

import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class   lowercase_   (  __lowercase					):
      UpperCamelCase_      :		str										=    ["image_processor", "tokenizer"]
      UpperCamelCase_      :		Tuple										=    "LayoutLMv2ImageProcessor"
      UpperCamelCase_      :		Tuple										=    ("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")



      def __init__(      self				:    List[str]      , A__				:    Any=None      , A__				:    Tuple=None      , **A__				:    Tuple		)   ->					Optional[int]:
        if "feature_extractor" in kwargs:
          warnings.warn(
              '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
              ''' instead.'''      , A__      , )
          _snake_case						=							kwargs.pop('''feature_extractor'''		)

        _snake_case						=							image_processor if image_processor is not None else feature_extractor
        if image_processor is None:
          raise ValueError('''You need to specify an `image_processor`.'''		)
        if tokenizer is None:
          raise ValueError('''You need to specify a `tokenizer`.'''		)

        super().__init__(A__      , A__		)



      def __call__(      self				:    int      , A__				:    Optional[int]      , A__				:    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None      , A__				:    Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None      , A__				:    Union[List[List[int]], List[List[List[int]]]] = None      , A__				:    Optional[Union[List[int], List[List[int]]]] = None      , A__				:    bool = True      , A__				:    Union[bool, str, PaddingStrategy] = False      , A__				:    Union[bool, str, TruncationStrategy] = None      , A__				:    Optional[int] = None      , A__				:    int = 0      , A__				:    Optional[int] = None      , A__				:    Optional[bool] = None      , A__				:    Optional[bool] = None      , A__				:    bool = False      , A__				:    bool = False      , A__				:    bool = False      , A__				:    bool = False      , A__				:    bool = True      , A__				:    Optional[Union[str, TensorType]] = None      , **A__				:    Any      , )   ->					BatchEncoding:
        # verify input
        if self.image_processor.apply_ocr and (boxes is not None):
          raise ValueError(
              '''You cannot provide bounding boxes '''
              '''if you initialized the image processor with apply_ocr set to True.'''		)

        if self.image_processor.apply_ocr and (word_labels is not None):
          raise ValueError(
              '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''		)

        if return_overflowing_tokens is True and return_offsets_mapping is False:
          raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.'''		)

        # first, apply the image processor
        _snake_case						=							self.image_processor(images=A__      , return_tensors=A__		)

        # second, apply the tokenizer
        if text is not None and self.image_processor.apply_ocr and text_pair is None:
          if isinstance(A__      , A__		):
            _snake_case						=							[text]  # add batch dimension (as the image processor always adds a batch dimension)
          _snake_case						=							features['''words''']

        _snake_case						=							self.tokenizer(
            text=text if text is not None else features['''words''']      , text_pair=text_pair if text_pair is not None else None      , boxes=boxes if boxes is not None else features['''boxes''']      , word_labels=A__      , add_special_tokens=A__      , padding=A__      , truncation=A__      , max_length=A__      , stride=A__      , pad_to_multiple_of=A__      , return_token_type_ids=A__      , return_attention_mask=A__      , return_overflowing_tokens=A__      , return_special_tokens_mask=A__      , return_offsets_mapping=A__      , return_length=A__      , verbose=A__      , return_tensors=A__      , **A__      , )

        # add pixel values
        _snake_case						=							features.pop('''pixel_values'''		)
        if return_overflowing_tokens is True:
          _snake_case						=							self.get_overflowing_images(A__      , encoded_inputs['''overflow_to_sample_mapping''']		)
        _snake_case						=							images

        return encoded_inputs



      def 						UpperCamelCase_		(      self				:    List[str]      , A__				:    Optional[int]      , A__				:    Union[str, Any]		)   ->					int:
        # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
        _snake_case						=							[]
        for sample_idx in overflow_to_sample_mapping:
          images_with_overflow.append(images[sample_idx]		)

        if len(A__		) != len(A__		):
          raise ValueError(
              '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
              f""" {len(A__		)} and {len(A__		)}"""		)

        return images_with_overflow



      def 						UpperCamelCase_		(      self				:    Optional[Any]      , *A__				:    List[Any]      , **A__				:    Optional[Any]		)   ->					Tuple:
        return self.tokenizer.batch_decode(*A__      , **A__		)



      def 						UpperCamelCase_		(      self				:    Optional[int]      , *A__				:    List[str]      , **A__				:    Any		)   ->					Dict:
        return self.tokenizer.decode(*A__      , **A__		)



