




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 				__UpperCamelCase      (			unittest.TestCase	):





				'''simple docstring'''



				def    __snake_case		(			self							,			UpperCAmelCase_    ):
											lowerCAmelCase =		list(tokenizer_class.max_model_input_sizes.keys()    )
											for model_name in sa_models[:1]:
																		lowerCAmelCase =		tokenizer_class.from_pretrained(UpperCAmelCase_    )
																		self.assertIsNotNone(UpperCAmelCase_    )
																		self.assertIsInstance(UpperCAmelCase_							,			UpperCAmelCase_    )
																		self.assertIsInstance(UpperCAmelCase_							,			UpperCAmelCase_    )

																		for special_tok in tokenizer.all_special_tokens:
																									self.assertIsInstance(UpperCAmelCase_							,			UpperCAmelCase_    )
																									lowerCAmelCase =		tokenizer.convert_tokens_to_ids(UpperCAmelCase_    )
																									self.assertIsInstance(UpperCAmelCase_							,			UpperCAmelCase_    )



				def    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_ = None    ):
											lowerCAmelCase =		pickle.dumps(UpperCAmelCase_    )
											self.assertIsNotNone(UpperCAmelCase_    )

											lowerCAmelCase =		pickle.loads(UpperCAmelCase_    )

											# 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(UpperCAmelCase_							,			UpperCAmelCase_    )    )
																		else:
																									self.assertEqual(UpperCAmelCase_							,			UpperCAmelCase_    )



				@slow
				def    __snake_case		(			self    ):
											self.check_tokenizer_from_pretrained(UpperCAmelCase_    )



				def    __snake_case		(			self    ):
											self.assertEqual(TensorType('''tf'''    )							,			TensorType.TENSORFLOW    )
											self.assertEqual(TensorType('''pt'''    )							,			TensorType.PYTORCH    )
											self.assertEqual(TensorType('''np'''    )							,			TensorType.NUMPY    )



				@require_tokenizers
				def    __snake_case		(			self    ):
											import numpy as np

											lowerCAmelCase =		BertTokenizer.from_pretrained('''bert-base-cased'''    )
											lowerCAmelCase =		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    __snake_case		(			self    ):
											import tensorflow as tf

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

											lowerCAmelCase =		BertTokenizer.from_pretrained('''bert-base-cased'''    )
											lowerCAmelCase =		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    )							,			UpperCAmelCase_    )

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



				@require_torch
				@require_tokenizers
				def    __snake_case		(			self    ):
											import torch

											lowerCAmelCase =		BertTokenizer.from_pretrained('''bert-base-cased'''    )
											lowerCAmelCase =		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    __snake_case		(			self    ):
											lowerCAmelCase =		BertTokenizer.from_pretrained('''bert-base-cased'''    )
											lowerCAmelCase =		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    __snake_case		(			self    ):
											lowerCAmelCase =		BertTokenizerFast.from_pretrained('''bert-base-cased'''    )
											lowerCAmelCase =		tokenizer_r(['''Test''', '''\xad''', '''test''']							,			is_split_into_words=UpperCAmelCase_    )

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



				def    __snake_case		(			self    ):
											lowerCAmelCase =		BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]}    )
											lowerCAmelCase =		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:
																		lowerCAmelCase =		batch.convert_to_tensors(tensor_type='''np'''    )
											self.assertFalse(len(cs.err    )							,			msg=F"""should have no warning, but got {cs.err}"""    )

											lowerCAmelCase =		BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0}    )
											lowerCAmelCase =		batch.convert_to_tensors(tensor_type='''np'''							,			prepend_batch_axis=UpperCAmelCase_    )
											self.assertEqual(tensor_batch['''inputs'''].shape							,			(1, 3)    )
											self.assertEqual(tensor_batch['''labels'''].shape							,			(1,)    )



				@require_torch
				def    __snake_case		(			self    ):
											lowerCAmelCase =		BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]}    )
											lowerCAmelCase =		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:
																		lowerCAmelCase =		batch.convert_to_tensors(tensor_type='''pt'''    )
											self.assertFalse(len(cs.err    )							,			msg=F"""should have no warning, but got {cs.err}"""    )

											lowerCAmelCase =		BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0}    )
											lowerCAmelCase =		batch.convert_to_tensors(tensor_type='''pt'''							,			prepend_batch_axis=UpperCAmelCase_    )
											self.assertEqual(tensor_batch['''inputs'''].shape							,			(1, 3)    )
											self.assertEqual(tensor_batch['''labels'''].shape							,			(1,)    )



				@require_tf
				def    __snake_case		(			self    ):
											lowerCAmelCase =		BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]}    )
											lowerCAmelCase =		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:
																		lowerCAmelCase =		batch.convert_to_tensors(tensor_type='''tf'''    )
											self.assertFalse(len(cs.err    )							,			msg=F"""should have no warning, but got {cs.err}"""    )

											lowerCAmelCase =		BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0}    )
											lowerCAmelCase =		batch.convert_to_tensors(tensor_type='''tf'''							,			prepend_batch_axis=UpperCAmelCase_    )
											self.assertEqual(tensor_batch['''inputs'''].shape							,			(1, 3)    )
											self.assertEqual(tensor_batch['''labels'''].shape							,			(1,)    )



