import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow






class 		_lowerCamelCase		:
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase   )       ->					Tuple:
     return None






class 		_lowerCamelCase		:
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase   )       ->					Dict:
     return None






class 		_lowerCamelCase		(   unittest.TestCase    ):
    __a  =      [
        # (model_name, model_kwargs)
        ("bert-base-cased", {}),
        ("gpt2", {"use_cache": False}),  # We don't support exporting GPT2 past keys anymore
    ]
    @require_tf
    @slow
    def 						UpperCamelCase_				(  self   )       ->					str:
     for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
      self._test_export(lowerCAmelCase , '''tf''' , 12 , **lowerCAmelCase   )
    @require_torch
    @slow
    def 						UpperCamelCase_				(  self   )       ->					Optional[int]:
     for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
      self._test_export(lowerCAmelCase , '''pt''' , 12 , **lowerCAmelCase   )
    @require_torch
    @slow
    def 						UpperCamelCase_				(  self   )       ->					Any:
     from transformers import BertModel

     SCREAMING_SNAKE_CASE__:   Dict=      ['''[UNK]''', '''[SEP]''', '''[CLS]''', '''[PAD]''', '''[MASK]''', '''some''', '''other''', '''words''']
     with NamedTemporaryFile(mode='''w+t'''   ) as vocab_file:
      vocab_file.write('''\n'''.join(lowerCAmelCase   )   )
      vocab_file.flush()
      SCREAMING_SNAKE_CASE__:   List[str]=      BertTokenizerFast(vocab_file.name   )

     with TemporaryDirectory() as bert_save_dir:
      SCREAMING_SNAKE_CASE__:   Any=      BertModel(BertConfig(vocab_size=len(lowerCAmelCase   )   )   )
      model.save_pretrained(lowerCAmelCase   )
      self._test_export(lowerCAmelCase , '''pt''' , 12 , lowerCAmelCase   )
    @require_tf
    @slow
    def 						UpperCamelCase_				(  self   )       ->					List[str]:
     for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
      SCREAMING_SNAKE_CASE__:   Tuple=      self._test_export(lowerCAmelCase , '''tf''' , 12 , **lowerCAmelCase   )
      SCREAMING_SNAKE_CASE__:   int=      quantize(Path(lowerCAmelCase   )   )

      # Ensure the actual quantized model is not bigger than the original one
      if quantized_path.stat().st_size >= Path(lowerCAmelCase   ).stat().st_size:
       self.fail('''Quantized model is bigger than initial ONNX model'''   )
    @require_torch
    @slow
    def 						UpperCamelCase_				(  self   )       ->					str:
     for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
      SCREAMING_SNAKE_CASE__:   Any=      self._test_export(lowerCAmelCase , '''pt''' , 12 , **lowerCAmelCase   )
      SCREAMING_SNAKE_CASE__:   List[str]=      quantize(lowerCAmelCase   )

      # Ensure the actual quantized model is not bigger than the original one
      if quantized_path.stat().st_size >= Path(lowerCAmelCase   ).stat().st_size:
       self.fail('''Quantized model is bigger than initial ONNX model'''   )
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase=None , **lowerCAmelCase   )       ->					Dict:
     try:
      # Compute path
      with TemporaryDirectory() as tempdir:
       SCREAMING_SNAKE_CASE__:   Union[str, Any]=      Path(lowerCAmelCase   ).joinpath('''model.onnx'''   )

      # Remove folder if exists
      if path.parent.exists():
       path.parent.rmdir()

      # Export
      convert(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , **lowerCAmelCase   )

      return path
     except Exception as e:
      self.fail(lowerCAmelCase   )
    @require_torch
    @require_tokenizers
    @slow
    def 						UpperCamelCase_				(  self   )       ->					str:
     from transformers import BertModel

     SCREAMING_SNAKE_CASE__:   Dict=      BertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random'''   )   )
     SCREAMING_SNAKE_CASE__:   Dict=      BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random'''   )
     self._test_infer_dynamic_axis(lowerCAmelCase , lowerCAmelCase , '''pt'''   )
    @require_tf
    @require_tokenizers
    @slow
    def 						UpperCamelCase_				(  self   )       ->					Tuple:
     from transformers import TFBertModel

     SCREAMING_SNAKE_CASE__:   Optional[Any]=      TFBertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random'''   )   )
     SCREAMING_SNAKE_CASE__:   Optional[int]=      BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random'''   )
     self._test_infer_dynamic_axis(lowerCAmelCase , lowerCAmelCase , '''tf'''   )
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase   )       ->					Dict:
     SCREAMING_SNAKE_CASE__:   Tuple=      FeatureExtractionPipeline(lowerCAmelCase , lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   List[str]=      ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''output_0''', '''output_1''']
     SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Optional[int]=      infer_shapes(lowerCAmelCase , lowerCAmelCase   )

