


import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
     snake_case							:       int										=	'''pt'''
elif is_tf_available():
     snake_case							:       List[str]										=	'''tf'''
else:
     snake_case							:       Tuple										=	'''jax'''







class 						_snake_case    ( _snake_case		,       unittest.TestCase				):
 SCREAMING_SNAKE_CASE__ =			ByTaTokenizer
 SCREAMING_SNAKE_CASE__ =			False




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       super().setUp()
       a		:Any									=			ByTaTokenizer()
       tokenizer.save_pretrained(self.tmpdirname       )




 @cached_property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       return ByTaTokenizer.from_pretrained('''google/byt5-small'''       )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    **_lowerCamelCase       ):
       return self.tokenizer_class.from_pretrained(self.tmpdirname   ,    **_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase=False   ,    _lowerCamelCase=20   ,    _lowerCamelCase=5       ):
       # XXX The default common tokenizer tests assume that every ID is decodable on its own.
       # This assumption is invalid for ByT5 because single bytes might not be
       # valid utf-8 (byte 128 for instance).
       # Here we're overriding the smallest possible method to provide
       # a clean sequence without making the same assumption.

       a		:Union[str, Any]									=			[]
       for i in range(len(_lowerCamelCase       )       ):
             try:
                   a		:Optional[int]									=			tokenizer.decode([i]   ,    clean_up_tokenization_spaces=_lowerCamelCase       )
             except UnicodeDecodeError:
                   pass
             toks.append((i, tok)       )

       a		:Optional[Any]									=			list(filter(lambda _lowerCamelCase       : re.match(R'''^[ a-zA-Z]+$'''   ,    t[1]       )   ,    _lowerCamelCase       )       )
       a		:List[str]									=			list(filter(lambda _lowerCamelCase       : [t[0]] == tokenizer.encode(t[1]   ,    add_special_tokens=_lowerCamelCase       )   ,    _lowerCamelCase       )       )
       if max_length is not None and len(_lowerCamelCase       ) > max_length:
             a		:Tuple									=			toks[:max_length]
       if min_length is not None and len(_lowerCamelCase       ) < min_length and len(_lowerCamelCase       ) > 0:
             while len(_lowerCamelCase       ) < min_length:
                   a		:List[str]									=			toks + toks
        # toks_str = [t[1] for t in toks]
       a		:str									=			[t[0] for t in toks]

       # Ensure consistency
       a		:Union[str, Any]									=			tokenizer.decode(_lowerCamelCase   ,    clean_up_tokenization_spaces=_lowerCamelCase       )
       if " " not in output_txt and len(_lowerCamelCase       ) > 1:
             a		:Union[str, Any]									=			(
                 tokenizer.decode([toks_ids[0]]   ,    clean_up_tokenization_spaces=_lowerCamelCase       )
                 + ''' '''
                 + tokenizer.decode(toks_ids[1:]   ,    clean_up_tokenization_spaces=_lowerCamelCase       )
             )
       if with_prefix_space:
             a		:str									=			''' ''' + output_txt
       a		:List[str]									=			tokenizer.encode(_lowerCamelCase   ,    add_special_tokens=_lowerCamelCase       )
       return output_txt, output_ids




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Dict									=			self.ta_base_tokenizer
       a		:List[Any]									=			tokenizer(['''hi</s>''', '''I went to the gym</s>''', '''</s>''']       )
       a		:str									=			tokenizer(['''hi''', '''I went to the gym''', '''''']       )
       self.assertListEqual(batch_with_eos_added['''input_ids''']   ,    batch_without_eos_added['''input_ids''']       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:List[str]									=			self.ta_base_tokenizer
       a		:List[Any]									=			'''Unicode €.'''
       a		:Union[str, Any]									=			tokenizer(_lowerCamelCase       )
       a		:Any									=			[88, 113, 108, 102, 114, 103, 104, 35, 229, 133, 175, 49, 1]
       self.assertEqual(encoded['''input_ids''']   ,    _lowerCamelCase       )

