




'''simple docstring'''



import copy
import random

from transformers import CLIPTokenizer







class 		lowercase_   (			A       ):


   """simple docstring"""

   def __init__(   self :     int  ,				*__lowerCamelCase :     int  ,				**__lowerCamelCase :     int   ):

      """simple docstring"""
      super().__init__(*__lowerCamelCase  ,				**__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				{}



   def 							lowerCAmelCase_      (   self :     List[str]  ,				__lowerCamelCase :     Dict  ,				*__lowerCamelCase :     List[Any]  ,				**__lowerCamelCase :     List[str]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				super().add_tokens(__lowerCamelCase  ,				*__lowerCamelCase  ,				**__lowerCamelCase   )
      if num_added_tokens == 0:
         raise ValueError(
             F"""The tokenizer already contains the token {placeholder_token}. Please pass a different"""
             " `placeholder_token` that is not already in the tokenizer."   )



   def 							lowerCAmelCase_      (   self :     List[Any]  ,				__lowerCamelCase :     Union[str, Any]  ,				*__lowerCamelCase :     Optional[Any]  ,				__lowerCamelCase :     List[str]=1  ,				**__lowerCamelCase :     Any   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				[]
      if num_vec_per_token == 1:
         self.try_adding_tokens(__lowerCamelCase  ,				*__lowerCamelCase  ,				**__lowerCamelCase   )
         output.append(__lowerCamelCase   )
      else:
         _SCREAMING_SNAKE_CASE	=				[]
         for i in range(__lowerCamelCase   ):
            _SCREAMING_SNAKE_CASE	=				placeholder_token + F"""_{i}"""
            self.try_adding_tokens(__lowerCamelCase  ,				*__lowerCamelCase  ,				**__lowerCamelCase   )
            output.append(__lowerCamelCase   )
        # handle cases where there is a new placeholder token that contains the current placeholder token but is larger
      for token in self.token_map:
         if token in placeholder_token:
            raise ValueError(
                F"""The tokenizer already has placeholder token {token} that can get confused with"""
                F""" {placeholder_token}keep placeholder tokens independent"""   )
      _SCREAMING_SNAKE_CASE	=				output



   def 							lowerCAmelCase_      (   self :     List[str]  ,				__lowerCamelCase :     List[Any]  ,				__lowerCamelCase :     List[Any]=False  ,				__lowerCamelCase :     List[str]=1.0   ):

      """simple docstring"""
      if isinstance(__lowerCamelCase  ,				__lowerCamelCase   ):
         _SCREAMING_SNAKE_CASE	=				[]
         for i in range(len(__lowerCamelCase   )   ):
            output.append(self.replace_placeholder_tokens_in_text(text[i]  ,				vector_shuffle=__lowerCamelCase   )   )
         return output
      for placeholder_token in self.token_map:
         if placeholder_token in text:
            _SCREAMING_SNAKE_CASE	=				self.token_map[placeholder_token]
            _SCREAMING_SNAKE_CASE	=				tokens[: 1 + int(len(__lowerCamelCase   ) * prop_tokens_to_load   )]
            if vector_shuffle:
               _SCREAMING_SNAKE_CASE	=				copy.copy(__lowerCamelCase   )
               random.shuffle(__lowerCamelCase   )
            _SCREAMING_SNAKE_CASE	=				text.replace(__lowerCamelCase  ,				" ".join(__lowerCamelCase   )   )
      return text



   def __call__(   self :     str  ,				__lowerCamelCase :     List[Any]  ,				*__lowerCamelCase :     List[str]  ,				__lowerCamelCase :     List[str]=False  ,				__lowerCamelCase :     Optional[Any]=1.0  ,				**__lowerCamelCase :     Any   ):

      """simple docstring"""
      return super().__call__(
          self.replace_placeholder_tokens_in_text(
              __lowerCamelCase  ,				vector_shuffle=__lowerCamelCase  ,				prop_tokens_to_load=__lowerCamelCase   )  ,				*__lowerCamelCase  ,				**__lowerCamelCase  ,				)



   def 							lowerCAmelCase_      (   self :     Optional[int]  ,				__lowerCamelCase :     Dict  ,				*__lowerCamelCase :     Any  ,				__lowerCamelCase :     Union[str, Any]=False  ,				__lowerCamelCase :     Optional[Any]=1.0  ,				**__lowerCamelCase :     Dict   ):

      """simple docstring"""
      return super().encode(
          self.replace_placeholder_tokens_in_text(
              __lowerCamelCase  ,				vector_shuffle=__lowerCamelCase  ,				prop_tokens_to_load=__lowerCamelCase   )  ,				*__lowerCamelCase  ,				**__lowerCamelCase  ,				)







