
import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
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     SCREAMING_SNAKE_CASE__      (							SCREAMING_SNAKE_CASE_				,						unittest.TestCase      ):

							"""simple docstring"""






							_snake_case       						=					BartTokenizer
							_snake_case       						=					BartTokenizerFast
							_snake_case       						=					True
							_snake_case       						=					filter_roberta_detectors



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





													'''simple docstring'''






													super().setUp()
													__UpperCamelCase       					=     [
													    '''l''',
													    '''o''',
													    '''w''',
													    '''e''',
													    '''r''',
													    '''s''',
													    '''t''',
													    '''i''',
													    '''d''',
													    '''n''',
													    '''\u0120''',
													    '''\u0120l''',
													    '''\u0120n''',
													    '''\u0120lo''',
													    '''\u0120low''',
													    '''er''',
													    '''\u0120lowest''',
													    '''\u0120newer''',
													    '''\u0120wider''',
													    '''<unk>''',
													]
													__UpperCamelCase       					=     dict(zip(SCREAMING_SNAKE_CASE_       ,		range(len(SCREAMING_SNAKE_CASE_     )     )     )     )
													__UpperCamelCase       					=     ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
													__UpperCamelCase       					=     {'''unk_token''': '''<unk>'''}

													__UpperCamelCase       					=     os.path.join(self.tmpdirname       ,		VOCAB_FILES_NAMES['''vocab_file''']     )
													__UpperCamelCase       					=     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(SCREAMING_SNAKE_CASE_     ) + '''\n'''     )
													with open(self.merges_file       ,		'''w'''       ,		encoding='''utf-8'''     ) as fp:
																			fp.write('''\n'''.join(SCREAMING_SNAKE_CASE_     )     )



							def       A__						(					self       ,		**SCREAMING_SNAKE_CASE_     )->				Dict:





													'''simple docstring'''






													kwargs.update(self.special_tokens_map     )
													return self.tokenizer_class.from_pretrained(self.tmpdirname       ,		**SCREAMING_SNAKE_CASE_     )



							def       A__						(					self       ,		**SCREAMING_SNAKE_CASE_     )->				str:





													'''simple docstring'''






													kwargs.update(self.special_tokens_map     )
													return self.rust_tokenizer_class.from_pretrained(self.tmpdirname       ,		**SCREAMING_SNAKE_CASE_     )



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				Optional[int]:





													'''simple docstring'''






													return "lower newer", "lower newer"



							@cached_property
							def       A__						(					self     )->				Tuple:





													'''simple docstring'''






													return BartTokenizer.from_pretrained('''facebook/bart-large'''     )



							@cached_property
							def       A__						(					self     )->				Tuple:





													'''simple docstring'''






													return BartTokenizerFast.from_pretrained('''facebook/bart-large'''     )



							@require_torch
							def       A__						(					self     )->				int:





													'''simple docstring'''






													__UpperCamelCase       					=     ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
													__UpperCamelCase       					=     [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

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

																			self.assertEqual((2, 9)       ,		batch.input_ids.shape     )
																			self.assertEqual((2, 9)       ,		batch.attention_mask.shape     )
																			__UpperCamelCase       					=     batch.input_ids.tolist()[0]
																			self.assertListEqual(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )
																			# Test that special tokens are reset



							@require_torch
							def       A__						(					self     )->				int:





													'''simple docstring'''






													__UpperCamelCase       					=     ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			__UpperCamelCase       					=     tokenizer(SCREAMING_SNAKE_CASE_       ,		padding=SCREAMING_SNAKE_CASE_       ,		return_tensors='''pt'''     )
																			# check if input_ids are returned and no labels
																			self.assertIn('''input_ids'''       ,		SCREAMING_SNAKE_CASE_     )
																			self.assertIn('''attention_mask'''       ,		SCREAMING_SNAKE_CASE_     )
																			self.assertNotIn('''labels'''       ,		SCREAMING_SNAKE_CASE_     )
																			self.assertNotIn('''decoder_attention_mask'''       ,		SCREAMING_SNAKE_CASE_     )



							@require_torch
							def       A__						(					self     )->				Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       					=     [
													    '''Summary of the text.''',
													    '''Another summary.''',
													]
													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			__UpperCamelCase       					=     tokenizer(text_target=SCREAMING_SNAKE_CASE_       ,		max_length=32       ,		padding='''max_length'''       ,		return_tensors='''pt'''     )
																			self.assertEqual(32       ,		targets['''input_ids'''].shape[1]     )



							@require_torch
							def       A__						(					self     )->				Optional[Any]:





													'''simple docstring'''






