


from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand






def 							__lowercase       (			__lowerCAmelCase							:    Namespace       ):
							return ConvertCommand(
							    args.model_type ,   args.tf_checkpoint ,   args.pytorch_dump_output ,   args.config ,   args.finetuning_task_name       )


snake_case							:	Union[str, Any]					      =				'''
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
'''







class 	snake_case_       (lowerCamelCase_				):
					@staticmethod
					def 			lowerCamelCase__(				__snake_case       :ArgumentParser				) ->   Any:
												a__					=					parser.add_parser(
												    'convert'      ,help='CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.'      ,)
												train_parser.add_argument('--model_type'      ,type=__snake_case      ,required=__snake_case      ,help='Model\'s type.'				)
												train_parser.add_argument(
												    '--tf_checkpoint'      ,type=__snake_case      ,required=__snake_case      ,help='TensorFlow checkpoint path or folder.'				)
												train_parser.add_argument(
												    '--pytorch_dump_output'      ,type=__snake_case      ,required=__snake_case      ,help='Path to the PyTorch saved model output.'				)
												train_parser.add_argument('--config'      ,type=__snake_case      ,default=''      ,help='Configuration file path or folder.'				)
												train_parser.add_argument(
												    '--finetuning_task_name'      ,type=__snake_case      ,default=__snake_case      ,help='Optional fine-tuning task name if the TF model was a finetuned model.'      ,)
												train_parser.set_defaults(func=__snake_case				)
					def __init__(				self       :Dict      ,__snake_case       :str      ,__snake_case       :str      ,__snake_case       :str      ,__snake_case       :str      ,__snake_case       :str      ,*__snake_case       :Optional[Any]      ,) ->   List[Any]:
												a__					=					logging.get_logger('transformers-cli/converting'				)

												self._logger.info(F'Loading model {model_type}'				)
												a__					=					model_type
												a__					=					tf_checkpoint
												a__					=					pytorch_dump_output
												a__					=					config
												a__					=					finetuning_task_name



					def 			lowerCamelCase__(				self       :Any				) ->   Dict:
												if self._model_type == "albert":
																			try:
																										from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
																										    convert_tf_checkpoint_to_pytorch,
																										)
																			except ImportError:
																										raise ImportError(__snake_case				)

																			convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,self._config      ,self._pytorch_dump_output				)
												elif self._model_type == "bert":
																			try:
																										from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
																										    convert_tf_checkpoint_to_pytorch,
																										)
																			except ImportError:
																										raise ImportError(__snake_case				)

																			convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,self._config      ,self._pytorch_dump_output				)
												elif self._model_type == "funnel":
																			try:
																										from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
																										    convert_tf_checkpoint_to_pytorch,
																										)
																			except ImportError:
																										raise ImportError(__snake_case				)

																			convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,self._config      ,self._pytorch_dump_output				)
												elif self._model_type == "t5":
																			try:
																										from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
																			except ImportError:
																										raise ImportError(__snake_case				)

																			convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,self._config      ,self._pytorch_dump_output				)
												elif self._model_type == "gpt":
																			from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
																			    convert_openai_checkpoint_to_pytorch,
																			)

																			convert_openai_checkpoint_to_pytorch(self._tf_checkpoint      ,self._config      ,self._pytorch_dump_output				)
												elif self._model_type == "transfo_xl":
																			try:
																										from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
																										    convert_transfo_xl_checkpoint_to_pytorch,
																										)
																			except ImportError:
																										raise ImportError(__snake_case				)

																			if "ckpt" in self._tf_checkpoint.lower():
																										a__					=					self._tf_checkpoint
																										a__					=					''
																			else:
																										a__					=					self._tf_checkpoint
																										a__					=					''
																			convert_transfo_xl_checkpoint_to_pytorch(
																			    __snake_case      ,self._config      ,self._pytorch_dump_output      ,__snake_case				)
												elif self._model_type == "gpt2":
																			try:
																										from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
																										    convert_gpta_checkpoint_to_pytorch,
																										)
																			except ImportError:
																										raise ImportError(__snake_case				)

