



# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from typing import List
def   UpperCAmelCase							(		A__				,  A__     )							->					Any:
	if name != "training_function" and name != "main":
		raise ValueError(f'''Incorrect function name passed: {name}, choose either \'main\' or \'training_function\''''     )
	_snake_case     , _snake_case					:							Union[str, Any]					     =   [], False
	for line in lines:
		if not found_start and f'''def {name}''' in line:
			_snake_case					:							Tuple					     =   True
			good_lines.append(A__     )
			continue
		if found_start:
			if name == "training_function" and "def main" in line:
				return good_lines
			if name == "main" and "if __name__" in line:
				return good_lines
			good_lines.append(A__     )
def   UpperCAmelCase							(		A__     )							->					Dict:
	return [line for line in lines if not line.lstrip().startswith("""#"""     ) and line != "\n"]


def   UpperCAmelCase							(		A__				,  A__				,  A__				,  A__ = None     )							->					str:
	with open(A__				,  """r"""     ) as f:
		_snake_case					:							Tuple					     =   f.readlines()
	with open(os.path.abspath(os.path.join("""examples"""				,  """nlp_example.py"""     )     )				,  """r"""     ) as f:
		_snake_case					:							Optional[Any]					     =   f.readlines()
	with open(A__				,  """r"""     ) as f:
		_snake_case					:							Optional[Any]					     =   f.readlines()
	if secondary_filename is not None:
		with open(A__				,  """r"""     ) as f:
			_snake_case					:							List[str]					     =   f.readlines()

    # This is our base, we remove all the code from here in our `full_filename` and `feature_filename` to find the new content
	if parser_only:
		_snake_case					:							Dict					     =   clean_lines(get_function_contents_by_name(A__				,  """main"""     )     )
		_snake_case					:							int					     =   clean_lines(get_function_contents_by_name(A__				,  """main"""     )     )
		_snake_case					:							Optional[Any]					     =   clean_lines(get_function_contents_by_name(A__				,  """main"""     )     )
		if secondary_filename is not None:
			_snake_case					:							Optional[Any]					     =   clean_lines(get_function_contents_by_name(A__				,  """main"""     )     )
	else:
		_snake_case					:							str					     =   clean_lines(get_function_contents_by_name(A__				,  """training_function"""     )     )
		_snake_case					:							List[Any]					     =   clean_lines(get_function_contents_by_name(A__				,  """training_function"""     )     )
		_snake_case					:							Union[str, Any]					     =   clean_lines(get_function_contents_by_name(A__				,  """training_function"""     )     )
		if secondary_filename is not None:
			_snake_case					:							List[Any]					     =   clean_lines(
			    get_function_contents_by_name(A__				,  """training_function"""     )     )

	_snake_case					:							str					     =   """train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)\n"""

	# Specific code in our script that differs from the full version, aka what is new
	_snake_case					:							Dict					     =   []
	_snake_case					:							str					     =   []  # We keep track of the idxs just in case it's a repeated statement
	_snake_case					:							Optional[Any]					     =   iter(A__     )
	for i in range(len(A__     ) - 1     ):
		if i not in passed_idxs:
			_snake_case					:							Any					     =   next(A__     )
			if (line not in full_file_func) and (line.lstrip() != _dl_line):
				if "TESTING_MOCKED_DATALOADERS" not in line:
					new_feature_code.append(A__     )
					passed_idxs.append(A__     )
				else:
					# Skip over the `config['num_epochs'] = 2` statement
					_snake_case					:							Optional[int]					     =   next(A__     )

    # Extract out just the new parts from the full_file_training_func
	_snake_case					:							Any					     =   []
	_snake_case					:							Tuple					     =   []  # We keep track of the idxs just in case it's a repeated statement
	for i, line in enumerate(A__     ):
		if i not in passed_idxs:
			if (line not in full_file_func) and (line.lstrip() != _dl_line):
				if "TESTING_MOCKED_DATALOADERS" not in line:
					new_full_example_parts.append(A__     )
					passed_idxs.append(A__     )