													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			__UpperCamelCase       					=     tokenizer(
																			    ['''I am a small frog''' * 1024, '''I am a small frog''']       ,		padding=SCREAMING_SNAKE_CASE_       ,		truncation=SCREAMING_SNAKE_CASE_       ,		return_tensors='''pt'''     )
																			self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )
																			self.assertEqual(batch.input_ids.shape       ,		(2, 1024)     )



							@require_torch
							def       A__						(					self     )->				Any:





													'''simple docstring'''






													__UpperCamelCase       					=     ['''A long paragraph for summarization.''']
													__UpperCamelCase       					=     [
													    '''Summary of the text.''',
													]
													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			__UpperCamelCase       					=     tokenizer(SCREAMING_SNAKE_CASE_       ,		return_tensors='''pt'''     )
																			__UpperCamelCase       					=     tokenizer(text_target=SCREAMING_SNAKE_CASE_       ,		return_tensors='''pt'''     )
																			__UpperCamelCase       					=     inputs['''input_ids''']
																			__UpperCamelCase       					=     targets['''input_ids''']
																			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       A__						(					self     )->				int:





													'''simple docstring'''






													pass



							def       A__						(					self     )->				int:





													'''simple docstring'''






													for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																			with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"     ):
																									__UpperCamelCase       					=     self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_       ,		**SCREAMING_SNAKE_CASE_     )
																									__UpperCamelCase       					=     self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_       ,		**SCREAMING_SNAKE_CASE_     )
																									__UpperCamelCase       					=     '''A, <mask> AllenNLP sentence.'''
																									__UpperCamelCase       					=     tokenizer_r.encode_plus(SCREAMING_SNAKE_CASE_       ,		add_special_tokens=SCREAMING_SNAKE_CASE_       ,		return_token_type_ids=SCREAMING_SNAKE_CASE_     )
																									__UpperCamelCase       					=     tokenizer_p.encode_plus(SCREAMING_SNAKE_CASE_       ,		add_special_tokens=SCREAMING_SNAKE_CASE_       ,		return_token_type_ids=SCREAMING_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''']     )       ,		)

																									__UpperCamelCase       					=     tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']     )
																									__UpperCamelCase       					=     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, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]     )
																									self.assertSequenceEqual(tokens_r['''input_ids''']       ,		[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]     )

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






import json
import os
from collections.abc import Mapping
from typing import Dict, List, Optional, Union

import numpy as np
import torch

from .imports import is_safetensors_available




def 		A_	(							snake_case							:				Union[str, Any]	, snake_case							:				str	, snake_case							:				Dict	, snake_case							:				Optional[Any]=None	) ->      Optional[Any]:



						'''simple docstring'''

						__UpperCamelCase       					=     None
						# Check the string instead of the dtype to be compatible with versions of PyTorch that don't have bfloat16.
						if str(weight.dtype	) == "torch.bfloat16":
												# Need to reinterpret the underlined data as int16 since NumPy does not handle bfloat16s.
												__UpperCamelCase       					=     weight.view(torch.intaa	)
												__UpperCamelCase       					=     '''bfloat16'''
						__UpperCamelCase       					=     weight.cpu().numpy()
						__UpperCamelCase       					=     os.path.join(snake_case	, f"{weight_name}.dat"	)
						if index is not None:
												if dtype is None:
																		__UpperCamelCase       					=     str(array.dtype	)
												__UpperCamelCase       					=     {'''dtype''': dtype, '''shape''': list(array.shape	)}
						if array.ndim == 0:
												__UpperCamelCase       					=     array[None]
						__UpperCamelCase       					=     np.memmap(snake_case	, dtype=array.dtype	, mode='''w+'''	, shape=array.shape	)
						__UpperCamelCase       					=     array[:]
						file_array.flush()
						return index




def 		A_	(							snake_case							:				Tuple	, snake_case							:				Union[str, Any]	) ->      Dict:



						'''simple docstring'''

						__UpperCamelCase       					=     tuple(weight_info['''shape''']	)
						if shape == ():
												# NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
												__UpperCamelCase       					=     (1,)

						__UpperCamelCase       					=     weight_info['''dtype''']
						if dtype == "bfloat16":
												# NumPy does not support bfloat16 so this was saved as a int16
												__UpperCamelCase       					=     '''int16'''

						__UpperCamelCase       					=     np.memmap(snake_case	, dtype=snake_case	, shape=snake_case	, mode='''r'''	)

						if len(weight_info['''shape''']	) == 0:
												__UpperCamelCase       					=     weight[0]
						__UpperCamelCase       					=     torch.tensor(snake_case	)
						if weight_info["dtype"] == "bfloat16":
												__UpperCamelCase       					=     weight.view(torch.bfloataa	)

						return weight




def 		A_	(							snake_case							:				Dict	, snake_case							:				int	) ->      str:



						'''simple docstring'''

						if index is None or len(snake_case	) == 0:
												# Nothing to save
												return

