





'''simple docstring'''



import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
	_SCREAMING_SNAKE_CASE					=    '''pt'''
elif is_tf_available():
	_SCREAMING_SNAKE_CASE					=    '''tf'''
else:
	_SCREAMING_SNAKE_CASE					=    '''jax'''






class 							__lowercase       (      lowerCAmelCase__ ,      unittest.TestCase						):







	'''simple docstring'''

	a    :				int											=       ByTaTokenizer
	a    :				Optional[Any]											=       False


	def 						_UpperCAmelCase     (self )							->							List[str]:







				'''simple docstring'''





				super().setUp()
				__lowercase										=							ByTaTokenizer()
				tokenizer.save_pretrained(self.tmpdirname )


	@cached_property
	def 						_UpperCAmelCase     (self )							->							Tuple:







				'''simple docstring'''





				return ByTaTokenizer.from_pretrained('''google/byt5-small''' )


	def 						_UpperCAmelCase     (self     ,**_lowerCamelCase )							->							ByTaTokenizer:







				'''simple docstring'''





				return self.tokenizer_class.from_pretrained(self.tmpdirname     ,**_lowerCamelCase )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase=False     ,_lowerCamelCase=20     ,_lowerCamelCase=5 )							->							Tuple[str, list]:







				'''simple docstring'''





				__lowercase										=							[]
				for i in range(len(_lowerCamelCase ) ):
							try:
										__lowercase										=							tokenizer.decode([i]     ,clean_up_tokenization_spaces=_lowerCamelCase )
							except UnicodeDecodeError:
										pass
							toks.append((i, tok) )

				__lowercase										=							list(filter(lambda _lowerCamelCase : re.match(R'''^[ a-zA-Z]+$'''     ,t[1] )     ,_lowerCamelCase ) )
				__lowercase										=							list(filter(lambda _lowerCamelCase : [t[0]] == tokenizer.encode(t[1]     ,add_special_tokens=_lowerCamelCase )     ,_lowerCamelCase ) )
				if max_length is not None and len(_lowerCamelCase ) > max_length:
							__lowercase										=							toks[:max_length]
				if min_length is not None and len(_lowerCamelCase ) < min_length and len(_lowerCamelCase ) > 0:
							while len(_lowerCamelCase ) < min_length:
										__lowercase										=							toks + toks
        # toks_str = [t[1] for t in toks]
				__lowercase										=							[t[0] for t in toks]

				# Ensure consistency
				__lowercase										=							tokenizer.decode(_lowerCamelCase     ,clean_up_tokenization_spaces=_lowerCamelCase )
				if " " not in output_txt and len(_lowerCamelCase ) > 1:
							__lowercase										=							(
							    tokenizer.decode([toks_ids[0]]     ,clean_up_tokenization_spaces=_lowerCamelCase )
							    + ''' '''
							    + tokenizer.decode(toks_ids[1:]     ,clean_up_tokenization_spaces=_lowerCamelCase )
							)
				if with_prefix_space:
							__lowercase										=							''' ''' + output_txt
				__lowercase										=							tokenizer.encode(_lowerCamelCase     ,add_special_tokens=_lowerCamelCase )
				return output_txt, output_ids


	def 						_UpperCAmelCase     (self )							->							Any:







				'''simple docstring'''





				__lowercase										=							self.ta_base_tokenizer
				__lowercase										=							tokenizer(['''hi</s>''', '''I went to the gym</s>''', '''</s>'''] )
				__lowercase										=							tokenizer(['''hi''', '''I went to the gym''', ''''''] )
				self.assertListEqual(batch_with_eos_added['''input_ids''']     ,batch_without_eos_added['''input_ids'''] )


	def 						_UpperCAmelCase     (self )							->							Optional[int]:







				'''simple docstring'''





				__lowercase										=							self.ta_base_tokenizer
				__lowercase										=							'''Unicode €.'''
				__lowercase										=							tokenizer(_lowerCamelCase )
				__lowercase										=							[88, 113, 108, 102, 114, 103, 104, 35, 229, 133, 175, 49, 1]
				self.assertEqual(encoded['''input_ids''']     ,_lowerCamelCase )

