

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   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__=None	):
						snake_case_       =     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.
												snake_case_       =     weight.view(torch.intaa	)
												snake_case_       =     '''bfloat16'''
						snake_case_       =     weight.cpu().numpy()
						snake_case_       =     os.path.join(SCREAMING_SNAKE_CASE__  ,		F'''{weight_name}.dat'''	)
						if index is not None:
												if dtype is None:
																		snake_case_       =     str(array.dtype	)
												snake_case_       =     {'''dtype''': dtype, '''shape''': list(array.shape	)}
						if array.ndim == 0:
												snake_case_       =     array[None]
						snake_case_       =     np.memmap(SCREAMING_SNAKE_CASE__  ,		dtype=array.dtype  ,		mode='''w+'''  ,		shape=array.shape	)
						snake_case_       =     array[:]
						file_array.flush()
						return index


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

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

						snake_case_       =     np.memmap(SCREAMING_SNAKE_CASE__  ,		dtype=SCREAMING_SNAKE_CASE__  ,		shape=SCREAMING_SNAKE_CASE__  ,		mode='''r'''	)

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

						return weight


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						if index is None or len(SCREAMING_SNAKE_CASE__	) == 0:
												# Nothing to save
												return

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

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


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						os.makedirs(SCREAMING_SNAKE_CASE__  ,		exist_ok=SCREAMING_SNAKE_CASE__	)
						snake_case_       =     {}
						for name, parameter in state_dict.items():
												snake_case_       =     offload_weight(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		index=SCREAMING_SNAKE_CASE__	)

						# Update index
						save_offload_index(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	)





class      snake_case_       (		__A ):







			'''simple docstring'''

			def __init__(					self			:    Optional[Any]							,       _UpperCamelCase			:    Mapping							,       _UpperCamelCase			:    str							)				->int:
									snake_case_       =     dataset
									snake_case_       =     prefix







			def __getitem__(					self			:    List[Any]							,       _UpperCamelCase			:    Any							)				->Tuple:
									return self.dataset[f'''{self.prefix}{key}''']







			def __iter__(					self			:    Optional[int]							)				->int:
									return iter([key for key in self.dataset if key.startswith(self.prefix							)]							)







			def __len__(					self			:    Dict							)				->int:
									return len(self.dataset							)






class      snake_case_       (		__A ):







			'''simple docstring'''

			def __init__(					self			:    str							,       _UpperCamelCase			:    Dict[str, torch.Tensor] = None							,       _UpperCamelCase			:    Optional[Union[str, os.PathLike]] = None							,       _UpperCamelCase			:    Mapping = None							,       _UpperCamelCase			:    Tuple=None							,       )				->Dict:
									if state_dict is None and save_folder is None:
															raise ValueError('''Need either a `state_dict` or a `save_folder` containing offloaded weights.'''							)

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







			def __getitem__(					self			:    int							,       _UpperCamelCase			:    str							)				->Union[str, Any]:
									# State dict gets priority
									if key in self.state_dict:
															return self.state_dict[key]
									snake_case_       =     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

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

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

									snake_case_       =     os.path.join(self.save_folder							,       f'''{key}.dat'''							)
									return load_offloaded_weight(_UpperCamelCase							,       _UpperCamelCase							)







			def __iter__(					self			:    int							)				->List[Any]:
									return iter(self.all_keys							)







			def __len__(					self			:    Optional[Any]							)				->Optional[Any]:
									return len(self.all_keys							)






def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						snake_case_       =     {}
						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


import concurrent.futures
import json
import os
import shutil
import tempfile
import unittest

from transformers import AutoTokenizer, PreTrainedTokenizerFast
from transformers.testing_utils import require_tokenizers

from ..test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class      snake_case_       (		__A     ,	unittest.TestCase ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE					:       str						      =    PreTrainedTokenizerFast
			SCREAMING_SNAKE_CASE					:       List[str]						      =    False
			SCREAMING_SNAKE_CASE					:       Tuple						      =    True
			SCREAMING_SNAKE_CASE					:       str						      =    "tokenizer_file"







