


from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PretrainedConfig, PreTrainedTokenizer
from ...onnx import OnnxConfig, PatchingSpec
from ...utils import TensorType, is_torch_available, logging


_A							     =       logging.get_logger(__name__)

_A							     =       {
    'microsoft/layoutlm-base-uncased': (
        'https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/config.json'
    ),
    'microsoft/layoutlm-large-uncased': (
        'https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/config.json'
    ),
}

class 			lowerCamelCase_					(    _SCREAMING_SNAKE_CASE    ):
							_lowerCamelCase				: Any       =					"""layoutlm"""





							def __init__(							self			,       _SCREAMING_SNAKE_CASE=3_0522			,       _SCREAMING_SNAKE_CASE=768			,       _SCREAMING_SNAKE_CASE=12			,       _SCREAMING_SNAKE_CASE=12			,       _SCREAMING_SNAKE_CASE=3072			,       _SCREAMING_SNAKE_CASE="gelu"			,       _SCREAMING_SNAKE_CASE=0.1			,       _SCREAMING_SNAKE_CASE=0.1			,       _SCREAMING_SNAKE_CASE=512			,       _SCREAMING_SNAKE_CASE=2			,       _SCREAMING_SNAKE_CASE=0.0_2			,       _SCREAMING_SNAKE_CASE=1E-12			,       _SCREAMING_SNAKE_CASE=0			,       _SCREAMING_SNAKE_CASE="absolute"			,       _SCREAMING_SNAKE_CASE=True			,       _SCREAMING_SNAKE_CASE=1024			,       **_SCREAMING_SNAKE_CASE			,       ):
								super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE			,       **_SCREAMING_SNAKE_CASE    )
								a_     			=   vocab_size
								a_     			=   hidden_size
								a_     			=   num_hidden_layers
								a_     			=   num_attention_heads
								a_     			=   hidden_act
								a_     			=   intermediate_size
								a_     			=   hidden_dropout_prob
								a_     			=   attention_probs_dropout_prob
								a_     			=   max_position_embeddings
								a_     			=   type_vocab_size
								a_     			=   initializer_range
								a_     			=   layer_norm_eps
								a_     			=   position_embedding_type
								a_     			=   use_cache
								a_     			=   max_ad_position_embeddings







class 			lowerCamelCase_					(    _SCREAMING_SNAKE_CASE    ):





							def __init__(							self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE = "default"			,       _SCREAMING_SNAKE_CASE = None			,       ):
								super().__init__(_SCREAMING_SNAKE_CASE			,       task=_SCREAMING_SNAKE_CASE			,       patching_specs=_SCREAMING_SNAKE_CASE    )
								a_     			=   config.max_ad_position_embeddings - 1





							@property
							def   __magic_name__      (							self    ):
								return OrderedDict(
								    [
								        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
								        ("""bbox""", {0: """batch""", 1: """sequence"""}),
								        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
								        ("""token_type_ids""", {0: """batch""", 1: """sequence"""}),
								    ]    )





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE = -1			,       _SCREAMING_SNAKE_CASE = -1			,       _SCREAMING_SNAKE_CASE = False			,       _SCREAMING_SNAKE_CASE = None			,       ):
								a_     			=   super().generate_dummy_inputs(
								    _SCREAMING_SNAKE_CASE			,       batch_size=_SCREAMING_SNAKE_CASE			,       seq_length=_SCREAMING_SNAKE_CASE			,       is_pair=_SCREAMING_SNAKE_CASE			,       framework=_SCREAMING_SNAKE_CASE    )

								# Generate a dummy bbox
								a_     			=   [48, 84, 73, 128]

								if not framework == TensorType.PYTORCH:
									raise NotImplementedError("""Exporting LayoutLM to ONNX is currently only supported for PyTorch."""    )

								if not is_torch_available():
									raise ValueError("""Cannot generate dummy inputs without PyTorch installed."""    )
								import torch

								a_			, a_     			=   input_dict["""input_ids"""].shape
								a_     			=   torch.tensor([*[box] * seq_length]    ).tile(_SCREAMING_SNAKE_CASE			,       1			,       1    )
								return input_dict



import random
import shutil
import tempfile
import unittest

import numpy as np

from transformers import TaTokenizer, TaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torch
from transformers.utils.import_utils import is_speech_available, is_torch_available


if is_torch_available():
			pass

if is_speech_available():
			from transformers import EncodecFeatureExtractor, MusicgenProcessor


_A							     =       random.Random()

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      List[Any]     ,					UpperCamelCase		:      Any=1.0     ,					UpperCamelCase		:      Union[str, Any]=None     ,					UpperCamelCase		:      Union[str, Any]=None	)     ->	List[Any]:



	"""simple docstring"""






	if rng is None:
		a_     			=   global_rng

	a_     			=   []
	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_sentencepiece
class 			lowerCamelCase_					(    unittest.TestCase    ):





							def   __magic_name__      (							self    ):
								a_     			=   """facebook/musicgen-small"""
								a_     			=   tempfile.mkdtemp()





							def   __magic_name__      (							self			,       **_SCREAMING_SNAKE_CASE    ):
								return TaTokenizer.from_pretrained(self.checkpoint			,       **_SCREAMING_SNAKE_CASE    )





							def   __magic_name__      (							self			,       **_SCREAMING_SNAKE_CASE    ):
								return EncodecFeatureExtractor.from_pretrained(self.checkpoint			,       **_SCREAMING_SNAKE_CASE    )