						__UpperCamelCase       					=     os.path.join(snake_case	, '''index.json'''	)
						if os.path.isfile(snake_case	):
												with open(snake_case	, '''r'''	, encoding='''utf-8'''	) as f:
																		__UpperCamelCase       					=     json.load(snake_case	)
						else:
												__UpperCamelCase       					=     {}
						current_index.update(snake_case	)

						with open(snake_case	, '''w'''	, encoding='''utf-8'''	) as f:
												json.dump(snake_case	, snake_case	, indent=2	)




def 		A_	(							snake_case							:				Union[str, os.PathLike]	, snake_case							:				Dict[str, torch.Tensor]	) ->      Dict:



						'''simple docstring'''

						os.makedirs(snake_case	, exist_ok=snake_case	)
						__UpperCamelCase       					=     {}
						for name, parameter in state_dict.items():
												__UpperCamelCase       					=     offload_weight(snake_case	, snake_case	, snake_case	, index=snake_case	)

						# Update index
						save_offload_index(snake_case	, snake_case	)






class     SCREAMING_SNAKE_CASE__      (							SCREAMING_SNAKE_CASE_      ):

							"""simple docstring"""






							def __init__(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )->				str:





													'''simple docstring'''






													__UpperCamelCase       					=     dataset
													__UpperCamelCase       					=     prefix



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





													'''simple docstring'''






													return self.dataset[F"{self.prefix}{key}"]



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





													'''simple docstring'''






													return iter([key for key in self.dataset if key.startswith(self.prefix     )]     )



							def __len__(					self     )->				str:





													'''simple docstring'''






													return len(self.dataset     )






class     SCREAMING_SNAKE_CASE__      (							SCREAMING_SNAKE_CASE_      ):

							"""simple docstring"""






							def __init__(					self       ,		SCREAMING_SNAKE_CASE_ = None       ,		SCREAMING_SNAKE_CASE_ = None       ,		SCREAMING_SNAKE_CASE_ = None       ,		SCREAMING_SNAKE_CASE_=None       ,		)->				Tuple:





													'''simple docstring'''






													if state_dict is None and save_folder is None:
																			raise ValueError('''Need either a `state_dict` or a `save_folder` containing offloaded weights.'''     )

													__UpperCamelCase       					=     {} if state_dict is None else state_dict
													__UpperCamelCase       					=     save_folder
													if index is None and save_folder is not None:
																			with open(os.path.join(SCREAMING_SNAKE_CASE_       ,		'''index.json'''     )     ) as f:
																									__UpperCamelCase       					=     json.load(SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     {} if index is None else index
													__UpperCamelCase       					=     list(self.state_dict.keys()     )
													self.all_keys.extend([key for key in self.index if key not in self.all_keys]     )
													__UpperCamelCase       					=     device



							def __getitem__(					self       ,		SCREAMING_SNAKE_CASE_     )->				Tuple:





													'''simple docstring'''






													if key in self.state_dict:
																			return self.state_dict[key]
													__UpperCamelCase       					=     self.index[key]
													if weight_info.get('''safetensors_file'''     ) is not None:
																			if not is_safetensors_available():
																									raise ImportError('''These offloaded weights require the use of safetensors: `pip install safetensors`.'''     )

																			from safetensors import safe_open

																			__UpperCamelCase       					=     '''cpu''' if self.device is None else self.device
																			with safe_open(weight_info['''safetensors_file''']       ,		framework='''pt'''       ,		device=SCREAMING_SNAKE_CASE_     ) as f:
																									__UpperCamelCase       					=     f.get_tensor(weight_info.get('''weight_name'''       ,		SCREAMING_SNAKE_CASE_     )     )

																			if "dtype" in weight_info:
																									return tensor.to(getattr(SCREAMING_SNAKE_CASE_       ,		weight_info['''dtype''']     )     )
																			else:
																									return tensor

													__UpperCamelCase       					=     os.path.join(self.save_folder       ,		F"{key}.dat"     )
													return load_offloaded_weight(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )



							def __iter__(					self     )->				Dict:





													'''simple docstring'''






													return iter(self.all_keys     )



							def __len__(					self     )->				Dict:





													'''simple docstring'''






													return len(self.all_keys     )





def 		A_	(							snake_case							:				Dict[str, torch.Tensor]	, snake_case							:				List[str]	) ->      Tuple:



						'''simple docstring'''

						__UpperCamelCase       					=     {}
						for module_name in submodule_names:
												# We want to catch module_name parameter (module_name.xxx) or potentially module_name, but not any of the
												# submodules that could being like module_name (transformers.h.1 and transformers.h.10 for instance)
												result.update(
												    {
												        key: param
												        for key, param in state_dict.items()
												        if key == module_name or key.startswith(module_name + '''.'''	)
												    }	)
						return result




