






import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
 import torch


lowerCamelCase      :       Optional[Any]    =						random.Random()





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case=1.0       ,			__snake_case=None       ,			__snake_case=None							):


     if rng is None:
          __lowerCAmelCase             =       global_rng

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

     return values



@require_torch
class 				_UpperCamelCase      (unittest.TestCase      ):


      def __init__(						self       ,    __UpperCamelCase       ,    __UpperCamelCase=7       ,    __UpperCamelCase=4_0_0       ,    __UpperCamelCase=2_0_0_0       ,    __UpperCamelCase=1       ,    __UpperCamelCase=0.0       ,    __UpperCamelCase=1_6_0_0_0       ,    __UpperCamelCase=True       ,    __UpperCamelCase=8_0       ,    __UpperCamelCase=1_6       ,    __UpperCamelCase=6_4       ,    __UpperCamelCase="hann_window"       ,    __UpperCamelCase=8_0       ,    __UpperCamelCase=7_6_0_0       ,    __UpperCamelCase=1e-10       ,    __UpperCamelCase=True       ,    )->						List[str]:
           __lowerCAmelCase             =       parent
           __lowerCAmelCase             =       batch_size
           __lowerCAmelCase             =       min_seq_length
           __lowerCAmelCase             =       max_seq_length
           __lowerCAmelCase             =       (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
           __lowerCAmelCase             =       feature_size
           __lowerCAmelCase             =       padding_value
           __lowerCAmelCase             =       sampling_rate
           __lowerCAmelCase             =       do_normalize
           __lowerCAmelCase             =       num_mel_bins
           __lowerCAmelCase             =       hop_length
           __lowerCAmelCase             =       win_length
           __lowerCAmelCase             =       win_function
           __lowerCAmelCase             =       fmin
           __lowerCAmelCase             =       fmax
           __lowerCAmelCase             =       mel_floor
           __lowerCAmelCase             =       return_attention_mask


      def 			__UpperCAmelCase		(						self						)->						Any:
           return {
               "feature_size": self.feature_size,
               "padding_value": self.padding_value,
               "sampling_rate": self.sampling_rate,
               "do_normalize": self.do_normalize,
               "num_mel_bins": self.num_mel_bins,
               "hop_length": self.hop_length,
               "win_length": self.win_length,
               "win_function": self.win_function,
               "fmin": self.fmin,
               "fmax": self.fmax,
               "mel_floor": self.mel_floor,
               "return_attention_mask": self.return_attention_mask,
           }


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase=False       ,    __UpperCamelCase=False						)->						Dict:






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

           if equal_length:
                __lowerCAmelCase             =       floats_list((self.batch_size, self.max_seq_length)						)
           else:
                # make sure that inputs increase in size
                __lowerCAmelCase             =       [
                    _flatten(floats_list((x, self.feature_size)						)						)
                    for x in range(self.min_seq_length       ,    self.max_seq_length       ,    self.seq_length_diff						)
                ]

           if numpify:
                __lowerCAmelCase             =       [np.asarray(__UpperCamelCase						) for x in speech_inputs]

           return speech_inputs


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase=False       ,    __UpperCamelCase=False						)->						str:






           if equal_length:
                __lowerCAmelCase             =       [floats_list((self.max_seq_length, self.num_mel_bins)						) for _ in range(self.batch_size						)]
           else:
                # make sure that inputs increase in size
                __lowerCAmelCase             =       [
                    floats_list((x, self.num_mel_bins)						)
                    for x in range(self.min_seq_length       ,    self.max_seq_length       ,    self.seq_length_diff						)
                ]

           if numpify:
                __lowerCAmelCase             =       [np.asarray(__UpperCamelCase						) for x in speech_inputs]

           return speech_inputs



@require_torch
class 				_UpperCamelCase      (__lowerCamelCase				,		unittest.TestCase      ):
      snake_case_	=				SpeechTaFeatureExtractor


      def 			__UpperCAmelCase		(						self						)->						int:
           __lowerCAmelCase             =       SpeechTaFeatureExtractionTester(self						)


