


'''simple docstring'''
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					:




 '''simple docstring'''


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  return None


class 						_lowerCamelCase					:




 '''simple docstring'''


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  return None


class 						_lowerCamelCase					(    unittest.TestCase				):




 '''simple docstring'''
 __lowercase				:		Any    							=						[
     # (model_name, model_kwargs)
     ('''bert-base-cased''', {}),
     ('''gpt2''', {'''use_cache''': False}),  # We don't support exporting GPT2 past keys anymore
 ]


 @require_tf
 @slow
 def  snake_case__   (      self						):

  """simple docstring"""







  for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
   self._test_export(__lowercase      ,      'tf'      ,      12      ,      **__lowercase						)


 @require_torch
 @slow
 def  snake_case__   (      self						):

  """simple docstring"""







  for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
   self._test_export(__lowercase      ,      'pt'      ,      12      ,      **__lowercase						)


 @require_torch
 @slow
 def  snake_case__   (      self						):

  """simple docstring"""







  from transformers import BertModel

  __A  :		Any       =			['[UNK]', '[SEP]', '[CLS]', '[PAD]', '[MASK]', 'some', 'other', 'words']
  with NamedTemporaryFile(mode='w+t'						) as vocab_file:
   vocab_file.write('\n'.join(__lowercase						)						)
   vocab_file.flush()
   __A  :		int       =			BertTokenizerFast(vocab_file.name						)

  with TemporaryDirectory() as bert_save_dir:
   __A  :		int       =			BertModel(BertConfig(vocab_size=len(__lowercase						)						)						)
   model.save_pretrained(__lowercase						)
   self._test_export(__lowercase      ,      'pt'      ,      12      ,      __lowercase						)


 @require_tf
 @slow
 def  snake_case__   (      self						):

  """simple docstring"""







  for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
   __A  :		Dict       =			self._test_export(__lowercase      ,      'tf'      ,      12      ,      **__lowercase						)
   __A  :		Any       =			quantize(Path(__lowercase						)						)

   # Ensure the actual quantized model is not bigger than the original one
   if quantized_path.stat().st_size >= Path(__lowercase						).stat().st_size:
    self.fail('Quantized model is bigger than initial ONNX model'						)


 @require_torch
 @slow
 def  snake_case__   (      self						):

  """simple docstring"""







  for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
   __A  :		str       =			self._test_export(__lowercase      ,      'pt'      ,      12      ,      **__lowercase						)
   __A  :		str       =			quantize(__lowercase						)

   # Ensure the actual quantized model is not bigger than the original one
   if quantized_path.stat().st_size >= Path(__lowercase						).stat().st_size:
    self.fail('Quantized model is bigger than initial ONNX model'						)


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase=None      ,      **__lowercase						):

  """simple docstring"""







  try:
   # Compute path
   with TemporaryDirectory() as tempdir:
    __A  :		Tuple       =			Path(__lowercase						).joinpath('model.onnx'						)

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

   # Export
   convert(__lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      **__lowercase						)

   return path
  except Exception as e:
   self.fail(__lowercase						)


 @require_torch
 @require_tokenizers
 @slow
 def  snake_case__   (      self						):

  """simple docstring"""







  from transformers import BertModel

  __A  :		Any       =			BertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'						)						)
  __A  :		Union[str, Any]       =			BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random'						)
  self._test_infer_dynamic_axis(__lowercase      ,      __lowercase      ,      'pt'						)


 @require_tf
 @require_tokenizers
 @slow
 def  snake_case__   (      self						):

  """simple docstring"""







  from transformers import TFBertModel

  __A  :		List[str]       =			TFBertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'						)						)
  __A  :		Any       =			BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random'						)
  self._test_infer_dynamic_axis(__lowercase      ,      __lowercase      ,      'tf'						)


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  __A  :		Dict       =			FeatureExtractionPipeline(__lowercase      ,      __lowercase						)

  __A  :		Union[str, Any]       =			['input_ids', 'token_type_ids', 'attention_mask', 'output_0', 'output_1']
  __A     ,__A     ,__A     ,__A  :		Optional[Any]       =			infer_shapes(__lowercase      ,      __lowercase						)

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

  # 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  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Optional[int]       =			['input_ids', 'attention_mask', 'token_type_ids']
  __A  :		List[str]       =			{'input_ids': [1, 2, 3, 4], 'attention_mask': [0, 0, 0, 0], 'token_type_ids': [1, 1, 1, 1]}
  __A     ,__A  :		List[str]       =			ensure_valid_input(FuncContiguousArgs()      ,      __lowercase      ,      __lowercase						)

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

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

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

  # Generated args are interleaved with another args (for instance parameter "past" in GPT2)
  __A     ,__A  :		int       =			ensure_valid_input(FuncNonContiguousArgs()      ,      __lowercase      ,      __lowercase						)

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

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







 def  snake_case__   (      self						):

