"""simple docstring"""
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

SCREAMING_SNAKE_CASE__      						=  random.Random()





def    lowerCAmelCase__    (			_UpperCamelCase       :				Union[str, Any] ,			_UpperCamelCase       :				Tuple=1.0 ,			_UpperCamelCase       :				Tuple=None ,			_UpperCamelCase       :				List[Any]=None					)					->				Tuple:





						"""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



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



				"""simple docstring"""

				def __init__(				self		,    lowerCAmelCase		,    lowerCAmelCase=7		,    lowerCAmelCase=4_00		,    lowerCAmelCase=20_00		,    lowerCAmelCase=24		,    lowerCAmelCase=24		,    lowerCAmelCase=0.0		,    lowerCAmelCase=1_60_00		,    lowerCAmelCase=True		,    lowerCAmelCase=True		,    ):






										"""simple docstring"""

										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								=			feature_size
										snake_case								=			num_mel_bins
										snake_case								=			padding_value
										snake_case								=			sampling_rate
										snake_case								=			return_attention_mask
										snake_case								=			do_normalize

				def 		snake_case (				self    ):






										"""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		,    lowerCAmelCase=False		,    lowerCAmelCase=False    ):






										"""simple docstring"""

										def _flatten(lowerCAmelCase    ):
																return list(itertools.chain(*lowerCAmelCase    )    )

										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(lowerCAmelCase    ) for x in speech_inputs]
										return speech_inputs





@require_torch
@require_torchaudio
class   lowerCAmelCase_    (			lowerCAmelCase     ,    unittest.TestCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    Any			  =						SpeechaTextFeatureExtractor if is_speech_available() else None

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			SpeechaTextFeatureExtractionTester(self    )

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

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

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
										# create three inputs of length 800, 1000, and 1200
										snake_case								=			[floats_list((1, x)    )[0] for x in range(8_00		,    14_00		,    2_00    )]
										snake_case								=			[np.asarray(lowerCAmelCase    ) for speech_input in speech_inputs]

										# Test feature size
										snake_case								=			feature_extractor(lowerCAmelCase		,    padding=lowerCAmelCase		,    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
										snake_case								=			feature_extractor(speech_inputs[0]		,    return_tensors='np'    ).input_features
										snake_case								=			feature_extractor(np_speech_inputs[0]		,    return_tensors='np'    ).input_features
										self.assertTrue(np.allclose(lowerCAmelCase		,    lowerCAmelCase		,    atol=1E-3    )    )

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

										# Test 2-D numpy arrays are batched.
										snake_case								=			[floats_list((1, x)    )[0] for x in (8_00, 8_00, 8_00)]
										snake_case								=			np.asarray(lowerCAmelCase    )
										snake_case								=			feature_extractor(lowerCAmelCase		,    return_tensors='np'    ).input_features
										snake_case								=			feature_extractor(lowerCAmelCase		,    return_tensors='np'    ).input_features
										for enc_seq_a, enc_seq_a in zip(lowerCAmelCase		,    lowerCAmelCase    ):
																self.assertTrue(np.allclose(lowerCAmelCase		,    lowerCAmelCase		,    atol=1E-3    )    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
										snake_case								=			[floats_list((1, x)    )[0] for x in range(8_00		,    14_00		,    2_00    )]

										snake_case								=			['longest', 'max_length', 'do_not_pad']
										snake_case								=			[None, 16, None]
										for max_length, padding in zip(lowerCAmelCase		,    lowerCAmelCase    ):
																snake_case								=			feature_extractor(
																    lowerCAmelCase		,    padding=lowerCAmelCase		,    max_length=lowerCAmelCase		,    return_attention_mask=lowerCAmelCase    )
																snake_case								=			inputs.input_features
																snake_case								=			inputs.attention_mask
																snake_case								=			[np.sum(lowerCAmelCase    ) 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    ):






