
"""simple docstring"""







import shutil
import tempfile
import unittest

import numpy as np
import pytest

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


if is_vision_available():
							from transformers import TvltImageProcessor

if is_speech_available():
							from transformers import TvltFeatureExtractor

from transformers import TvltProcessor




@require_torch
class  A__       (	unittest.TestCase):

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Any									=      "ZinengTang/tvlt-base"
			__lowerCAmelCase			:						int									=      tempfile.mkdtemp()

		def 							__lowerCamelCase							(      self       ,		**_SCREAMING_SNAKE_CASE ):
			return TvltImageProcessor.from_pretrained(self.checkpoint       ,		**_a )

		def 							__lowerCamelCase							(      self       ,		**_SCREAMING_SNAKE_CASE ):
			return TvltFeatureExtractor.from_pretrained(self.checkpoint       ,		**_a )

		def 							__lowerCamelCase							(      self ):
			shutil.rmtree(self.tmpdirname )

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Tuple									=      self.get_image_processor()
			__lowerCAmelCase			:						str									=      self.get_feature_extractor()

			__lowerCAmelCase			:						List[Any]									=      TvltProcessor(image_processor=_a       ,		feature_extractor=_a )
			processor.save_pretrained(self.tmpdirname )
			__lowerCAmelCase			:						Any									=      TvltProcessor.from_pretrained(self.tmpdirname )

			self.assertIsInstance(processor.feature_extractor       ,		_a )
			self.assertIsInstance(processor.image_processor       ,		_a )

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Union[str, Any]									=      self.get_image_processor()
			__lowerCAmelCase			:						Union[str, Any]									=      self.get_feature_extractor()

			__lowerCAmelCase			:						Any									=      TvltProcessor(image_processor=_a       ,		feature_extractor=_a )

			__lowerCAmelCase			:						List[Any]									=      np.ones([1_20_00] )

			__lowerCAmelCase			:						Optional[int]									=      feature_extractor(_a       ,		return_tensors='np' )
			__lowerCAmelCase			:						List[str]									=      processor(audio=_a       ,		return_tensors='np' )

			for key in audio_dict.keys():
				self.assertAlmostEqual(audio_dict[key].sum()       ,		input_processor[key].sum()       ,		delta=1E-2 )

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						int									=      self.get_image_processor()
			__lowerCAmelCase			:						Union[str, Any]									=      self.get_feature_extractor()

			__lowerCAmelCase			:						int									=      TvltProcessor(image_processor=_a       ,		feature_extractor=_a )

			__lowerCAmelCase			:						int									=      np.ones([3, 2_24, 2_24] )

			__lowerCAmelCase			:						int									=      image_processor(_a       ,		return_tensors='np' )
			__lowerCAmelCase			:						List[str]									=      processor(images=_a       ,		return_tensors='np' )

			for key in image_dict.keys():
				self.assertAlmostEqual(image_dict[key].sum()       ,		input_processor[key].sum()       ,		delta=1E-2 )

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Union[str, Any]									=      self.get_image_processor()
			__lowerCAmelCase			:						Dict									=      self.get_feature_extractor()

			__lowerCAmelCase			:						Tuple									=      TvltProcessor(image_processor=_a       ,		feature_extractor=_a )

			__lowerCAmelCase			:						Tuple									=      np.ones([1_20_00] )
			__lowerCAmelCase			:						List[Any]									=      np.ones([3, 2_24, 2_24] )

			__lowerCAmelCase			:						str									=      processor(audio=_a       ,		images=_a )

			self.assertListEqual(list(inputs.keys() )       ,		['audio_values', 'audio_mask', 'pixel_values', 'pixel_mask'] )

			# test if it raises when no input is passed
			with pytest.raises(_a ):
				processor()







		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Tuple									=      self.get_image_processor()
			__lowerCAmelCase			:						str									=      self.get_feature_extractor()

			__lowerCAmelCase			:						List[Any]									=      TvltProcessor(image_processor=_a       ,		feature_extractor=_a )

			self.assertListEqual(
			    processor.model_input_names       ,		image_processor.model_input_names + feature_extractor.model_input_names       ,		msg='`processor` and `image_processor`+`feature_extractor` model input names do not match'       ,		)

import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase






def   lowerCAmelCase_						(					_snake_case      :  List[str]      ,   _snake_case      :  Tuple      ,   _snake_case      :  List[Any]      ,   _snake_case      :  Optional[Any]   )				->		Optional[Any]:







	'''simple docstring'''


	if isinstance(_snake_case      ,   _snake_case   ):
		__magic_name__		:		Union[str, Any]	   =     np.full((len(_snake_case   ), sequence_length, 2)      ,   _snake_case   )
	else:
		__magic_name__		:		List[Any]	   =     np.full((len(_snake_case   ), sequence_length)      ,   _snake_case   )

	for i, tensor in enumerate(_snake_case   ):
		if padding_side == "right":
			if isinstance(_snake_case      ,   _snake_case   ):
				__magic_name__		:		Optional[Any]	   =     tensor[:sequence_length]
			else:
				__magic_name__		:		Union[str, Any]	   =     tensor[:sequence_length]
		else:
			if isinstance(_snake_case      ,   _snake_case   ):
				__magic_name__		:		List[Any]	   =     tensor[:sequence_length]
			else:
				__magic_name__		:		Optional[Any]	   =     tensor[:sequence_length]

	return out_tensor.tolist()






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







	'''simple docstring'''


	__magic_name__		:		Union[str, Any]	   =     ord(_snake_case   )
	if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
		return True
	__magic_name__		:		Any	   =     unicodedata.category(_snake_case   )
	if cat.startswith("P"   ):
		return True
	return False





@dataclass
class       _snake_case					(     snake_case				):
			UpperCamelCase__          =    42
			UpperCamelCase__          =    True
			UpperCamelCase__          =    None
			UpperCamelCase__          =    None
			UpperCamelCase__          =    -100
			UpperCamelCase__          =    "pt"







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				import torch

				__magic_name__		:		List[str]	   =     "label" if "label" in features[0].keys() else "labels"
				__magic_name__		:		Union[str, Any]	   =     [feature[label_name] for feature in features] if label_name in features[0].keys() else None
				__magic_name__		:		Optional[int]	   =     self.tokenizer.pad(
				    _a						,   padding=self.padding						,   max_length=self.max_length						,   pad_to_multiple_of=self.pad_to_multiple_of						,   return_tensors="pt" if labels is None else None						,   )

				if labels is None:
					return batch

				__magic_name__		:		Dict	   =     torch.tensor(batch["entity_ids"]		).shape[1]
				__magic_name__		:		List[Any]	   =     self.tokenizer.padding_side
				if padding_side == "right":
					__magic_name__		:		str	   =     [
					    list(_a		) + [self.label_pad_token_id] * (sequence_length - len(_a		)) for label in labels
					]
				else:
					__magic_name__		:		int	   =     [
					    [self.label_pad_token_id] * (sequence_length - len(_a		)) + list(_a		) for label in labels
					]

				__magic_name__		:		Dict	   =     [feature["ner_tags"] for feature in features]
				__magic_name__		:		List[Any]	   =     padding_tensor(_a						,   -1						,   _a						,   _a		)
				__magic_name__		:		Any	   =     [feature["original_entity_spans"] for feature in features]
				__magic_name__		:		Any	   =     padding_tensor(_a						,   (-1, -1)						,   _a						,   _a		)
				__magic_name__		:		List[Any]	   =     {k: torch.tensor(_a						,   dtype=torch.intaa		) for k, v in batch.items()}

				return batch
