






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 			__a  (    unittest.TestCase       ):







 @slow
 def 					__lowercase					(		self					:   Optional[Any]						):


      '''simple docstring'''






      for model_name in ["bert-base-uncased"]:
           UpperCamelCase__			:   Optional[Any]       				=  AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE						)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE						)
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)

           UpperCamelCase__			:   Union[str, Any]       				=  TFAutoModel.from_pretrained(SCREAMING_SNAKE_CASE							,   from_pt=SCREAMING_SNAKE_CASE						)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE						)
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)

           UpperCamelCase__			:   int       				=  AutoModel.from_pretrained(SCREAMING_SNAKE_CASE							,   from_tf=SCREAMING_SNAKE_CASE						)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE						)
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)







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


      '''simple docstring'''






      for model_name in ["bert-base-uncased"]:
           UpperCamelCase__			:   str       				=  AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE						)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE						)
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)

           UpperCamelCase__			:   Union[str, Any]       				=  TFAutoModelForPreTraining.from_pretrained(SCREAMING_SNAKE_CASE							,   from_pt=SCREAMING_SNAKE_CASE						)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE						)
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)

           UpperCamelCase__			:   Dict       				=  AutoModelForPreTraining.from_pretrained(SCREAMING_SNAKE_CASE							,   from_tf=SCREAMING_SNAKE_CASE						)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE						)
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)







 @slow
 def 					__lowercase					(		self					:   str						):


      '''simple docstring'''






      for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           UpperCamelCase__			:   Union[str, Any]       				=  AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE						)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE						)
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)

           UpperCamelCase__			:   Optional[int]       				=  TFAutoModelForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE							,   from_pt=SCREAMING_SNAKE_CASE						)
           UpperCamelCase__				,     UpperCamelCase__			:   Union[str, Any]       				=  TFAutoModelForCausalLM.from_pretrained(
               SCREAMING_SNAKE_CASE							,   output_loading_info=SCREAMING_SNAKE_CASE							,   from_pt=SCREAMING_SNAKE_CASE						)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE						)
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)

           UpperCamelCase__			:   List[str]       				=  AutoModelForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE							,   from_tf=SCREAMING_SNAKE_CASE						)
           UpperCamelCase__				,     UpperCamelCase__			:   int       				=  AutoModelForCausalLM.from_pretrained(
               SCREAMING_SNAKE_CASE							,   output_loading_info=SCREAMING_SNAKE_CASE							,   from_tf=SCREAMING_SNAKE_CASE						)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE						)
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)







 @slow
 def 					__lowercase					(		self					:   Dict						):


      '''simple docstring'''






      for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           UpperCamelCase__			:   Tuple       				=  AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE						)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE						)
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)

           UpperCamelCase__			:   Union[str, Any]       				=  TFAutoModelWithLMHead.from_pretrained(SCREAMING_SNAKE_CASE							,   from_pt=SCREAMING_SNAKE_CASE						)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE						)
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)

           UpperCamelCase__			:   List[Any]       				=  AutoModelWithLMHead.from_pretrained(SCREAMING_SNAKE_CASE							,   from_tf=SCREAMING_SNAKE_CASE						)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE						)
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)







 @slow
 def 					__lowercase					(		self					:   int						):


      '''simple docstring'''






      for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           UpperCamelCase__			:   List[Any]       				=  AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE						)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE						)
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)

           UpperCamelCase__			:   Any       				=  TFAutoModelForMaskedLM.from_pretrained(SCREAMING_SNAKE_CASE							,   from_pt=SCREAMING_SNAKE_CASE						)
           UpperCamelCase__				,     UpperCamelCase__			:   Tuple       				=  TFAutoModelForMaskedLM.from_pretrained(
               SCREAMING_SNAKE_CASE							,   output_loading_info=SCREAMING_SNAKE_CASE							,   from_pt=SCREAMING_SNAKE_CASE						)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE						)
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)

           UpperCamelCase__			:   int       				=  AutoModelForMaskedLM.from_pretrained(SCREAMING_SNAKE_CASE							,   from_tf=SCREAMING_SNAKE_CASE						)
           UpperCamelCase__				,     UpperCamelCase__			:   Optional[int]       				=  AutoModelForMaskedLM.from_pretrained(
               SCREAMING_SNAKE_CASE							,   output_loading_info=SCREAMING_SNAKE_CASE							,   from_tf=SCREAMING_SNAKE_CASE						)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE						)
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)







 @slow
 def 					__lowercase					(		self					:   Any						):


