



import importlib.metadata
import warnings
from copy import deepcopy

from packaging import version

from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available


if is_bitsandbytes_available():
					import bitsandbytes as bnb
					import torch
					import torch.nn as nn

					from ..pytorch_utils import ConvaD

if is_accelerate_available():
					from accelerate import init_empty_weights
					from accelerate.utils import find_tied_parameters

SCREAMING_SNAKE_CASE_				= logging.get_logger(__name__)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__=None, SCREAMING_SNAKE_CASE__=None	)			->      List[Any]:
		# Recurse if needed
		if "." in tensor_name:
				a_     :      List[str] 						=     tensor_name.split("."	)
				for split in splits[:-1]:
						a_     :      Union[str, Any] 						=     getattr(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
						if new_module is None:
								raise ValueError(F"""{module} has no attribute {split}."""	)
						a_     :      int 						=     new_module
				a_     :      Any 						=     splits[-1]

		if tensor_name not in module._parameters and tensor_name not in module._buffers:
				raise ValueError(F"""{module} does not have a parameter or a buffer named {tensor_name}."""	)
		a_     :      Optional[int] 						=     tensor_name in module._buffers
		a_     :      Any 						=     getattr(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)

		if old_value.device == torch.device("meta"	) and device not in ["meta", torch.device("meta"	)] and value is None:
				raise ValueError(F"""{tensor_name} is on the meta device, we need a `value` to put in on {device}."""	)

		a_     :      Optional[int] 						=     False
		a_     :      Tuple 						=     False
		if is_buffer or not is_bitsandbytes_available():
				a_     :      Union[str, Any] 						=     False
				a_     :      List[Any] 						=     False
		else:
				a_     :      Dict 						=     hasattr(bnb.nn, "Params4bit"	) and isinstance(module._parameters[tensor_name], bnb.nn.Paramsabit	)
				a_     :      Dict 						=     isinstance(module._parameters[tensor_name], bnb.nn.IntaParams	)

		if is_abit or is_abit:
				a_     :      Tuple 						=     module._parameters[tensor_name]
				if param.device.type != "cuda":
						if value is None:
								a_     :      int 						=     old_value.to(SCREAMING_SNAKE_CASE__	)
						elif isinstance(SCREAMING_SNAKE_CASE__, torch.Tensor	):
								a_     :      Any 						=     value.to("cpu"	)
								if value.dtype == torch.inta:
										a_     :      str 						=     version.parse(importlib.metadata.version("bitsandbytes"	)	) > version.parse(
										    "0.37.2"	)
										if not is_abit_serializable:
												raise ValueError(
												    "Detected int8 weights but the version of bitsandbytes is not compatible with int8 serialization. "
												    "Make sure to download the latest `bitsandbytes` version. `pip install --upgrade bitsandbytes`."	)
						else:
								a_     :      Dict 						=     torch.tensor(SCREAMING_SNAKE_CASE__, device="cpu"	)

						# Support models using `Conv1D` in place of `nn.Linear` (e.g. gpt2) by transposing the weight matrix prior to quantization.
						# Since weights are saved in the correct "orientation", we skip transposing when loading.
						if issubclass(module.source_cls, SCREAMING_SNAKE_CASE__	) and fpaa_statistics is None:
								a_     :      Optional[Any] 						=     new_value.T

						a_     :      Union[str, Any] 						=     old_value.__dict__
						if is_abit:
								a_     :      List[Any] 						=     bnb.nn.IntaParams(SCREAMING_SNAKE_CASE__, requires_grad=SCREAMING_SNAKE_CASE__, **SCREAMING_SNAKE_CASE__	).to(SCREAMING_SNAKE_CASE__	)
						elif is_abit:
								a_     :      Optional[Any] 						=     bnb.nn.Paramsabit(SCREAMING_SNAKE_CASE__, requires_grad=SCREAMING_SNAKE_CASE__, **SCREAMING_SNAKE_CASE__	).to(SCREAMING_SNAKE_CASE__	)

						a_     :      List[Any] 						=     new_value
						if fpaa_statistics is not None:
								setattr(module.weight, "SCB", fpaa_statistics.to(SCREAMING_SNAKE_CASE__	)	)