										"""simple docstring"""

										snake_case								=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
										snake_case								=			[floats_list((1, x)    )[0] for x in range(8_00		,    14_00		,    2_00    )]

										snake_case								=			['longest', 'max_length', 'do_not_pad']
										snake_case								=			[None, 16, None]
										for max_length, padding in zip(lowerCAmelCase		,    lowerCAmelCase    ):
																snake_case								=			feature_extractor(
																    lowerCAmelCase		,    max_length=lowerCAmelCase		,    padding=lowerCAmelCase		,    return_tensors='np'		,    return_attention_mask=lowerCAmelCase    )
																snake_case								=			inputs.input_features
																snake_case								=			inputs.attention_mask
																snake_case								=			[np.sum(lowerCAmelCase    ) 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    ):






										"""simple docstring"""

										snake_case								=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
										snake_case								=			[floats_list((1, x)    )[0] for x in range(8_00		,    14_00		,    2_00    )]
										snake_case								=			feature_extractor(
										    lowerCAmelCase		,    padding='max_length'		,    max_length=4		,    truncation=lowerCAmelCase		,    return_tensors='np'		,    return_attention_mask=lowerCAmelCase		,    )
										snake_case								=			inputs.input_features
										snake_case								=			inputs.attention_mask
										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    ):






										"""simple docstring"""

										snake_case								=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
										snake_case								=			[floats_list((1, x)    )[0] for x in range(8_00		,    14_00		,    2_00    )]
										snake_case								=			feature_extractor(
										    lowerCAmelCase		,    padding='longest'		,    max_length=4		,    truncation=lowerCAmelCase		,    return_tensors='np'		,    return_attention_mask=lowerCAmelCase		,    )
										snake_case								=			inputs.input_features
										snake_case								=			inputs.attention_mask
										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)    )

										snake_case								=			[floats_list((1, x)    )[0] for x in range(8_00		,    14_00		,    2_00    )]
										snake_case								=			feature_extractor(
										    lowerCAmelCase		,    padding='longest'		,    max_length=16		,    truncation=lowerCAmelCase		,    return_tensors='np'		,    return_attention_mask=lowerCAmelCase		,    )
										snake_case								=			inputs.input_features
										snake_case								=			inputs.attention_mask
										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    ):






										"""simple docstring"""

										import torch

										snake_case								=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
										snake_case								=			np.random.rand(1_00		,    32    ).astype(np.floataa    )
										snake_case								=			np_speech_inputs.tolist()

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

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										from datasets import load_dataset

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

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

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			np.array([
										    -1.57_45, -1.77_13, -1.70_20, -1.60_69, -1.22_50, -1.11_05, -0.90_72, -0.82_41,
										    -1.23_10, -0.80_98, -0.33_20, -0.41_01, -0.79_85, -0.49_96, -0.82_13, -0.91_28,
										    -1.04_20, -1.12_86, -1.04_40, -0.79_99, -0.84_05, -1.22_75, -1.54_43, -1.46_25,
										]    )
										# fmt: on

										snake_case								=			self._load_datasamples(1    )
										snake_case								=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
										snake_case								=			feature_extractor(lowerCAmelCase		,    return_tensors='pt'    ).input_features
										self.assertEquals(input_features.shape		,    (1, 5_84, 24)    )
										self.assertTrue(np.allclose(input_features[0, 0, :30]		,    lowerCAmelCase		,    atol=1E-4    )    )



"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
					from ...processing_utils import ProcessorMixin
					from ...utils import TensorType


SCREAMING_SNAKE_CASE__      						=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__      						=  {
    "microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json",
}



class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    Dict			  =						"""layoutlmv3"""