'''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 		lowercase_   :


   """simple docstring"""

   def 							lowerCAmelCase_      (   self :     int  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     Optional[int]  ,				__lowerCamelCase :     Tuple   ):

      """simple docstring"""
      return None







class 		lowercase_   :


   """simple docstring"""

   def 							lowerCAmelCase_      (   self :     Union[str, Any]  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     List[str]  ,				__lowerCamelCase :     Union[str, Any]  ,				__lowerCamelCase :     List[Any]   ):

      """simple docstring"""
      return None







class 		lowercase_   (			unittest.TestCase       ):


   """simple docstring"""

   lowerCamelCase_      =						[
       # (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 :     Union[str, Any]   ):

      """simple docstring"""
      for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         self._test_export(__lowerCamelCase  ,				"tf"  ,				1_2  ,				**__lowerCamelCase   )



   @require_torch
   @slow
   def 							lowerCAmelCase_      (   self :     List[Any]   ):

      """simple docstring"""
      for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         self._test_export(__lowerCamelCase  ,				"pt"  ,				1_2  ,				**__lowerCamelCase   )



   @require_torch
   @slow
   def 							lowerCAmelCase_      (   self :     List[Any]   ):

      """simple docstring"""
      from transformers import BertModel

      _SCREAMING_SNAKE_CASE	=				["[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	=				BertTokenizerFast(vocab_file.name   )

      with TemporaryDirectory() as bert_save_dir:
         _SCREAMING_SNAKE_CASE	=				BertModel(BertConfig(vocab_size=len(__lowerCamelCase   )   )   )
         model.save_pretrained(__lowerCamelCase   )
         self._test_export(__lowerCamelCase  ,				"pt"  ,				1_2  ,				__lowerCamelCase   )



   @require_tf
   @slow
   def 							lowerCAmelCase_      (   self :     Optional[int]   ):

      """simple docstring"""
      for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         _SCREAMING_SNAKE_CASE	=				self._test_export(__lowerCamelCase  ,				"tf"  ,				1_2  ,				**__lowerCamelCase   )
         _SCREAMING_SNAKE_CASE	=				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 							lowerCAmelCase_      (   self :     Union[str, Any]   ):

      """simple docstring"""
      for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         _SCREAMING_SNAKE_CASE	=				self._test_export(__lowerCamelCase  ,				"pt"  ,				1_2  ,				**__lowerCamelCase   )
         _SCREAMING_SNAKE_CASE	=				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 							lowerCAmelCase_      (   self :     Tuple  ,				__lowerCamelCase :     Optional[Any]  ,				__lowerCamelCase :     Any  ,				__lowerCamelCase :     Dict  ,				__lowerCamelCase :     Optional[int]=None  ,				**__lowerCamelCase :     int   ):

      """simple docstring"""
      try:
         # Compute path
         with TemporaryDirectory() as tempdir:
            _SCREAMING_SNAKE_CASE	=				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 							lowerCAmelCase_      (   self :     Tuple   ):

      """simple docstring"""
      from transformers import BertModel

      _SCREAMING_SNAKE_CASE	=				BertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"   )   )
      _SCREAMING_SNAKE_CASE	=				BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"   )
      self._test_infer_dynamic_axis(__lowerCamelCase  ,				__lowerCamelCase  ,				"pt"   )



   @require_tf
   @require_tokenizers
   @slow
   def 							lowerCAmelCase_      (   self :     Any   ):

      """simple docstring"""
      from transformers import TFBertModel

      _SCREAMING_SNAKE_CASE	=				TFBertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"   )   )
      _SCREAMING_SNAKE_CASE	=				BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"   )
      self._test_infer_dynamic_axis(__lowerCamelCase  ,				__lowerCamelCase  ,				"tf"   )



   def 							lowerCAmelCase_      (   self :     List[Any]  ,				__lowerCamelCase :     Tuple  ,				__lowerCamelCase :     Optional[Any]  ,				__lowerCamelCase :     List[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				FeatureExtractionPipeline(__lowerCamelCase  ,				__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE	=				["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
      _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				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 							lowerCAmelCase_      (   self :     Union[str, Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				["input_ids", "attention_mask", "token_type_ids"]
      _SCREAMING_SNAKE_CASE	=				{"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	=				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	=				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 							lowerCAmelCase_      (   self :     Optional[int]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				generate_identified_filename(Path("/home/something/my_fake_model.onnx"   )  ,				"-test"   )
      self.assertEqual("/home/something/my_fake_model-test.onnx"  ,				generated.as_posix()   )