		else:
				if value is None:
						a_     :      str 						=     old_value.to(SCREAMING_SNAKE_CASE__	)
				elif isinstance(SCREAMING_SNAKE_CASE__, torch.Tensor	):
						a_     :      Optional[int] 						=     value.to(SCREAMING_SNAKE_CASE__	)
				else:
						a_     :      int 						=     torch.tensor(SCREAMING_SNAKE_CASE__, device=SCREAMING_SNAKE_CASE__	)

				if is_buffer:
						a_     :      int 						=     new_value
				else:
						a_     :      Optional[int] 						=     nn.Parameter(SCREAMING_SNAKE_CASE__, requires_grad=old_value.requires_grad	)
						a_     :      Any 						=     new_value



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__=None, SCREAMING_SNAKE_CASE__=None, SCREAMING_SNAKE_CASE__=None, SCREAMING_SNAKE_CASE__=False	)			->      Optional[Any]:
		for name, module in model.named_children():
				if current_key_name is None:
						a_     :      Tuple 						=     []
				current_key_name.append(SCREAMING_SNAKE_CASE__	)

				if (isinstance(SCREAMING_SNAKE_CASE__, nn.Linear	) or isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)) and name not in modules_to_not_convert:
						# Check if the current key is not in the `modules_to_not_convert`
						if not any(key in ".".join(SCREAMING_SNAKE_CASE__	) for key in modules_to_not_convert	):
								with init_empty_weights():
										if isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	):
												a_     :      Optional[Any] 						=     module.weight.shape
										else:
												a_     :      int 						=     module.in_features
												a_     :      Union[str, Any] 						=     module.out_features

										if quantization_config.quantization_method() == "llm_int8":
												a_     :      str 						=     bnb.nn.LinearabitLt(
												    SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, module.bias is not None, has_fpaa_weights=quantization_config.llm_inta_has_fpaa_weight, threshold=quantization_config.llm_inta_threshold, )
												a_     :      int 						=     True
										else:
												if (
												    quantization_config.llm_inta_skip_modules is not None
												    and name in quantization_config.llm_inta_skip_modules
												):
														pass
												else:
														a_     :      int 						=     bnb.nn.Linearabit(
														    SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, module.bias is not None, quantization_config.bnb_abit_compute_dtype, compress_statistics=quantization_config.bnb_abit_use_double_quant, quant_type=quantization_config.bnb_abit_quant_type, )
														a_     :      int 						=     True
                    # Store the module class in case we need to transpose the weight later
										a_     :      Union[str, Any] 						=     type(SCREAMING_SNAKE_CASE__	)
										# Force requires grad to False to avoid unexpected errors
										model._modules[name].requires_grad_(SCREAMING_SNAKE_CASE__	)
				if len(list(module.children()	)	) > 0:
						a_     :      str 						=     _replace_with_bnb_linear(
						    SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, has_been_replaced=SCREAMING_SNAKE_CASE__, )
				# Remove the last key for recursion
				current_key_name.pop(-1	)
		return model, has_been_replaced



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__=None, SCREAMING_SNAKE_CASE__=None, SCREAMING_SNAKE_CASE__=None	)			->      Tuple:
		a_     :      List[str] 						=     ["lm_head"] if modules_to_not_convert is None else modules_to_not_convert
		a_     :      Dict 						=     _replace_with_bnb_linear(
		    SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)

		if not has_been_replaced:
				logger.warning(
				    "You are loading your model in 8bit or 4bit but no linear modules were found in your model."
				    " Please double check your model architecture, or submit an issue on github if you think this is"
				    " a bug."	)

		return model



def        lowerCAmelCase_						(							*SCREAMING_SNAKE_CASE__, **SCREAMING_SNAKE_CASE__	)			->      Tuple:
		warnings.warn(
		    "`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead", SCREAMING_SNAKE_CASE__, )
		return replace_with_bnb_linear(*SCREAMING_SNAKE_CASE__, **SCREAMING_SNAKE_CASE__	)



def        lowerCAmelCase_						(							*SCREAMING_SNAKE_CASE__, **SCREAMING_SNAKE_CASE__	)			->      Dict:
		warnings.warn(
		    "`set_module_8bit_tensor_to_device` will be deprecated in a future version, please use `set_module_quantized_tensor_to_device` instead", SCREAMING_SNAKE_CASE__, )
		return set_module_quantized_tensor_to_device(*SCREAMING_SNAKE_CASE__, **SCREAMING_SNAKE_CASE__	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      Optional[int]:
		a_     :      Optional[Any] 						=     deepcopy(SCREAMING_SNAKE_CASE__	)  # this has 0 cost since it is done inside `init_empty_weights` context manager`
		tied_model.tie_weights()

