





import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
	from transformers import SpeechaTextFeatureExtractor

_lowerCamelCase 					=      random.Random()






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				int       , UpperCamelCase__:				Union[str, Any]=1.0       , UpperCamelCase__:				str=None       , UpperCamelCase__:				int=None     ):
		if rng is None:
				SCREAMING_SNAKE_CASE__								=   global_rng

		SCREAMING_SNAKE_CASE__								=   []
		for batch_idx in range(shape[0]     ):
				values.append([]     )
				for _ in range(shape[1]     ):
						values[-1].append(rng.random() * scale     )

		return values




@require_torch
@require_torchaudio
class 	UpperCamelCase_							(  unittest.TestCase     ):


							def __init__(				self     :Optional[Any]				,					__A     :str				,					__A     :int=7				,					__A     :Optional[Any]=400				,					__A     :Tuple=2000				,					__A     :Any=24				,					__A     :Optional[Any]=24				,					__A     :List[str]=0.0				,					__A     :Optional[int]=1_6000				,					__A     :Dict=True				,					__A     :List[Any]=True				,					) -> List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   parent
									SCREAMING_SNAKE_CASE__								=   batch_size
									SCREAMING_SNAKE_CASE__								=   min_seq_length
									SCREAMING_SNAKE_CASE__								=   max_seq_length
									SCREAMING_SNAKE_CASE__								=   (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
									SCREAMING_SNAKE_CASE__								=   feature_size
									SCREAMING_SNAKE_CASE__								=   num_mel_bins
									SCREAMING_SNAKE_CASE__								=   padding_value
									SCREAMING_SNAKE_CASE__								=   sampling_rate
									SCREAMING_SNAKE_CASE__								=   return_attention_mask
									SCREAMING_SNAKE_CASE__								=   do_normalize


							def   _snake_case	(				self     :Tuple		) -> Optional[int]:



									"""simple docstring"""



									return {
									    "feature_size": self.feature_size,
									    "num_mel_bins": self.num_mel_bins,
									    "padding_value": self.padding_value,
									    "sampling_rate": self.sampling_rate,
									    "return_attention_mask": self.return_attention_mask,
									    "do_normalize": self.do_normalize,
									}


							def   _snake_case	(				self     :List[str]				,					__A     :str=False				,					__A     :Any=False		) -> Any:



									"""simple docstring"""



									def _flatten(__A     :Tuple		):
											return list(itertools.chain(*__A		)		)

									if equal_length:
											SCREAMING_SNAKE_CASE__								=   [floats_list((self.max_seq_length, self.feature_size)		) for _ in range(self.batch_size		)]
									else:
											# make sure that inputs increase in size
											SCREAMING_SNAKE_CASE__								=   [
											    floats_list((x, self.feature_size)		)
											    for x in range(self.min_seq_length				,					self.max_seq_length				,					self.seq_length_diff		)
											]
									if numpify:
											SCREAMING_SNAKE_CASE__								=   [np.asarray(__A		) for x in speech_inputs]
									return speech_inputs




@require_torch
@require_torchaudio
class 	UpperCamelCase_							(  UpperCamelCase__					,	unittest.TestCase     ):
							lowerCamelCase_							     =       SpeechaTextFeatureExtractor if is_speech_available() else None


							def   _snake_case	(				self     :Tuple		) -> List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   SpeechaTextFeatureExtractionTester(self		)


							def   _snake_case	(				self     :Dict				,					__A     :Union[str, Any]		) -> Union[str, Any]:



									"""simple docstring"""



									self.assertTrue(np.all(np.mean(__A				,					axis=0		) < 1E-3		)		)
									self.assertTrue(np.all(np.abs(np.var(__A				,					axis=0		) - 1		) < 1E-3		)		)


							def   _snake_case	(				self     :Optional[Any]		) -> str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
									# create three inputs of length 800, 1000, and 1200
									SCREAMING_SNAKE_CASE__								=   [floats_list((1, x)		)[0] for x in range(800				,					1400				,					200		)]
									SCREAMING_SNAKE_CASE__								=   [np.asarray(__A		) for speech_input in speech_inputs]

