids=lowerCamelCase_  )
							self.parent.assertEqual(result.logits.shape       ,       (self.batch_size, self.seq_length, self.num_labels)  )




				def   __a		(      self  )       ->   Dict:
							lowerCAmelCase_      			=			self.prepare_config_and_inputs()
							(
							    lowerCAmelCase_       
							)      			=			config_and_inputs
							lowerCAmelCase_      			=			{"""input_ids""": input_ids, """attention_mask""": input_mask}
							return config, inputs_dict





@require_torch
class 			_lowerCAmelCase      (			lowercase_      ,   lowercase_      ,   lowercase_      ,   unittest.TestCase ):
				_lowercase    	=(
				    (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
				    if is_torch_available()
				    else ()
				)
				_lowercase    	=(BioGptForCausalLM,) if is_torch_available() else ()
				_lowercase    	=(
				    {
				        '''feature-extraction''': BioGptModel,
				        '''text-classification''': BioGptForSequenceClassification,
				        '''text-generation''': BioGptForCausalLM,
				        '''token-classification''': BioGptForTokenClassification,
				        '''zero-shot''': BioGptForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				_lowercase    	=False




				def   __a		(      self  )       ->   Optional[int]:
							lowerCAmelCase_      			=			BioGptModelTester(self  )
							lowerCAmelCase_      			=			ConfigTester(self       ,       config_class=lowerCamelCase_       ,       hidden_size=37  )




				def   __a		(      self  )       ->   Dict:
							self.config_tester.run_common_tests()




				def   __a		(      self  )       ->   Tuple:
							lowerCAmelCase_      			=			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowerCamelCase_  )




				def   __a		(      self  )       ->   List[Any]:
							lowerCAmelCase_      			=			self.model_tester.prepare_config_and_inputs()
							for type in ["absolute", "relative_key", "relative_key_query"]:
										lowerCAmelCase_      			=			type
										self.model_tester.create_and_check_model(*lowerCamelCase_  )




				def   __a		(      self  )       ->   Union[str, Any]:
							lowerCAmelCase_      			=			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_biogpt_model_attention_mask_past(*lowerCamelCase_  )




				def   __a		(      self  )       ->   int:
							lowerCAmelCase_      			=			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_forward_and_backwards(*lowerCamelCase_       ,       gradient_checkpointing=lowerCamelCase_  )




				def   __a		(      self  )       ->   List[Any]:
							lowerCAmelCase_      			=			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_biogpt_model_past_large_inputs(*lowerCamelCase_  )




				def   __a		(      self  )       ->   Tuple:
							lowerCAmelCase_      			=			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_biogpt_weight_initialization(*lowerCamelCase_  )




				def   __a		(      self  )       ->   Dict:
							lowerCAmelCase_      			=			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_biogpt_for_token_classification(*lowerCamelCase_  )




				@slow
				def   __a		(      self  )       ->   Union[str, Any]:
							lowerCAmelCase_      			=			BioGptForCausalLM.from_pretrained("microsoft/biogpt"  )
							model.to(lowerCamelCase_  )
							lowerCAmelCase_      			=			BioGptTokenizer.from_pretrained("microsoft/biogpt"  )

							lowerCAmelCase_      			=			"""left"""

							# Define PAD Token = EOS Token = 50256
							lowerCAmelCase_      			=			tokenizer.eos_token
							lowerCAmelCase_      			=			model.config.eos_token_id

							# use different length sentences to test batching
							lowerCAmelCase_      			=			[
							    """Hello, my dog is a little""",
							    """Today, I""",
							]

							lowerCAmelCase_      			=			tokenizer(lowerCamelCase_       ,       return_tensors="pt"       ,       padding=lowerCamelCase_  )
							lowerCAmelCase_      			=			inputs["""input_ids"""].to(lowerCamelCase_  )

							lowerCAmelCase_      			=			model.generate(
							    input_ids=lowerCamelCase_       ,       attention_mask=inputs["attention_mask"].to(lowerCamelCase_  )       ,       )

							lowerCAmelCase_      			=			tokenizer(sentences[0]       ,       return_tensors="pt"  ).input_ids.to(lowerCamelCase_  )
							lowerCAmelCase_      			=			model.generate(input_ids=lowerCamelCase_  )