      @property
      def 						UpperCamelCase_		(      self				:    List[str]		)   ->					List[Any]:
        return ["input_ids", "bbox", "attention_mask", "image"]



      @property
      def 						UpperCamelCase_		(      self				:    List[Any]		)   ->					Union[str, Any]:
        warnings.warn(
            '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''      , A__      , )
        return self.image_processor_class







      @property
      def 						UpperCamelCase_		(      self				:    List[Any]		)   ->					Optional[int]:
        warnings.warn(
            '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''      , A__      , )
        return self.image_processor




import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
 import torch

if is_datasets_available():
 from datasets import load_dataset

__A      		=     random.Random()



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase=1.0			,	_UpperCamelCase=None			,	_UpperCamelCase=None					)							->     Optional[int]:



  """simple docstring"""






  if rng is None:
    _snake_case						=							global_rng

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

  return values



class   lowercase_   (  unittest.TestCase					):



      def __init__(      self				:    List[Any]      , A__				:    List[Any]      , A__				:    int=7      , A__				:    Tuple=400      , A__				:    int=2000      , A__				:    Any=2048      , A__				:    List[Any]=128      , A__				:    Optional[int]=1      , A__				:    Optional[Any]=512      , A__				:    Any=30      , A__				:    Any=44100      , )   ->					int:
        _snake_case						=							parent
        _snake_case						=							batch_size
        _snake_case						=							min_seq_length
        _snake_case						=							max_seq_length
        _snake_case						=							(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        _snake_case						=							spectrogram_length
        _snake_case						=							feature_size
        _snake_case						=							num_audio_channels
        _snake_case						=							hop_length
        _snake_case						=							chunk_length
        _snake_case						=							sampling_rate



      def 						UpperCamelCase_		(      self				:    str		)   ->					Optional[int]:
        return {
            "spectrogram_length": self.spectrogram_length,
            "feature_size": self.feature_size,
            "num_audio_channels": self.num_audio_channels,
            "hop_length": self.hop_length,
            "chunk_length": self.chunk_length,
            "sampling_rate": self.sampling_rate,
        }







      def 						UpperCamelCase_		(      self				:    Any      , A__				:    Any=False      , A__				:    List[str]=False		)   ->					Tuple:
        def _flatten(A__				:    List[str]		):
          return list(itertools.chain(*A__		)		)

        if equal_length:
          _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
          _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:
          _snake_case						=							[np.asarray(A__		) for x in speech_inputs]
        return speech_inputs



@require_torch
@require_torchaudio
class   lowercase_   (  __lowercase       ,							unittest.TestCase					):
      UpperCamelCase_      :		Optional[int]										=    TvltFeatureExtractor



      def 						UpperCamelCase_		(      self				:    Dict		)   ->					List[str]:
        _snake_case						=							TvltFeatureExtractionTester(self		)



      def 						UpperCamelCase_		(      self				:    int		)   ->					Optional[int]:
        _snake_case						=							self.feature_extraction_class(**self.feat_extract_dict		)
        self.assertTrue(hasattr(A__      , '''spectrogram_length'''		)		)
        self.assertTrue(hasattr(A__      , '''feature_size'''		)		)
        self.assertTrue(hasattr(A__      , '''num_audio_channels'''		)		)
        self.assertTrue(hasattr(A__      , '''hop_length'''		)		)
        self.assertTrue(hasattr(A__      , '''chunk_length'''		)		)
        self.assertTrue(hasattr(A__      , '''sampling_rate'''		)		)



      def 						UpperCamelCase_		(      self				:    Any		)   ->					Union[str, Any]:
        _snake_case						=							self.feature_extraction_class(**self.feat_extract_dict		)

        with tempfile.TemporaryDirectory() as tmpdirname:
          _snake_case						=							feat_extract_first.save_pretrained(A__		)[0]
          check_json_file_has_correct_format(A__		)
          _snake_case						=							self.feature_extraction_class.from_pretrained(A__		)