				@require_flax
				def    __snake_case		(			self    ):
											lowerCAmelCase =		BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]}    )
											lowerCAmelCase =		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:
																		lowerCAmelCase =		batch.convert_to_tensors(tensor_type='''jax'''    )
											self.assertFalse(len(cs.err    )							,			msg=F"""should have no warning, but got {cs.err}"""    )

											lowerCAmelCase =		BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0}    )
											lowerCAmelCase =		batch.convert_to_tensors(tensor_type='''jax'''							,			prepend_batch_axis=UpperCAmelCase_    )
											self.assertEqual(tensor_batch['''inputs'''].shape							,			(1, 3)    )
											self.assertEqual(tensor_batch['''labels'''].shape							,			(1,)    )



				def    __snake_case		(			self    ):
											lowerCAmelCase =		[{'''input_ids''': np.array([0, 1, 2]    )}, {'''input_ids''': np.array([0, 1, 2, 3]    )}]
											lowerCAmelCase =		BertTokenizer.from_pretrained('''bert-base-cased'''    )

											lowerCAmelCase =		tokenizer.pad(UpperCAmelCase_							,			padding=UpperCAmelCase_    )
											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]]    )
											lowerCAmelCase =		tokenizer.pad(UpperCAmelCase_							,			padding=UpperCAmelCase_							,			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    __snake_case		(			self    ):
											import torch

											lowerCAmelCase =		[{'''input_ids''': torch.tensor([0, 1, 2]    )}, {'''input_ids''': torch.tensor([0, 1, 2, 3]    )}]
											lowerCAmelCase =		BertTokenizer.from_pretrained('''bert-base-cased'''    )

											lowerCAmelCase =		tokenizer.pad(UpperCAmelCase_							,			padding=UpperCAmelCase_    )
											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]]    )
											lowerCAmelCase =		tokenizer.pad(UpperCAmelCase_							,			padding=UpperCAmelCase_							,			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    __snake_case		(			self    ):
											import tensorflow as tf

											lowerCAmelCase =		[{'''input_ids''': tf.constant([0, 1, 2]    )}, {'''input_ids''': tf.constant([0, 1, 2, 3]    )}]
											lowerCAmelCase =		BertTokenizer.from_pretrained('''bert-base-cased'''    )

											lowerCAmelCase =		tokenizer.pad(UpperCAmelCase_							,			padding=UpperCAmelCase_    )
											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]]    )
											lowerCAmelCase =		tokenizer.pad(UpperCAmelCase_							,			padding=UpperCAmelCase_							,			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    __snake_case		(			self    ):
											lowerCAmelCase =		Tokenizer(WordPiece(unk_token='''[UNK]'''    )    )
											PreTrainedTokenizerFast(tokenizer_object=UpperCAmelCase_    )






				@require_tokenizers
				def    __snake_case		(			self    ):
											lowerCAmelCase =		Tokenizer(WordPiece(unk_token='''[UNK]'''    )    )
											with tempfile.TemporaryDirectory() as tmpdirname:
																		bert_tokenizer.save(os.path.join(UpperCAmelCase_							,			'''tokenizer.json'''    )    )
																		PreTrainedTokenizerFast(tokenizer_file=os.path.join(UpperCAmelCase_							,			'''tokenizer.json'''    )    )









import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()



def    UpperCAmelCase							(  _snake_case    ,			_snake_case				):
							# Initialize PyTorch model
							lowerCAmelCase =		CanineConfig()
							lowerCAmelCase =		CanineModel(_snake_case				)
							model.eval()

							print(F"""Building PyTorch model from configuration: {config}"""				)

							# Load weights from tf checkpoint
							load_tf_weights_in_canine(_snake_case    ,			_snake_case    ,			_snake_case				)

							# Save pytorch-model (weights and configuration)
							print(F"""Save PyTorch model to {pytorch_dump_path}"""				)
							model.save_pretrained(_snake_case				)

							# Save tokenizer files
							lowerCAmelCase =		CanineTokenizer()
							print(F"""Save tokenizer files to {pytorch_dump_path}"""				)
							tokenizer.save_pretrained(_snake_case				)


if __name__ == "__main__":
		UpperCAmelCase_    =argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--tf_checkpoint_path""",
		    default=None,
		    type=str,
		    required=True,
		    help="""Path to the TensorFlow checkpoint. Should end with model.ckpt""",
		)
		parser.add_argument(
		    """--pytorch_dump_path""",
		    default=None,
		    type=str,
		    required=True,
		    help="""Path to a folder where the PyTorch model will be placed.""",
		)
		UpperCAmelCase_    =parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)