      '''simple docstring'''






      for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           UpperCamelCase__			:   List[Any]       				=  AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE						)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE						)
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)

           UpperCamelCase__			:   str       				=  TFAutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE							,   from_pt=SCREAMING_SNAKE_CASE						)
           UpperCamelCase__				,     UpperCamelCase__			:   Optional[Any]       				=  TFAutoModelForSeqaSeqLM.from_pretrained(
               SCREAMING_SNAKE_CASE							,   output_loading_info=SCREAMING_SNAKE_CASE							,   from_pt=SCREAMING_SNAKE_CASE						)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE						)
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)

           UpperCamelCase__			:   List[Any]       				=  AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE							,   from_tf=SCREAMING_SNAKE_CASE						)
           UpperCamelCase__				,     UpperCamelCase__			:   List[str]       				=  AutoModelForSeqaSeqLM.from_pretrained(
               SCREAMING_SNAKE_CASE							,   output_loading_info=SCREAMING_SNAKE_CASE							,   from_tf=SCREAMING_SNAKE_CASE						)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE						)
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)







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


      '''simple docstring'''






      for model_name in ["bert-base-uncased"]:
           UpperCamelCase__			:   Optional[int]       				=  AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE						)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE						)
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)

           UpperCamelCase__			:   Dict       				=  TFAutoModelForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE							,   from_pt=SCREAMING_SNAKE_CASE						)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE						)
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)

           UpperCamelCase__			:   Dict       				=  AutoModelForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE							,   from_tf=SCREAMING_SNAKE_CASE						)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE						)
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)







 @slow
 def 					__lowercase					(		self					:   Tuple						):


      '''simple docstring'''






      for model_name in ["bert-base-uncased"]:
           UpperCamelCase__			:   List[str]       				=  AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE						)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE						)
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)

           UpperCamelCase__			:   List[str]       				=  TFAutoModelForQuestionAnswering.from_pretrained(SCREAMING_SNAKE_CASE							,   from_pt=SCREAMING_SNAKE_CASE						)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE						)
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)

           UpperCamelCase__			:   Optional[int]       				=  AutoModelForQuestionAnswering.from_pretrained(SCREAMING_SNAKE_CASE							,   from_tf=SCREAMING_SNAKE_CASE						)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE						)
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   List[Any]						):


      '''simple docstring'''






      UpperCamelCase__			:   int       				=  TFAutoModelWithLMHead.from_pretrained(SCREAMING_SNAKE_CASE							,   from_pt=SCREAMING_SNAKE_CASE						)
      self.assertIsInstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)
      self.assertEqual(model.num_parameters()							,   1_44_10						)
      self.assertEqual(model.num_parameters(only_trainable=SCREAMING_SNAKE_CASE						)							,   1_44_10						)

      UpperCamelCase__			:   Any       				=  AutoModelWithLMHead.from_pretrained(SCREAMING_SNAKE_CASE							,   from_tf=SCREAMING_SNAKE_CASE						)
      self.assertIsInstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)
      self.assertEqual(model.num_parameters()							,   1_44_10						)
      self.assertEqual(model.num_parameters(only_trainable=SCREAMING_SNAKE_CASE						)							,   1_44_10						)







 def 					__lowercase					(		self					:   str						):


      '''simple docstring'''






      UpperCamelCase__			:   List[Any]       				=  TFAutoModelWithLMHead.from_pretrained(SCREAMING_SNAKE_CASE							,   from_pt=SCREAMING_SNAKE_CASE						)
      self.assertIsInstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)
      self.assertEqual(model.num_parameters()							,   1_44_10						)
      self.assertEqual(model.num_parameters(only_trainable=SCREAMING_SNAKE_CASE						)							,   1_44_10						)

      UpperCamelCase__			:   Optional[int]       				=  AutoModelWithLMHead.from_pretrained(SCREAMING_SNAKE_CASE							,   from_tf=SCREAMING_SNAKE_CASE						)
      self.assertIsInstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)
      self.assertEqual(model.num_parameters()							,   1_44_10						)
      self.assertEqual(model.num_parameters(only_trainable=SCREAMING_SNAKE_CASE						)							,   1_44_10						)







from math import factorial





class 			__a  :







 def __init__(		self					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   Tuple							,   SCREAMING_SNAKE_CASE					:   Optional[int]						):


      '''simple docstring'''






      UpperCamelCase__			:   Tuple       				=  real
      if isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
           UpperCamelCase__			:   Union[str, Any]       				=  [1] * rank
      else:
           UpperCamelCase__			:   int       				=  rank







 def __repr__(		self					:   Tuple						):