		a_     :      int 						=     find_tied_parameters(SCREAMING_SNAKE_CASE__	)
		# For compatibility with Accelerate < 0.18
		if isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	):
				a_     :      List[Any] 						=     sum(list(tied_params.values()	), []	) + list(tied_params.keys()	)
		else:
				a_     :      int 						=     sum(SCREAMING_SNAKE_CASE__, []	)
		a_     :      Optional[Any] 						=     len(SCREAMING_SNAKE_CASE__	) > 0

		# Check if it is a base model
		a_     :      Optional[Any] 						=     not hasattr(SCREAMING_SNAKE_CASE__, model.base_model_prefix	)

		# Ignore this for base models (BertModel, GPT2Model, etc.)
		if (not has_tied_params) and is_base_model:
				return []

		# otherwise they have an attached head
		a_     :      Tuple 						=     list(model.named_children()	)
		a_     :      Dict 						=     [list_modules[-1][0]]

		# add last module together with tied weights
		a_     :      Tuple 						=     set(SCREAMING_SNAKE_CASE__	) - set(SCREAMING_SNAKE_CASE__	)
		a_     :      Any 						=     list(set(SCREAMING_SNAKE_CASE__	)	) + list(SCREAMING_SNAKE_CASE__	)

		# remove ".weight" from the keys
		a_     :      List[str] 						=     [".weight", ".bias"]
		a_     :      Optional[Any] 						=     []
		for name in list_untouched:
				for name_to_remove in names_to_remove:
						if name_to_remove in name:
								a_     :      int 						=     name.replace(SCREAMING_SNAKE_CASE__, ""	)
				filtered_module_names.append(SCREAMING_SNAKE_CASE__	)

		return filtered_module_names




"""simple docstring"""
import unittest

from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE_				= get_tests_dir("""fixtures/test_sentencepiece_bpe_char.model""")




@require_sentencepiece
@require_tokenizers
class 			snake_case_						(						a_     ,unittest.TestCase				):
							__lowerCAmelCase    					=					SpeechTaTokenizer
							__lowerCAmelCase    					=					False
							__lowerCAmelCase    					=					True






							def snake_case_  (   self      ):
									super().setUp()

									# We have a SentencePiece fixture for testing
									a_     :      Any 						=     SpeechTaTokenizer(a_      )

									a_     :      Optional[int] 						=     AddedToken("<mask>"			,     lstrip=a_			,     rstrip=a_      )
									a_     :      Any 						=     mask_token
									tokenizer.add_special_tokens({"mask_token": mask_token}      )
									tokenizer.add_tokens(["<ctc_blank>"]      )

									tokenizer.save_pretrained(self.tmpdirname      )






							def snake_case_  (   self			,     a_      ):
									a_     :      Tuple 						=     "this is a test"
									a_     :      Any 						=     "this is a test"
									return input_text, output_text






							def snake_case_  (   self			,     a_			,     a_=False			,     a_=2_0			,     a_=5      ):
									a_						,				a_     :      Optional[Any] 						=     self.get_input_output_texts(a_      )
									a_     :      Optional[Any] 						=     tokenizer.encode(a_			,     add_special_tokens=a_      )
									a_     :      Dict 						=     tokenizer.decode(a_			,     clean_up_tokenization_spaces=a_      )
									return text, ids






							def snake_case_  (   self      ):
									a_     :      List[Any] 						=     "<pad>"
									a_     :      Optional[int] 						=     1

									self.assertEqual(self.get_tokenizer()._convert_token_to_id(a_      )			,     a_      )
									self.assertEqual(self.get_tokenizer()._convert_id_to_token(a_      )			,     a_      )






							def snake_case_  (   self      ):
									a_     :      List[Any] 						=     list(self.get_tokenizer().get_vocab().keys()      )