				# decoding
				__lowercase										=							tokenizer.decode(_lowerCamelCase )
				self.assertEqual(_lowerCamelCase     ,'''Unicode €.</s>''' )

				__lowercase										=							tokenizer('''e è é ê ë''' )
				__lowercase										=							[104, 35, 198, 171, 35, 198, 172, 35, 198, 173, 35, 198, 174, 1]
				self.assertEqual(encoded['''input_ids''']     ,_lowerCamelCase )
				# decoding
				__lowercase										=							tokenizer.decode(_lowerCamelCase )
				self.assertEqual(_lowerCamelCase     ,'''e è é ê ë</s>''' )

				# encode/decode, but with `encode` instead of `__call__`
				self.assertEqual(tokenizer.decode(tokenizer.encode('''e è é ê ë''' ) )     ,'''e è é ê ë</s>''' )


	def 						_UpperCAmelCase     (self )							->							List[Any]:







				'''simple docstring'''





				__lowercase										=							self.ta_base_tokenizer
				__lowercase										=							['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
				# fmt: off
				__lowercase										=							[68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 1, 0]
				# fmt: on
				__lowercase										=							tokenizer(_lowerCamelCase     ,padding=_lowerCamelCase     ,return_tensors=_lowerCamelCase )
				self.assertIsInstance(_lowerCamelCase     ,_lowerCamelCase )

				if FRAMEWORK != "jax":
							__lowercase										=							list(batch.input_ids.numpy()[0] )
				else:
							__lowercase										=							list(batch.input_ids.tolist()[0] )

				self.assertListEqual(_lowerCamelCase     ,_lowerCamelCase )

				self.assertEqual((2, 37)     ,batch.input_ids.shape )
				self.assertEqual((2, 37)     ,batch.attention_mask.shape )


	def 						_UpperCAmelCase     (self )							->							List[Any]:







				'''simple docstring'''





				__lowercase										=							self.ta_base_tokenizer
				__lowercase										=							['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
				__lowercase										=							tokenizer(_lowerCamelCase     ,padding=_lowerCamelCase     ,return_tensors=_lowerCamelCase )
				# check if input_ids are returned and no decoder_input_ids
				self.assertIn('''input_ids'''     ,_lowerCamelCase )
				self.assertIn('''attention_mask'''     ,_lowerCamelCase )
				self.assertNotIn('''decoder_input_ids'''     ,_lowerCamelCase )
				self.assertNotIn('''decoder_attention_mask'''     ,_lowerCamelCase )


	def 						_UpperCAmelCase     (self )							->							int:







				'''simple docstring'''





				__lowercase										=							self.ta_base_tokenizer
				__lowercase										=							[
				    '''Summary of the text.''',
				    '''Another summary.''',
				]
				__lowercase										=							tokenizer(
				    text_target=_lowerCamelCase     ,max_length=32     ,padding='''max_length'''     ,truncation=_lowerCamelCase     ,return_tensors=_lowerCamelCase )
				self.assertEqual(32     ,targets['''input_ids'''].shape[1] )


	def 						_UpperCAmelCase     (self )							->							Optional[int]:







				'''simple docstring'''





				__lowercase										=							self.ta_base_tokenizer
				__lowercase										=							['''A long paragraph for summarization. </s>''']
				__lowercase										=							['''Summary of the text. </s>''']
				# fmt: off
				__lowercase										=							[68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 35, 1]
				__lowercase										=							[86, 120, 112, 112, 100, 117, 124, 35, 114, 105, 35, 119, 107, 104, 35, 119, 104, 123, 119, 49, 35, 1]
				# fmt: on

				__lowercase										=							tokenizer(_lowerCamelCase     ,text_target=_lowerCamelCase )

				self.assertEqual(_lowerCamelCase     ,batch['''input_ids'''][0] )
				self.assertEqual(_lowerCamelCase     ,batch['''labels'''][0] )


	def 						_UpperCAmelCase     (self )							->							List[str]:







				'''simple docstring'''





				__lowercase										=							self.get_tokenizers()
				for tokenizer in tokenizers:
							with self.subTest(f"{tokenizer.__class__.__name__}" ):
										self.assertNotEqual(tokenizer.model_max_length     ,42 )

