




import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
					import torch
					from torch import nn

					from transformers import (
					    Adafactor,
					    AdamW,
					    get_constant_schedule,
					    get_constant_schedule_with_warmup,
					    get_cosine_schedule_with_warmup,
					    get_cosine_with_hard_restarts_schedule_with_warmup,
					    get_inverse_sqrt_schedule,
					    get_linear_schedule_with_warmup,
					    get_polynomial_decay_schedule_with_warmup,
					)







def 			lowerCamelCase_		(       UpperCamelCase__				:      List[Any] ,     UpperCamelCase__				:      Union[str, Any]=10      )     ->       str:





				"""simple docstring"""


				__lowerCamelCase		       =    []
				for _ in range(UpperCamelCase__      ):
								lrs.append(scheduler.get_lr()[0]      )
								scheduler.step()
				return lrs







def 			lowerCamelCase_		(       UpperCamelCase__				:      Optional[int] ,     UpperCamelCase__				:      Any=10      )     ->       Any:





				"""simple docstring"""


				__lowerCamelCase		       =    []
				for step in range(UpperCamelCase__      ):
								lrs.append(scheduler.get_lr()[0]      )
								scheduler.step()
								if step == num_steps // 2:
												with tempfile.TemporaryDirectory() as tmpdirname:
																__lowerCamelCase		       =    os.path.join(UpperCamelCase__ ,     'schedule.bin'      )
																torch.save(scheduler.state_dict() ,     UpperCamelCase__      )

																__lowerCamelCase		       =    torch.load(UpperCamelCase__      )
																scheduler.load_state_dict(UpperCamelCase__      )
				return lrs







@require_torch
class     __lowerCAmelCase (       unittest.TestCase ):

			"""simple docstring"""







			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      Any:



							'''simple docstring'''
							self.assertEqual(len(lowerCamelCase__							)			,						len(lowerCamelCase__							)							)
							for a, b in zip(lowerCamelCase__			,						lowerCamelCase__							):
											self.assertAlmostEqual(lowerCamelCase__			,						lowerCamelCase__			,						delta=lowerCamelCase__							)


			def       lowercase_    (      self							)    ->      Any:



							'''simple docstring'''
							__lowerCamelCase		       =    torch.tensor([0.1, -0.2, -0.1]			,						requires_grad=lowerCamelCase__							)
							__lowerCamelCase		       =    torch.tensor([0.4, 0.2, -0.5]							)
							__lowerCamelCase		       =    nn.MSELoss()
							# No warmup, constant schedule, no gradient clipping
							__lowerCamelCase		       =    AdamW(params=[w]			,						lr=2e-1			,						weight_decay=0.0							)
							for _ in range(100							):
											__lowerCamelCase		       =    criterion(lowerCamelCase__			,						lowerCamelCase__							)
											loss.backward()
											optimizer.step()
											w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
											w.grad.zero_()
							self.assertListAlmostEqual(w.tolist()			,						[0.4, 0.2, -0.5]			,						tol=1e-2							)






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



							'''simple docstring'''
							__lowerCamelCase		       =    torch.tensor([0.1, -0.2, -0.1]			,						requires_grad=lowerCamelCase__							)
							__lowerCamelCase		       =    torch.tensor([0.4, 0.2, -0.5]							)
							__lowerCamelCase		       =    nn.MSELoss()
							# No warmup, constant schedule, no gradient clipping
							__lowerCamelCase		       =    Adafactor(
							    params=[w]			,						lr=1e-2			,						eps=(1e-30, 1e-3)			,						clip_threshold=1.0			,						decay_rate=-0.8			,						betaa=lowerCamelCase__			,						weight_decay=0.0			,						relative_step=lowerCamelCase__			,						scale_parameter=lowerCamelCase__			,						warmup_init=lowerCamelCase__			,						)
							for _ in range(1_000							):
											__lowerCamelCase		       =    criterion(lowerCamelCase__			,						lowerCamelCase__							)
											loss.backward()
											optimizer.step()
											w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
											w.grad.zero_()
							self.assertListAlmostEqual(w.tolist()			,						[0.4, 0.2, -0.5]			,						tol=1e-2							)







@require_torch
class     __lowerCAmelCase (       unittest.TestCase ):

			"""simple docstring"""







			snake_case_   		=       nn.Linear(50			,				50 ) if is_torch_available() else None
			snake_case_   		=       AdamW(m.parameters()			,				lr=1_0.0 ) if is_torch_available() else None
			snake_case_   		=       10


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__=None							)    ->      Any:



							'''simple docstring'''
							self.assertEqual(len(lowerCamelCase__							)			,						len(lowerCamelCase__							)							)
							for a, b in zip(lowerCamelCase__			,						lowerCamelCase__							):
											self.assertAlmostEqual(lowerCamelCase__			,						lowerCamelCase__			,						delta=lowerCamelCase__			,						msg=lowerCamelCase__							)