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase						)->						Union[str, Any]:
           self.assertTrue(np.all(np.mean(__UpperCamelCase       ,    axis=0						) < 1e-3						)						)
           self.assertTrue(np.all(np.abs(np.var(__UpperCamelCase       ,    axis=0						) - 1						) < 1e-3						)						)


      def 			__UpperCAmelCase		(						self						)->						Union[str, Any]:
           __lowerCAmelCase             =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
           # create three inputs of length 800, 1000, and 1200
           __lowerCAmelCase             =       [floats_list((1, x)						)[0] for x in range(8_0_0       ,    1_4_0_0       ,    2_0_0						)]
           __lowerCAmelCase             =       [np.asarray(__UpperCamelCase						) for speech_input in speech_inputs]

           # Test not batched input
           __lowerCAmelCase             =       feat_extract(speech_inputs[0]       ,    return_tensors="np"						).input_values
           __lowerCAmelCase             =       feat_extract(np_speech_inputs[0]       ,    return_tensors="np"						).input_values
           self.assertTrue(np.allclose(__UpperCamelCase       ,    __UpperCamelCase       ,    atol=1e-3						)						)

           # Test batched
           __lowerCAmelCase             =       feat_extract(__UpperCamelCase       ,    return_tensors="np"						).input_values
           __lowerCAmelCase             =       feat_extract(__UpperCamelCase       ,    return_tensors="np"						).input_values
           for enc_seq_a, enc_seq_a in zip(__UpperCamelCase       ,    __UpperCamelCase						):
                self.assertTrue(np.allclose(__UpperCamelCase       ,    __UpperCamelCase       ,    atol=1e-3						)						)


      def 			__UpperCAmelCase		(						self						)->						Dict:
           __lowerCAmelCase             =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
           __lowerCAmelCase             =       [floats_list((1, x)						)[0] for x in range(8_0_0       ,    1_4_0_0       ,    2_0_0						)]

           __lowerCAmelCase             =       ["longest", "max_length", "do_not_pad"]
           __lowerCAmelCase             =       [None, 1_6_0_0, None]
           for max_length, padding in zip(__UpperCamelCase       ,    __UpperCamelCase						):
                __lowerCAmelCase             =       feat_extract(__UpperCamelCase       ,    padding=__UpperCamelCase       ,    max_length=__UpperCamelCase       ,    return_tensors="np"						)
                __lowerCAmelCase             =       processed.input_values

                self._check_zero_mean_unit_variance(input_values[0][:8_0_0]						)
                self.assertTrue(input_values[0][8_0_0:].sum() < 1e-6						)
                self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]						)
                self.assertTrue(input_values[0][1_0_0_0:].sum() < 1e-6						)
                self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]						)


      def 			__UpperCAmelCase		(						self						)->						Optional[int]:
           __lowerCAmelCase             =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
           __lowerCAmelCase             =       range(8_0_0       ,    1_4_0_0       ,    2_0_0						)
           __lowerCAmelCase             =       [floats_list((1, x)						)[0] for x in lengths]

           __lowerCAmelCase             =       ["longest", "max_length", "do_not_pad"]
           __lowerCAmelCase             =       [None, 1_6_0_0, None]

           for max_length, padding in zip(__UpperCamelCase       ,    __UpperCamelCase						):
                __lowerCAmelCase             =       feat_extract(__UpperCamelCase       ,    max_length=__UpperCamelCase       ,    padding=__UpperCamelCase						)
                __lowerCAmelCase             =       processed.input_values

                self._check_zero_mean_unit_variance(input_values[0][:8_0_0]						)
                self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]						)
                self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]						)