       # decoding
       a		:Dict									=			tokenizer.decode(_lowerCamelCase       )
       self.assertEqual(_lowerCamelCase   ,    '''Unicode €.</s>'''       )

       a		:Any									=			tokenizer('''e è é ê ë'''       )
       a		:Optional[Any]									=			[104, 35, 198, 171, 35, 198, 172, 35, 198, 173, 35, 198, 174, 1]
       self.assertEqual(encoded['''input_ids''']   ,    _lowerCamelCase       )
       # decoding
       a		:List[str]									=			tokenizer.decode(_lowerCamelCase       )
       self.assertEqual(_lowerCamelCase   ,    '''e è é ê ë</s>'''       )

       # encode/decode, but with `encode` instead of `__call__`
       self.assertEqual(tokenizer.decode(tokenizer.encode('''e è é ê ë'''       )       )   ,    '''e è é ê ë</s>'''       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:int									=			self.ta_base_tokenizer
       a		:Dict									=			['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
       # fmt: off
       a		:Any									=			[68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 1, 0]
       # fmt: on
       a		:Tuple									=			tokenizer(_lowerCamelCase   ,    padding=_lowerCamelCase   ,    return_tensors=_lowerCamelCase       )
       self.assertIsInstance(_lowerCamelCase   ,    _lowerCamelCase       )

       if FRAMEWORK != "jax":
             a		:Tuple									=			list(batch.input_ids.numpy()[0]       )
       else:
             a		:List[str]									=			list(batch.input_ids.tolist()[0]       )

       self.assertListEqual(_lowerCamelCase   ,    _lowerCamelCase       )

       self.assertEqual((2, 37)   ,    batch.input_ids.shape       )
       self.assertEqual((2, 37)   ,    batch.attention_mask.shape       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Any									=			self.ta_base_tokenizer
       a		:Tuple									=			['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
       a		:Union[str, Any]									=			tokenizer(_lowerCamelCase   ,    padding=_lowerCamelCase   ,    return_tensors=_lowerCamelCase       )
       # check if input_ids are returned and no decoder_input_ids
       self.assertIn('''input_ids'''   ,    _lowerCamelCase       )
       self.assertIn('''attention_mask'''   ,    _lowerCamelCase       )
       self.assertNotIn('''decoder_input_ids'''   ,    _lowerCamelCase       )
       self.assertNotIn('''decoder_attention_mask'''   ,    _lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Any									=			self.ta_base_tokenizer
       a		:Dict									=			[
           '''Summary of the text.''',
           '''Another summary.''',
       ]
       a		:Tuple									=			tokenizer(
           text_target=_lowerCamelCase   ,    max_length=32   ,    padding='''max_length'''   ,    truncation=_lowerCamelCase   ,    return_tensors=_lowerCamelCase       )
       self.assertEqual(32   ,    targets['''input_ids'''].shape[1]       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:str									=			self.ta_base_tokenizer
       a		:Optional[Any]									=			['''A long paragraph for summarization. </s>''']
       a		:Tuple									=			['''Summary of the text. </s>''']
       # fmt: off
       a		:List[str]									=			[68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 35, 1]
       a		:List[Any]									=			[86, 120, 112, 112, 100, 117, 124, 35, 114, 105, 35, 119, 107, 104, 35, 119, 104, 123, 119, 49, 35, 1]
       # fmt: on

       a		:Optional[int]									=			tokenizer(_lowerCamelCase   ,    text_target=_lowerCamelCase       )

       self.assertEqual(_lowerCamelCase   ,    batch['''input_ids'''][0]       )
       self.assertEqual(_lowerCamelCase   ,    batch['''labels'''][0]       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       # safety check on max_len default value so we are sure the test works
       a		:Any									=			self.get_tokenizers()
       for tokenizer in tokenizers:
             with self.subTest(F'''{tokenizer.__class__.__name__}'''       ):
                   self.assertNotEqual(tokenizer.model_max_length   ,    42       )