				def __init__(				self		,    lowerCAmelCase=5_02_65		,    lowerCAmelCase=7_68		,    lowerCAmelCase=12		,    lowerCAmelCase=12		,    lowerCAmelCase=30_72		,    lowerCAmelCase="gelu"		,    lowerCAmelCase=0.1		,    lowerCAmelCase=0.1		,    lowerCAmelCase=5_12		,    lowerCAmelCase=2		,    lowerCAmelCase=0.02		,    lowerCAmelCase=1E-5		,    lowerCAmelCase=1		,    lowerCAmelCase=0		,    lowerCAmelCase=2		,    lowerCAmelCase=10_24		,    lowerCAmelCase=1_28		,    lowerCAmelCase=1_28		,    lowerCAmelCase=True		,    lowerCAmelCase=32		,    lowerCAmelCase=1_28		,    lowerCAmelCase=64		,    lowerCAmelCase=2_56		,    lowerCAmelCase=True		,    lowerCAmelCase=True		,    lowerCAmelCase=True		,    lowerCAmelCase=2_24		,    lowerCAmelCase=3		,    lowerCAmelCase=16		,    lowerCAmelCase=None		,    **lowerCAmelCase		,    ):






										"""simple docstring"""

										super().__init__(
										    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		,    initializer_range=lowerCAmelCase		,    layer_norm_eps=lowerCAmelCase		,    pad_token_id=lowerCAmelCase		,    bos_token_id=lowerCAmelCase		,    eos_token_id=lowerCAmelCase		,    **lowerCAmelCase		,    )
										snake_case								=			max_ad_position_embeddings
										snake_case								=			coordinate_size
										snake_case								=			shape_size
										snake_case								=			has_relative_attention_bias
										snake_case								=			rel_pos_bins
										snake_case								=			max_rel_pos
										snake_case								=			has_spatial_attention_bias
										snake_case								=			rel_ad_pos_bins
										snake_case								=			max_rel_ad_pos
										snake_case								=			text_embed
										snake_case								=			visual_embed
										snake_case								=			input_size
										snake_case								=			num_channels
										snake_case								=			patch_size
										snake_case								=			classifier_dropout





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    List[str]			  =						version.parse("""1.12"""					)

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										if self.task in ["question-answering", "sequence-classification"]:
																return OrderedDict(
																    [
																        ('input_ids', {0: 'batch', 1: 'sequence'}),
																        ('attention_mask', {0: 'batch', 1: 'sequence'}),
																        ('bbox', {0: 'batch', 1: 'sequence'}),
																        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
																    ]    )
										else:
																return OrderedDict(
																    [
																        ('input_ids', {0: 'batch', 1: 'sequence'}),
																        ('bbox', {0: 'batch', 1: 'sequence'}),
																        ('attention_mask', {0: 'batch', 1: 'sequence'}),
																        ('pixel_values', {0: 'batch', 1: 'num_channels'}),
																    ]    )

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										return 1E-5

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										return 12

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase = -1		,    lowerCAmelCase = -1		,    lowerCAmelCase = False		,    lowerCAmelCase = None		,    lowerCAmelCase = 3		,    lowerCAmelCase = 40		,    lowerCAmelCase = 40		,    ):






										"""simple docstring"""

										setattr(processor.image_processor		,    'apply_ocr'		,    lowerCAmelCase    )

										# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
										snake_case								=			compute_effective_axis_dimension(
										    lowerCAmelCase		,    fixed_dimension=OnnxConfig.default_fixed_batch		,    num_token_to_add=0    )
										# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
										snake_case								=			processor.tokenizer.num_special_tokens_to_add(lowerCAmelCase    )
										snake_case								=			compute_effective_axis_dimension(
										    lowerCAmelCase		,    fixed_dimension=OnnxConfig.default_fixed_sequence		,    num_token_to_add=lowerCAmelCase    )
										# Generate dummy inputs according to compute batch and sequence
										snake_case								=			[[' '.join([processor.tokenizer.unk_token]    ) * seq_length]] * batch_size

										# Generate dummy bounding boxes
										snake_case								=			[[[48, 84, 73, 1_28]]] * batch_size

										# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
										# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
										snake_case								=			self._generate_dummy_images(lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase    )

										snake_case								=			dict(
										    processor(
										        lowerCAmelCase		,    text=lowerCAmelCase		,    boxes=lowerCAmelCase		,    return_tensors=lowerCAmelCase		,    )    )

										return inputs