									# Test feature size
									SCREAMING_SNAKE_CASE__								=   feature_extractor(__A				,					padding=__A				,					return_tensors="""np"""		).input_features
									self.assertTrue(input_features.ndim == 3		)
									self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size		)

									# Test not batched input
									SCREAMING_SNAKE_CASE__								=   feature_extractor(speech_inputs[0]				,					return_tensors="""np"""		).input_features
									SCREAMING_SNAKE_CASE__								=   feature_extractor(np_speech_inputs[0]				,					return_tensors="""np"""		).input_features
									self.assertTrue(np.allclose(__A				,					__A				,					atol=1E-3		)		)

									# Test batched
									SCREAMING_SNAKE_CASE__								=   feature_extractor(__A				,					return_tensors="""np"""		).input_features
									SCREAMING_SNAKE_CASE__								=   feature_extractor(__A				,					return_tensors="""np"""		).input_features
									for enc_seq_a, enc_seq_a in zip(__A				,					__A		):
											self.assertTrue(np.allclose(__A				,					__A				,					atol=1E-3		)		)

									# Test 2-D numpy arrays are batched.
									SCREAMING_SNAKE_CASE__								=   [floats_list((1, x)		)[0] for x in (800, 800, 800)]
									SCREAMING_SNAKE_CASE__								=   np.asarray(__A		)
									SCREAMING_SNAKE_CASE__								=   feature_extractor(__A				,					return_tensors="""np"""		).input_features
									SCREAMING_SNAKE_CASE__								=   feature_extractor(__A				,					return_tensors="""np"""		).input_features
									for enc_seq_a, enc_seq_a in zip(__A				,					__A		):
											self.assertTrue(np.allclose(__A				,					__A				,					atol=1E-3		)		)


							def   _snake_case	(				self     :Dict		) -> str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
									SCREAMING_SNAKE_CASE__								=   [floats_list((1, x)		)[0] for x in range(800				,					1400				,					200		)]

									SCREAMING_SNAKE_CASE__								=   ["""longest""", """max_length""", """do_not_pad"""]
									SCREAMING_SNAKE_CASE__								=   [None, 16, None]
									for max_length, padding in zip(__A				,					__A		):
											SCREAMING_SNAKE_CASE__								=   feature_extractor(
											    __A				,					padding=__A				,					max_length=__A				,					return_attention_mask=__A		)
											SCREAMING_SNAKE_CASE__								=   inputs.input_features
											SCREAMING_SNAKE_CASE__								=   inputs.attention_mask
											SCREAMING_SNAKE_CASE__								=   [np.sum(__A		) for x in attention_mask]

											self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]		)
											self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]		)
											self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]		)


							def   _snake_case	(				self     :Tuple		) -> Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
									SCREAMING_SNAKE_CASE__								=   [floats_list((1, x)		)[0] for x in range(800				,					1400				,					200		)]

									SCREAMING_SNAKE_CASE__								=   ["""longest""", """max_length""", """do_not_pad"""]
									SCREAMING_SNAKE_CASE__								=   [None, 16, None]
									for max_length, padding in zip(__A				,					__A		):
											SCREAMING_SNAKE_CASE__								=   feature_extractor(
											    __A				,					max_length=__A				,					padding=__A				,					return_tensors="""np"""				,					return_attention_mask=__A		)
											SCREAMING_SNAKE_CASE__								=   inputs.input_features
											SCREAMING_SNAKE_CASE__								=   inputs.attention_mask
											SCREAMING_SNAKE_CASE__								=   [np.sum(__A		) for x in attention_mask]

											self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]		)
											self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1E-6		)
											self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]		)
											self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1E-6		)
											self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]		)


							def   _snake_case	(				self     :Union[str, Any]		) -> Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
									SCREAMING_SNAKE_CASE__								=   [floats_list((1, x)		)[0] for x in range(800				,					1400				,					200		)]
									SCREAMING_SNAKE_CASE__								=   feature_extractor(
									    __A				,					padding="""max_length"""				,					max_length=4				,					truncation=__A				,					return_tensors="""np"""				,					return_attention_mask=__A				,					)
									SCREAMING_SNAKE_CASE__								=   inputs.input_features
									SCREAMING_SNAKE_CASE__								=   inputs.attention_mask
									SCREAMING_SNAKE_CASE__								=   np.sum(attention_mask == 1				,					axis=1		)