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



							'''simple docstring'''
							__lowerCamelCase		       =    {'num_warmup_steps': 2, 'num_training_steps': 10}
							# schedulers doct format
							# function: (sched_args_dict, expected_learning_rates)
							__lowerCamelCase		       =    {
							    get_constant_schedule: ({}, [10.0] * self.num_steps),
							    get_constant_schedule_with_warmup: (
							        {'num_warmup_steps': 4},
							        [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
							    ),
							    get_linear_schedule_with_warmup: (
							        {**common_kwargs},
							        [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
							    ),
							    get_cosine_schedule_with_warmup: (
							        {**common_kwargs},
							        [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
							    ),
							    get_cosine_with_hard_restarts_schedule_with_warmup: (
							        {**common_kwargs, 'num_cycles': 2},
							        [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
							    ),
							    get_polynomial_decay_schedule_with_warmup: (
							        {**common_kwargs, 'power': 2.0, 'lr_end': 1e-7},
							        [0.0, 5.0, 10.0, 7.6_56, 5.6_25, 3.9_06, 2.5, 1.4_06, 0.6_25, 0.1_56],
							    ),
							    get_inverse_sqrt_schedule: (
							        {'num_warmup_steps': 2},
							        [0.0, 5.0, 10.0, 8.1_65, 7.0_71, 6.3_25, 5.7_74, 5.3_45, 5.0, 4.7_14],
							    ),
							}

							for scheduler_func, data in scheds.items():
											__lowerCamelCase       ,						__lowerCamelCase		       =    data

											__lowerCamelCase		       =    scheduler_func(self.optimizer			,						**lowerCamelCase__							)
											self.assertEqual(len([scheduler.get_lr()[0]]							)			,						1							)
											__lowerCamelCase		       =    unwrap_schedule(lowerCamelCase__			,						self.num_steps							)
											self.assertListAlmostEqual(
											    lowerCamelCase__			,						lowerCamelCase__			,						tol=1e-2			,						msg=f"""failed for {scheduler_func} in normal scheduler"""			,						)

											__lowerCamelCase		       =    scheduler_func(self.optimizer			,						**lowerCamelCase__							)
											if scheduler_func.__name__ != "get_constant_schedule":
															LambdaScheduleWrapper.wrap_scheduler(lowerCamelCase__							)  # wrap to test picklability of the schedule
											__lowerCamelCase		       =    unwrap_and_save_reload_schedule(lowerCamelCase__			,						self.num_steps							)
											self.assertListEqual(lowerCamelCase__			,						lowerCamelCase__			,						msg=f"""failed for {scheduler_func} in save and reload"""							)







class     __lowerCAmelCase :

			"""simple docstring"""







			def __init__(      self			,						lowerCamelCase__							)    ->      Tuple:



							'''simple docstring'''
							__lowerCamelCase		       =    fn


			def __call__(      self			,						*lowerCamelCase__			,						**lowerCamelCase__							)    ->      Any:



							'''simple docstring'''
							return self.fn(*lowerCamelCase__			,						**lowerCamelCase__							)






			@classmethod
			def       lowercase_    (      self			,						lowerCamelCase__							)    ->      Any:



							'''simple docstring'''
							__lowerCamelCase		       =    list(map(self			,						scheduler.lr_lambdas							)							)








import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation


__A        =    logging.get_logger(__name__)

__A        =    {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

__A        =    {
    "tokenizer_file": {
        "EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json",
    },
}

__A        =    {
    "gpt-neox-20b": 20_48,
}







class     __lowerCAmelCase (       __magic_name__ ):

			"""simple docstring"""







			snake_case_   		=       VOCAB_FILES_NAMES
			snake_case_   		=       PRETRAINED_VOCAB_FILES_MAP
			snake_case_   		=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			snake_case_   		=       ['''input_ids''', '''attention_mask''']


			def __init__(      self			,						lowerCamelCase__=None			,						lowerCamelCase__=None			,						lowerCamelCase__=None			,						lowerCamelCase__="<|endoftext|>"			,						lowerCamelCase__="<|endoftext|>"			,						lowerCamelCase__="<|endoftext|>"			,						lowerCamelCase__=False			,						**lowerCamelCase__			,						)    ->      int:



							'''simple docstring'''
							super().__init__(
							    lowerCamelCase__			,						lowerCamelCase__			,						tokenizer_file=lowerCamelCase__			,						unk_token=lowerCamelCase__			,						bos_token=lowerCamelCase__			,						eos_token=lowerCamelCase__			,						add_prefix_space=lowerCamelCase__			,						**lowerCamelCase__			,						)

							__lowerCamelCase		       =    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
							if pre_tok_state.get('add_prefix_space'			,						lowerCamelCase__							) != add_prefix_space:
											__lowerCamelCase		       =    getattr(lowerCamelCase__			,						pre_tok_state.pop('type'							)							)
											__lowerCamelCase		       =    add_prefix_space
											__lowerCamelCase		       =    pre_tok_class(**lowerCamelCase__							)

							__lowerCamelCase		       =    add_prefix_space


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__ = None							)    ->      Tuple[str]:



							'''simple docstring'''
							__lowerCamelCase		       =    self._tokenizer.model.save(lowerCamelCase__			,						name=lowerCamelCase__							)
							return tuple(lowerCamelCase__							)






			def       lowercase_    (      self			,						lowerCamelCase__							)    ->      List[int]:



							'''simple docstring'''
							__lowerCamelCase		       =    []
							for is_user, text in conversation.iter_texts():
											input_ids.extend(self.encode(lowerCamelCase__			,						add_special_tokens=lowerCamelCase__							) + [self.eos_token_id]							)

							if len(lowerCamelCase__							) > self.model_max_length:
											__lowerCamelCase		       =    input_ids[-self.model_max_length :]
							return input_ids