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





							def   __magic_name__      (							self    ):
								a_     			=   self.get_tokenizer()
								a_     			=   self.get_feature_extractor()

								a_     			=   MusicgenProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,       feature_extractor=_SCREAMING_SNAKE_CASE    )

								processor.save_pretrained(self.tmpdirname    )
								a_     			=   MusicgenProcessor.from_pretrained(self.tmpdirname    )

								self.assertEqual(processor.tokenizer.get_vocab()			,       tokenizer.get_vocab()    )
								self.assertIsInstance(processor.tokenizer			,       _SCREAMING_SNAKE_CASE    )

								self.assertEqual(processor.feature_extractor.to_json_string()			,       feature_extractor.to_json_string()    )
								self.assertIsInstance(processor.feature_extractor			,       _SCREAMING_SNAKE_CASE    )





							def   __magic_name__      (							self    ):
								a_     			=   MusicgenProcessor(tokenizer=self.get_tokenizer()			,       feature_extractor=self.get_feature_extractor()    )
								processor.save_pretrained(self.tmpdirname    )

								a_     			=   self.get_tokenizer(bos_token="""(BOS)"""			,       eos_token="""(EOS)"""    )
								a_     			=   self.get_feature_extractor(do_normalize=_SCREAMING_SNAKE_CASE			,       padding_value=1.0    )

								a_     			=   MusicgenProcessor.from_pretrained(
								    self.tmpdirname			,       bos_token="""(BOS)"""			,       eos_token="""(EOS)"""			,       do_normalize=_SCREAMING_SNAKE_CASE			,       padding_value=1.0    )

								self.assertEqual(processor.tokenizer.get_vocab()			,       tokenizer_add_kwargs.get_vocab()    )
								self.assertIsInstance(processor.tokenizer			,       _SCREAMING_SNAKE_CASE    )

								self.assertEqual(processor.feature_extractor.to_json_string()			,       feature_extractor_add_kwargs.to_json_string()    )
								self.assertIsInstance(processor.feature_extractor			,       _SCREAMING_SNAKE_CASE    )





							def   __magic_name__      (							self    ):
								a_     			=   self.get_feature_extractor()
								a_     			=   self.get_tokenizer()

								a_     			=   MusicgenProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,       feature_extractor=_SCREAMING_SNAKE_CASE    )

								a_     			=   floats_list((3, 1000)    )

								a_     			=   feature_extractor(_SCREAMING_SNAKE_CASE			,       return_tensors="""np"""    )
								a_     			=   processor(_SCREAMING_SNAKE_CASE			,       return_tensors="""np"""    )

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





							def   __magic_name__      (							self    ):
								a_     			=   self.get_feature_extractor()
								a_     			=   self.get_tokenizer()

								a_     			=   MusicgenProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,       feature_extractor=_SCREAMING_SNAKE_CASE    )

								a_     			=   """This is a test string"""

								a_     			=   processor(text=_SCREAMING_SNAKE_CASE    )

								a_     			=   tokenizer(_SCREAMING_SNAKE_CASE    )

								for key in encoded_tok.keys():
									self.assertListEqual(encoded_tok[key]			,       encoded_processor[key]    )





							def   __magic_name__      (							self    ):
								a_     			=   self.get_feature_extractor()
								a_     			=   self.get_tokenizer()

								a_     			=   MusicgenProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,       feature_extractor=_SCREAMING_SNAKE_CASE    )

								a_     			=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

								a_     			=   processor.batch_decode(sequences=_SCREAMING_SNAKE_CASE    )
								a_     			=   tokenizer.batch_decode(_SCREAMING_SNAKE_CASE    )

								self.assertListEqual(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    )





							def   __magic_name__      (							self    ):
								a_     			=   self.get_feature_extractor()
								a_     			=   self.get_tokenizer()

								a_     			=   MusicgenProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,       feature_extractor=_SCREAMING_SNAKE_CASE    )

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





							def   __magic_name__      (							self    ):
								a_     			=   self.get_feature_extractor(padding_side="""left"""    )
								a_     			=   self.get_tokenizer()

								a_     			=   MusicgenProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,       feature_extractor=_SCREAMING_SNAKE_CASE    )

								a_     			=   [floats_list((1, x)    )[0] for x in range(5			,       20			,       5    )]
								a_     			=   processor(_SCREAMING_SNAKE_CASE    ).padding_mask

								a_     			=   np.asarray(floats_list((3, 20)    )    )[:, None, :]
								a_     			=   processor.batch_decode(_SCREAMING_SNAKE_CASE			,       padding_mask=_SCREAMING_SNAKE_CASE    )

								self.assertIsInstance(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    )

								for audio in decoded_audios:
									self.assertIsInstance(_SCREAMING_SNAKE_CASE			,       np.ndarray    )

								self.assertTrue(decoded_audios[0].shape == (1, 10)    )
								self.assertTrue(decoded_audios[1].shape == (1, 15)    )
								self.assertTrue(decoded_audios[2].shape == (1, 20)    )