
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      __lowerCAmelCase  (						self			:			Union[str, Any]					, SCREAMING_SNAKE_CASE__			:			List[str]					, SCREAMING_SNAKE_CASE__			:			Any					, SCREAMING_SNAKE_CASE__			:			Optional[Any]   ):






      """simple docstring"""



      return None




class       _lowerCAmelCase  :

 """simple docstring"""

 def      __lowerCAmelCase  (						self			:			List[Any]					, SCREAMING_SNAKE_CASE__			:			Optional[Any]					, SCREAMING_SNAKE_CASE__			:			str					, SCREAMING_SNAKE_CASE__			:			int					, SCREAMING_SNAKE_CASE__			:			Dict   ):






      """simple docstring"""



      return None




class       _lowerCAmelCase  (		unittest.TestCase							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					List[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      __lowerCAmelCase  (						self			:			Any   ):






      """simple docstring"""



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

 @require_torch
 @slow
 def      __lowerCAmelCase  (						self			:			List[str]   ):






      """simple docstring"""



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

 @require_torch
 @slow
 def      __lowerCAmelCase  (						self			:			Dict   ):






      """simple docstring"""



      from transformers import BertModel

      UpperCamelCase						    =					['[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()
           UpperCamelCase						    =					BertTokenizerFast(vocab_file.name   )

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

 @require_tf
 @slow
 def      __lowerCAmelCase  (						self			:			Tuple   ):






      """simple docstring"""



      for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
           UpperCamelCase						    =					self._test_export(SCREAMING_SNAKE_CASE__					, 'tf'					, 12					, **SCREAMING_SNAKE_CASE__   )
           UpperCamelCase						    =					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      __lowerCAmelCase  (						self			:			int   ):






      """simple docstring"""



      for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
           UpperCamelCase						    =					self._test_export(SCREAMING_SNAKE_CASE__					, 'pt'					, 12					, **SCREAMING_SNAKE_CASE__   )
           UpperCamelCase						    =					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      __lowerCAmelCase  (						self			:			Dict					, SCREAMING_SNAKE_CASE__			:			Optional[int]					, SCREAMING_SNAKE_CASE__			:			Tuple					, SCREAMING_SNAKE_CASE__			:			Dict					, SCREAMING_SNAKE_CASE__			:			int=None					, **SCREAMING_SNAKE_CASE__			:			Optional[Any]   ):






      """simple docstring"""



      try:
           # Compute path
           with TemporaryDirectory() as tempdir:
                UpperCamelCase						    =					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      __lowerCAmelCase  (						self			:			Union[str, Any]   ):






      """simple docstring"""



      from transformers import BertModel

      UpperCamelCase						    =					BertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'   )   )
      UpperCamelCase						    =					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      __lowerCAmelCase  (						self			:			int   ):






      """simple docstring"""



      from transformers import TFBertModel

      UpperCamelCase						    =					TFBertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'   )   )
      UpperCamelCase						    =					BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random'   )
      self._test_infer_dynamic_axis(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, 'tf'   )

 def      __lowerCAmelCase  (						self			:			List[str]					, SCREAMING_SNAKE_CASE__			:			Optional[int]					, SCREAMING_SNAKE_CASE__			:			Optional[Any]					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					FeatureExtractionPipeline(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )

      UpperCamelCase						    =					['input_ids', 'token_type_ids', 'attention_mask', 'output_0', 'output_1']
      UpperCamelCase    ,		UpperCamelCase    ,		UpperCamelCase    ,		UpperCamelCase						    =					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      __lowerCAmelCase  (						self			:			str   ):






      """simple docstring"""



      UpperCamelCase						    =					['input_ids', 'attention_mask', 'token_type_ids']
      UpperCamelCase						    =					{'input_ids': [1, 2, 3, 4], 'attention_mask': [0, 0, 0, 0], 'token_type_ids': [1, 1, 1, 1]}
      UpperCamelCase    ,		UpperCamelCase						    =					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)
      UpperCamelCase    ,		UpperCamelCase						    =					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      __lowerCAmelCase  (						self			:			List[Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					generate_identified_filename(Path('/home/something/my_fake_model.onnx'   )					, '-test'   )
      self.assertEqual('/home/something/my_fake_model-test.onnx'					, generated.as_posix()   )




import logging

from transformers import PretrainedConfig


_snake_case      = logging.getLogger(__name__)


_snake_case      = {
    '''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json''',
}
class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					int       						="bertabs"

 def __init__(						self			:			Optional[int]					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=3_05_22					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=5_12					, SCREAMING_SNAKE_CASE__			:			List[Any]=6					, SCREAMING_SNAKE_CASE__			:			List[str]=5_12					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=8					, SCREAMING_SNAKE_CASE__			:			List[str]=5_12					, SCREAMING_SNAKE_CASE__			:			List[Any]=0.2					, SCREAMING_SNAKE_CASE__			:			Tuple=6					, SCREAMING_SNAKE_CASE__			:			Any=7_68					, SCREAMING_SNAKE_CASE__			:			str=8					, SCREAMING_SNAKE_CASE__			:			Dict=20_48					, SCREAMING_SNAKE_CASE__			:			List[str]=0.2					, **SCREAMING_SNAKE_CASE__			:			List[str]					, ):






      """simple docstring"""



      super().__init__(**SCREAMING_SNAKE_CASE__   )

      UpperCamelCase						    =					vocab_size
      UpperCamelCase						    =					max_pos

      UpperCamelCase						    =					enc_layers
      UpperCamelCase						    =					enc_hidden_size
      UpperCamelCase						    =					enc_heads
      UpperCamelCase						    =					enc_ff_size
      UpperCamelCase						    =					enc_dropout

      UpperCamelCase						    =					dec_layers
      UpperCamelCase						    =					dec_hidden_size
      UpperCamelCase						    =					dec_heads
      UpperCamelCase						    =					dec_ff_size
      UpperCamelCase						    =					dec_dropout