      '''simple docstring'''






      return (
          F'{self.real}+'
          F'{"+".join(str(SCREAMING_SNAKE_CASE						)+"E"+str(n+1						)for n,dual in enumerate(self.duals						)						)}'
      )







 def 					__lowercase					(		self					:   Tuple						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[Any]       				=  self.duals.copy()
      while cur[-1] == 0:
           cur.pop(-1						)
      return Dual(self.real							,   SCREAMING_SNAKE_CASE						)







 def __add__(		self					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   Dict						):


      '''simple docstring'''






      if not isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
           return Dual(self.real + other							,   self.duals						)
      UpperCamelCase__			:   Optional[int]       				=  self.duals.copy()
      UpperCamelCase__			:   Any       				=  other.duals.copy()
      if len(SCREAMING_SNAKE_CASE						) > len(SCREAMING_SNAKE_CASE						):
           o_dual.extend([1] * (len(SCREAMING_SNAKE_CASE						) - len(SCREAMING_SNAKE_CASE						))						)
      elif len(SCREAMING_SNAKE_CASE						) < len(SCREAMING_SNAKE_CASE						):
           s_dual.extend([1] * (len(SCREAMING_SNAKE_CASE						) - len(SCREAMING_SNAKE_CASE						))						)
      UpperCamelCase__			:   Optional[int]       				=  []
      for i in range(len(SCREAMING_SNAKE_CASE						)						):
           new_duals.append(s_dual[i] + o_dual[i]						)
      return Dual(self.real + other.real							,   SCREAMING_SNAKE_CASE						)

 _lowerCAmelCase       :       Dict        = __add__







 def __sub__(		self					:   Tuple							,   SCREAMING_SNAKE_CASE					:   Tuple						):


      '''simple docstring'''






      return self + other * -1







 def __mul__(		self					:   int							,   SCREAMING_SNAKE_CASE					:   Dict						):


      '''simple docstring'''






      if not isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
           UpperCamelCase__			:   str       				=  []
           for i in self.duals:
                new_duals.append(i * other						)
           return Dual(self.real * other							,   SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   List[Any]       				=  [0] * (len(self.duals						) + len(other.duals						) + 1)
      for i, item in enumerate(self.duals						):
           for j, jtem in enumerate(other.duals						):
                new_duals[i + j + 1] += item * jtem
      for k in range(len(self.duals						)						):
           new_duals[k] += self.duals[k] * other.real
      for index in range(len(other.duals						)						):
           new_duals[index] += other.duals[index] * self.real
      return Dual(self.real * other.real							,   SCREAMING_SNAKE_CASE						)

 _lowerCAmelCase       :       Union[str, Any]        = __mul__







 def __truediv__(		self					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   Any						):


      '''simple docstring'''






      if not isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
           UpperCamelCase__			:   str       				=  []
           for i in self.duals:
                new_duals.append(i / other						)
           return Dual(self.real / other							,   SCREAMING_SNAKE_CASE						)
      raise ValueError







 def __floordiv__(		self					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   Tuple						):


      '''simple docstring'''






      if not isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
           UpperCamelCase__			:   Dict       				=  []
           for i in self.duals:
                new_duals.append(i // other						)
           return Dual(self.real // other							,   SCREAMING_SNAKE_CASE						)
      raise ValueError







 def __pow__(		self					:   str							,   SCREAMING_SNAKE_CASE					:   int						):


      '''simple docstring'''






      if n < 0 or isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
           raise ValueError("power must be a positive integer"						)
      if n == 0:
           return 1
      if n == 1:
           return self
      UpperCamelCase__			:   str       				=  self
      for _ in range(n - 1						):
           x *= self
      return x







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase			) ->	str:
     if not callable(__lowerCAmelCase			):
          raise ValueError("differentiate() requires a function as input for func"			)
     if not isinstance(__lowerCAmelCase					,    (float, int)			):
          raise ValueError("differentiate() requires a float as input for position"			)
     if not isinstance(__lowerCAmelCase					,    __lowerCAmelCase			):
          raise ValueError("differentiate() requires an int as input for order"			)
     UpperCamelCase__			:   Optional[Any]       				=  Dual(__lowerCAmelCase					,    1			)
     UpperCamelCase__			:   Any       				=  func(__lowerCAmelCase			)
     if order == 0:
          return result.real
     return result.duals[order - 1] * factorial(__lowerCAmelCase			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()







 def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	List[Any]:
      return y**2 * y**4




 print(differentiate(f, 9, 2))