									self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]		)
									self._check_zero_mean_unit_variance(input_features[1]		)
									self._check_zero_mean_unit_variance(input_features[2]		)


							def   _snake_case	(				self     :Optional[Any]		) -> str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
									SCREAMING_SNAKE_CASE__								=   [floats_list((1, x)		)[0] for x in range(800				,					1400				,					200		)]
									SCREAMING_SNAKE_CASE__								=   feature_extractor(
									    __A				,					padding="""longest"""				,					max_length=4				,					truncation=__A				,					return_tensors="""np"""				,					return_attention_mask=__A				,					)
									SCREAMING_SNAKE_CASE__								=   inputs.input_features
									SCREAMING_SNAKE_CASE__								=   inputs.attention_mask
									SCREAMING_SNAKE_CASE__								=   np.sum(attention_mask == 1				,					axis=1		)

									self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]		)
									self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]		)
									self._check_zero_mean_unit_variance(input_features[2]		)

									# make sure that if max_length < longest -> then pad to max_length
									self.assertEqual(input_features.shape				,					(3, 4, 24)		)

									SCREAMING_SNAKE_CASE__								=   [floats_list((1, x)		)[0] for x in range(800				,					1400				,					200		)]
									SCREAMING_SNAKE_CASE__								=   feature_extractor(
									    __A				,					padding="""longest"""				,					max_length=16				,					truncation=__A				,					return_tensors="""np"""				,					return_attention_mask=__A				,					)
									SCREAMING_SNAKE_CASE__								=   inputs.input_features
									SCREAMING_SNAKE_CASE__								=   inputs.attention_mask
									SCREAMING_SNAKE_CASE__								=   np.sum(attention_mask == 1				,					axis=1		)

									self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]		)
									self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]		)
									self._check_zero_mean_unit_variance(input_features[2]		)

									# make sure that if max_length < longest -> then pad to max_length
									self.assertEqual(input_features.shape				,					(3, 6, 24)		)


							def   _snake_case	(				self     :Any		) -> Union[str, Any]:



									"""simple docstring"""



									import torch

									SCREAMING_SNAKE_CASE__								=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
									SCREAMING_SNAKE_CASE__								=   np.random.rand(100				,					32		).astype(np.floataa		)
									SCREAMING_SNAKE_CASE__								=   np_speech_inputs.tolist()

									for inputs in [py_speech_inputs, np_speech_inputs]:
											SCREAMING_SNAKE_CASE__								=   feature_extractor.pad([{"""input_features""": inputs}]				,					return_tensors="""np"""		)
											self.assertTrue(np_processed.input_features.dtype == np.floataa		)
											SCREAMING_SNAKE_CASE__								=   feature_extractor.pad([{"""input_features""": inputs}]				,					return_tensors="""pt"""		)
											self.assertTrue(pt_processed.input_features.dtype == torch.floataa		)


							def   _snake_case	(				self     :Union[str, Any]				,					__A     :Union[str, Any]		) -> Tuple:



									"""simple docstring"""



									from datasets import load_dataset

									SCREAMING_SNAKE_CASE__								=   load_dataset("""hf-internal-testing/librispeech_asr_dummy"""				,					"""clean"""				,					split="""validation"""		)
									# automatic decoding with librispeech
									SCREAMING_SNAKE_CASE__								=   ds.sort("""id"""		).select(range(__A		)		)[:num_samples]["""audio"""]

									return [x["array"] for x in speech_samples]


							def   _snake_case	(				self     :Dict		) -> List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   np.array([
									    -1.5_7_4_5, -1.7_7_1_3, -1.7_0_2_0, -1.6_0_6_9, -1.2_2_5_0, -1.1_1_0_5, -0.9_0_7_2, -0.8_2_4_1,
									    -1.2_3_1_0, -0.8_0_9_8, -0.3_3_2_0, -0.4_1_0_1, -0.7_9_8_5, -0.4_9_9_6, -0.8_2_1_3, -0.9_1_2_8,
									    -1.0_4_2_0, -1.1_2_8_6, -1.0_4_4_0, -0.7_9_9_9, -0.8_4_0_5, -1.2_2_7_5, -1.5_4_4_3, -1.4_6_2_5,
									]		)
									# fmt: on