        _snake_case						=							feat_extract_first.to_dict()
        _snake_case						=							feat_extract_second.to_dict()
        _snake_case						=							dict_first.pop('''mel_filters'''		)
        _snake_case						=							dict_second.pop('''mel_filters'''		)
        self.assertTrue(np.allclose(A__      , A__		)		)
        self.assertEqual(A__      , A__		)



      def 						UpperCamelCase_		(      self				:    int		)   ->					Union[str, Any]:
        _snake_case						=							self.feature_extraction_class(**self.feat_extract_dict		)

        with tempfile.TemporaryDirectory() as tmpdirname:
          _snake_case						=							os.path.join(A__      , '''feat_extract.json'''		)
          feat_extract_first.to_json_file(A__		)
          _snake_case						=							self.feature_extraction_class.from_json_file(A__		)

        _snake_case						=							feat_extract_first.to_dict()
        _snake_case						=							feat_extract_second.to_dict()
        _snake_case						=							dict_first.pop('''mel_filters'''		)
        _snake_case						=							dict_second.pop('''mel_filters'''		)
        self.assertTrue(np.allclose(A__      , A__		)		)
        self.assertEqual(A__      , A__		)



      def 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					Any:
        # Initialize feature_extractor
        _snake_case						=							self.feature_extraction_class(**self.feat_extract_dict		)

        # create three inputs of length 800, 1000, and 1200
        _snake_case						=							[floats_list((1, x)		)[0] for x in range(800      , 1400      , 200		)]
        _snake_case						=							[np.asarray(A__		) for speech_input in speech_inputs]

        # Test not batched input
        _snake_case						=							feature_extractor(np_speech_inputs[0]      , return_tensors='''np'''      , sampling_rate=44100		).audio_values

        self.assertTrue(encoded_audios.ndim == 4		)
        self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size		)
        self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length		)
        self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels		)

        # Test batched
        _snake_case						=							feature_extractor(A__      , return_tensors='''np'''      , sampling_rate=44100		).audio_values

        self.assertTrue(encoded_audios.ndim == 4		)
        self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size		)
        self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length		)
        self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels		)

        # Test audio masking
        _snake_case						=							feature_extractor(
            A__      , return_tensors='''np'''      , sampling_rate=44100      , mask_audio=A__		).audio_values

        self.assertTrue(encoded_audios.ndim == 4		)
        self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size		)
        self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length		)
        self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels		)

        # Test 2-D numpy arrays are batched.
        _snake_case						=							[floats_list((1, x)		)[0] for x in (800, 800, 800)]
        _snake_case						=							np.asarray(A__		)
        _snake_case						=							feature_extractor(A__      , return_tensors='''np'''      , sampling_rate=44100		).audio_values
        self.assertTrue(encoded_audios.ndim == 4		)
        self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size		)
        self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length		)
        self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels		)



      def 						UpperCamelCase_		(      self				:    Optional[Any]      , A__				:    Any		)   ->					Optional[int]:
        _snake_case						=							load_dataset('''hf-internal-testing/librispeech_asr_dummy'''      , '''clean'''      , split='''validation'''		)
        # automatic decoding with librispeech
        _snake_case						=							ds.sort('''id'''		).select(range(A__		)		)[:num_samples]['''audio''']

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







      def 						UpperCamelCase_		(      self				:    List[str]		)   ->					Optional[Any]:
        _snake_case						=							self._load_datasamples(1		)
        _snake_case						=							TvltFeatureExtractor()
        _snake_case						=							feature_extractor(A__      , return_tensors='''pt'''		).audio_values

        self.assertEquals(audio_values.shape      , (1, 1, 192, 128)		)

        _snake_case						=							torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]]		)
        self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]      , A__      , atol=1e-4		)		)