        # Now let's start the test
				__lowercase										=							self.get_tokenizers()
				for tokenizer in tokenizers:
							with self.subTest(f"{tokenizer.__class__.__name__}" ):
										# Isolate this from the other tests because we save additional tokens/etc
										__lowercase										=							tempfile.mkdtemp()

										__lowercase										=							''' He is very happy, UNwant\u00E9d,running'''
										__lowercase										=							tokenizer.encode(_lowerCamelCase     ,add_special_tokens=_lowerCamelCase )
										tokenizer.save_pretrained(_lowerCamelCase )

										__lowercase										=							tokenizer.__class__.from_pretrained(_lowerCamelCase )
										__lowercase										=							after_tokenizer.encode(_lowerCamelCase     ,add_special_tokens=_lowerCamelCase )
										self.assertListEqual(_lowerCamelCase     ,_lowerCamelCase )

										shutil.rmtree(_lowerCamelCase )

				__lowercase										=							self.get_tokenizers(model_max_length=42 )
				for tokenizer in tokenizers:
							with self.subTest(f"{tokenizer.__class__.__name__}" ):
										# Isolate this from the other tests because we save additional tokens/etc
										__lowercase										=							tempfile.mkdtemp()

										__lowercase										=							''' He is very happy, UNwant\u00E9d,running'''
										tokenizer.add_tokens(['''bim''', '''bambam'''] )
										__lowercase										=							tokenizer.additional_special_tokens
										additional_special_tokens.append('''new_additional_special_token''' )
										tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens} )
										__lowercase										=							tokenizer.encode(_lowerCamelCase     ,add_special_tokens=_lowerCamelCase )
										tokenizer.save_pretrained(_lowerCamelCase )

										__lowercase										=							tokenizer.__class__.from_pretrained(_lowerCamelCase )
										__lowercase										=							after_tokenizer.encode(_lowerCamelCase     ,add_special_tokens=_lowerCamelCase )
										self.assertListEqual(_lowerCamelCase     ,_lowerCamelCase )
										self.assertIn('''new_additional_special_token'''     ,after_tokenizer.additional_special_tokens )
										self.assertEqual(after_tokenizer.model_max_length     ,42 )

										__lowercase										=							tokenizer.__class__.from_pretrained(_lowerCamelCase     ,model_max_length=43 )
										self.assertEqual(tokenizer.model_max_length     ,43 )

										shutil.rmtree(_lowerCamelCase )


	def 						_UpperCAmelCase     (self )							->							str:







				'''simple docstring'''





				__lowercase										=							[]
				if self.test_slow_tokenizer:
							tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )

				if self.test_rust_tokenizer:
							tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )

				for tokenizer_class, tokenizer_utils in tokenizer_list:
							with tempfile.TemporaryDirectory() as tmp_dir:
										tokenizer_utils.save_pretrained(_lowerCamelCase )

										with open(os.path.join(_lowerCamelCase     ,'''special_tokens_map.json''' )     ,encoding='''utf-8''' ) as json_file:
													__lowercase										=							json.load(_lowerCamelCase )

										with open(os.path.join(_lowerCamelCase     ,'''tokenizer_config.json''' )     ,encoding='''utf-8''' ) as json_file:
													__lowercase										=							json.load(_lowerCamelCase )

										__lowercase										=							[f"<extra_id_{i}>" for i in range(125 )]

										__lowercase										=							added_tokens_extra_ids + [
										    '''an_additional_special_token'''
										]
										__lowercase										=							added_tokens_extra_ids + [
										    '''an_additional_special_token'''
										]

										with open(os.path.join(_lowerCamelCase     ,'''special_tokens_map.json''' )     ,'''w'''     ,encoding='''utf-8''' ) as outfile:
													json.dump(_lowerCamelCase     ,_lowerCamelCase )
										with open(os.path.join(_lowerCamelCase     ,'''tokenizer_config.json''' )     ,'''w'''     ,encoding='''utf-8''' ) as outfile:
													json.dump(_lowerCamelCase     ,_lowerCamelCase )

										# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
										# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
										# "special_tokens_map.json" files
										__lowercase										=							tokenizer_class.from_pretrained(
										    _lowerCamelCase     ,)
										self.assertIn(
										    '''an_additional_special_token'''     ,tokenizer_without_change_in_init.additional_special_tokens )
										# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
										self.assertEqual(
										    ['''an_additional_special_token''']     ,tokenizer_without_change_in_init.convert_ids_to_tokens(
										        tokenizer_without_change_in_init.convert_tokens_to_ids(['''an_additional_special_token'''] ) )     ,)

										# Now we test that we can change the value of additional_special_tokens in the from_pretrained
										__lowercase										=							added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''     ,lstrip=_lowerCamelCase )]
										__lowercase										=							tokenizer_class.from_pretrained(
										    _lowerCamelCase     ,additional_special_tokens=_lowerCamelCase     ,)

										self.assertIn('''a_new_additional_special_token'''     ,tokenizer.additional_special_tokens )
										self.assertEqual(
										    ['''a_new_additional_special_token''']     ,tokenizer.convert_ids_to_tokens(
										        tokenizer.convert_tokens_to_ids(['''a_new_additional_special_token'''] ) )     ,)


	def 						_UpperCAmelCase     (self )							->							Optional[Any]:







				'''simple docstring'''





				__lowercase										=							[]
				if self.test_slow_tokenizer:
							tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )

				if self.test_rust_tokenizer:
							tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )

				for tokenizer_class, tokenizer_utils in tokenizer_list:
							with tempfile.TemporaryDirectory() as tmp_dir:
										tokenizer_utils.save_pretrained(_lowerCamelCase )

										__lowercase										=							tokenizer_class.from_pretrained(_lowerCamelCase )

										self.assertTrue(tokenizer.decode([255] ) == '''''' )


	def 						_UpperCAmelCase     (self )							->							List[str]:







				'''simple docstring'''





				pass


	def 						_UpperCAmelCase     (self )							->							Union[str, Any]:







				'''simple docstring'''





				pass


	def 						_UpperCAmelCase     (self )							->							List[Any]:







				'''simple docstring'''





				pass


	def 						_UpperCAmelCase     (self )							->							Union[str, Any]:







				'''simple docstring'''





				pass


	def 						_UpperCAmelCase     (self )							->							str:







				'''simple docstring'''





				__lowercase										=							self.get_tokenizers(fast=_lowerCamelCase     ,do_lower_case=_lowerCamelCase )
				for tokenizer in tokenizers:
							with self.subTest(f"{tokenizer.__class__.__name__}" ):
										__lowercase										=							['''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''x''', '''t''', '''</s>''']
										__lowercase										=							tokenizer.convert_tokens_to_string(_lowerCamelCase )

										self.assertIsInstance(_lowerCamelCase     ,_lowerCamelCase )


	def 						_UpperCAmelCase     (self )							->							Any:







				'''simple docstring'''





				__lowercase										=							self.get_tokenizers()
				for tokenizer in tokenizers:
							with self.subTest(f"{tokenizer.__class__.__name__}" ):
										__lowercase										=							[
										    '''bos_token''',
										    '''eos_token''',
										    '''unk_token''',
										    '''sep_token''',
										    '''pad_token''',
										    '''cls_token''',
										    '''mask_token''',
										]

										__lowercase										=							0
										__lowercase										=							tokenizer.convert_ids_to_tokens(
										    _lowerCamelCase     ,skip_special_tokens=_lowerCamelCase )

										for attr in attributes_list:
													setattr(_lowerCamelCase     ,attr + '''_id'''     ,_lowerCamelCase )
													self.assertEqual(getattr(_lowerCamelCase     ,_lowerCamelCase )     ,_lowerCamelCase )
													self.assertEqual(getattr(_lowerCamelCase     ,attr + '''_id''' )     ,_lowerCamelCase )

													setattr(_lowerCamelCase     ,attr + '''_id'''     ,_lowerCamelCase )
													self.assertEqual(getattr(_lowerCamelCase     ,_lowerCamelCase )     ,_lowerCamelCase )
													self.assertEqual(getattr(_lowerCamelCase     ,attr + '''_id''' )     ,_lowerCamelCase )