																			convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint      ,self._config      ,self._pytorch_dump_output				)
												elif self._model_type == "xlnet":
																			try:
																										from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
																										    convert_xlnet_checkpoint_to_pytorch,
																										)
																			except ImportError:
																										raise ImportError(__snake_case				)

																			convert_xlnet_checkpoint_to_pytorch(
																			    self._tf_checkpoint      ,self._config      ,self._pytorch_dump_output      ,self._finetuning_task_name				)
												elif self._model_type == "xlm":
																			from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
																			    convert_xlm_checkpoint_to_pytorch,
																			)

																			convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint      ,self._pytorch_dump_output				)
												elif self._model_type == "lxmert":
																			from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
																			    convert_lxmert_checkpoint_to_pytorch,
																			)

																			convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint      ,self._pytorch_dump_output				)
												elif self._model_type == "rembert":
																			from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
																			    convert_rembert_tf_checkpoint_to_pytorch,
																			)

																			convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,self._config      ,self._pytorch_dump_output				)
												else:
																			raise ValueError(
																			    '--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]'				)








import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors







@require_tokenizers
class 	snake_case_       (lowerCamelCase_     ,						unittest.TestCase				):
					UpperCAmelCase__ :   int   				=      MvpTokenizer
					UpperCAmelCase__ :   List[Any]   				=      MvpTokenizerFast
					UpperCAmelCase__ :   Optional[int]   				=      True
					UpperCAmelCase__ :   Tuple   				=      filter_roberta_detectors
					def 			lowerCamelCase__(				self       :Optional[Any]				) ->   Union[str, Any]:
												super().setUp()
												a__					=					[
												    'l',
												    'o',
												    'w',
												    'e',
												    'r',
												    's',
												    't',
												    'i',
												    'd',
												    'n',
												    '\u0120',
												    '\u0120l',
												    '\u0120n',
												    '\u0120lo',
												    '\u0120low',
												    'er',
												    '\u0120lowest',
												    '\u0120newer',
												    '\u0120wider',
												    '<unk>',
												]
												a__					=					dict(zip(__snake_case      ,range(len(__snake_case				)				)				)				)
												a__					=					['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
												a__					=					{'unk_token': '<unk>'}

												a__					=					os.path.join(self.tmpdirname      ,VOCAB_FILES_NAMES['vocab_file']				)
												a__					=					os.path.join(self.tmpdirname      ,VOCAB_FILES_NAMES['merges_file']				)
												with open(self.vocab_file      ,'w'      ,encoding='utf-8'				) as fp:
																			fp.write(json.dumps(__snake_case				) + '\n'				)
												with open(self.merges_file      ,'w'      ,encoding='utf-8'				) as fp:
																			fp.write('\n'.join(__snake_case				)				)
					def 			lowerCamelCase__(				self       :Optional[Any]      ,**__snake_case       :Tuple				) ->   int:
												kwargs.update(self.special_tokens_map				)
												return self.tokenizer_class.from_pretrained(self.tmpdirname      ,**__snake_case				)
					def 			lowerCamelCase__(				self       :Optional[Any]      ,**__snake_case       :Dict				) ->   Dict:
												kwargs.update(self.special_tokens_map				)
												return self.rust_tokenizer_class.from_pretrained(self.tmpdirname      ,**__snake_case				)
					def 			lowerCamelCase__(				self       :Any      ,__snake_case       :Union[str, Any]				) ->   List[Any]:
												return "lower newer", "lower newer"
					@cached_property
					def 			lowerCamelCase__(				self       :Tuple				) ->   Optional[int]:
												return MvpTokenizer.from_pretrained('RUCAIBox/mvp'				)
					@cached_property
					def 			lowerCamelCase__(				self       :Optional[int]				) ->   str:
												return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp'				)
					@require_torch
					def 			lowerCamelCase__(				self       :Optional[int]				) ->   str:
												a__					=					['A long paragraph for summarization.', 'Another paragraph for summarization.']
												a__					=					[0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			a__					=					tokenizer(__snake_case      ,max_length=len(__snake_case				)      ,padding=__snake_case      ,return_tensors='pt'				)
																			self.assertIsInstance(__snake_case      ,__snake_case				)