									SCREAMING_SNAKE_CASE__								=   self._load_datasamples(1		)
									SCREAMING_SNAKE_CASE__								=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
									SCREAMING_SNAKE_CASE__								=   feature_extractor(__A				,					return_tensors="""pt"""		).input_features
									self.assertEquals(input_features.shape				,					(1, 584, 24)		)
									self.assertTrue(np.allclose(input_features[0, 0, :30]				,					__A				,					atol=1E-4		)		)






'''simple docstring'''




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      UpperCamelCase__					(					nn.Module			):




					"""simple docstring"""


					def __init__(						self				:			Dict      ,						lowerCamelCase_				:			Union[str, Any]						):

							'''simple docstring'''



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



					def lowerCamelCase_			(						self				:			List[str]      ,						lowerCamelCase_				:			List[Any]						):

							'''simple docstring'''



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







class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""


					def __init__(						self				:			str      ,						lowerCamelCase_				:			List[Any]      ,						lowerCamelCase_				:			List[str]      ,						lowerCamelCase_				:			List[Any]      ,						lowerCamelCase_				:			List[Any]      ,						lowerCamelCase_				:			Tuple						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Tuple		=			[json.loads(lowerCamelCase_						) for l in open(lowerCamelCase_						)]
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			os.path.dirname(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Tuple		=			tokenizer
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			labels
							SCREAMING_SNAKE_CASE :				str		=			len(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				int		=			max_seq_length

							SCREAMING_SNAKE_CASE :				List[Any]		=			transforms


					def __len__(						self				:			Any						):

							'''simple docstring'''



							return len(self.data						)


					def __getitem__(						self				:			Optional[int]      ,						lowerCamelCase_				:			List[str]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[Any]		=			torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]      ,						add_special_tokens=lowerCamelCase_						)						)
							SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE :				Tuple		=			sentence[0], sentence[1:-1], sentence[-1]
							SCREAMING_SNAKE_CASE :				int		=			sentence[: self.max_seq_length]

							SCREAMING_SNAKE_CASE :				List[Any]		=			torch.zeros(self.n_classes						)
							SCREAMING_SNAKE_CASE :				List[str]		=			1

							SCREAMING_SNAKE_CASE :				int		=			Image.open(os.path.join(self.data_dir      ,						self.data[index]["""img"""]						)						).convert("""RGB"""						)
							SCREAMING_SNAKE_CASE :				str		=			self.transforms(lowerCamelCase_						)

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



					def lowerCamelCase_			(						self				:			Optional[Any]						):

							'''simple docstring'''



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







def 		__A		(  lowerCamelCase_	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				Tuple		=			[len(row["""sentence"""]	) for row in batch]
		SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE :				Any		=			len(lowerCamelCase_	), max(lowerCamelCase_	)

		SCREAMING_SNAKE_CASE :				int		=			torch.zeros(lowerCamelCase_			,			lowerCamelCase_			,			dtype=torch.long	)
		SCREAMING_SNAKE_CASE :				Any		=			torch.zeros(lowerCamelCase_			,			lowerCamelCase_			,			dtype=torch.long	)

		for i_batch, (input_row, length) in enumerate(zip(lowerCamelCase_			,			lowerCamelCase_	)	):
				SCREAMING_SNAKE_CASE :				Union[str, Any]		=			input_row["""sentence"""]
				SCREAMING_SNAKE_CASE :				Union[str, Any]		=			1

		SCREAMING_SNAKE_CASE :				List[str]		=			torch.stack([row["""image"""] for row in batch]	)
		SCREAMING_SNAKE_CASE :				int		=			torch.stack([row["""label"""] for row in batch]	)
		SCREAMING_SNAKE_CASE :				str		=			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		(  ):


		"""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 		__A		(  ):


		"""simple docstring"""



		return transforms.Compose(
		    [
		        transforms.Resize(2_56	),
		        transforms.CenterCrop(2_24	),
		        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]			,			),
		    ]	)