  """simple docstring"""







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










'''simple docstring'''
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast

@require_vision
class 						_lowerCamelCase					(    unittest.TestCase				):




 '''simple docstring'''


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		int       =			tempfile.mkdtemp()

  __A  :		List[Any]       =			BlipImageProcessor()
  __A  :		List[str]       =			BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-BertModel'						)

  __A  :		Union[str, Any]       =			BlipProcessor(__lowercase      ,      __lowercase						)

  processor.save_pretrained(self.tmpdirname						)


 def  snake_case__   (      self      ,      **__lowercase						):

  """simple docstring"""







  return AutoProcessor.from_pretrained(self.tmpdirname      ,      **__lowercase						).tokenizer


 def  snake_case__   (      self      ,      **__lowercase						):

  """simple docstring"""







  return AutoProcessor.from_pretrained(self.tmpdirname      ,      **__lowercase						).image_processor


 def  snake_case__   (      self						):

  """simple docstring"""







  shutil.rmtree(self.tmpdirname						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Optional[Any]       =			[np.random.randint(255      ,      size=(3, 30, 400)      ,      dtype=np.uinta						)]

  __A  :		Union[str, Any]       =			[Image.fromarray(np.moveaxis(__lowercase      ,      0      ,      -1						)						) for x in image_inputs]

  return image_inputs


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Any       =			BlipProcessor(tokenizer=self.get_tokenizer()      ,      image_processor=self.get_image_processor()						)
  processor.save_pretrained(self.tmpdirname						)

  __A  :		Optional[int]       =			self.get_tokenizer(bos_token='(BOS)'      ,      eos_token='(EOS)'						)
  __A  :		Optional[Any]       =			self.get_image_processor(do_normalize=__lowercase      ,      padding_value=1.0						)

  __A  :		List[str]       =			BlipProcessor.from_pretrained(
      self.tmpdirname      ,      bos_token='(BOS)'      ,      eos_token='(EOS)'      ,      do_normalize=__lowercase      ,      padding_value=1.0						)

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

  self.assertEqual(processor.image_processor.to_json_string()      ,      image_processor_add_kwargs.to_json_string()						)
  self.assertIsInstance(processor.image_processor      ,      __lowercase						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Optional[Any]       =			self.get_image_processor()
  __A  :		Union[str, Any]       =			self.get_tokenizer()

  __A  :		Dict       =			BlipProcessor(tokenizer=__lowercase      ,      image_processor=__lowercase						)

  __A  :		Dict       =			self.prepare_image_inputs()

  __A  :		str       =			image_processor(__lowercase      ,      return_tensors='np'						)
  __A  :		List[Any]       =			processor(images=__lowercase      ,      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  snake_case__   (      self						):

  """simple docstring"""







  __A  :		int       =			self.get_image_processor()
  __A  :		List[str]       =			self.get_tokenizer()

  __A  :		str       =			BlipProcessor(tokenizer=__lowercase      ,      image_processor=__lowercase						)

  __A  :		List[Any]       =			'lower newer'

  __A  :		Any       =			processor(text=__lowercase						)

  __A  :		int       =			tokenizer(__lowercase      ,      return_token_type_ids=__lowercase						)

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


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		str       =			self.get_image_processor()
  __A  :		Dict       =			self.get_tokenizer()

  __A  :		Any       =			BlipProcessor(tokenizer=__lowercase      ,      image_processor=__lowercase						)

  __A  :		List[Any]       =			'lower newer'
  __A  :		List[str]       =			self.prepare_image_inputs()

  __A  :		Optional[Any]       =			processor(text=__lowercase      ,      images=__lowercase						)

  self.assertListEqual(list(inputs.keys()						)      ,      ['pixel_values', 'input_ids', 'attention_mask']						)

  # test if it raises when no input is passed
  with pytest.raises(__lowercase						):
   processor()


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		int       =			self.get_image_processor()
  __A  :		Optional[int]       =			self.get_tokenizer()

  __A  :		List[str]       =			BlipProcessor(tokenizer=__lowercase      ,      image_processor=__lowercase						)

  __A  :		str       =			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

  __A  :		Tuple       =			processor.batch_decode(__lowercase						)
  __A  :		Tuple       =			tokenizer.batch_decode(__lowercase						)

  self.assertListEqual(__lowercase      ,      __lowercase						)







 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Optional[int]       =			self.get_image_processor()
  __A  :		Any       =			self.get_tokenizer()

  __A  :		Any       =			BlipProcessor(tokenizer=__lowercase      ,      image_processor=__lowercase						)

  __A  :		Optional[int]       =			'lower newer'
  __A  :		List[str]       =			self.prepare_image_inputs()

  __A  :		int       =			processor(text=__lowercase      ,      images=__lowercase						)

  # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
  self.assertListEqual(list(inputs.keys()						)      ,      ['pixel_values', 'input_ids', 'attention_mask']						)