      def 			__UpperCAmelCase		(						self						)->						List[Any]:
           __lowerCAmelCase             =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
           __lowerCAmelCase             =       [floats_list((1, x)						)[0] for x in range(8_0_0       ,    1_4_0_0       ,    2_0_0						)]
           __lowerCAmelCase             =       feat_extract(
               __UpperCamelCase       ,    truncation=__UpperCamelCase       ,    max_length=1_0_0_0       ,    padding="max_length"       ,    return_tensors="np"						)
           __lowerCAmelCase             =       processed.input_values

           self._check_zero_mean_unit_variance(input_values[0, :8_0_0]						)
           self._check_zero_mean_unit_variance(input_values[1]						)
           self._check_zero_mean_unit_variance(input_values[2]						)


      def 			__UpperCAmelCase		(						self						)->						str:
           __lowerCAmelCase             =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
           __lowerCAmelCase             =       [floats_list((1, x)						)[0] for x in range(8_0_0       ,    1_4_0_0       ,    2_0_0						)]
           __lowerCAmelCase             =       feat_extract(
               __UpperCamelCase       ,    truncation=__UpperCamelCase       ,    max_length=1_0_0_0       ,    padding="longest"       ,    return_tensors="np"						)
           __lowerCAmelCase             =       processed.input_values

           self._check_zero_mean_unit_variance(input_values[0, :8_0_0]						)
           self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]						)
           self._check_zero_mean_unit_variance(input_values[2]						)

           # make sure that if max_length < longest -> then pad to max_length
           self.assertTrue(input_values.shape == (3, 1_0_0_0)						)

           __lowerCAmelCase             =       [floats_list((1, x)						)[0] for x in range(8_0_0       ,    1_4_0_0       ,    2_0_0						)]
           __lowerCAmelCase             =       feat_extract(
               __UpperCamelCase       ,    truncation=__UpperCamelCase       ,    max_length=2_0_0_0       ,    padding="longest"       ,    return_tensors="np"						)
           __lowerCAmelCase             =       processed.input_values

           self._check_zero_mean_unit_variance(input_values[0, :8_0_0]						)
           self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]						)
           self._check_zero_mean_unit_variance(input_values[2]						)

           # make sure that if max_length > longest -> then pad to longest
           self.assertTrue(input_values.shape == (3, 1_2_0_0)						)


      def 			__UpperCAmelCase		(						self						)->						List[Any]:
           __lowerCAmelCase             =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
           __lowerCAmelCase             =       np.random.rand(1_0_0						).astype(np.floataa						)
           __lowerCAmelCase             =       np_speech_inputs.tolist()

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


      def 			__UpperCAmelCase		(						self						)->						Optional[Any]:
           __lowerCAmelCase             =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
           # create three inputs of length 800, 1000, and 1200
           __lowerCAmelCase             =       [floats_list((1, x)						)[0] for x in range(8_0_0       ,    1_4_0_0       ,    2_0_0						)]
           __lowerCAmelCase             =       [np.asarray(__UpperCamelCase						) for speech_input in speech_inputs]

           # Test feature size
           __lowerCAmelCase             =       feature_extractor(audio_target=__UpperCamelCase       ,    padding=__UpperCamelCase       ,    return_tensors="np"						).input_values
           self.assertTrue(input_values.ndim == 3						)
           self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins						)

           # Test not batched input
           __lowerCAmelCase             =       feature_extractor(speech_inputs[0]       ,    return_tensors="np"						).input_values
           __lowerCAmelCase             =       feature_extractor(np_speech_inputs[0]       ,    return_tensors="np"						).input_values
           self.assertTrue(np.allclose(__UpperCamelCase       ,    __UpperCamelCase       ,    atol=1e-3						)						)

           # Test batched
           __lowerCAmelCase             =       feature_extractor(__UpperCamelCase       ,    return_tensors="np"						).input_values
           __lowerCAmelCase             =       feature_extractor(__UpperCamelCase       ,    return_tensors="np"						).input_values
           for enc_seq_a, enc_seq_a in zip(__UpperCamelCase       ,    __UpperCamelCase						):
                self.assertTrue(np.allclose(__UpperCamelCase       ,    __UpperCamelCase       ,    atol=1e-3						)						)

