


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 			SCREAMING_SNAKE_CASE__  :
      def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					,							SCREAMING_SNAKE_CASE__    :	Optional[int]					,							SCREAMING_SNAKE_CASE__    :	Optional[int]					,							SCREAMING_SNAKE_CASE__    :	Any					)		->      Tuple:
             return None
class 			SCREAMING_SNAKE_CASE__  :
      def 	SCREAMING_SNAKE_CASE							(	self    :	int					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]					,							SCREAMING_SNAKE_CASE__    :	Dict					,							SCREAMING_SNAKE_CASE__    :	List[Any]					)		->      Any:
             return None
class 			SCREAMING_SNAKE_CASE__  (    unittest.TestCase      ):
      snake_case__    :					Optional[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 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					)		->      List[str]:
             for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                    self._test_export(SCREAMING_SNAKE_CASE__					,							'tf'					,							1_2					,							**SCREAMING_SNAKE_CASE__					)
      @require_torch
      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					)		->      List[Any]:
             for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                    self._test_export(SCREAMING_SNAKE_CASE__					,							'pt'					,							1_2					,							**SCREAMING_SNAKE_CASE__					)
      @require_torch
      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					)		->      str:
             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(SCREAMING_SNAKE_CASE__					)					)
                    vocab_file.flush()
                    a_ :		Any							=  BertTokenizerFast(vocab_file.name					)

             with TemporaryDirectory() as bert_save_dir:
                    a_ :		List[str]							=  BertModel(BertConfig(vocab_size=len(SCREAMING_SNAKE_CASE__					)					)					)
                    model.save_pretrained(SCREAMING_SNAKE_CASE__					)
                    self._test_export(SCREAMING_SNAKE_CASE__					,							'pt'					,							1_2					,							SCREAMING_SNAKE_CASE__					)
      @require_tf
      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	int					)		->      Optional[int]:
             for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                    a_ :		Union[str, Any]							=  self._test_export(SCREAMING_SNAKE_CASE__					,							'tf'					,							1_2					,							**SCREAMING_SNAKE_CASE__					)
                    a_ :		Any							=  quantize(Path(SCREAMING_SNAKE_CASE__					)					)

                    # Ensure the actual quantized model is not bigger than the original one
                    if quantized_path.stat().st_size >= Path(SCREAMING_SNAKE_CASE__					).stat().st_size:
                           self.fail('Quantized model is bigger than initial ONNX model'					)
      @require_torch
      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					)		->      List[str]:
             for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                    a_ :		Optional[int]							=  self._test_export(SCREAMING_SNAKE_CASE__					,							'pt'					,							1_2					,							**SCREAMING_SNAKE_CASE__					)
                    a_ :		Union[str, Any]							=  quantize(SCREAMING_SNAKE_CASE__					)

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

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

                    # Export
                    convert(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							**SCREAMING_SNAKE_CASE__					)

                    return path
             except Exception as e:
                    self.fail(SCREAMING_SNAKE_CASE__					)
      @require_torch
      @require_tokenizers
      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					)		->      List[str]:
             from transformers import BertModel

             a_ :		Tuple							=  BertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'					)					)
             a_ :		Tuple							=  BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random'					)
             self._test_infer_dynamic_axis(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							'pt'					)
      @require_tf
      @require_tokenizers
      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[Any]					)		->      List[Any]:
             from transformers import TFBertModel

             a_ :		Any							=  TFBertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'					)					)
             a_ :		List[str]							=  BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random'					)
             self._test_infer_dynamic_axis(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							'tf'					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[Any]					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]					,							SCREAMING_SNAKE_CASE__    :	str					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]					)		->      Optional[Any]:
             a_ :		Union[str, Any]							=  FeatureExtractionPipeline(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)

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

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

             # 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 	SCREAMING_SNAKE_CASE							(	self    :	Dict					)		->      List[str]:
             a_ :		Any							=  ['input_ids', 'attention_mask', 'token_type_ids']
             a_ :		Tuple							=  {'input_ids': [1, 2, 3, 4], 'attention_mask': [0, 0, 0, 0], 'token_type_ids': [1, 1, 1, 1]}
             a_ :		List[Any]							=  ensure_valid_input(FuncContiguousArgs()					,							SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)

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

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

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

             # Generated args are interleaved with another args (for instance parameter "past" in GPT2)
             a_ :		Any							=  ensure_valid_input(FuncNonContiguousArgs()					,							SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)

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

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

      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					)		->      Dict:
             a_ :		Tuple							=  generate_identified_filename(Path('/home/something/my_fake_model.onnx'					)					,							'-test'					)
             self.assertEqual('/home/something/my_fake_model-test.onnx'					,							generated.as_posix()					)






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_				:			Optional[Any]   =	{'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PLBartConfig']}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_				:			Union[str, Any]   =	['PLBartTokenizer']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_				:			List[str]   =	[
       'PLBART_PRETRAINED_MODEL_ARCHIVE_LIST',
       'PLBartForCausalLM',
       'PLBartForConditionalGeneration',
       'PLBartForSequenceClassification',
       'PLBartModel',
       'PLBartPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_plbart import PLBartTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_plbart import (
          PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
          PLBartForCausalLM,
          PLBartForConditionalGeneration,
          PLBartForSequenceClassification,
          PLBartModel,
          PLBartPreTrainedModel,
      )


else:
   import sys

   UpperCAmelCase_				:			Union[str, Any]   =	_LazyModule(__name__, globals()['__file__'], _import_structure)


