




"""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 lowerCAmelCase__      :






			'''simple docstring'''



			def    A_       (    self    , lowercase    , lowercase    , lowercase    ):
							return None






class lowerCAmelCase__      :






			'''simple docstring'''



			def    A_       (    self    , lowercase    , lowercase    , lowercase    , lowercase    ):
							return None






class lowerCAmelCase__      (	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    A_       (    self    ):
							for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
											self._test_export(lowercase    , 'tf'    , 12    , **lowercase    )
			@require_torch
			@slow
			def    A_       (    self    ):
							for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
											self._test_export(lowercase    , 'pt'    , 12    , **lowercase    )
			@require_torch
			@slow
			def    A_       (    self    ):
							from transformers import BertModel

							_lowerCamelCase	:     Union[str, Any]       =       ['[UNK]', '[SEP]', '[CLS]', '[PAD]', '[MASK]', 'some', 'other', 'words']
							with NamedTemporaryFile(mode='w+t'    ) as vocab_file:
											vocab_file.write('\n'.join(lowercase    )    )
											vocab_file.flush()
											_lowerCamelCase	:     List[str]       =       BertTokenizerFast(vocab_file.name    )

							with TemporaryDirectory() as bert_save_dir:
											_lowerCamelCase	:     str       =       BertModel(BertConfig(vocab_size=len(lowercase    )    )    )
											model.save_pretrained(lowercase    )
											self._test_export(lowercase    , 'pt'    , 12    , lowercase    )
			@require_tf
			@slow
			def    A_       (    self    ):
							for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
											_lowerCamelCase	:     int       =       self._test_export(lowercase    , 'tf'    , 12    , **lowercase    )
											_lowerCamelCase	:     Any       =       quantize(Path(lowercase    )    )

											# Ensure the actual quantized model is not bigger than the original one
											if quantized_path.stat().st_size >= Path(lowercase    ).stat().st_size:
															self.fail('Quantized model is bigger than initial ONNX model'    )
			@require_torch
			@slow
			def    A_       (    self    ):
							for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
											_lowerCamelCase	:     Optional[Any]       =       self._test_export(lowercase    , 'pt'    , 12    , **lowercase    )
											_lowerCamelCase	:     Dict       =       quantize(lowercase    )

											# Ensure the actual quantized model is not bigger than the original one
											if quantized_path.stat().st_size >= Path(lowercase    ).stat().st_size:
															self.fail('Quantized model is bigger than initial ONNX model'    )
			def    A_       (    self    , lowercase    , lowercase    , lowercase    , lowercase=None    , **lowercase    ):
							try:
											# Compute path
											with TemporaryDirectory() as tempdir:
															_lowerCamelCase	:     List[str]       =       Path(lowercase    ).joinpath('model.onnx'    )

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

											# Export
											convert(lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , **lowercase    )

											return path
							except Exception as e:
											self.fail(lowercase    )
			@require_torch
			@require_tokenizers
			@slow
			def    A_       (    self    ):
							from transformers import BertModel

							_lowerCamelCase	:     Tuple       =       BertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'    )    )
							_lowerCamelCase	:     List[str]       =       BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random'    )
							self._test_infer_dynamic_axis(lowercase    , lowercase    , 'pt'    )
			@require_tf
			@require_tokenizers
			@slow
			def    A_       (    self    ):
							from transformers import TFBertModel

							_lowerCamelCase	:     Dict       =       TFBertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'    )    )
							_lowerCamelCase	:     Union[str, Any]       =       BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random'    )
							self._test_infer_dynamic_axis(lowercase    , lowercase    , 'tf'    )
			def    A_       (    self    , lowercase    , lowercase    , lowercase    ):
							_lowerCamelCase	:     Dict       =       FeatureExtractionPipeline(lowercase    , lowercase    )

							_lowerCamelCase	:     int       =       ['input_ids', 'token_type_ids', 'attention_mask', 'output_0', 'output_1']
							_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase	:     Optional[int]       =       infer_shapes(lowercase    , lowercase    )

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

							# 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    A_       (    self    ):
							_lowerCamelCase	:     Tuple       =       ['input_ids', 'attention_mask', 'token_type_ids']
							_lowerCamelCase	:     List[Any]       =       {'input_ids': [1, 2, 3, 4], 'attention_mask': [0, 0, 0, 0], 'token_type_ids': [1, 1, 1, 1]}
							_lowerCamelCase,					_lowerCamelCase	:     Dict       =       ensure_valid_input(FuncContiguousArgs()    , lowercase    , lowercase    )

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

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

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

							# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
							_lowerCamelCase,					_lowerCamelCase	:     Optional[int]       =       ensure_valid_input(FuncNonContiguousArgs()    , lowercase    , lowercase    )

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

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






			def    A_       (    self    ):
							_lowerCamelCase	:     str       =       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"""






import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer






class lowerCAmelCase__      (	tf.keras.layers.Layer		):






			'''simple docstring'''



			def __init__(    self    , lowercase    , lowercase    , lowercase = None    , lowercase = None    ):
							super().__init__()
							_lowerCamelCase	:     Optional[Any]       =       pad_token_id
							_lowerCamelCase	:     Dict       =       max_length
							_lowerCamelCase	:     Tuple       =       vocab
							_lowerCamelCase	:     List[str]       =       merges
							_lowerCamelCase	:     int       =       BytePairTokenizer(lowercase    , lowercase    , sequence_length=lowercase    )
			@classmethod
			def    A_       (    cls    , lowercase    , *lowercase    , **lowercase    ):
							_lowerCamelCase	:     str       =       [' '.join(lowercase    ) for m in tokenizer.bpe_ranks.keys()]
							_lowerCamelCase	:     Union[str, Any]       =       tokenizer.get_vocab()
							return cls(lowercase    , lowercase    , *lowercase    , **lowercase    )
			@classmethod
			def    A_       (    cls    , lowercase    , *lowercase    , **lowercase    ):
							_lowerCamelCase	:     Optional[int]       =       GPTaTokenizer.from_pretrained(lowercase    , *lowercase    , **lowercase    )
							return cls.from_tokenizer(lowercase    , *lowercase    , **lowercase    )
			@classmethod
			def    A_       (    cls    , lowercase    ):
							return cls(**lowercase    )
			def    A_       (    self    ):
							return {
							    "vocab": self.vocab,
							    "merges": self.merges,
							    "max_length": self.max_length,
							    "pad_token_id": self.pad_token_id,
							}






			def    A_       (    self    , lowercase    , lowercase = None    ):
							_lowerCamelCase	:     int       =       self.tf_tokenizer(lowercase    )
							_lowerCamelCase	:     Optional[int]       =       tf.ones_like(lowercase    )

							if self.pad_token_id is not None:
											# pad the tokens up to max length
											_lowerCamelCase	:     str       =       max_length if max_length is not None else self.max_length

											if max_length is not None:
															_lowerCamelCase,					_lowerCamelCase	:     Union[str, Any]       =       pad_model_inputs(
															    lowercase    , max_seq_length=lowercase    , pad_value=self.pad_token_id    )

							return {"attention_mask": attention_mask, "input_ids": input_ids}