





'''simple docstring'''

import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
  import tensorflow as tf

if is_tensorflow_text_available():
  from transformers.models.bert import TFBertTokenizer


a        =						["bert-base-uncased", "bert-base-cased"]
a        =						"hf-internal-testing/tiny-bert-tf-only"

if is_tf_available():







  class __a (   tf.keras.Model			):





     def __init__(					self				:			Union[str, Any]				,lowerCamelCase				:			Tuple    ):

        '''simple docstring'''

        super().__init__()
        __SCREAMING_SNAKE_CASE      							=       tokenizer
        __SCREAMING_SNAKE_CASE      							=       AutoConfig.from_pretrained(lowerCamelCase    )
        __SCREAMING_SNAKE_CASE      							=       TFAutoModel.from_config(lowerCamelCase    )





     def      UpperCAmelCase__     (					self				:			Dict				,lowerCamelCase				:			Dict    ):

        '''simple docstring'''

        __SCREAMING_SNAKE_CASE      							=       self.tokenizer(lowerCamelCase    )
        __SCREAMING_SNAKE_CASE      							=       self.bert(**lowerCamelCase    )
        return out["pooler_output"]







@require_tf
@require_tensorflow_text
class __a (   unittest.TestCase			):





   def      UpperCAmelCase__     (					self				:			Optional[Any]    ):

      '''simple docstring'''

      super().setUp()

      __SCREAMING_SNAKE_CASE      							=       [
          BertTokenizer.from_pretrained(lowerCamelCase    ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
      ]  # repeat for when fast_bert_tokenizer=false
      __SCREAMING_SNAKE_CASE      							=       [TFBertTokenizer.from_pretrained(lowerCamelCase    ) for checkpoint in TOKENIZER_CHECKPOINTS] + [
          TFBertTokenizer.from_pretrained(lowerCamelCase				,use_fast_bert_tokenizer=lowerCamelCase    )
          for checkpoint in TOKENIZER_CHECKPOINTS
      ]
      assert len(self.tokenizers    ) == len(self.tf_tokenizers    )

      __SCREAMING_SNAKE_CASE      							=       [
          """This is a straightforward English test sentence.""",
          """This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.""",
          """Now we're going to add some Chinese: 一 二 三 一二三""",
          """And some much more rare Chinese: 齉 堃 齉堃""",
          """Je vais aussi écrire en français pour tester les accents""",
          """Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ""",
      ]
      __SCREAMING_SNAKE_CASE      							=       list(zip(self.test_sentences				,self.test_sentences[::-1]    )    )





   def      UpperCAmelCase__     (					self				:			List[Any]    ):

      '''simple docstring'''

      for tokenizer, tf_tokenizer in zip(self.tokenizers				,self.tf_tokenizers    ):
         for test_inputs in (self.test_sentences, self.paired_sentences):
            __SCREAMING_SNAKE_CASE      							=       tokenizer(lowerCamelCase				,return_tensors="""tf"""				,padding="""longest"""    )
            __SCREAMING_SNAKE_CASE      							=       tf_tokenizer(lowerCamelCase    )

            for key in python_outputs.keys():
               self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape    )    )
               self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]				,tf.intaa    ) == tf_outputs[key]    )    )





   @slow
   def      UpperCAmelCase__     (					self				:			int    ):

      '''simple docstring'''

      for tf_tokenizer in self.tf_tokenizers:
         __SCREAMING_SNAKE_CASE      							=       tf_tokenizer(self.paired_sentences    )
         __SCREAMING_SNAKE_CASE      							=       tf_tokenizer(
             text=[sentence[0] for sentence in self.paired_sentences]				,text_pair=[sentence[1] for sentence in self.paired_sentences]				,)
         for key in merged_outputs.keys():
            self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]				,tf.intaa    ) == separated_outputs[key]    )    )





   @slow
   def      UpperCAmelCase__     (					self				:			List[str]    ):

      '''simple docstring'''

      for tf_tokenizer in self.tf_tokenizers:
         __SCREAMING_SNAKE_CASE      							=       tf.function(lowerCamelCase    )
         for test_inputs in (self.test_sentences, self.paired_sentences):
            __SCREAMING_SNAKE_CASE      							=       tf.constant(lowerCamelCase    )
            __SCREAMING_SNAKE_CASE      							=       compiled_tokenizer(lowerCamelCase    )
            __SCREAMING_SNAKE_CASE      							=       tf_tokenizer(lowerCamelCase    )

            for key in eager_outputs.keys():
               self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]    )    )