							lowerCAmelCase_      			=			inputs_non_padded.shape[-1] - inputs["""attention_mask"""][-1].long().sum().cpu().item()
							lowerCAmelCase_      			=			tokenizer(sentences[1]       ,       return_tensors="pt"  ).input_ids.to(lowerCamelCase_  )
							lowerCAmelCase_      			=			model.generate(input_ids=lowerCamelCase_       ,       max_length=model.config.max_length - num_paddings  )

							lowerCAmelCase_      			=			tokenizer.batch_decode(lowerCamelCase_       ,       skip_special_tokens=lowerCamelCase_  )
							lowerCAmelCase_      			=			tokenizer.decode(output_non_padded[0]       ,       skip_special_tokens=lowerCamelCase_  )
							lowerCAmelCase_      			=			tokenizer.decode(output_padded[0]       ,       skip_special_tokens=lowerCamelCase_  )

							lowerCAmelCase_      			=			[
							    """Hello, my dog is a little bit bigger than a little bit.""",
							    """Today, I have a good idea of how to use the information""",
							]
							self.assertListEqual(lowerCamelCase_       ,       lowerCamelCase_  )
							self.assertListEqual(lowerCamelCase_       ,       [non_padded_sentence, padded_sentence]  )




				@slow
				def   __a		(      self  )       ->   List[str]:



							for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowerCAmelCase_      			=			BioGptModel.from_pretrained(lowerCamelCase_  )
										self.assertIsNotNone(lowerCamelCase_  )




				def   __a		(      self  )       ->   Tuple:
							lowerCAmelCase_      			=			self.model_tester.prepare_config_and_inputs_for_common()
							lowerCAmelCase_      			=			3
							lowerCAmelCase_      			=			input_dict["""input_ids"""]
							lowerCAmelCase_      			=			input_ids.ne(1  ).to(lowerCamelCase_  )
							lowerCAmelCase_      			=			ids_tensor([self.model_tester.batch_size]       ,       self.model_tester.type_sequence_label_size  )
							lowerCAmelCase_      			=			BioGptForSequenceClassification(lowerCamelCase_  )
							model.to(lowerCamelCase_  )
							model.eval()
							lowerCAmelCase_      			=			model(lowerCamelCase_       ,       attention_mask=lowerCamelCase_       ,       labels=lowerCamelCase_  )
							self.assertEqual(result.logits.shape       ,       (self.model_tester.batch_size, self.model_tester.num_labels)  )




				def   __a		(      self  )       ->   List[Any]:
							lowerCAmelCase_      			=			self.model_tester.prepare_config_and_inputs_for_common()
							lowerCAmelCase_      			=			3
							lowerCAmelCase_      			=			"""multi_label_classification"""
							lowerCAmelCase_      			=			input_dict["""input_ids"""]
							lowerCAmelCase_      			=			input_ids.ne(1  ).to(lowerCamelCase_  )
							lowerCAmelCase_      			=			ids_tensor(
							    [self.model_tester.batch_size, config.num_labels]       ,       self.model_tester.type_sequence_label_size  ).to(torch.float  )
							lowerCAmelCase_      			=			BioGptForSequenceClassification(lowerCamelCase_  )
							model.to(lowerCamelCase_  )
							model.eval()
							lowerCAmelCase_      			=			model(lowerCamelCase_       ,       attention_mask=lowerCamelCase_       ,       labels=lowerCamelCase_  )
							self.assertEqual(result.logits.shape       ,       (self.model_tester.batch_size, self.model_tester.num_labels)  )





@require_torch
class 			_lowerCAmelCase      (			unittest.TestCase ):




				@slow
				def   __a		(      self  )       ->   Any:
							lowerCAmelCase_      			=			BioGptForCausalLM.from_pretrained("microsoft/biogpt"  )
							lowerCAmelCase_      			=			torch.tensor([[2, 4_805, 9, 656, 21]]  )
							lowerCAmelCase_      			=			model(lowerCamelCase_  )[0]

							lowerCAmelCase_      			=			42_384

							lowerCAmelCase_      			=			torch.Size((1, 5, vocab_size)  )
							self.assertEqual(output.shape       ,       lowerCamelCase_  )

							lowerCAmelCase_      			=			torch.tensor(
							    [[[-9.5236, -9.8918, 10.4557], [-11.0469, -9.6423, 8.1022], [-8.8664, -7.8826, 5.5325]]]  )

							self.assertTrue(torch.allclose(output[:, :3, :3]       ,       lowerCamelCase_       ,       atol=1e-4  )  )




