





from __future__ import annotations

import unittest

from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow


if is_tf_available():
       from transformers import (
           AutoConfig,
           BertConfig,
           GPTaConfig,
           TaConfig,
           TFAutoModel,
           TFAutoModelForCausalLM,
           TFAutoModelForMaskedLM,
           TFAutoModelForPreTraining,
           TFAutoModelForQuestionAnswering,
           TFAutoModelForSeqaSeqLM,
           TFAutoModelForSequenceClassification,
           TFAutoModelWithLMHead,
           TFBertForMaskedLM,
           TFBertForPreTraining,
           TFBertForQuestionAnswering,
           TFBertForSequenceClassification,
           TFBertModel,
           TFGPTaLMHeadModel,
           TFRobertaForMaskedLM,
           TFTaForConditionalGeneration,
       )
       from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
       from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
       from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST

if is_torch_available():
       from transformers import (
           AutoModel,
           AutoModelForCausalLM,
           AutoModelForMaskedLM,
           AutoModelForPreTraining,
           AutoModelForQuestionAnswering,
           AutoModelForSeqaSeqLM,
           AutoModelForSequenceClassification,
           AutoModelWithLMHead,
           BertForMaskedLM,
           BertForPreTraining,
           BertForQuestionAnswering,
           BertForSequenceClassification,
           BertModel,
           GPTaLMHeadModel,
           RobertaForMaskedLM,
           TaForConditionalGeneration,
       )


@is_pt_tf_cross_test
class        __SCREAMING_SNAKE_CASE      (			unittest.TestCase ):







       @slow
       def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->str:
          # for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          for model_name in ["bert-base-uncased"]:
             lowerCamelCase__       :						List[str]        =							AutoConfig.from_pretrained(A      )
             self.assertIsNotNone(A      )
             self.assertIsInstance(A		, A      )

             lowerCamelCase__       :						Optional[int]        =							TFAutoModel.from_pretrained(A		, from_pt=A      )
             self.assertIsNotNone(A      )
             self.assertIsInstance(A		, A      )

             lowerCamelCase__       :						Union[str, Any]        =							AutoModel.from_pretrained(A		, from_tf=A      )
             self.assertIsNotNone(A      )
             self.assertIsInstance(A		, A      )







       @slow
       def 						__lowerCamelCase   (				self					:     int      )       ->str:
          # for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          for model_name in ["bert-base-uncased"]:
             lowerCamelCase__       :						Tuple        =							AutoConfig.from_pretrained(A      )
             self.assertIsNotNone(A      )
             self.assertIsInstance(A		, A      )

             lowerCamelCase__       :						List[Any]        =							TFAutoModelForPreTraining.from_pretrained(A		, from_pt=A      )
             self.assertIsNotNone(A      )
             self.assertIsInstance(A		, A      )

             lowerCamelCase__       :						Dict        =							AutoModelForPreTraining.from_pretrained(A		, from_tf=A      )
             self.assertIsNotNone(A      )
             self.assertIsInstance(A		, A      )







       @slow
       def 						__lowerCamelCase   (				self					:     str      )       ->str:
          for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowerCamelCase__       :						int        =							AutoConfig.from_pretrained(A      )
             self.assertIsNotNone(A      )
             self.assertIsInstance(A		, A      )

             lowerCamelCase__       :						Any        =							TFAutoModelForCausalLM.from_pretrained(A		, from_pt=A      )
             lowerCamelCase__       :						str        =							TFAutoModelForCausalLM.from_pretrained(
                 A		, output_loading_info=A		, from_pt=A      )
             self.assertIsNotNone(A      )
             self.assertIsInstance(A		, A      )

             lowerCamelCase__       :						Tuple        =							AutoModelForCausalLM.from_pretrained(A		, from_tf=A      )
             lowerCamelCase__       :						List[Any]        =							AutoModelForCausalLM.from_pretrained(
                 A		, output_loading_info=A		, from_tf=A      )
             self.assertIsNotNone(A      )
             self.assertIsInstance(A		, A      )







       @slow
       def 						__lowerCamelCase   (				self					:     List[str]      )       ->Union[str, Any]:
          for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowerCamelCase__       :						Optional[Any]        =							AutoConfig.from_pretrained(A      )
             self.assertIsNotNone(A      )
             self.assertIsInstance(A		, A      )

             lowerCamelCase__       :						Optional[int]        =							TFAutoModelWithLMHead.from_pretrained(A		, from_pt=A      )
             self.assertIsNotNone(A      )
             self.assertIsInstance(A		, A      )

             lowerCamelCase__       :						int        =							AutoModelWithLMHead.from_pretrained(A		, from_tf=A      )
             self.assertIsNotNone(A      )
             self.assertIsInstance(A		, A      )







       @slow
       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->str:
          for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowerCamelCase__       :						Union[str, Any]        =							AutoConfig.from_pretrained(A      )
             self.assertIsNotNone(A      )
             self.assertIsInstance(A		, A      )

             lowerCamelCase__       :						Tuple        =							TFAutoModelForMaskedLM.from_pretrained(A		, from_pt=A      )
             lowerCamelCase__       :						str        =							TFAutoModelForMaskedLM.from_pretrained(
                 A		, output_loading_info=A		, from_pt=A      )
             self.assertIsNotNone(A      )
             self.assertIsInstance(A		, A      )

             lowerCamelCase__       :						List[str]        =							AutoModelForMaskedLM.from_pretrained(A		, from_tf=A      )
             lowerCamelCase__       :						Union[str, Any]        =							AutoModelForMaskedLM.from_pretrained(
                 A		, output_loading_info=A		, from_tf=A      )
             self.assertIsNotNone(A      )
             self.assertIsInstance(A		, A      )