   @slow
   def      UpperCAmelCase__     (					self				:			Dict    ):

      '''simple docstring'''

      for tf_tokenizer in self.tf_tokenizers:
         __SCREAMING_SNAKE_CASE      							=       ModelToSave(tokenizer=lowerCamelCase    )
         __SCREAMING_SNAKE_CASE      							=       tf.convert_to_tensor(self.test_sentences    )
         __SCREAMING_SNAKE_CASE      							=       model(lowerCamelCase    )  # Build model with some sample inputs
         with TemporaryDirectory() as tempdir:
            __SCREAMING_SNAKE_CASE      							=       Path(lowerCamelCase    ) / """saved.model"""
            model.save(lowerCamelCase    )
            __SCREAMING_SNAKE_CASE      							=       tf.keras.models.load_model(lowerCamelCase    )
         __SCREAMING_SNAKE_CASE      							=       loaded_model(lowerCamelCase    )
         # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
         self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output    )    )				,1E-5    )












'''simple docstring'''

import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


a        =						logging.get_logger(__name__)







class __a (   _snake_case			):
   __UpperCamelCase   : int	      =	'linear'
   __UpperCamelCase   : Tuple	      =	'cosine'
   __UpperCamelCase   : Tuple	      =	'cosine_with_restarts'
   __UpperCamelCase   : List[Any]	      =	'polynomial'
   __UpperCamelCase   : Optional[Any]	      =	'constant'
   __UpperCamelCase   : Optional[int]	      =	'constant_with_warmup'
   __UpperCamelCase   : List[Any]	      =	'piecewise_constant'





def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase = -1      )						->					int:

   '''simple docstring'''







   return LambdaLR(__UpperCAmelCase							,	lambda __UpperCAmelCase      : 1							,	last_epoch=__UpperCAmelCase      )

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase = -1      )						->					List[Any]:

   '''simple docstring'''







   def lr_lambda(__UpperCAmelCase      ):
      if current_step < num_warmup_steps:
         return float(__UpperCAmelCase      ) / float(max(1.0							,	__UpperCAmelCase      )      )
      return 1.0

   return LambdaLR(__UpperCAmelCase							,	__UpperCAmelCase							,	last_epoch=__UpperCAmelCase      )

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase = -1      )						->					int:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       {}
   __SCREAMING_SNAKE_CASE      							=       step_rules.split(""","""      )
   for rule_str in rule_list[:-1]:
      __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       rule_str.split(""":"""      )
      __SCREAMING_SNAKE_CASE      							=       int(__UpperCAmelCase      )
      __SCREAMING_SNAKE_CASE      							=       float(__UpperCAmelCase      )
      __SCREAMING_SNAKE_CASE      							=       value
   __SCREAMING_SNAKE_CASE      							=       float(rule_list[-1]      )

   def create_rules_function(__UpperCAmelCase							,	__UpperCAmelCase      ):
      def rule_func(__UpperCAmelCase      ) -> float:
         __SCREAMING_SNAKE_CASE      							=       sorted(rules_dict.keys()      )
         for i, sorted_step in enumerate(__UpperCAmelCase      ):
            if steps < sorted_step:
               return rules_dict[sorted_steps[i]]
         return last_lr_multiple

      return rule_func

   __SCREAMING_SNAKE_CASE      							=       create_rules_function(__UpperCAmelCase							,	__UpperCAmelCase      )

   return LambdaLR(__UpperCAmelCase							,	__UpperCAmelCase							,	last_epoch=__UpperCAmelCase      )

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase=-1      )						->					int:

   '''simple docstring'''







   def lr_lambda(__UpperCAmelCase      ):
      if current_step < num_warmup_steps:
         return float(__UpperCAmelCase      ) / float(max(1							,	__UpperCAmelCase      )      )
      return max(
          0.0							,	float(num_training_steps - current_step      ) / float(max(1							,	num_training_steps - num_warmup_steps      )      )      )

   return LambdaLR(__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase = 0.5							,	__UpperCAmelCase = -1      )						->					Dict:

   '''simple docstring'''







   def lr_lambda(__UpperCAmelCase      ):
      if current_step < num_warmup_steps:
         return float(__UpperCAmelCase      ) / float(max(1							,	__UpperCAmelCase      )      )
      __SCREAMING_SNAKE_CASE      							=       float(current_step - num_warmup_steps      ) / float(max(1							,	num_training_steps - num_warmup_steps      )      )
      return max(0.0							,	0.5 * (1.0 + math.cos(math.pi * float(__UpperCAmelCase      ) * 2.0 * progress      ))      )

   return LambdaLR(__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase = 1							,	__UpperCAmelCase = -1      )						->					Tuple:

   '''simple docstring'''







   def lr_lambda(__UpperCAmelCase      ):
      if current_step < num_warmup_steps:
         return float(__UpperCAmelCase      ) / float(max(1							,	__UpperCAmelCase      )      )
      __SCREAMING_SNAKE_CASE      							=       float(current_step - num_warmup_steps      ) / float(max(1							,	num_training_steps - num_warmup_steps      )      )
      if progress >= 1.0:
         return 0.0
      return max(0.0							,	0.5 * (1.0 + math.cos(math.pi * ((float(__UpperCAmelCase      ) * progress) % 1.0)      ))      )

   return LambdaLR(__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase=1e-7							,	__UpperCAmelCase=1.0							,	__UpperCAmelCase=-1      )						->					Tuple:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       optimizer.defaults["""lr"""]
   if not (lr_init > lr_end):
      raise ValueError(f"""lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})"""      )

   def lr_lambda(__UpperCAmelCase      ):
      if current_step < num_warmup_steps:
         return float(__UpperCAmelCase      ) / float(max(1							,	__UpperCAmelCase      )      )
      elif current_step > num_training_steps:
         return lr_end / lr_init  # as LambdaLR multiplies by lr_init
      else:
         __SCREAMING_SNAKE_CASE      							=       lr_init - lr_end
         __SCREAMING_SNAKE_CASE      							=       num_training_steps - num_warmup_steps
         __SCREAMING_SNAKE_CASE      							=       1 - (current_step - num_warmup_steps) / decay_steps
         __SCREAMING_SNAKE_CASE      							=       lr_range * pct_remaining**power + lr_end
         return decay / lr_init  # as LambdaLR multiplies by lr_init

   return LambdaLR(__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )


a        =						{
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}







def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase = None							,	__UpperCAmelCase = None							,	__UpperCAmelCase = None							,	__UpperCAmelCase = 1							,	__UpperCAmelCase = 1.0							,	__UpperCAmelCase = -1							,	)						->					str:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       SchedulerType(__UpperCAmelCase      )
   __SCREAMING_SNAKE_CASE      							=       TYPE_TO_SCHEDULER_FUNCTION[name]
   if name == SchedulerType.CONSTANT:
      return schedule_func(__UpperCAmelCase							,	last_epoch=__UpperCAmelCase      )

   if name == SchedulerType.PIECEWISE_CONSTANT:
      return schedule_func(__UpperCAmelCase							,	step_rules=__UpperCAmelCase							,	last_epoch=__UpperCAmelCase      )

   # All other schedulers require `num_warmup_steps`
   if num_warmup_steps is None:
      raise ValueError(f"""{name} requires `num_warmup_steps`, please provide that argument."""      )

   if name == SchedulerType.CONSTANT_WITH_WARMUP:
      return schedule_func(__UpperCAmelCase							,	num_warmup_steps=__UpperCAmelCase							,	last_epoch=__UpperCAmelCase      )

   # All other schedulers require `num_training_steps`
   if num_training_steps is None:
      raise ValueError(f"""{name} requires `num_training_steps`, please provide that argument."""      )

   if name == SchedulerType.COSINE_WITH_RESTARTS:
      return schedule_func(
          __UpperCAmelCase							,	num_warmup_steps=__UpperCAmelCase							,	num_training_steps=__UpperCAmelCase							,	num_cycles=__UpperCAmelCase							,	last_epoch=__UpperCAmelCase							,	)

   if name == SchedulerType.POLYNOMIAL:
      return schedule_func(
          __UpperCAmelCase							,	num_warmup_steps=__UpperCAmelCase							,	num_training_steps=__UpperCAmelCase							,	power=__UpperCAmelCase							,	last_epoch=__UpperCAmelCase							,	)

   return schedule_func(
       __UpperCAmelCase							,	num_warmup_steps=__UpperCAmelCase							,	num_training_steps=__UpperCAmelCase							,	last_epoch=__UpperCAmelCase      )