									self.assertEqual(vocab_keys[0]			,     "<s>"      )
									self.assertEqual(vocab_keys[1]			,     "<pad>"      )
									self.assertEqual(vocab_keys[-4]			,     "œ"      )
									self.assertEqual(vocab_keys[-2]			,     "<mask>"      )
									self.assertEqual(vocab_keys[-1]			,     "<ctc_blank>"      )
									self.assertEqual(len(a_      )			,     8_1      )






							def snake_case_  (   self      ):
									self.assertEqual(self.get_tokenizer().vocab_size			,     7_9      )






							def snake_case_  (   self      ):
									a_     :      Any 						=     self.get_tokenizers(do_lower_case=a_      )
									for tokenizer in tokenizers:
											with self.subTest(F"""{tokenizer.__class__.__name__}"""      ):
													a_     :      Dict 						=     tokenizer.vocab_size
													a_     :      List[str] 						=     len(a_      )

													self.assertNotEqual(a_			,     0      )

													# We usually have added tokens from the start in tests because our vocab fixtures are
													# smaller than the original vocabs - let's not assert this
													# self.assertEqual(vocab_size, all_size)

													a_     :      Optional[int] 						=     ["aaaaa bbbbbb", "cccccccccdddddddd"]
													a_     :      int 						=     tokenizer.add_tokens(a_      )
													a_     :      List[Any] 						=     tokenizer.vocab_size
													a_     :      Tuple 						=     len(a_      )

													self.assertNotEqual(a_			,     0      )
													self.assertEqual(a_			,     a_      )
													self.assertEqual(a_			,     len(a_      )      )
													self.assertEqual(a_			,     all_size + len(a_      )      )

													a_     :      str 						=     tokenizer.encode("aaaaa bbbbbb low cccccccccdddddddd l"			,     add_special_tokens=a_      )

													self.assertGreaterEqual(len(a_      )			,     4      )
													self.assertGreater(tokens[0]			,     tokenizer.vocab_size - 1      )
													self.assertGreater(tokens[-3]			,     tokenizer.vocab_size - 1      )

													a_     :      Tuple 						=     {"eos_token": ">>>>|||<||<<|<<", "pad_token": "<<<<<|||>|>>>>|>"}
													a_     :      Dict 						=     tokenizer.add_special_tokens(a_      )
													a_     :      Optional[Any] 						=     tokenizer.vocab_size
													a_     :      Any 						=     len(a_      )

													self.assertNotEqual(a_			,     0      )
													self.assertEqual(a_			,     a_      )
													self.assertEqual(a_			,     len(a_      )      )
													self.assertEqual(a_			,     all_size_a + len(a_      )      )

													a_     :      Any 						=     tokenizer.encode(
													    ">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l"			,     add_special_tokens=a_      )

													self.assertGreaterEqual(len(a_      )			,     6      )
													self.assertGreater(tokens[0]			,     tokenizer.vocab_size - 1      )
													self.assertGreater(tokens[0]			,     tokens[1]      )
													self.assertGreater(tokens[-3]			,     tokenizer.vocab_size - 1      )
													self.assertGreater(tokens[-3]			,     tokens[-4]      )
													self.assertEqual(tokens[0]			,     tokenizer.eos_token_id      )
													self.assertEqual(tokens[-3]			,     tokenizer.pad_token_id      )






							def snake_case_  (   self      ):
									pass






							def snake_case_  (   self      ):
									pass






							def snake_case_  (   self      ):
									a_     :      Union[str, Any] 						=     self.get_tokenizer()

									a_     :      Any 						=     tokenizer.tokenize("This is a test"      )
									# fmt: off
									self.assertListEqual(a_			,     [SPIECE_UNDERLINE, "T", "h", "i", "s", SPIECE_UNDERLINE, "i", "s", SPIECE_UNDERLINE, "a", SPIECE_UNDERLINE, "t", "e", "s", "t"]      )
									# fmt: on

									self.assertListEqual(
									    tokenizer.convert_tokens_to_ids(a_      )			,     [4, 3_2, 1_1, 1_0, 1_2, 4, 1_0, 1_2, 4, 7, 4, 6, 5, 1_2, 6]			,     )