        # Now let's start the test
       a		:Tuple									=			self.get_tokenizers()
       for tokenizer in tokenizers:
             with self.subTest(F'''{tokenizer.__class__.__name__}'''       ):
                   # Isolate this from the other tests because we save additional tokens/etc
                   a		:Tuple									=			tempfile.mkdtemp()

                   a		:Optional[Any]									=			''' He is very happy, UNwant\u00E9d,running'''
                   a		:str									=			tokenizer.encode(_lowerCamelCase   ,    add_special_tokens=_lowerCamelCase       )
                   tokenizer.save_pretrained(_lowerCamelCase       )

                   a		:Tuple									=			tokenizer.__class__.from_pretrained(_lowerCamelCase       )
                   a		:List[Any]									=			after_tokenizer.encode(_lowerCamelCase   ,    add_special_tokens=_lowerCamelCase       )
                   self.assertListEqual(_lowerCamelCase   ,    _lowerCamelCase       )

                   shutil.rmtree(_lowerCamelCase       )

       a		:Optional[Any]									=			self.get_tokenizers(model_max_length=42       )
       for tokenizer in tokenizers:
             with self.subTest(F'''{tokenizer.__class__.__name__}'''       ):
                   # Isolate this from the other tests because we save additional tokens/etc
                   a		:str									=			tempfile.mkdtemp()

                   a		:Optional[Any]									=			''' He is very happy, UNwant\u00E9d,running'''
                   tokenizer.add_tokens(['''bim''', '''bambam''']       )
                   a		:List[str]									=			tokenizer.additional_special_tokens
                   additional_special_tokens.append('''new_additional_special_token'''       )
                   tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens}       )
                   a		:Union[str, Any]									=			tokenizer.encode(_lowerCamelCase   ,    add_special_tokens=_lowerCamelCase       )
                   tokenizer.save_pretrained(_lowerCamelCase       )

                   a		:Optional[int]									=			tokenizer.__class__.from_pretrained(_lowerCamelCase       )
                   a		:int									=			after_tokenizer.encode(_lowerCamelCase   ,    add_special_tokens=_lowerCamelCase       )
                   self.assertListEqual(_lowerCamelCase   ,    _lowerCamelCase       )
                   self.assertIn('''new_additional_special_token'''   ,    after_tokenizer.additional_special_tokens       )
                   self.assertEqual(after_tokenizer.model_max_length   ,    42       )

                   a		:List[str]									=			tokenizer.__class__.from_pretrained(_lowerCamelCase   ,    model_max_length=43       )
                   self.assertEqual(tokenizer.model_max_length   ,    43       )

                   shutil.rmtree(_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:List[Any]									=			[]
       if self.test_slow_tokenizer:
             tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())       )

       if self.test_rust_tokenizer:
             tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())       )

       for tokenizer_class, tokenizer_utils in tokenizer_list:
             with tempfile.TemporaryDirectory() as tmp_dir:
                   tokenizer_utils.save_pretrained(_lowerCamelCase       )

                   with open(os.path.join(_lowerCamelCase   ,    '''special_tokens_map.json'''       )   ,    encoding='''utf-8'''       ) as json_file:
                         a		:Dict									=			json.load(_lowerCamelCase       )

                   with open(os.path.join(_lowerCamelCase   ,    '''tokenizer_config.json'''       )   ,    encoding='''utf-8'''       ) as json_file:
                         a		:Optional[Any]									=			json.load(_lowerCamelCase       )

                   a		:str									=			[F'''<extra_id_{i}>''' for i in range(125       )]

                   a		:Optional[int]									=			added_tokens_extra_ids + [
                       '''an_additional_special_token'''
                   ]
                   a		:int									=			added_tokens_extra_ids + [
                       '''an_additional_special_token'''
                   ]

                   with open(os.path.join(_lowerCamelCase   ,    '''special_tokens_map.json'''       )   ,    '''w'''   ,    encoding='''utf-8'''       ) as outfile:
                         json.dump(_lowerCamelCase   ,    _lowerCamelCase       )
                   with open(os.path.join(_lowerCamelCase   ,    '''tokenizer_config.json'''       )   ,    '''w'''   ,    encoding='''utf-8'''       ) as outfile:
                         json.dump(_lowerCamelCase   ,    _lowerCamelCase       )

                   # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                   # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                   # "special_tokens_map.json" files
                   a		:int									=			tokenizer_class.from_pretrained(
                       _lowerCamelCase   ,    )
                   self.assertIn(
                       '''an_additional_special_token'''   ,    tokenizer_without_change_in_init.additional_special_tokens       )
                   # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
                   self.assertEqual(
                       ['''an_additional_special_token''']   ,    tokenizer_without_change_in_init.convert_ids_to_tokens(
                           tokenizer_without_change_in_init.convert_tokens_to_ids(['''an_additional_special_token''']       )       )   ,    )