     # Assert all variables are present
     self.assertEqual(len(lowerCAmelCase   ) , len(lowerCAmelCase   )   )
     self.assertTrue(all(var_name in shapes for var_name in variable_names   )   )
     self.assertSequenceEqual(variable_names[:3] , lowerCAmelCase   )
     self.assertSequenceEqual(variable_names[3:] , lowerCAmelCase   )

     # Assert inputs are {0: batch, 1: sequence}
     for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
      self.assertDictEqual(shapes[var_name] , {0: '''batch''', 1: '''sequence'''}   )

     # Assert outputs are {0: batch, 1: sequence} and {0: batch}
     self.assertDictEqual(shapes['''output_0'''] , {0: '''batch''', 1: '''sequence'''}   )
     self.assertDictEqual(shapes['''output_1'''] , {0: '''batch'''}   )
    def 						UpperCamelCase_				(  self   )       ->					int:
     SCREAMING_SNAKE_CASE__:   Any=      ['''input_ids''', '''attention_mask''', '''token_type_ids''']
     SCREAMING_SNAKE_CASE__:   List[str]=      {'''input_ids''': [1, 2, 3, 4], '''attention_mask''': [0, 0, 0, 0], '''token_type_ids''': [1, 1, 1, 1]}
     SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Optional[int]=      ensure_valid_input(FuncContiguousArgs() , lowerCAmelCase , lowerCAmelCase   )

     # Should have exactly the same number of args (all are valid)
     self.assertEqual(len(lowerCAmelCase   ) , 3   )

     # Should have exactly the same input names
     self.assertEqual(set(lowerCAmelCase   ) , set(lowerCAmelCase   )   )

     # Parameter should be reordered according to their respective place in the function:
     # (input_ids, token_type_ids, attention_mask)
     self.assertEqual(lowerCAmelCase , (tokens['''input_ids'''], tokens['''token_type_ids'''], tokens['''attention_mask'''])   )

     # Generated args are interleaved with another args (for instance parameter "past" in GPT2)
     SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Optional[int]=      ensure_valid_input(FuncNonContiguousArgs() , lowerCAmelCase , lowerCAmelCase   )

     # Should have exactly the one arg (all before the one not provided "some_other_args")
     self.assertEqual(len(lowerCAmelCase   ) , 1   )
     self.assertEqual(len(lowerCAmelCase   ) , 1   )

     # Should have only "input_ids"
     self.assertEqual(inputs_args[0] , tokens['''input_ids''']   )
     self.assertEqual(ordered_input_names[0] , '''input_ids'''   )



    def 						UpperCamelCase_				(  self   )       ->					List[str]:
     SCREAMING_SNAKE_CASE__:   int=      generate_identified_filename(Path('''/home/something/my_fake_model.onnx'''   ) , '''-test'''   )
     self.assertEqual('''/home/something/my_fake_model-test.onnx''' , generated.as_posix()   )


import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_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_speech_available():
      from transformers import WhisperFeatureExtractor

if is_torch_available():
      import torch

lowercase_     :   Any 				=				random.Random()







def 			A__      (	snake_case_ :		int       ,				snake_case_ :		Tuple=1.0       ,				snake_case_ :		Optional[Any]=None       ,				snake_case_ :		List[Any]=None      ):
 if rng is None:
  SCREAMING_SNAKE_CASE__:   Tuple=      global_rng

 SCREAMING_SNAKE_CASE__:   List[str]=      []
 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=2000 , lowerCAmelCase=10 , lowerCAmelCase=160 , lowerCAmelCase=8 , lowerCAmelCase=0.0 , lowerCAmelCase=4000 , lowerCAmelCase=False , lowerCAmelCase=True , )       ->					Tuple:
     SCREAMING_SNAKE_CASE__:   Any=      parent
     SCREAMING_SNAKE_CASE__:   Dict=      batch_size
     SCREAMING_SNAKE_CASE__:   List[Any]=      min_seq_length
     SCREAMING_SNAKE_CASE__:   Tuple=      max_seq_length
     SCREAMING_SNAKE_CASE__:   Optional[int]=      (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
     SCREAMING_SNAKE_CASE__:   str=      padding_value
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      sampling_rate
     SCREAMING_SNAKE_CASE__:   List[str]=      return_attention_mask
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      do_normalize
     SCREAMING_SNAKE_CASE__:   Optional[int]=      feature_size
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      chunk_length
     SCREAMING_SNAKE_CASE__:   List[Any]=      hop_length
    def 						UpperCamelCase_				(  self   )       ->					str:
     return {
         "feature_size": self.feature_size,
         "hop_length": self.hop_length,
         "chunk_length": self.chunk_length,
         "padding_value": self.padding_value,
         "sampling_rate": self.sampling_rate,
         "return_attention_mask": self.return_attention_mask,
         "do_normalize": self.do_normalize,
     }