           # Test 2-D numpy arrays are batched.
           __lowerCAmelCase             =       [floats_list((1, x)						)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
           __lowerCAmelCase             =       np.asarray(__UpperCamelCase						)
           __lowerCAmelCase             =       feature_extractor(__UpperCamelCase       ,    return_tensors="np"						).input_values
           __lowerCAmelCase             =       feature_extractor(__UpperCamelCase       ,    return_tensors="np"						).input_values
           for enc_seq_a, enc_seq_a in zip(__UpperCamelCase       ,    __UpperCamelCase						):
                self.assertTrue(np.allclose(__UpperCamelCase       ,    __UpperCamelCase       ,    atol=1e-3						)						)


      def 			__UpperCAmelCase		(						self						)->						Any:
           __lowerCAmelCase             =       self.feat_extract_tester.prepare_inputs_for_target()
           __lowerCAmelCase             =       self.feature_extraction_class(**self.feat_extract_dict						)
           __lowerCAmelCase             =       feat_extract.model_input_names[0]

           __lowerCAmelCase             =       BatchFeature({input_name: speech_inputs}						)

           self.assertTrue(all(len(__UpperCamelCase						) == len(__UpperCamelCase						) for x, y in zip(__UpperCamelCase       ,    processed_features[input_name]						)						)						)

           __lowerCAmelCase             =       self.feat_extract_tester.prepare_inputs_for_target(equal_length=__UpperCamelCase						)
           __lowerCAmelCase             =       BatchFeature({input_name: speech_inputs}       ,    tensor_type="np"						)

           __lowerCAmelCase             =       processed_features[input_name]

           if len(batch_features_input.shape						) < 3:
                __lowerCAmelCase             =       batch_features_input[:, :, None]

           self.assertTrue(
               batch_features_input.shape
               == (self.feat_extract_tester.batch_size, len(speech_inputs[0]						), self.feat_extract_tester.num_mel_bins)						)


      @require_torch
      def 			__UpperCAmelCase		(						self						)->						str:
           __lowerCAmelCase             =       self.feat_extract_tester.prepare_inputs_for_target(equal_length=__UpperCamelCase						)
           __lowerCAmelCase             =       self.feature_extraction_class(**self.feat_extract_dict						)
           __lowerCAmelCase             =       feat_extract.model_input_names[0]

           __lowerCAmelCase             =       BatchFeature({input_name: speech_inputs}       ,    tensor_type="pt"						)

           __lowerCAmelCase             =       processed_features[input_name]

           if len(batch_features_input.shape						) < 3:
                __lowerCAmelCase             =       batch_features_input[:, :, None]

           self.assertTrue(
               batch_features_input.shape
               == (self.feat_extract_tester.batch_size, len(speech_inputs[0]						), self.feat_extract_tester.num_mel_bins)						)


      @require_torch
      def 			__UpperCAmelCase		(						self						)->						Union[str, Any]:
           __lowerCAmelCase             =       self.feature_extraction_class(**self.feat_extract_dict						)
           __lowerCAmelCase             =       self.feat_extract_tester.prepare_inputs_for_target()
           __lowerCAmelCase             =       feat_extract.model_input_names[0]