    # Finally, get the overall diff
	_snake_case					:							Optional[int]					     =   [line for line in new_feature_code if line not in new_full_example_parts]
	if secondary_filename is not None:
		_snake_case					:							Optional[int]					     =   [line for line in full_file_contents if line not in secondary_file_func]
		_snake_case					:							int					     =   [line for line in diff_from_example if line not in diff_from_two]

	return diff_from_example










import os
import pickle
import tempfile
import unittest
from typing import Callable, Optional

import numpy as np

from transformers import (
    BatchEncoding,
    BertTokenizer,
    BertTokenizerFast,
    PreTrainedTokenizer,
    PreTrainedTokenizerFast,
    TensorType,
    TokenSpan,
    is_tokenizers_available,
)
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import CaptureStderr, require_flax, require_tf, require_tokenizers, require_torch, slow


if is_tokenizers_available():
						from tokenizers import Tokenizer
						from tokenizers.models import WordPiece





class 		__SCREAMING_SNAKE_CASE      (				unittest.TestCase      ):






				"""simple docstring"""

				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							Any					     =   list(tokenizer_class.max_model_input_sizes.keys()      )
					for model_name in sa_models[:1]:
						_snake_case					:							Optional[Any]					     =   tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__      )
						self.assertIsNotNone(SCREAMING_SNAKE_CASE__      )
						self.assertIsInstance(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )
						self.assertIsInstance(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )

						for special_tok in tokenizer.all_special_tokens:
							self.assertIsInstance(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )
							_snake_case					:							int					     =   tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__      )
							self.assertIsInstance(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = None      ):






					"""simple docstring"""

					_snake_case					:							int					     =   pickle.dumps(SCREAMING_SNAKE_CASE__      )
					self.assertIsNotNone(SCREAMING_SNAKE_CASE__      )

					_snake_case					:							Dict					     =   pickle.loads(SCREAMING_SNAKE_CASE__      )

					# Ensure is_fast is correctly restored
					self.assertEqual(be_restored.is_fast						,	be_original.is_fast      )

					# Ensure encodings are potentially correctly restored
					if be_original.is_fast:
						self.assertIsNotNone(be_restored.encodings      )
					else:
						self.assertIsNone(be_restored.encodings      )

					# Ensure the keys are the same
					for original_v, restored_v in zip(be_original.values()						,	be_restored.values()      ):
						if equal_op:
							self.assertTrue(equal_op(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )      )
						else:
							self.assertEqual(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )







				@slow
				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					self.check_tokenizer_from_pretrained(SCREAMING_SNAKE_CASE__      )







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					self.assertEqual(TensorType("""tf"""      )						,	TensorType.TENSORFLOW      )
					self.assertEqual(TensorType("""pt"""      )						,	TensorType.PYTORCH      )
					self.assertEqual(TensorType("""np"""      )						,	TensorType.NUMPY      )







				@require_tokenizers
				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					import numpy as np

					_snake_case					:							Optional[int]					     =   BertTokenizer.from_pretrained("""bert-base-cased"""      )
					_snake_case					:							Any					     =   BertTokenizerFast.from_pretrained("""bert-base-cased"""      )

					# Python no tensor
					with self.subTest("""BatchEncoding (Python, return_tensors=None)"""      ):
						self.assert_dump_and_restore(tokenizer_p("""Small example to encode"""      )      )

					with self.subTest("""BatchEncoding (Python, return_tensors=NUMPY)"""      ):
						self.assert_dump_and_restore(
						    tokenizer_p("""Small example to encode"""						,	return_tensors=TensorType.NUMPY      )						,	np.array_equal      )

					with self.subTest("""BatchEncoding (Rust, return_tensors=None)"""      ):
						self.assert_dump_and_restore(tokenizer_r("""Small example to encode"""      )      )

					with self.subTest("""BatchEncoding (Rust, return_tensors=NUMPY)"""      ):
						self.assert_dump_and_restore(
						    tokenizer_r("""Small example to encode"""						,	return_tensors=TensorType.NUMPY      )						,	np.array_equal      )