                   # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                   a		:List[str]									=			added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''   ,    lstrip=_lowerCamelCase       )]
                   a		:List[str]									=			tokenizer_class.from_pretrained(
                       _lowerCamelCase   ,    additional_special_tokens=_lowerCamelCase   ,    )

                   self.assertIn('''a_new_additional_special_token'''   ,    tokenizer.additional_special_tokens       )
                   self.assertEqual(
                       ['''a_new_additional_special_token''']   ,    tokenizer.convert_ids_to_tokens(
                           tokenizer.convert_tokens_to_ids(['''a_new_additional_special_token''']       )       )   ,    )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Any									=			[]
       if self.test_slow_tokenizer:
             tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())       )

       if self.test_rust_tokenizer:
             tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())       )

       for tokenizer_class, tokenizer_utils in tokenizer_list:
             with tempfile.TemporaryDirectory() as tmp_dir:
                   tokenizer_utils.save_pretrained(_lowerCamelCase       )

                   a		:int									=			tokenizer_class.from_pretrained(_lowerCamelCase       )

                   self.assertTrue(tokenizer.decode([255]       ) == ''''''       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       pass




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       pass




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       pass




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       pass




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       # The default common tokenizer tests uses invalid tokens for ByT5 that can only accept one-character strings
       # and special added tokens as tokens
       a		:int									=			self.get_tokenizers(fast=_lowerCamelCase   ,    do_lower_case=_lowerCamelCase       )
       for tokenizer in tokenizers:
             with self.subTest(F'''{tokenizer.__class__.__name__}'''       ):
                   a		:Optional[Any]									=			['''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''x''', '''t''', '''</s>''']
                   a		:Union[str, Any]									=			tokenizer.convert_tokens_to_string(_lowerCamelCase       )

                   self.assertIsInstance(_lowerCamelCase   ,    _lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Optional[int]									=			self.get_tokenizers()
       for tokenizer in tokenizers:
             with self.subTest(F'''{tokenizer.__class__.__name__}'''       ):
                   a		:Optional[Any]									=			[
                       '''bos_token''',
                       '''eos_token''',
                       '''unk_token''',
                       '''sep_token''',
                       '''pad_token''',
                       '''cls_token''',
                       '''mask_token''',
                   ]

                   a		:Optional[int]									=			0
                   a		:Dict									=			tokenizer.convert_ids_to_tokens(
                       _lowerCamelCase   ,    skip_special_tokens=_lowerCamelCase       )

                   for attr in attributes_list:
                         setattr(_lowerCamelCase   ,    attr + '''_id'''   ,    _lowerCamelCase       )
                         self.assertEqual(getattr(_lowerCamelCase   ,    _lowerCamelCase       )   ,    _lowerCamelCase       )
                         self.assertEqual(getattr(_lowerCamelCase   ,    attr + '''_id'''       )   ,    _lowerCamelCase       )

                         setattr(_lowerCamelCase   ,    attr + '''_id'''   ,    _lowerCamelCase       )
                         self.assertEqual(getattr(_lowerCamelCase   ,    _lowerCamelCase       )   ,    _lowerCamelCase       )
                         self.assertEqual(getattr(_lowerCamelCase   ,    attr + '''_id'''       )   ,    _lowerCamelCase       )

                   setattr(_lowerCamelCase   ,    '''additional_special_tokens_ids'''   ,    []       )
                   self.assertListEqual(getattr(_lowerCamelCase   ,    '''additional_special_tokens'''       )   ,    []       )
                   self.assertListEqual(getattr(_lowerCamelCase   ,    '''additional_special_tokens_ids'''       )   ,    []       )

                   setattr(_lowerCamelCase   ,    '''additional_special_tokens_ids'''   ,    [token_id_to_test_setters]       )
                   self.assertListEqual(getattr(_lowerCamelCase   ,    '''additional_special_tokens'''       )   ,    [token_to_test_setters]       )
                   self.assertListEqual(getattr(_lowerCamelCase   ,    '''additional_special_tokens_ids'''       )   ,    [token_id_to_test_setters]       )









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_		:						str					,    UpperCAmelCase_		:						Union[str, Any]=10		):




      """simple docstring"""





      a		:Tuple									=			[]
      for _ in range(UpperCAmelCase_		):
            lrs.append(scheduler.get_lr()[0]		)
            scheduler.step()
      return lrs
def 							__lowerCamelCase		(					UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						Tuple=10		):




