


import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class 						lowerCamelCase_							(					lowerCAmelCase__  ,  unittest.TestCase					):



					'''simple docstring'''







					__UpperCAmelCase							  =					None
					__UpperCAmelCase							  =					BloomTokenizerFast
					__UpperCAmelCase							  =					BloomTokenizerFast
					__UpperCAmelCase							  =					True
					__UpperCAmelCase							  =					False
					__UpperCAmelCase							  =					"tokenizer_file"
					__UpperCAmelCase							  =					{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}






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







								'''simple docstring'''

								super().setUp()
								__lowercase			       = BloomTokenizerFast.from_pretrained('''bigscience/tokenizer'''    )
								tokenizer.save_pretrained(self.tmpdirname    )






					def A   (					self			,							**snake_case_    ) ->      Dict:







								'''simple docstring'''

								kwargs.update(self.special_tokens_map    )
								return BloomTokenizerFast.from_pretrained(self.tmpdirname			,							**snake_case_    )






					def A   (					self    ) ->      Tuple:







								'''simple docstring'''

								__lowercase			       = self.get_rust_tokenizer()

								__lowercase			       = ['''The quick brown fox</s>''', '''jumps over the lazy dog</s>''']
								__lowercase			       = [[2_1_7_5, 2_3_7_1_4, 7_3_1_7_3, 1_4_4_2_5_2, 2], [7_7, 1_3_2_6_1_9, 3_4_7_8, 3_6_8, 1_0_9_5_8_6, 3_5_4_3_3, 2]]

								__lowercase			       = tokenizer.batch_encode_plus(snake_case_    )['''input_ids''']
								self.assertListEqual(snake_case_			,							snake_case_    )

								__lowercase			       = tokenizer.batch_decode(snake_case_    )
								self.assertListEqual(snake_case_			,							snake_case_    )






					def A   (					self			,							snake_case_=6    ) ->      str:







								'''simple docstring'''

								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'    ):
														__lowercase			       = self.rust_tokenizer_class.from_pretrained(snake_case_			,							**snake_case_    )
														# tokenizer_r.pad_token = None # Hotfixing padding = None
														# Simple input
														__lowercase			       = '''This is a simple input'''
														__lowercase			       = ['''This is a simple input 1''', '''This is a simple input 2''']
														__lowercase			       = ('''This is a simple input''', '''This is a pair''')
														__lowercase			       = [
														    ('''This is a simple input 1''', '''This is a simple input 2'''),
														    ('''This is a simple pair 1''', '''This is a simple pair 2'''),
														]

														# Simple input tests
														try:
																	tokenizer_r.encode(snake_case_			,							max_length=snake_case_    )
																	tokenizer_r.encode_plus(snake_case_			,							max_length=snake_case_    )

																	tokenizer_r.batch_encode_plus(snake_case_			,							max_length=snake_case_    )
																	tokenizer_r.encode(snake_case_			,							max_length=snake_case_    )
																	tokenizer_r.batch_encode_plus(snake_case_			,							max_length=snake_case_    )
														except ValueError:
																	self.fail('''Bloom Tokenizer should be able to deal with padding'''    )

														__lowercase			       = None  # Hotfixing padding = None
														self.assertRaises(snake_case_			,							tokenizer_r.encode			,							snake_case_			,							max_length=snake_case_			,							padding='''max_length'''    )

														# Simple input
														self.assertRaises(snake_case_			,							tokenizer_r.encode_plus			,							snake_case_			,							max_length=snake_case_			,							padding='''max_length'''    )

														# Simple input
														self.assertRaises(
														    snake_case_			,							tokenizer_r.batch_encode_plus			,							snake_case_			,							max_length=snake_case_			,							padding='''max_length'''			,							)

														# Pair input
														self.assertRaises(snake_case_			,							tokenizer_r.encode			,							snake_case_			,							max_length=snake_case_			,							padding='''max_length'''    )

														# Pair input
														self.assertRaises(snake_case_			,							tokenizer_r.encode_plus			,							snake_case_			,							max_length=snake_case_			,							padding='''max_length'''    )

														# Pair input
														self.assertRaises(
														    snake_case_			,							tokenizer_r.batch_encode_plus			,							snake_case_			,							max_length=snake_case_			,							padding='''max_length'''			,							)






					def A   (					self    ) ->      Dict:







								'''simple docstring'''

								__lowercase			       = self.get_rust_tokenizer()
								__lowercase			       = load_dataset('''xnli'''			,							'''all_languages'''			,							split='''test'''			,							streaming=snake_case_    )

								__lowercase			       = next(iter(snake_case_    )    )['''premise''']  # pick up one data
								__lowercase			       = list(sample_data.values()    )

								__lowercase			       = list(map(tokenizer.encode			,							snake_case_    )    )
								__lowercase			       = [tokenizer.decode(snake_case_			,							clean_up_tokenization_spaces=snake_case_    ) for x in output_tokens]
								self.assertListEqual(snake_case_			,							snake_case_    )






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







								'''simple docstring'''

								self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map    )			,							1    )
								self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()    )[0]    )			,							1    )






import argparse
from pathlib import Path

import torch

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


logging.set_verbosity_info()
a			:			List[str]												=					logging.get_logger(__name__)


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = torch.load(_UpperCamelCase							,      map_location='''cpu'''	)
			if "model" in sd.keys():
						__lowercase			       = torch.load(_UpperCamelCase							,      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(_UpperCamelCase	)

			__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(_UpperCamelCase	)

			__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(_UpperCamelCase							,      depth // 3							,      dim=0	)

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

			return sd


@torch.no_grad()
def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase=None	):


			'''simple docstring'''







			__lowercase			       = load_checkpoint(_UpperCamelCase	)

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

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

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


if __name__ == "__main__":
	a			:			List[Any]												=					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.''')
	a			:			Any												=					parser.parse_args()
	convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)


