"""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 		UpperCamelCase      :



    def  _lowercase					(self    :				Any     ,  _A    :				List[str]     ,  _A    :				List[Any]     ,  _A    :				Optional[int])   ->     Union[str, Any]:
     return None







class 		UpperCamelCase      :



    def  _lowercase					(self    :				List[Any]     ,  _A    :				Optional[Any]     ,  _A    :				Optional[int]     ,  _A    :				List[str]     ,  _A    :				Union[str, Any])   ->     List[Any]:
     return None







class 		UpperCamelCase      (   unittest.TestCase						):
    UpperCAmelCase    :				List[str]   =		[
        # (model_name, model_kwargs)
        ("""bert-base-cased""", {}),
        ("""gpt2""", {"""use_cache""": False}),  # We don't support exporting GPT2 past keys anymore
    ]



    @require_tf
    @slow
    def  _lowercase					(self    :				List[Any])   ->     Any:
     for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
      self._test_export(_A     ,  'tf'     ,  12     ,  **_A)



    @require_torch
    @slow
    def  _lowercase					(self    :				int)   ->     Optional[Any]:
     for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
      self._test_export(_A     ,  'pt'     ,  12     ,  **_A)



    @require_torch
    @slow
    def  _lowercase					(self    :				Any)   ->     str:
     from transformers import BertModel

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

     with TemporaryDirectory() as bert_save_dir:
      __snake_case      :							List[Any]						=			BertModel(BertConfig(vocab_size=len(_A)))
      model.save_pretrained(_A)
      self._test_export(_A     ,  'pt'     ,  12     ,  _A)



    @require_tf
    @slow
    def  _lowercase					(self    :				Union[str, Any])   ->     Dict:
     for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
      __snake_case      :							int						=			self._test_export(_A     ,  'tf'     ,  12     ,  **_A)
      __snake_case      :							List[Any]						=			quantize(Path(_A))

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



    @require_torch
    @slow
    def  _lowercase					(self    :				Dict)   ->     Any:
     for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
      __snake_case      :							Dict						=			self._test_export(_A     ,  'pt'     ,  12     ,  **_A)
      __snake_case      :							Optional[Any]						=			quantize(_A)

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



    def  _lowercase					(self    :				Union[str, Any]     ,  _A    :				str     ,  _A    :				Optional[int]     ,  _A    :				List[Any]     ,  _A    :				List[str]=None     ,  **_A    :				str)   ->     str:
     try:
      # Compute path
      with TemporaryDirectory() as tempdir:
       __snake_case      :							List[Any]						=			Path(_A).joinpath('model.onnx')

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

      # Export
      convert(_A     ,  _A     ,  _A     ,  _A     ,  _A     ,  **_A)

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



    @require_torch
    @require_tokenizers
    @slow
    def  _lowercase					(self    :				Tuple)   ->     int:
     from transformers import BertModel

     __snake_case      :							Any						=			BertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'))
     __snake_case      :							Optional[Any]						=			BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random')
     self._test_infer_dynamic_axis(_A     ,  _A     ,  'pt')



    @require_tf
    @require_tokenizers
    @slow
    def  _lowercase					(self    :				str)   ->     Optional[int]:
     from transformers import TFBertModel

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



    def  _lowercase					(self    :				List[str]     ,  _A    :				Any     ,  _A    :				Any     ,  _A    :				int)   ->     Optional[Any]:
     __snake_case      :							Optional[int]						=			FeatureExtractionPipeline(_A     ,  _A)

     __snake_case      :							List[str]						=			['input_ids', 'token_type_ids', 'attention_mask', 'output_0', 'output_1']
     __snake_case      :							str						=			infer_shapes(_A     ,  _A)

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

     # 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  _lowercase					(self    :				Optional[Any])   ->     List[str]:
     __snake_case      :							Tuple						=			['input_ids', 'attention_mask', 'token_type_ids']
     __snake_case      :							Any						=			{'input_ids': [1, 2, 3, 4], 'attention_mask': [0, 0, 0, 0], 'token_type_ids': [1, 1, 1, 1]}
     __snake_case      :							int						=			ensure_valid_input(FuncContiguousArgs()     ,  _A     ,  _A)

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

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

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

     # Generated args are interleaved with another args (for instance parameter "past" in GPT2)
     __snake_case      :							List[Any]						=			ensure_valid_input(FuncNonContiguousArgs()     ,  _A     ,  _A)

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

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






    def  _lowercase					(self    :				List[Any])   ->     List[str]:
     __snake_case      :							List[Any]						=			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"""







from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Namespace  )							->      Union[str, Any]:

	'''simple docstring'''





	return ConvertCommand(
	    args.model_type      ,							args.tf_checkpoint      ,							args.pytorch_dump_output      ,							args.config      ,							args.finetuning_task_name  )


_a					:					str=			"\ntransformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires\nTensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.\n"

class 		UpperCamelCase      (   lowercase						):