																			self.assertEqual((2, 9)      ,batch.input_ids.shape				)
																			self.assertEqual((2, 9)      ,batch.attention_mask.shape				)
																			a__					=					batch.input_ids.tolist()[0]
																			self.assertListEqual(__snake_case      ,__snake_case				)
																			# Test that special tokens are reset
					@require_torch
					def 			lowerCamelCase__(				self       :Union[str, Any]				) ->   List[Any]:
												a__					=					['A long paragraph for summarization.', 'Another paragraph for summarization.']
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			a__					=					tokenizer(__snake_case      ,padding=__snake_case      ,return_tensors='pt'				)
																			# check if input_ids are returned and no labels
																			self.assertIn('input_ids'      ,__snake_case				)
																			self.assertIn('attention_mask'      ,__snake_case				)
																			self.assertNotIn('labels'      ,__snake_case				)
																			self.assertNotIn('decoder_attention_mask'      ,__snake_case				)
					@require_torch
					def 			lowerCamelCase__(				self       :Any				) ->   int:
												a__					=					[
												    'Summary of the text.',
												    'Another summary.',
												]
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			a__					=					tokenizer(text_target=__snake_case      ,max_length=32      ,padding='max_length'      ,return_tensors='pt'				)
																			self.assertEqual(32      ,targets['input_ids'].shape[1]				)
					@require_torch
					def 			lowerCamelCase__(				self       :int				) ->   Union[str, Any]:
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			a__					=					tokenizer(
																			    ['I am a small frog' * 10_24, 'I am a small frog']      ,padding=__snake_case      ,truncation=__snake_case      ,return_tensors='pt'				)
																			self.assertIsInstance(__snake_case      ,__snake_case				)
																			self.assertEqual(batch.input_ids.shape      ,(2, 10_24)				)
					@require_torch
					def 			lowerCamelCase__(				self       :List[Any]				) ->   Any:
												a__					=					['A long paragraph for summarization.']
												a__					=					[
												    'Summary of the text.',
												]
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			a__					=					tokenizer(__snake_case      ,text_target=__snake_case      ,return_tensors='pt'				)
																			a__					=					inputs['input_ids']
																			a__					=					inputs['labels']
																			self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()				)
																			self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()				)
																			self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()				)
																			self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()				)
					def 			lowerCamelCase__(				self       :Union[str, Any]				) ->   Tuple:
												pass



					def 			lowerCamelCase__(				self       :Tuple				) ->   Optional[Any]:
												for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																			with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'				):
																										a__					=					self.rust_tokenizer_class.from_pretrained(__snake_case      ,**__snake_case				)
																										a__					=					self.tokenizer_class.from_pretrained(__snake_case      ,**__snake_case				)
																										a__					=					'A, <mask> AllenNLP sentence.'
																										a__					=					tokenizer_r.encode_plus(__snake_case      ,add_special_tokens=__snake_case      ,return_token_type_ids=__snake_case				)
																										a__					=					tokenizer_p.encode_plus(__snake_case      ,add_special_tokens=__snake_case      ,return_token_type_ids=__snake_case				)

																										# token_type_ids should put 0 everywhere
																										self.assertEqual(sum(tokens_r['token_type_ids']				)      ,sum(tokens_p['token_type_ids']				)				)

																										# attention_mask should put 1 everywhere, so sum over length should be 1
																										self.assertEqual(
																										    sum(tokens_r['attention_mask']				) / len(tokens_r['attention_mask']				)      ,sum(tokens_p['attention_mask']				) / len(tokens_p['attention_mask']				)      ,)

																										a__					=					tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']				)
																										a__					=					tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']				)

																										# Rust correctly handles the space before the mask while python doesnt
																										self.assertSequenceEqual(tokens_p['input_ids']      ,[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]				)
																										self.assertSequenceEqual(tokens_r['input_ids']      ,[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]				)

																										self.assertSequenceEqual(
																										    __snake_case      ,['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']				)
																										self.assertSequenceEqual(
																										    __snake_case      ,['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']				)