           __lowerCAmelCase             =       BatchFeature({input_name: speech_inputs}						)

           __lowerCAmelCase             =       feat_extract.num_mel_bins  # hack!

           __lowerCAmelCase             =       feat_extract.pad(__UpperCamelCase       ,    padding="longest"       ,    return_tensors="np"						)[input_name]
           __lowerCAmelCase             =       feat_extract.pad(__UpperCamelCase       ,    padding="longest"       ,    return_tensors="pt"						)[input_name]

           self.assertTrue(abs(input_np.astype(np.floataa						).sum() - input_pt.numpy().astype(np.floataa						).sum()						) < 1e-2						)


      def 			__UpperCAmelCase		(						self						)->						Optional[Any]:
           __lowerCAmelCase             =       self.feat_extract_dict
           __lowerCAmelCase             =       True
           __lowerCAmelCase             =       self.feature_extraction_class(**__UpperCamelCase						)
           __lowerCAmelCase             =       self.feat_extract_tester.prepare_inputs_for_target()
           __lowerCAmelCase             =       [len(__UpperCamelCase						) for x in speech_inputs]
           __lowerCAmelCase             =       feat_extract.model_input_names[0]

           __lowerCAmelCase             =       BatchFeature({input_name: speech_inputs}						)

           __lowerCAmelCase             =       feat_extract.num_mel_bins  # hack!

           __lowerCAmelCase             =       feat_extract.pad(__UpperCamelCase       ,    padding="longest"       ,    return_tensors="np"						)
           self.assertIn("attention_mask"       ,    __UpperCamelCase						)
           self.assertListEqual(list(processed.attention_mask.shape						)       ,    list(processed[input_name].shape[:2]						)						)
           self.assertListEqual(processed.attention_mask.sum(-1						).tolist()       ,    __UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						List[Any]:
           __lowerCAmelCase             =       self.feat_extract_dict
           __lowerCAmelCase             =       True
           __lowerCAmelCase             =       self.feature_extraction_class(**__UpperCamelCase						)
           __lowerCAmelCase             =       self.feat_extract_tester.prepare_inputs_for_target()
           __lowerCAmelCase             =       [len(__UpperCamelCase						) for x in speech_inputs]
           __lowerCAmelCase             =       feat_extract.model_input_names[0]

           __lowerCAmelCase             =       BatchFeature({input_name: speech_inputs}						)
           __lowerCAmelCase             =       min(__UpperCamelCase						)

           __lowerCAmelCase             =       feat_extract.num_mel_bins  # hack!

           __lowerCAmelCase             =       feat_extract.pad(
               __UpperCamelCase       ,    padding="max_length"       ,    max_length=__UpperCamelCase       ,    truncation=__UpperCamelCase       ,    return_tensors="np"						)
           self.assertIn("attention_mask"       ,    __UpperCamelCase						)
           self.assertListEqual(
               list(processed_pad.attention_mask.shape						)       ,    [processed_pad[input_name].shape[0], max_length]						)
           self.assertListEqual(
               processed_pad.attention_mask[:, :max_length].sum(-1						).tolist()       ,    [max_length for x in speech_inputs]						)


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase						)->						Optional[Any]:
           from datasets import load_dataset

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

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


      def 			__UpperCAmelCase		(						self						)->						Union[str, Any]:
           __lowerCAmelCase             =       torch.tensor(
               [2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03,
                3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03,
                2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04,
                4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03,
                7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04,
                4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03]						)
           # fmt: on

           __lowerCAmelCase             =       self._load_datasamples(1						)
           __lowerCAmelCase             =       SpeechTaFeatureExtractor()
           __lowerCAmelCase             =       feature_extractor(__UpperCamelCase       ,    return_tensors="pt"						).input_values
           self.assertEquals(input_values.shape       ,    (1, 9_3_6_8_0)						)
           self.assertTrue(torch.allclose(input_values[0, :3_0]       ,    __UpperCamelCase       ,    atol=1e-6						)						)