				@staticmethod
				def  _lowercase					(_A    :				ArgumentParser)   ->     Tuple:
					__snake_case      :							Optional[Any]						=			parser.add_parser(
					    'convert'     ,  help='CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.'     ,  )
					train_parser.add_argument('--model_type'     ,  type=_A     ,  required=_A     ,  help='Model\'s type.')
					train_parser.add_argument(
					    '--tf_checkpoint'     ,  type=_A     ,  required=_A     ,  help='TensorFlow checkpoint path or folder.')
					train_parser.add_argument(
					    '--pytorch_dump_output'     ,  type=_A     ,  required=_A     ,  help='Path to the PyTorch saved model output.')
					train_parser.add_argument('--config'     ,  type=_A     ,  default=''     ,  help='Configuration file path or folder.')
					train_parser.add_argument(
					    '--finetuning_task_name'     ,  type=_A     ,  default=_A     ,  help='Optional fine-tuning task name if the TF model was a finetuned model.'     ,  )
					train_parser.set_defaults(func=_A)



				def __init__(self    :				List[str]     ,  _A    :				str     ,  _A    :				str     ,  _A    :				str     ,  _A    :				str     ,  _A    :				str     ,  *_A    :				Any     ,  )   ->     Optional[Any]:
					__snake_case      :							List[Any]						=			logging.get_logger('transformers-cli/converting')

					self._logger.info(f"Loading model {model_type}")
					__snake_case      :							List[str]						=			model_type
					__snake_case      :							int						=			tf_checkpoint
					__snake_case      :							Optional[int]						=			pytorch_dump_output
					__snake_case      :							Optional[Any]						=			config
					__snake_case      :							Optional[Any]						=			finetuning_task_name






				def  _lowercase					(self    :				List[str])   ->     str:
					if self._model_type == "albert":
						try:
							from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
							    convert_tf_checkpoint_to_pytorch,
							)
						except ImportError:
							raise ImportError(_A)

						convert_tf_checkpoint_to_pytorch(self._tf_checkpoint     ,  self._config     ,  self._pytorch_dump_output)
					elif self._model_type == "bert":
						try:
							from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
							    convert_tf_checkpoint_to_pytorch,
							)
						except ImportError:
							raise ImportError(_A)

						convert_tf_checkpoint_to_pytorch(self._tf_checkpoint     ,  self._config     ,  self._pytorch_dump_output)
					elif self._model_type == "funnel":
						try:
							from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
							    convert_tf_checkpoint_to_pytorch,
							)
						except ImportError:
							raise ImportError(_A)

						convert_tf_checkpoint_to_pytorch(self._tf_checkpoint     ,  self._config     ,  self._pytorch_dump_output)
					elif self._model_type == "t5":
						try:
							from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
						except ImportError:
							raise ImportError(_A)

						convert_tf_checkpoint_to_pytorch(self._tf_checkpoint     ,  self._config     ,  self._pytorch_dump_output)
					elif self._model_type == "gpt":
						from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
						    convert_openai_checkpoint_to_pytorch,
						)

						convert_openai_checkpoint_to_pytorch(self._tf_checkpoint     ,  self._config     ,  self._pytorch_dump_output)
					elif self._model_type == "transfo_xl":
						try:
							from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
							    convert_transfo_xl_checkpoint_to_pytorch,
							)
						except ImportError:
							raise ImportError(_A)

						if "ckpt" in self._tf_checkpoint.lower():
							__snake_case      :							Union[str, Any]						=			self._tf_checkpoint
							__snake_case      :							List[Any]						=			''
						else:
							__snake_case      :							Optional[Any]						=			self._tf_checkpoint
							__snake_case      :							List[Any]						=			''
						convert_transfo_xl_checkpoint_to_pytorch(
						    _A     ,  self._config     ,  self._pytorch_dump_output     ,  _A)
					elif self._model_type == "gpt2":
						try:
							from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
							    convert_gpta_checkpoint_to_pytorch,
							)
						except ImportError:
							raise ImportError(_A)

						convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint     ,  self._config     ,  self._pytorch_dump_output)
					elif self._model_type == "xlnet":
						try:
							from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
							    convert_xlnet_checkpoint_to_pytorch,
							)
						except ImportError:
							raise ImportError(_A)

						convert_xlnet_checkpoint_to_pytorch(
						    self._tf_checkpoint     ,  self._config     ,  self._pytorch_dump_output     ,  self._finetuning_task_name)
					elif self._model_type == "xlm":
						from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
						    convert_xlm_checkpoint_to_pytorch,
						)

						convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint     ,  self._pytorch_dump_output)
					elif self._model_type == "lxmert":
						from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
						    convert_lxmert_checkpoint_to_pytorch,
						)

						convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint     ,  self._pytorch_dump_output)
					elif self._model_type == "rembert":
						from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
						    convert_rembert_tf_checkpoint_to_pytorch,
						)

						convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint     ,  self._config     ,  self._pytorch_dump_output)
					else:
						raise ValueError(
						    '--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]')