									a_     :      Optional[int] 						=     tokenizer.tokenize("I was born in 92000, and this is falsé."      )
									self.assertListEqual(
									    a_			,     [SPIECE_UNDERLINE, "I", SPIECE_UNDERLINE, "w", "a", "s", SPIECE_UNDERLINE, "b", "o", "r", "n", SPIECE_UNDERLINE, "i", "n", SPIECE_UNDERLINE, "92000", ",", SPIECE_UNDERLINE, "a", "n", "d", SPIECE_UNDERLINE, "t", "h", "i", "s", SPIECE_UNDERLINE, "i", "s", SPIECE_UNDERLINE, "f", "a", "l", "s", "é", "."]      )

									a_     :      Tuple 						=     tokenizer.convert_tokens_to_ids(a_      )
									# fmt: off
									self.assertListEqual(a_			,     [4, 3_0, 4, 2_0, 7, 1_2, 4, 2_5, 8, 1_3, 9, 4, 1_0, 9, 4, 3, 2_3, 4, 7, 9, 1_4, 4, 6, 1_1, 1_0, 1_2, 4, 1_0, 1_2, 4, 1_9, 7, 1_5, 1_2, 7_3, 2_6]      )
									# fmt: on

									a_     :      Tuple 						=     tokenizer.convert_ids_to_tokens(a_      )
									self.assertListEqual(
									    a_			,     [SPIECE_UNDERLINE, "I", SPIECE_UNDERLINE, "w", "a", "s", SPIECE_UNDERLINE, "b", "o", "r", "n", SPIECE_UNDERLINE, "i", "n", SPIECE_UNDERLINE, "<unk>", ",", SPIECE_UNDERLINE, "a", "n", "d", SPIECE_UNDERLINE, "t", "h", "i", "s", SPIECE_UNDERLINE, "i", "s", SPIECE_UNDERLINE, "f", "a", "l", "s", "é", "."]      )






							@slow
							def snake_case_  (   self      ):
									# Use custom sequence because this tokenizer does not handle numbers.
									a_     :      List[Any] 						=     [
									    "Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides "
									    "general-purpose architectures (BERT, GPT, RoBERTa, XLM, DistilBert, XLNet...) for Natural "
									    "Language Understanding (NLU) and Natural Language Generation (NLG) with over thirty-two pretrained "
									    "models in one hundred plus languages and deep interoperability between Jax, PyTorch and TensorFlow.",
									    "BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly "
									    "conditioning on both left and right context in all layers.",
									    "The quick brown fox jumps over the lazy dog.",
									]

									# fmt: off
									a_     :      Tuple 						=     {
									    "input_ids": [
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									        [4, 4_0, 4_7, 5_4, 3_2, 4, 1_0, 1_2, 4, 1_4, 5, 1_2, 1_0, 2_1, 9, 5, 1_4, 4, 6, 8, 4, 2_4, 1_3, 5, 3_9, 6, 1_3, 7, 1_0, 9, 4, 1_4, 5, 5, 2_4, 4, 2_5, 1_0, 1_4, 1_0, 1_3, 5, 1_7, 6, 1_0, 8, 9, 7, 1_5, 4, 1_3, 5, 2_4, 1_3, 5, 1_2, 5, 9, 6, 7, 6, 1_0, 8, 9, 1_2, 4, 1_9, 1_3, 8, 1_8, 4, 1_6, 9, 1_5, 7, 2_5, 5, 1_5, 5, 1_4, 4, 6, 5, 3_7, 6, 4, 2_5, 2_2, 4, 4_6, 8, 1_0, 9, 6, 1_5, 2_2, 4, 1_7, 8, 9, 1_4, 1_0, 6, 1_0, 8, 9, 1_0, 9, 2_1, 4, 8, 9, 4, 2_5, 8, 6, 1_1, 4, 1_5, 5, 1_9, 6, 4, 7, 9, 1_4, 4, 1_3, 1_0, 2_1, 1_1, 6, 4, 1_7, 8, 9, 6, 5, 3_7, 6, 4, 1_0, 9, 4, 7, 1_5, 1_5, 4, 1_5, 7, 2_2, 5, 1_3, 1_2, 2_6, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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									    ],
									    "attention_mask": [
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									    ]
									}
									# fmt: on

									self.tokenizer_integration_test_util(
									    expected_encoding=a_			,     model_name="microsoft/speecht5_asr"			,     revision="c5ef64c71905caeccde0e4462ef3f9077224c524"			,     sequences=a_			,     )