    def 						UpperCamelCase_				(  self , lowerCAmelCase=False , lowerCAmelCase=False   )       ->					Optional[Any]:
     def _flatten(lowerCAmelCase   ):
      return list(itertools.chain(*lowerCAmelCase   )   )

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






@require_torch
@require_torchaudio
class 		_lowerCamelCase		(   UpperCamelCase_    ,     unittest.TestCase    ):
    __a  =      WhisperFeatureExtractor if is_speech_available() else None
    def 						UpperCamelCase_				(  self   )       ->					Dict:
     SCREAMING_SNAKE_CASE__:   Any=      WhisperFeatureExtractionTester(self   )
    def 						UpperCamelCase_				(  self   )       ->					int:
     SCREAMING_SNAKE_CASE__:   List[str]=      self.feature_extraction_class(**self.feat_extract_dict   )

     with tempfile.TemporaryDirectory() as tmpdirname:
      SCREAMING_SNAKE_CASE__:   List[str]=      feat_extract_first.save_pretrained(lowerCAmelCase   )[0]
      check_json_file_has_correct_format(lowerCAmelCase   )
      SCREAMING_SNAKE_CASE__:   str=      self.feature_extraction_class.from_pretrained(lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   int=      feat_extract_first.to_dict()
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      feat_extract_second.to_dict()
     SCREAMING_SNAKE_CASE__:   int=      feat_extract_first.mel_filters
     SCREAMING_SNAKE_CASE__:   Any=      feat_extract_second.mel_filters
     self.assertTrue(np.allclose(lowerCAmelCase , lowerCAmelCase   )   )
     self.assertEqual(lowerCAmelCase , lowerCAmelCase   )
    def 						UpperCamelCase_				(  self   )       ->					Tuple:
     SCREAMING_SNAKE_CASE__:   List[Any]=      self.feature_extraction_class(**self.feat_extract_dict   )

     with tempfile.TemporaryDirectory() as tmpdirname:
      SCREAMING_SNAKE_CASE__:   List[str]=      os.path.join(lowerCAmelCase , '''feat_extract.json'''   )
      feat_extract_first.to_json_file(lowerCAmelCase   )
      SCREAMING_SNAKE_CASE__:   Tuple=      self.feature_extraction_class.from_json_file(lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   Tuple=      feat_extract_first.to_dict()
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      feat_extract_second.to_dict()
     SCREAMING_SNAKE_CASE__:   List[str]=      feat_extract_first.mel_filters
     SCREAMING_SNAKE_CASE__:   Optional[int]=      feat_extract_second.mel_filters
     self.assertTrue(np.allclose(lowerCAmelCase , lowerCAmelCase   )   )
     self.assertEqual(lowerCAmelCase , lowerCAmelCase   )
    def 						UpperCamelCase_				(  self   )       ->					Tuple:
     # Tests that all call wrap to encode_plus and batch_encode_plus
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
     # create three inputs of length 800, 1000, and 1200
     SCREAMING_SNAKE_CASE__:   str=      [floats_list((1, x)   )[0] for x in range(800 , 1400 , 200   )]
     SCREAMING_SNAKE_CASE__:   Tuple=      [np.asarray(lowerCAmelCase   ) for speech_input in speech_inputs]

     # Test feature size
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      feature_extractor(lowerCAmelCase , padding='''max_length''' , return_tensors='''np'''   ).input_features
     self.assertTrue(input_features.ndim == 3   )
     self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames   )
     self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size   )

     # Test not batched input
     SCREAMING_SNAKE_CASE__:   List[str]=      feature_extractor(speech_inputs[0] , return_tensors='''np'''   ).input_features
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      feature_extractor(np_speech_inputs[0] , return_tensors='''np'''   ).input_features
     self.assertTrue(np.allclose(lowerCAmelCase , lowerCAmelCase , atol=1e-3   )   )

