




'''simple docstring'''



import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class 		lowercase_   (			A					,  unittest.TestCase       ):


   """simple docstring"""

   lowerCamelCase_      =						None
   lowerCamelCase_      =						BloomTokenizerFast
   lowerCamelCase_      =						BloomTokenizerFast
   lowerCamelCase_      =						True
   lowerCamelCase_      =						False
   lowerCamelCase_      =						'''tokenizer_file'''
   lowerCamelCase_      =						{'''bos_token''': '''<s>''', '''eos_token''': '''</s>''', '''unk_token''': '''<unk>''', '''pad_token''': '''<pad>'''}



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

      """simple docstring"""
      super().setUp()
      _SCREAMING_SNAKE_CASE	=				BloomTokenizerFast.from_pretrained("bigscience/tokenizer"   )
      tokenizer.save_pretrained(self.tmpdirname   )



   def 							lowerCAmelCase_      (   self :     str  ,				**__lowerCamelCase :     Tuple   ):

      """simple docstring"""
      kwargs.update(self.special_tokens_map   )
      return BloomTokenizerFast.from_pretrained(self.tmpdirname  ,				**__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     Optional[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.get_rust_tokenizer()

      _SCREAMING_SNAKE_CASE	=				["The quick brown fox</s>", "jumps over the lazy dog</s>"]
      _SCREAMING_SNAKE_CASE	=				[[2_1_7_5, 2_3_7_1_4, 7_3_1_7_3, 1_4_4_2_5_2, 2], [7_7, 1_3_2_6_1_9, 3_4_7_8, 3_6_8, 1_0_9_5_8_6, 3_5_4_3_3, 2]]

      _SCREAMING_SNAKE_CASE	=				tokenizer.batch_encode_plus(__lowerCamelCase   )["input_ids"]
      self.assertListEqual(__lowerCamelCase  ,				__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE	=				tokenizer.batch_decode(__lowerCamelCase   )
      self.assertListEqual(__lowerCamelCase  ,				__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     Optional[Any]  ,				__lowerCamelCase :     int=6   ):

      """simple docstring"""
      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""   ):
            _SCREAMING_SNAKE_CASE	=				self.rust_tokenizer_class.from_pretrained(__lowerCamelCase  ,				**__lowerCamelCase   )
            # tokenizer_r.pad_token = None # Hotfixing padding = None
            # Simple input
            _SCREAMING_SNAKE_CASE	=				"This is a simple input"
            _SCREAMING_SNAKE_CASE	=				["This is a simple input 1", "This is a simple input 2"]
            _SCREAMING_SNAKE_CASE	=				("This is a simple input", "This is a pair")
            _SCREAMING_SNAKE_CASE	=				[
                ("This is a simple input 1", "This is a simple input 2"),
                ("This is a simple pair 1", "This is a simple pair 2"),
            ]

            # Simple input tests
            try:
               tokenizer_r.encode(__lowerCamelCase  ,				max_length=__lowerCamelCase   )
               tokenizer_r.encode_plus(__lowerCamelCase  ,				max_length=__lowerCamelCase   )

               tokenizer_r.batch_encode_plus(__lowerCamelCase  ,				max_length=__lowerCamelCase   )
               tokenizer_r.encode(__lowerCamelCase  ,				max_length=__lowerCamelCase   )
               tokenizer_r.batch_encode_plus(__lowerCamelCase  ,				max_length=__lowerCamelCase   )
            except ValueError:
               self.fail("Bloom Tokenizer should be able to deal with padding"   )

            _SCREAMING_SNAKE_CASE	=				None  # Hotfixing padding = None
            self.assertRaises(__lowerCamelCase  ,				tokenizer_r.encode  ,				__lowerCamelCase  ,				max_length=__lowerCamelCase  ,				padding="max_length"   )

            # Simple input
            self.assertRaises(__lowerCamelCase  ,				tokenizer_r.encode_plus  ,				__lowerCamelCase  ,				max_length=__lowerCamelCase  ,				padding="max_length"   )

            # Simple input
            self.assertRaises(
                __lowerCamelCase  ,				tokenizer_r.batch_encode_plus  ,				__lowerCamelCase  ,				max_length=__lowerCamelCase  ,				padding="max_length"  ,				)

            # Pair input
            self.assertRaises(__lowerCamelCase  ,				tokenizer_r.encode  ,				__lowerCamelCase  ,				max_length=__lowerCamelCase  ,				padding="max_length"   )

            # Pair input
            self.assertRaises(__lowerCamelCase  ,				tokenizer_r.encode_plus  ,				__lowerCamelCase  ,				max_length=__lowerCamelCase  ,				padding="max_length"   )

            # Pair input
            self.assertRaises(
                __lowerCamelCase  ,				tokenizer_r.batch_encode_plus  ,				__lowerCamelCase  ,				max_length=__lowerCamelCase  ,				padding="max_length"  ,				)



   def 							lowerCAmelCase_      (   self :     List[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.get_rust_tokenizer()
      _SCREAMING_SNAKE_CASE	=				load_dataset("xnli"  ,				"all_languages"  ,				split="test"  ,				streaming=__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE	=				next(iter(__lowerCamelCase   )   )["premise"]  # pick up one data
      _SCREAMING_SNAKE_CASE	=				list(sample_data.values()   )

      _SCREAMING_SNAKE_CASE	=				list(map(tokenizer.encode  ,				__lowerCamelCase   )   )
      _SCREAMING_SNAKE_CASE	=				[tokenizer.decode(__lowerCamelCase  ,				clean_up_tokenization_spaces=__lowerCamelCase   ) for x in output_tokens]
      self.assertListEqual(__lowerCamelCase  ,				__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     str   ):

      """simple docstring"""
      # The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
      # any sequence length constraints. This test of the parent class will fail since it relies on the
      # maximum sequence length of the positoonal embeddings.
      self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map   )  ,				1   )
      self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()   )[0]   )  ,				1   )







'''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()   )