										setattr(_lowerCamelCase     ,'''additional_special_tokens_ids'''     ,[] )
										self.assertListEqual(getattr(_lowerCamelCase     ,'''additional_special_tokens''' )     ,[] )
										self.assertListEqual(getattr(_lowerCamelCase     ,'''additional_special_tokens_ids''' )     ,[] )

										setattr(_lowerCamelCase     ,'''additional_special_tokens_ids'''     ,[token_id_to_test_setters] )
										self.assertListEqual(getattr(_lowerCamelCase     ,'''additional_special_tokens''' )     ,[token_to_test_setters] )
										self.assertListEqual(getattr(_lowerCamelCase     ,'''additional_special_tokens_ids''' )     ,[token_id_to_test_setters] )












'''simple docstring'''



import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE					=    logging.get_logger(__name__)




def    _lowerCAmelCase						(				lowerCamelCase_			:  int				):
			__lowercase										=							torch.load(lowerCamelCase_		,       map_location='''cpu'''				)
			if "model" in sd.keys():
						__lowercase										=							torch.load(lowerCamelCase_		,       map_location='''cpu'''				)['''model''']

			# pop unnecessary weights
			__lowercase										=							[
			    '''decoder.version''',
			    '''decoder.output_projection.weight''',
			]
			for key in keys_to_delete:
						if key in sd:
									sd.pop(lowerCamelCase_				)

			__lowercase										=							{
			    '''decoder.project_in_dim.weight''': '''decoder.project_in.weight''',
			    '''decoder.project_out_dim.weight''': '''decoder.project_out.weight''',
			    '''decoder.layer_norm.weight''': '''decoder.final_layer_norm.weight''',
			    '''decoder.layer_norm.bias''': '''decoder.final_layer_norm.bias''',
			}
			for old_key, new_key in keys_to_rename.items():
						if old_key in sd:
									__lowercase										=							sd.pop(lowerCamelCase_				)

			__lowercase										=							list(sd.keys()				)
			for key in keys:
						if ".qkv_proj." in key:
									__lowercase										=							sd[key]
									# We split QKV in separate Q,K,V

									__lowercase										=							key.replace('''.qkv_proj.'''		,       '''.q_proj.'''				)
									__lowercase										=							key.replace('''.qkv_proj.'''		,       '''.k_proj.'''				)
									__lowercase										=							key.replace('''.qkv_proj.'''		,       '''.v_proj.'''				)

									__lowercase										=							value.shape[0]
									assert depth % 3 == 0
									# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
									# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
									__lowercase						,		__lowercase						,		__lowercase										=							torch.split(lowerCamelCase_		,       depth // 3		,       dim=0				)

									__lowercase										=							q
									__lowercase										=							k
									__lowercase										=							v
									del sd[key]

			return sd




@torch.no_grad()
def    _lowerCAmelCase						(				lowerCamelCase_			:  Union[str, Any]		,       lowerCamelCase_			:  Tuple		,       lowerCamelCase_			:  Union[str, Any]=None				):
			__lowercase										=							load_checkpoint(lowerCamelCase_				)

			if config is not None:
						__lowercase										=							OPTConfig.from_pretrained(lowerCamelCase_				)
			else:
						__lowercase										=							OPTConfig()

			__lowercase										=							OPTModel(lowerCamelCase_				).half().eval()
			model.load_state_dict(lowerCamelCase_				)

			# Check results
			Path(lowerCamelCase_				).mkdir(exist_ok=lowerCamelCase_				)
			model.save_pretrained(lowerCamelCase_				)


if __name__ == "__main__":
	_SCREAMING_SNAKE_CASE					=    argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--fairseq_path''',
	    type=str,
	    help=(
	        '''path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'''
	        ''' https://huggingface.co/models?other=opt_metasq'''
	    ),
	)
	parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
	parser.add_argument('''--hf_config''', default=None, type=str, help='''Define HF config.''')
	_SCREAMING_SNAKE_CASE					=    parser.parse_args()
	convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)





