

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

lowercase_             =     random.Random()
def   a   (    A__			:     Dict   ,     A__			:     Union[str, Any]=1.0   ,     A__			:     Any=None   ,     A__			:     List[str]=None  )					->   Optional[int]:





      """simple docstring"""



      if rng is None:
            _lowercase			 =global_rng

      _lowercase			 =[]
      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							):




  def __init__(       self							,     lowerCAmelCase							,     lowerCAmelCase=7							,     lowerCAmelCase=400							,     lowerCAmelCase=2_000							,     lowerCAmelCase=24							,     lowerCAmelCase=24							,     lowerCAmelCase=0.0							,     lowerCAmelCase=16_000							,     lowerCAmelCase=True							,     lowerCAmelCase=True							,     )						->				int:
        '''simple docstring'''






        _lowercase			 =parent
        _lowercase			 =batch_size
        _lowercase			 =min_seq_length
        _lowercase			 =max_seq_length
        _lowercase			 =(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        _lowercase			 =feature_size
        _lowercase			 =num_mel_bins
        _lowercase			 =padding_value
        _lowercase			 =sampling_rate
        _lowercase			 =return_attention_mask
        _lowercase			 =do_normalize




  def 						A__  (       self      )						->				Any:
        '''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 						A__  (       self							,     lowerCAmelCase=False							,     lowerCAmelCase=False      )						->				Tuple:
        '''simple docstring'''






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

        if equal_length:
              _lowercase			 =[floats_list((self.max_seq_length, self.feature_size)      ) for _ in range(self.batch_size      )]
        else:
              # make sure that inputs increase in size
              _lowercase			 =[
                  floats_list((x, self.feature_size)      )
                  for x in range(self.min_seq_length							,     self.max_seq_length							,     self.seq_length_diff      )
              ]
        if numpify:
              _lowercase			 =[np.asarray(lowerCAmelCase      ) for x in speech_inputs]
        return speech_inputs





@require_torch
@require_torchaudio
class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE    ,    unittest.TestCase							):
  _a		    =						SpeechaTextFeatureExtractor if is_speech_available() else None




  def 						A__  (       self      )						->				Any:
        '''simple docstring'''






        _lowercase			 =SpeechaTextFeatureExtractionTester(self      )




  def 						A__  (       self							,     lowerCAmelCase      )						->				str:
        '''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 						A__  (       self      )						->				Dict:
        '''simple docstring'''






        _lowercase			 =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
        # create three inputs of length 800, 1000, and 1200
        _lowercase			 =[floats_list((1, x)      )[0] for x in range(800							,     1_400							,     200      )]
        _lowercase			 =[np.asarray(lowerCAmelCase      ) for speech_input in speech_inputs]

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

        # Test batched
        _lowercase			 =feature_extractor(lowerCAmelCase							,     return_tensors='np'      ).input_features
        _lowercase			 =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.
        _lowercase			 =[floats_list((1, x)      )[0] for x in (800, 800, 800)]
        _lowercase			 =np.asarray(lowerCAmelCase      )
        _lowercase			 =feature_extractor(lowerCAmelCase							,     return_tensors='np'      ).input_features
        _lowercase			 =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 						A__  (       self      )						->				List[str]:
        '''simple docstring'''






        _lowercase			 =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
        _lowercase			 =[floats_list((1, x)      )[0] for x in range(800							,     1_400							,     200      )]

        _lowercase			 =['longest', 'max_length', 'do_not_pad']
        _lowercase			 =[None, 16, None]
        for max_length, padding in zip(lowerCAmelCase							,     lowerCAmelCase      ):
              _lowercase			 =feature_extractor(
                  lowerCAmelCase							,     padding=lowerCAmelCase							,     max_length=lowerCAmelCase							,     return_attention_mask=lowerCAmelCase      )
              _lowercase			 =inputs.input_features
              _lowercase			 =inputs.attention_mask
              _lowercase			 =[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 						A__  (       self      )						->				Optional[int]:
        '''simple docstring'''






        _lowercase			 =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
        _lowercase			 =[floats_list((1, x)      )[0] for x in range(800							,     1_400							,     200      )]

        _lowercase			 =['longest', 'max_length', 'do_not_pad']
        _lowercase			 =[None, 16, None]
        for max_length, padding in zip(lowerCAmelCase							,     lowerCAmelCase      ):
              _lowercase			 =feature_extractor(
                  lowerCAmelCase							,     max_length=lowerCAmelCase							,     padding=lowerCAmelCase							,     return_tensors='np'							,     return_attention_mask=lowerCAmelCase      )
              _lowercase			 =inputs.input_features
              _lowercase			 =inputs.attention_mask
              _lowercase			 =[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 						A__  (       self      )						->				int:
        '''simple docstring'''






        _lowercase			 =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
        _lowercase			 =[floats_list((1, x)      )[0] for x in range(800							,     1_400							,     200      )]
        _lowercase			 =feature_extractor(
            lowerCAmelCase							,     padding='max_length'							,     max_length=4							,     truncation=lowerCAmelCase							,     return_tensors='np'							,     return_attention_mask=lowerCAmelCase							,     )
        _lowercase			 =inputs.input_features
        _lowercase			 =inputs.attention_mask
        _lowercase			 =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 						A__  (       self      )						->				Union[str, Any]:
        '''simple docstring'''