				@slow
				def   __a		(      self  )       ->   List[Any]:
							lowerCAmelCase_      			=			BioGptTokenizer.from_pretrained("microsoft/biogpt"  )
							lowerCAmelCase_      			=			BioGptForCausalLM.from_pretrained("microsoft/biogpt"  )
							model.to(lowerCamelCase_  )

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			tokenizer("COVID-19 is"       ,       return_tensors="pt"  ).to(lowerCamelCase_  )
							lowerCAmelCase_      			=			model.generate(
							    **lowerCamelCase_       ,       min_length=100       ,       max_length=1_024       ,       num_beams=5       ,       early_stopping=lowerCamelCase_       ,       )
							lowerCAmelCase_      			=			tokenizer.decode(output_ids[0]       ,       skip_special_tokens=lowerCamelCase_  )

							lowerCAmelCase_      			=			(
							    """COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the"""
							    """ causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and"""
							    """ territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),"""
							    """ and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and"""
							    """ more than 800,000 deaths."""
							)
							self.assertEqual(lowerCamelCase_       ,       lowerCamelCase_  )





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


_A     =  {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 			_lowerCAmelCase      (			nn.Module ):




				def __init__(      self       ,       _UpperCamelCase  )       ->   Optional[Any]:
							super().__init__()
							lowerCAmelCase_      			=			torchvision.models.resnetaaa(pretrained=_UpperCamelCase  )
							lowerCAmelCase_      			=			list(model.children()  )[:-2]
							lowerCAmelCase_      			=			nn.Sequential(*_UpperCamelCase  )
							lowerCAmelCase_      			=			nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]  )




				def   __a		(      self       ,       _UpperCamelCase  )       ->   Dict:
							# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
							lowerCAmelCase_      			=			self.pool(self.model(_UpperCamelCase  )  )
							lowerCAmelCase_      			=			torch.flatten(_UpperCamelCase       ,       start_dim=2  )
							lowerCAmelCase_      			=			out.transpose(1       ,       2  ).contiguous()
							return out  # BxNx2048





class 			_lowerCAmelCase      (			__a ):




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   List[str]:
							lowerCAmelCase_      			=			[json.loads(_UpperCamelCase  ) for l in open(_UpperCamelCase  )]
							lowerCAmelCase_      			=			os.path.dirname(_UpperCamelCase  )
							lowerCAmelCase_      			=			tokenizer
							lowerCAmelCase_      			=			labels
							lowerCAmelCase_      			=			len(_UpperCamelCase  )
							lowerCAmelCase_      			=			max_seq_length

							lowerCAmelCase_      			=			transforms




				def __len__(      self  )       ->   Any:
							return len(self.data  )




				def __getitem__(      self       ,       _UpperCamelCase  )       ->   Any:
							lowerCAmelCase_      			=			torch.LongTensor(self.tokenizer.encode(self.data[index]["text"]       ,       add_special_tokens=_UpperCamelCase  )  )
							lowerCAmelCase_			,						lowerCAmelCase_			,						lowerCAmelCase_      			=			sentence[0], sentence[1:-1], sentence[-1]
							lowerCAmelCase_      			=			sentence[: self.max_seq_length]

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

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

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




				def   __a		(      self  )       ->   str:
							lowerCAmelCase_      			=			Counter()
							for row in self.data:
										label_freqs.update(row["label"]  )
							return label_freqs






def      lowerCamelCase__       (       __lowerCAmelCase						:						List[str]       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			[len(row["sentence"]       ) for row in batch]
			lowerCAmelCase_			,						lowerCAmelCase_      			=			len(__lowerCAmelCase       ), max(__lowerCAmelCase       )

			lowerCAmelCase_      			=			torch.zeros(__lowerCAmelCase					,							__lowerCAmelCase					,							dtype=torch.long       )
			lowerCAmelCase_      			=			torch.zeros(__lowerCAmelCase					,							__lowerCAmelCase					,							dtype=torch.long       )

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

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




			"""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      lowerCamelCase__       (       ):




			"""simple docstring"""


			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]					,							),
			    ]       )