				@require_tf
				@require_tokenizers
				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					import tensorflow as tf

					def tf_array_equals(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      ):
						return tf.reduce_all(tf.equal(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )      )

					_snake_case					:							int					     =   BertTokenizer.from_pretrained("""bert-base-cased"""      )
					_snake_case					:							str					     =   BertTokenizerFast.from_pretrained("""bert-base-cased"""      )

					with self.subTest("""BatchEncoding (Python, return_tensors=TENSORFLOW)"""      ):
						self.assert_dump_and_restore(
						    tokenizer_p("""Small example to encode"""						,	return_tensors=TensorType.TENSORFLOW      )						,	SCREAMING_SNAKE_CASE__      )

					with self.subTest("""BatchEncoding (Rust, return_tensors=TENSORFLOW)"""      ):
						self.assert_dump_and_restore(
						    tokenizer_r("""Small example to encode"""						,	return_tensors=TensorType.TENSORFLOW      )						,	SCREAMING_SNAKE_CASE__      )







				@require_torch
				@require_tokenizers
				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					import torch

					_snake_case					:							Union[str, Any]					     =   BertTokenizer.from_pretrained("""bert-base-cased"""      )
					_snake_case					:							Dict					     =   BertTokenizerFast.from_pretrained("""bert-base-cased"""      )

					with self.subTest("""BatchEncoding (Python, return_tensors=PYTORCH)"""      ):
						self.assert_dump_and_restore(
						    tokenizer_p("""Small example to encode"""						,	return_tensors=TensorType.PYTORCH      )						,	torch.equal      )

					with self.subTest("""BatchEncoding (Rust, return_tensors=PYTORCH)"""      ):
						self.assert_dump_and_restore(
						    tokenizer_r("""Small example to encode"""						,	return_tensors=TensorType.PYTORCH      )						,	torch.equal      )







				@require_tokenizers
				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					_snake_case					:							Optional[int]					     =   BertTokenizer.from_pretrained("""bert-base-cased"""      )
					_snake_case					:							int					     =   BertTokenizerFast.from_pretrained("""bert-base-cased"""      )

					with self.subTest("""Python Tokenizer"""      ):
						self.assertFalse(tokenizer_p("""Small example to_encode"""      ).is_fast      )

					with self.subTest("""Rust Tokenizer"""      ):
						self.assertTrue(tokenizer_r("""Small example to_encode"""      ).is_fast      )







				@require_tokenizers
				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					_snake_case					:							Optional[int]					     =   BertTokenizerFast.from_pretrained("""bert-base-cased"""      )
					_snake_case					:							Dict					     =   tokenizer_r(["""Test""", """\xad""", """test"""]						,	is_split_into_words=SCREAMING_SNAKE_CASE__      )

					self.assertEqual(encoded.word_to_tokens(0      )						,	TokenSpan(start=1						,	end=2      )      )
					self.assertEqual(encoded.word_to_tokens(1      )						,	SCREAMING_SNAKE_CASE__      )
					self.assertEqual(encoded.word_to_tokens(2      )						,	TokenSpan(start=2						,	end=3      )      )







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					_snake_case					:							List[Any]					     =   BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]}      )
					_snake_case					:							Union[str, Any]					     =   batch.convert_to_tensors(tensor_type="""np"""      )
					self.assertEqual(tensor_batch["""inputs"""].shape						,	(2, 3)      )
					self.assertEqual(tensor_batch["""labels"""].shape						,	(2,)      )
					# test converting the converted
					with CaptureStderr() as cs:
						_snake_case					:							Optional[int]					     =   batch.convert_to_tensors(tensor_type="""np"""      )
					self.assertFalse(len(cs.err      )						,	msg=f'''should have no warning, but got {cs.err}'''      )