      """simple docstring"""





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

                        a		:int									=			torch.load(UpperCAmelCase_		)
                        scheduler.load_state_dict(UpperCAmelCase_		)
      return lrs







@require_torch
class 						_snake_case    ( unittest.TestCase				):




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase       ):
       self.assertEqual(len(_lowerCamelCase       )   ,    len(_lowerCamelCase       )       )
       for a, b in zip(_lowerCamelCase   ,    _lowerCamelCase       ):
             self.assertAlmostEqual(_lowerCamelCase   ,    _lowerCamelCase   ,    delta=_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:List[str]									=			torch.tensor([0.1, -0.2, -0.1]   ,    requires_grad=_lowerCamelCase       )
       a		:int									=			torch.tensor([0.4, 0.2, -0.5]       )
       a		:int									=			nn.MSELoss()
       # No warmup, constant schedule, no gradient clipping
       a		:Tuple									=			AdamW(params=[w]   ,    lr=2e-1   ,    weight_decay=0.0       )
       for _ in range(100       ):
             a		:int									=			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 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:List[Any]									=			torch.tensor([0.1, -0.2, -0.1]   ,    requires_grad=_lowerCamelCase       )
       a		:Union[str, Any]									=			torch.tensor([0.4, 0.2, -0.5]       )
       a		:List[Any]									=			nn.MSELoss()
       # No warmup, constant schedule, no gradient clipping
       a		:Any									=			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(1000       ):
             a		:str									=			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 						_snake_case    ( unittest.TestCase				):
 SCREAMING_SNAKE_CASE__ =			nn.Linear(50		,       50				) if is_torch_available() else None
 SCREAMING_SNAKE_CASE__ =			AdamW(m.parameters()		,       lr=10.0				) if is_torch_available() else None
 SCREAMING_SNAKE_CASE__ =			10




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase=None       ):
       self.assertEqual(len(_lowerCamelCase       )   ,    len(_lowerCamelCase       )       )
       for a, b in zip(_lowerCamelCase   ,    _lowerCamelCase       ):
             self.assertAlmostEqual(_lowerCamelCase   ,    _lowerCamelCase   ,    delta=_lowerCamelCase   ,    msg=_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Union[str, Any]									=			{'''num_warmup_steps''': 2, '''num_training_steps''': 10}
       # schedulers doct format
       # function: (sched_args_dict, expected_learning_rates)
       a		:Any									=			{
           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, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
           ),
           get_inverse_sqrt_schedule: (
               {'''num_warmup_steps''': 2},
               [0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
           ),
       }

       for scheduler_func, data in scheds.items():
             a				,							a		:List[str]									=			data

             a		:str									=			scheduler_func(self.optimizer   ,    **_lowerCamelCase       )
             self.assertEqual(len([scheduler.get_lr()[0]]       )   ,    1       )
             a		:Optional[Any]									=			unwrap_schedule(_lowerCamelCase   ,    self.num_steps       )
             self.assertListAlmostEqual(
                 _lowerCamelCase   ,    _lowerCamelCase   ,    tol=1e-2   ,    msg=F'''failed for {scheduler_func} in normal scheduler'''   ,    )

             a		:List[Any]									=			scheduler_func(self.optimizer   ,    **_lowerCamelCase       )
             if scheduler_func.__name__ != "get_constant_schedule":
                   LambdaScheduleWrapper.wrap_scheduler(_lowerCamelCase       )  # wrap to test picklability of the schedule
             a		:Dict									=			unwrap_and_save_reload_schedule(_lowerCamelCase   ,    self.num_steps       )
             self.assertListEqual(_lowerCamelCase   ,    _lowerCamelCase   ,    msg=F'''failed for {scheduler_func} in save and reload'''       )







class 						_snake_case    :




 def __init__(						self   ,    _lowerCamelCase       ):
       a		:Dict									=			fn




 def __call__(						self   ,    *_lowerCamelCase   ,    **_lowerCamelCase       ):
       return self.fn(*_lowerCamelCase   ,    **_lowerCamelCase       )




 @classmethod
 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase       ):
       a		:str									=			list(map(self   ,    scheduler.lr_lambdas       )       )