      def 			__UpperCAmelCase		(						self						)->						List[str]:
           __lowerCAmelCase             =       torch.tensor(
               [-2.6_8_7_0, -3.0_1_0_4, -3.1_3_5_6, -3.5_3_5_2, -3.0_0_4_4, -3.0_3_5_3, -3.4_7_1_9, -3.6_7_7_7,
                -3.1_5_2_0, -2.9_4_3_5, -2.6_5_5_3, -2.8_7_9_5, -2.9_9_4_4, -2.5_9_2_1, -3.0_2_7_9, -3.0_3_8_6,
                -3.0_8_6_4, -3.1_2_9_1, -3.2_3_5_3, -2.7_4_4_4, -2.6_8_3_1, -2.7_2_8_7, -3.1_7_6_1, -3.1_5_7_1,
                -3.2_7_2_6, -3.0_5_8_2, -3.1_0_0_7, -3.4_5_3_3, -3.4_6_9_5, -3.0_9_9_8]						)
           # fmt: on

           __lowerCAmelCase             =       self._load_datasamples(1						)
           __lowerCAmelCase             =       SpeechTaFeatureExtractor()
           __lowerCAmelCase             =       feature_extractor(audio_target=__UpperCamelCase       ,    return_tensors="pt"						).input_values
           self.assertEquals(input_values.shape       ,    (1, 3_6_6, 8_0)						)
           self.assertTrue(torch.allclose(input_values[0, 0, :3_0]       ,    __UpperCamelCase       ,    atol=1e-4						)						)








from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase					   =       logging.get_logger(__name__)

__lowerCAmelCase					   =       {
    'alibaba-damo/mgp-str-base': 'https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json',
}





class     lowerCamelCase      (       __lowerCamelCase					):
	UpperCamelCase_  :					Union[str, Any]    		=	'mgp-str'







	def __init__(     self      :Union[str, Any]   ,     lowercase      :Union[str, Any]=[3_2, 1_2_8]   ,     lowercase      :Tuple=4   ,     lowercase      :Optional[int]=3   ,     lowercase      :Union[str, Any]=2_7   ,     lowercase      :List[str]=3_8   ,     lowercase      :Optional[Any]=5_0_2_5_7   ,     lowercase      :str=3_0_5_2_2   ,     lowercase      :Dict=7_6_8   ,     lowercase      :List[Any]=1_2   ,     lowercase      :List[str]=1_2   ,     lowercase      :Optional[Any]=4.0   ,     lowercase      :Optional[Any]=True   ,     lowercase      :str=False   ,     lowercase      :Optional[Any]=1e-5   ,     lowercase      :List[str]=0.0   ,     lowercase      :List[Any]=0.0   ,     lowercase      :Optional[int]=0.0   ,     lowercase      :Dict=False   ,     lowercase      :Optional[Any]=0.02   ,     **lowercase      :Optional[Any]   ,     )		->      Dict:







				"""simple docstring"""






				super().__init__(**lowercase			)

				SCREAMING_SNAKE_CASE     						= image_size
				SCREAMING_SNAKE_CASE     						= patch_size
				SCREAMING_SNAKE_CASE     						= num_channels
				SCREAMING_SNAKE_CASE     						= max_token_length
				SCREAMING_SNAKE_CASE     						= num_character_labels
				SCREAMING_SNAKE_CASE     						= num_bpe_labels
				SCREAMING_SNAKE_CASE     						= num_wordpiece_labels
				SCREAMING_SNAKE_CASE     						= hidden_size
				SCREAMING_SNAKE_CASE     						= num_hidden_layers
				SCREAMING_SNAKE_CASE     						= num_attention_heads
				SCREAMING_SNAKE_CASE     						= mlp_ratio
				SCREAMING_SNAKE_CASE     						= distilled
				SCREAMING_SNAKE_CASE     						= layer_norm_eps
				SCREAMING_SNAKE_CASE     						= drop_rate
				SCREAMING_SNAKE_CASE     						= qkv_bias
				SCREAMING_SNAKE_CASE     						= attn_drop_rate
				SCREAMING_SNAKE_CASE     						= drop_path_rate
				SCREAMING_SNAKE_CASE     						= output_aa_attentions
				SCREAMING_SNAKE_CASE     						= initializer_range