					_snake_case					:							str					     =   BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}      )
					_snake_case					:							Optional[int]					     =   batch.convert_to_tensors(tensor_type="""np"""						,	prepend_batch_axis=SCREAMING_SNAKE_CASE__      )
					self.assertEqual(tensor_batch["""inputs"""].shape						,	(1, 3)      )
					self.assertEqual(tensor_batch["""labels"""].shape						,	(1,)      )







				@require_torch
				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					_snake_case					:							List[Any]					     =   BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]}      )
					_snake_case					:							Union[str, Any]					     =   batch.convert_to_tensors(tensor_type="""pt"""      )
					self.assertEqual(tensor_batch["""inputs"""].shape						,	(2, 3)      )
					self.assertEqual(tensor_batch["""labels"""].shape						,	(2,)      )
					# test converting the converted
					with CaptureStderr() as cs:
						_snake_case					:							Optional[Any]					     =   batch.convert_to_tensors(tensor_type="""pt"""      )
					self.assertFalse(len(cs.err      )						,	msg=f'''should have no warning, but got {cs.err}'''      )

					_snake_case					:							Union[str, Any]					     =   BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}      )
					_snake_case					:							Union[str, Any]					     =   batch.convert_to_tensors(tensor_type="""pt"""						,	prepend_batch_axis=SCREAMING_SNAKE_CASE__      )
					self.assertEqual(tensor_batch["""inputs"""].shape						,	(1, 3)      )
					self.assertEqual(tensor_batch["""labels"""].shape						,	(1,)      )







				@require_tf
				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					_snake_case					:							Optional[int]					     =   BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]}      )
					_snake_case					:							str					     =   batch.convert_to_tensors(tensor_type="""tf"""      )
					self.assertEqual(tensor_batch["""inputs"""].shape						,	(2, 3)      )
					self.assertEqual(tensor_batch["""labels"""].shape						,	(2,)      )
					# test converting the converted
					with CaptureStderr() as cs:
						_snake_case					:							int					     =   batch.convert_to_tensors(tensor_type="""tf"""      )
					self.assertFalse(len(cs.err      )						,	msg=f'''should have no warning, but got {cs.err}'''      )

					_snake_case					:							Optional[Any]					     =   BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}      )
					_snake_case					:							List[str]					     =   batch.convert_to_tensors(tensor_type="""tf"""						,	prepend_batch_axis=SCREAMING_SNAKE_CASE__      )
					self.assertEqual(tensor_batch["""inputs"""].shape						,	(1, 3)      )
					self.assertEqual(tensor_batch["""labels"""].shape						,	(1,)      )







				@require_flax
				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					_snake_case					:							List[Any]					     =   BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]}      )
					_snake_case					:							Any					     =   batch.convert_to_tensors(tensor_type="""jax"""      )
					self.assertEqual(tensor_batch["""inputs"""].shape						,	(2, 3)      )
					self.assertEqual(tensor_batch["""labels"""].shape						,	(2,)      )
					# test converting the converted
					with CaptureStderr() as cs:
						_snake_case					:							Dict					     =   batch.convert_to_tensors(tensor_type="""jax"""      )
					self.assertFalse(len(cs.err      )						,	msg=f'''should have no warning, but got {cs.err}'''      )

					_snake_case					:							List[str]					     =   BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}      )
					_snake_case					:							List[Any]					     =   batch.convert_to_tensors(tensor_type="""jax"""						,	prepend_batch_axis=SCREAMING_SNAKE_CASE__      )
					self.assertEqual(tensor_batch["""inputs"""].shape						,	(1, 3)      )
					self.assertEqual(tensor_batch["""labels"""].shape						,	(1,)      )







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					_snake_case					:							Any					     =   [{"""input_ids""": np.array([0, 1, 2]      )}, {"""input_ids""": np.array([0, 1, 2, 3]      )}]
					_snake_case					:							int					     =   BertTokenizer.from_pretrained("""bert-base-cased"""      )