     # Test batched
     SCREAMING_SNAKE_CASE__:   Optional[int]=      feature_extractor(lowerCAmelCase , return_tensors='''np'''   ).input_features
     SCREAMING_SNAKE_CASE__:   List[Any]=      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.
     SCREAMING_SNAKE_CASE__:   Optional[int]=      [floats_list((1, x)   )[0] for x in (800, 800, 800)]
     SCREAMING_SNAKE_CASE__:   Any=      np.asarray(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   List[str]=      feature_extractor(lowerCAmelCase , return_tensors='''np'''   ).input_features
     SCREAMING_SNAKE_CASE__:   List[Any]=      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 truncation required
     SCREAMING_SNAKE_CASE__:   str=      [floats_list((1, x)   )[0] for x in range(200 , (feature_extractor.n_samples + 500) , 200   )]
     SCREAMING_SNAKE_CASE__:   str=      [np.asarray(lowerCAmelCase   ) for speech_input in speech_inputs]

     SCREAMING_SNAKE_CASE__:   str=      [x[: feature_extractor.n_samples] for x in speech_inputs]
     SCREAMING_SNAKE_CASE__:   int=      [np.asarray(lowerCAmelCase   ) for speech_input in speech_inputs_truncated]

     SCREAMING_SNAKE_CASE__:   Dict=      feature_extractor(lowerCAmelCase , return_tensors='''np'''   ).input_features
     SCREAMING_SNAKE_CASE__:   List[str]=      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 						UpperCamelCase_				(  self   )       ->					Dict:
     import torch

     SCREAMING_SNAKE_CASE__:   List[Any]=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
     SCREAMING_SNAKE_CASE__:   Dict=      np.random.rand(100 , 32   ).astype(np.floataa   )
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      np_speech_inputs.tolist()

     for inputs in [py_speech_inputs, np_speech_inputs]:
      SCREAMING_SNAKE_CASE__:   Tuple=      feature_extractor.pad([{'''input_features''': inputs}] , return_tensors='''np'''   )
      self.assertTrue(np_processed.input_features.dtype == np.floataa   )
      SCREAMING_SNAKE_CASE__:   Any=      feature_extractor.pad([{'''input_features''': inputs}] , return_tensors='''pt'''   )
      self.assertTrue(pt_processed.input_features.dtype == torch.floataa   )
    def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					str:
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      load_dataset('''hf-internal-testing/librispeech_asr_dummy''' , '''clean''' , split='''validation'''   )
     # automatic decoding with librispeech
     SCREAMING_SNAKE_CASE__:   List[str]=      ds.sort('''id'''   ).select(range(lowerCAmelCase   )   )[:num_samples]['''audio''']

     return [x["array"] for x in speech_samples]
    def 						UpperCamelCase_				(  self   )       ->					int:
     # fmt: off
     SCREAMING_SNAKE_CASE__:   Dict=      torch.tensor(
         [
             0.1193, -0.0946, -0.1098, -0.0196, 0.0225, -0.0690, -0.1736, 0.0951,
             0.0971, -0.0817, -0.0702, 0.0162, 0.0260, 0.0017, -0.0192, -0.1678,
             0.0709, -0.1867, -0.0655, -0.0274, -0.0234, -0.1884, -0.0516, -0.0554,
             -0.0274, -0.1425, -0.1423, 0.0837, 0.0377, -0.0854
         ]   )
     # fmt: on

     SCREAMING_SNAKE_CASE__:   str=      self._load_datasamples(1   )
     SCREAMING_SNAKE_CASE__:   Dict=      WhisperFeatureExtractor()
     SCREAMING_SNAKE_CASE__:   Optional[int]=      feature_extractor(lowerCAmelCase , return_tensors='''pt'''   ).input_features
     self.assertEqual(input_features.shape , (1, 80, 3000)   )
     self.assertTrue(torch.allclose(input_features[0, 0, :30] , lowerCAmelCase , atol=1e-4   )   )



    def 						UpperCamelCase_				(  self   )       ->					Optional[Any]:
     SCREAMING_SNAKE_CASE__:   List[str]=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
     SCREAMING_SNAKE_CASE__:   Dict=      self._load_datasamples(1   )[0]
     SCREAMING_SNAKE_CASE__:   str=      ((audio - audio.min()) / (audio.max() - audio.min())) * 65535  # Rescale to [0, 65535] to show issue
     SCREAMING_SNAKE_CASE__:   Optional[int]=      feat_extract.zero_mean_unit_var_norm([audio] , attention_mask=lowerCAmelCase   )[0]

     self.assertTrue(np.all(np.mean(lowerCAmelCase   ) < 1e-3   )   )
     self.assertTrue(np.all(np.abs(np.var(lowerCAmelCase   ) - 1   ) < 1e-3   )   )