        _lowercase			 =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
        _lowercase			 =[floats_list((1, x)      )[0] for x in range(800							,     1_400							,     200      )]
        _lowercase			 =feature_extractor(
            lowerCAmelCase							,     padding='longest'							,     max_length=4							,     truncation=lowerCAmelCase							,     return_tensors='np'							,     return_attention_mask=lowerCAmelCase							,     )
        _lowercase			 =inputs.input_features
        _lowercase			 =inputs.attention_mask
        _lowercase			 =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)      )

        _lowercase			 =[floats_list((1, x)      )[0] for x in range(800							,     1_400							,     200      )]
        _lowercase			 =feature_extractor(
            lowerCAmelCase							,     padding='longest'							,     max_length=16							,     truncation=lowerCAmelCase							,     return_tensors='np'							,     return_attention_mask=lowerCAmelCase							,     )
        _lowercase			 =inputs.input_features
        _lowercase			 =inputs.attention_mask
        _lowercase			 =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 						A__  (       self      )						->				str:
        '''simple docstring'''






        import torch

        _lowercase			 =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
        _lowercase			 =np.random.rand(100							,     32      ).astype(np.floataa      )
        _lowercase			 =np_speech_inputs.tolist()

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




  def 						A__  (       self							,     lowerCAmelCase      )						->				List[str]:
        '''simple docstring'''






        from datasets import load_dataset

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

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




  def 						A__  (       self      )						->				Tuple:
        '''simple docstring'''






        _lowercase			 =np.array([
            -1.5745, -1.7713, -1.7020, -1.6069, -1.2250, -1.1105, -0.9072, -0.8241,
            -1.2310, -0.8098, -0.3320, -0.4101, -0.7985, -0.4996, -0.8213, -0.9128,
            -1.0420, -1.1286, -1.0440, -0.7999, -0.8405, -1.2275, -1.5443, -1.4625,
        ]      )
        # fmt: on

        _lowercase			 =self._load_datasamples(1      )
        _lowercase			 =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
        _lowercase			 =feature_extractor(lowerCAmelCase							,     return_tensors='pt'      ).input_features
        self.assertEquals(input_features.shape							,     (1, 584, 24)      )
        self.assertTrue(np.allclose(input_features[0, 0, :30]							,     lowerCAmelCase							,     atol=1e-4      )      )





import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()
def   a   (    A__			:     Dict   ,     A__			:     Optional[Any]   ,     A__			:     Optional[Any]   ,     A__			:     Optional[int]   ,     A__			:     Union[str, Any]  )					->   Optional[int]:





      """simple docstring"""



      _lowercase			 =TapasConfig.from_json_file(A__  )
      # set absolute/relative position embeddings parameter
      _lowercase			 =reset_position_index_per_cell

      # set remaining parameters of TapasConfig as well as the model based on the task
      if task == "SQA":
            _lowercase			 =TapasForQuestionAnswering(config=A__  )
      elif task == "WTQ":
            # run_task_main.py hparams
            _lowercase			 =4
            _lowercase			 =True
            # hparam_utils.py hparams
            _lowercase			 =0.664694
            _lowercase			 =0.207951
            _lowercase			 =0.121194
            _lowercase			 =True
            _lowercase			 =True
            _lowercase			 =False
            _lowercase			 =0.0352513

            _lowercase			 =TapasForQuestionAnswering(config=A__  )
      elif task == "WIKISQL_SUPERVISED":
            # run_task_main.py hparams
            _lowercase			 =4
            _lowercase			 =False
            # hparam_utils.py hparams
            _lowercase			 =36.4519
            _lowercase			 =0.903421
            _lowercase			 =222.088
            _lowercase			 =True
            _lowercase			 =True
            _lowercase			 =True
            _lowercase			 =0.763141

            _lowercase			 =TapasForQuestionAnswering(config=A__  )
      elif task == "TABFACT":
            _lowercase			 =TapasForSequenceClassification(config=A__  )
      elif task == "MLM":
            _lowercase			 =TapasForMaskedLM(config=A__  )
      elif task == "INTERMEDIATE_PRETRAINING":
            _lowercase			 =TapasModel(config=A__  )
      else:
            raise ValueError(F'''Task {task} not supported.'''  )

      print(F'''Building PyTorch model from configuration: {config}'''  )
      # Load weights from tf checkpoint
      load_tf_weights_in_tapas(A__   ,     A__   ,     A__  )

      # Save pytorch-model (weights and configuration)
      print(F'''Save PyTorch model to {pytorch_dump_path}'''  )
      model.save_pretrained(A__  )

      # Save tokenizer files
      print(F'''Save tokenizer files to {pytorch_dump_path}'''  )
      _lowercase			 =TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + 'vocab.txt'   ,     model_max_length=512  )
      tokenizer.save_pretrained(A__  )

      print('Used relative position embeddings:'   ,     model.config.reset_position_index_per_cell  )


if __name__ == "__main__":
  lowercase_             =     argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--task', default='SQA', type=str, help='Model task for which to convert a checkpoint. Defaults to SQA.'
  )
  parser.add_argument(
      '--reset_position_index_per_cell',
      default=False,
      action='store_true',
      help='Whether to use relative position embeddings or not. Defaults to True.',
  )
  parser.add_argument(
      '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
  )
  parser.add_argument(
      '--tapas_config_file',
      default=None,
      type=str,
      required=True,
      help=(
          'The config json file corresponding to the pre-trained TAPAS model. \n'
          'This specifies the model architecture.'
      ),
  )
  parser.add_argument(
      '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  lowercase_             =     parser.parse_args()
  convert_tf_checkpoint_to_pytorch(
      args.task,
      args.reset_position_index_per_cell,
      args.tf_checkpoint_path,
      args.tapas_config_file,
      args.pytorch_dump_path,
  )