					_snake_case					:							List[Any]					     =   tokenizer.pad(SCREAMING_SNAKE_CASE__						,	padding=SCREAMING_SNAKE_CASE__      )
					self.assertTrue(isinstance(batch["""input_ids"""]						,	np.ndarray      )      )
					self.assertEqual(batch["""input_ids"""].tolist()						,	[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]      )
					_snake_case					:							Optional[int]					     =   tokenizer.pad(SCREAMING_SNAKE_CASE__						,	padding=SCREAMING_SNAKE_CASE__						,	return_tensors="""np"""      )
					self.assertTrue(isinstance(batch["""input_ids"""]						,	np.ndarray      )      )
					self.assertEqual(batch["""input_ids"""].tolist()						,	[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]      )







				@require_torch
				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					import torch

					_snake_case					:							str					     =   [{"""input_ids""": torch.tensor([0, 1, 2]      )}, {"""input_ids""": torch.tensor([0, 1, 2, 3]      )}]
					_snake_case					:							Tuple					     =   BertTokenizer.from_pretrained("""bert-base-cased"""      )

					_snake_case					:							Dict					     =   tokenizer.pad(SCREAMING_SNAKE_CASE__						,	padding=SCREAMING_SNAKE_CASE__      )
					self.assertTrue(isinstance(batch["""input_ids"""]						,	torch.Tensor      )      )
					self.assertEqual(batch["""input_ids"""].tolist()						,	[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]      )
					_snake_case					:							Tuple					     =   tokenizer.pad(SCREAMING_SNAKE_CASE__						,	padding=SCREAMING_SNAKE_CASE__						,	return_tensors="""pt"""      )
					self.assertTrue(isinstance(batch["""input_ids"""]						,	torch.Tensor      )      )
					self.assertEqual(batch["""input_ids"""].tolist()						,	[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]      )







				@require_tf
				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					import tensorflow as tf

					_snake_case					:							Optional[Any]					     =   [{"""input_ids""": tf.constant([0, 1, 2]      )}, {"""input_ids""": tf.constant([0, 1, 2, 3]      )}]
					_snake_case					:							Optional[Any]					     =   BertTokenizer.from_pretrained("""bert-base-cased"""      )

					_snake_case					:							Optional[Any]					     =   tokenizer.pad(SCREAMING_SNAKE_CASE__						,	padding=SCREAMING_SNAKE_CASE__      )
					self.assertTrue(isinstance(batch["""input_ids"""]						,	tf.Tensor      )      )
					self.assertEqual(batch["""input_ids"""].numpy().tolist()						,	[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]      )
					_snake_case					:							List[Any]					     =   tokenizer.pad(SCREAMING_SNAKE_CASE__						,	padding=SCREAMING_SNAKE_CASE__						,	return_tensors="""tf"""      )
					self.assertTrue(isinstance(batch["""input_ids"""]						,	tf.Tensor      )      )
					self.assertEqual(batch["""input_ids"""].numpy().tolist()						,	[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]      )







				@require_tokenizers
				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					_snake_case					:							Optional[int]					     =   Tokenizer(WordPiece(unk_token="""[UNK]"""      )      )
					PreTrainedTokenizerFast(tokenizer_object=SCREAMING_SNAKE_CASE__      )







				@require_tokenizers
				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					_snake_case					:							Optional[Any]					     =   Tokenizer(WordPiece(unk_token="""[UNK]"""      )      )
					with tempfile.TemporaryDirectory() as tmpdirname:
						bert_tokenizer.save(os.path.join(SCREAMING_SNAKE_CASE__						,	"""tokenizer.json"""      )      )
						PreTrainedTokenizerFast(tokenizer_file=os.path.join(SCREAMING_SNAKE_CASE__						,	"""tokenizer.json"""      )      )