       @slow
       def 						__lowerCamelCase   (				self					:     List[str]      )       ->Union[str, Any]:
          for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowerCamelCase__       :						Any        =							AutoConfig.from_pretrained(A      )
             self.assertIsNotNone(A      )
             self.assertIsInstance(A		, A      )

             lowerCamelCase__       :						Any        =							TFAutoModelForSeqaSeqLM.from_pretrained(A		, from_pt=A      )
             lowerCamelCase__       :						Optional[Any]        =							TFAutoModelForSeqaSeqLM.from_pretrained(
                 A		, output_loading_info=A		, from_pt=A      )
             self.assertIsNotNone(A      )
             self.assertIsInstance(A		, A      )

             lowerCamelCase__       :						str        =							AutoModelForSeqaSeqLM.from_pretrained(A		, from_tf=A      )
             lowerCamelCase__       :						List[str]        =							AutoModelForSeqaSeqLM.from_pretrained(
                 A		, output_loading_info=A		, from_tf=A      )
             self.assertIsNotNone(A      )
             self.assertIsInstance(A		, A      )







       @slow
       def 						__lowerCamelCase   (				self					:     List[Any]      )       ->Optional[int]:
          # for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          for model_name in ["bert-base-uncased"]:
             lowerCamelCase__       :						Optional[int]        =							AutoConfig.from_pretrained(A      )
             self.assertIsNotNone(A      )
             self.assertIsInstance(A		, A      )

             lowerCamelCase__       :						str        =							TFAutoModelForSequenceClassification.from_pretrained(A		, from_pt=A      )
             self.assertIsNotNone(A      )
             self.assertIsInstance(A		, A      )

             lowerCamelCase__       :						Tuple        =							AutoModelForSequenceClassification.from_pretrained(A		, from_tf=A      )
             self.assertIsNotNone(A      )
             self.assertIsInstance(A		, A      )







       @slow
       def 						__lowerCamelCase   (				self					:     Optional[Any]      )       ->int:
          # for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          for model_name in ["bert-base-uncased"]:
             lowerCamelCase__       :						Any        =							AutoConfig.from_pretrained(A      )
             self.assertIsNotNone(A      )
             self.assertIsInstance(A		, A      )

             lowerCamelCase__       :						str        =							TFAutoModelForQuestionAnswering.from_pretrained(A		, from_pt=A      )
             self.assertIsNotNone(A      )
             self.assertIsInstance(A		, A      )

             lowerCamelCase__       :						int        =							AutoModelForQuestionAnswering.from_pretrained(A		, from_tf=A      )
             self.assertIsNotNone(A      )
             self.assertIsInstance(A		, A      )







       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->str:
          lowerCamelCase__       :						Optional[int]        =							TFAutoModelWithLMHead.from_pretrained(A		, from_pt=A      )
          self.assertIsInstance(A		, A      )
          self.assertEqual(model.num_parameters()		, 1_4_4_1_0      )
          self.assertEqual(model.num_parameters(only_trainable=A      )		, 1_4_4_1_0      )

          lowerCamelCase__       :						str        =							AutoModelWithLMHead.from_pretrained(A		, from_tf=A      )
          self.assertIsInstance(A		, A      )
          self.assertEqual(model.num_parameters()		, 1_4_4_1_0      )
          self.assertEqual(model.num_parameters(only_trainable=A      )		, 1_4_4_1_0      )







       def 						__lowerCamelCase   (				self					:     List[str]      )       ->List[str]:
          lowerCamelCase__       :						List[Any]        =							TFAutoModelWithLMHead.from_pretrained(A		, from_pt=A      )
          self.assertIsInstance(A		, A      )
          self.assertEqual(model.num_parameters()		, 1_4_4_1_0      )
          self.assertEqual(model.num_parameters(only_trainable=A      )		, 1_4_4_1_0      )

          lowerCamelCase__       :						str        =							AutoModelWithLMHead.from_pretrained(A		, from_tf=A      )
          self.assertIsInstance(A		, A      )
          self.assertEqual(model.num_parameters()		, 1_4_4_1_0      )
          self.assertEqual(model.num_parameters(only_trainable=A      )		, 1_4_4_1_0      )










import math
import random



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase = False					)    ->    float:







   """simple docstring"""
   if deriv:
      return value * (1 - value)
   return 1 / (1 + math.exp(-value					))


# Initial Value
_A :   Tuple  			=  0.02



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    float:







   """simple docstring"""
   lowerCamelCase__       :						List[Any]        =							float(2 * (random.randint(1      ,  100					)) - 1					)

   for _ in range(UpperCAmelCase					):
      # Forward propagation
      lowerCamelCase__       :						Optional[int]        =							sigmoid_function(INITIAL_VALUE * weight					)
      # How much did we miss?
      lowerCamelCase__       :						int        =							(expected / 100) - layer_a
      # Error delta
      lowerCamelCase__       :						Union[str, Any]        =							layer_1_error * sigmoid_function(UpperCAmelCase      ,  UpperCAmelCase					)
      # Update weight
      weight += INITIAL_VALUE * layer_1_delta

   return layer_a * 100


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       _A :   Union[str, Any]  			=  int(input('Expected value: '))
       _A :   int  			=  int(input('Number of propagations: '))
       print(forward_propagation(expected, number_propagations))