			def     snake_case__(					self			:    Dict							)				->int:
									snake_case_       =     False  # because we don't have pretrained_vocab_files_map
									super().setUp()
									snake_case_       =     True

									snake_case_       =     ['''robot-test/dummy-tokenizer-fast''', '''robot-test/dummy-tokenizer-wordlevel''']
									snake_case_       =     '''SaulLu/dummy-tokenizer-bytelevel-bpe'''

									# Inclusion of 2 tokenizers to test different types of models (Unigram and WordLevel for the moment)
									snake_case_       =     [(PreTrainedTokenizerFast, model_path, {}) for model_path in model_paths]

									snake_case_       =     PreTrainedTokenizerFast.from_pretrained(model_paths[0]							)
									tokenizer.save_pretrained(self.tmpdirname							)







			def     snake_case__(					self			:    Union[str, Any]							)				->Dict:
									# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
									# model
									pass







			def     snake_case__(					self			:    str							)				->Any:
									# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
									# model
									pass







			def     snake_case__(					self			:    List[Any]							)				->str:
									# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
									# model
									pass







			def     snake_case__(					self			:    str							)				->List[Any]:
									# PreTrainedTokenizerFast doesn't have tokenizer_file in its signature
									pass







			def     snake_case__(					self			:    Any							)				->Union[str, Any]:
									snake_case_       =     self.tmpdirname
									# Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
																					try:
																											snake_case_       =     tempfile.mkdtemp()
																											snake_case_       =     self.rust_tokenizer_class.from_pretrained(_UpperCamelCase							,       **_UpperCamelCase							)

																											tokenizer.save_pretrained(self.tmpdirname							)
																											super().test_training_new_tokenizer()
																					finally:
																											# Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
																											# is restored
																											shutil.rmtree(self.tmpdirname							)
																											snake_case_       =     tmpdirname_orig







			def     snake_case__(					self			:    List[str]							)				->List[Any]:
									snake_case_       =     self.tmpdirname
									# Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
																					try:
																											snake_case_       =     tempfile.mkdtemp()
																											snake_case_       =     self.rust_tokenizer_class.from_pretrained(_UpperCamelCase							,       **_UpperCamelCase							)

																											tokenizer.save_pretrained(self.tmpdirname							)
																											super().test_training_new_tokenizer_with_special_tokens_change()
																					finally:
																											# Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
																											# is restored
																											shutil.rmtree(self.tmpdirname							)
																											snake_case_       =     tmpdirname_orig







			def     snake_case__(					self			:    Optional[int]							)				->Optional[Any]:
									snake_case_       =     self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name							)

									snake_case_       =     ('''a''' for _ in range(1_0_0_0							))
									snake_case_       =     tokenizer.train_new_from_iterator(text_iterator=_UpperCamelCase							,       length=1_0_0_0							,       vocab_size=5_0							)

									snake_case_       =     new_tokenizer.encode('''a🤗'''							)
									self.assertEqual(_UpperCamelCase							,       [6_4, 1_7_2, 2_5_3, 9_7, 2_4_5]							)






@require_tokenizers
class      snake_case_       (		unittest.TestCase ):







			'''simple docstring'''

			def     snake_case__(					self			:    Optional[int]							)				->int:
									snake_case_       =     AutoTokenizer.from_pretrained('''bert-base-cased'''							)
									snake_case_       =     json.loads(tokenizer._tokenizer.to_str()							)
									snake_case_       =     len(_UpperCamelCase							)

									with tempfile.TemporaryDirectory() as tmp_dir:
															# Hack to save this in the tokenizer_config.json
															snake_case_       =     ['''tokenizer.4.0.0.json''']
															tokenizer.save_pretrained(_UpperCamelCase							)
															json.dump(_UpperCamelCase							,       open(os.path.join(_UpperCamelCase							,       '''tokenizer.4.0.0.json'''							)							,       '''w'''							)							)

															# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
															snake_case_       =     AutoTokenizer.from_pretrained(_UpperCamelCase							)
															self.assertEqual(len(_UpperCamelCase							)							,       len(_UpperCamelCase							) + 1							)
															snake_case_       =     json.loads(new_tokenizer._tokenizer.to_str()							)
															self.assertIn('''huggingface'''							,       json_tokenizer['''model''']['''vocab''']							)

															# Will need to be adjusted if we reach v42 and this test is still here.
															# Should pick the old tokenizer file as the version of Transformers is < 4.0.0
															shutil.move(os.path.join(_UpperCamelCase							,       '''tokenizer.4.0.0.json'''							)							,       os.path.join(_UpperCamelCase							,       '''tokenizer.42.0.0.json'''							)							)
															snake_case_       =     ['''tokenizer.42.0.0.json''']
															tokenizer.save_pretrained(_UpperCamelCase							)
															snake_case_       =     AutoTokenizer.from_pretrained(_UpperCamelCase							)
															self.assertEqual(len(_UpperCamelCase							)							,       len(_UpperCamelCase							)							)
															snake_case_       =     json.loads(new_tokenizer._tokenizer.to_str()							)
															self.assertNotIn('''huggingface'''							,       json_tokenizer['''model''']['''vocab''']							)







			def     snake_case__(					self			:    List[str]							)				->Optional[Any]:
									# This repo has two tokenizer files, one for v4.0.0 and above with an added token, one for versions lower.
									snake_case_       =     '''hf-internal-testing/test-two-tokenizers'''

									# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
									snake_case_       =     AutoTokenizer.from_pretrained(_UpperCamelCase							)
									self.assertEqual(len(_UpperCamelCase							)							,       2_8_9_9_7							)
									snake_case_       =     json.loads(tokenizer._tokenizer.to_str()							)
									self.assertIn('''huggingface'''							,       json_tokenizer['''model''']['''vocab''']							)

									# Testing an older version by monkey-patching the version in the module it's used.
									import transformers as old_transformers

									snake_case_       =     '''3.0.0'''
									snake_case_       =     old_transformers.models.auto.AutoTokenizer.from_pretrained(_UpperCamelCase							)
									self.assertEqual(len(_UpperCamelCase							)							,       2_8_9_9_6							)
									snake_case_       =     json.loads(old_tokenizer._tokenizer.to_str()							)
									self.assertNotIn('''huggingface'''							,       json_tokenizer['''model''']['''vocab''']							)






@require_tokenizers
class      snake_case_       (		unittest.TestCase ):







			'''simple docstring'''

			def     snake_case__(					self			:    str							)				->Optional[int]:
									# See https://github.com/huggingface/transformers/pull/12550
									# and https://github.com/huggingface/tokenizers/issues/537
									snake_case_       =     PreTrainedTokenizerFast.from_pretrained('''robot-test/dummy-tokenizer-wordlevel'''							)
									snake_case_       =     '''The Matrix is a 1999 science fiction action film.'''

									with concurrent.futures.ThreadPoolExecutor() as executor:
															snake_case_       =     [executor.submit(self.fetch							,       _UpperCamelCase							,       _UpperCamelCase							) for i in range(1_0							)]
															snake_case_       =     [future.result() for future in futures]
															self.assertEqual(_UpperCamelCase							,       [[1, 1_0, 0, 8, 0, 1_8, 0, 0, 0, 2] for i in range(1_0							)]							)







			def     snake_case__(					self			:    Optional[int]							,       _UpperCamelCase			:    Dict							,       _UpperCamelCase			:    str							)				->Tuple:
									return tokenizer.encode(_UpperCamelCase							,       truncation='''longest_first'''							,       padding='''longest'''							)