

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 _UpperCamelCase							(							unittest.TestCase   ):
		'''simple docstring'''







		@slow
		def 							__UpperCamelCase (			self							: int	)							->       Optional[Any]:

			"""simple docstring"""

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

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       TFAutoModel.from_pretrained(a      ,      from_pt=a	)
				self.assertIsNotNone(a	)
				self.assertIsInstance(a      ,      a	)

				SCREAMING_SNAKE_CASE   :      Dict				     =       AutoModel.from_pretrained(a      ,      from_tf=a	)
				self.assertIsNotNone(a	)
				self.assertIsInstance(a      ,      a	)
		@slow
		def 							__UpperCamelCase (			self							: Any	)							->       Optional[Any]:

			"""simple docstring"""

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

				SCREAMING_SNAKE_CASE   :      str				     =       TFAutoModelForPreTraining.from_pretrained(a      ,      from_pt=a	)
				self.assertIsNotNone(a	)
				self.assertIsInstance(a      ,      a	)

				SCREAMING_SNAKE_CASE   :      str				     =       AutoModelForPreTraining.from_pretrained(a      ,      from_tf=a	)
				self.assertIsNotNone(a	)
				self.assertIsInstance(a      ,      a	)
		@slow
		def 							__UpperCamelCase (			self							: Optional[Any]	)							->       List[Any]:

			"""simple docstring"""

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

				SCREAMING_SNAKE_CASE   :      Dict				     =       TFAutoModelForCausalLM.from_pretrained(a      ,      from_pt=a	)
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       TFAutoModelForCausalLM.from_pretrained(
				    a      ,      output_loading_info=a      ,      from_pt=a	)
				self.assertIsNotNone(a	)
				self.assertIsInstance(a      ,      a	)

				SCREAMING_SNAKE_CASE   :      Tuple				     =       AutoModelForCausalLM.from_pretrained(a      ,      from_tf=a	)
				SCREAMING_SNAKE_CASE   :      List[str]				     =       AutoModelForCausalLM.from_pretrained(
				    a      ,      output_loading_info=a      ,      from_tf=a	)
				self.assertIsNotNone(a	)
				self.assertIsInstance(a      ,      a	)
		@slow
		def 							__UpperCamelCase (			self							: Optional[Any]	)							->       Tuple:

			"""simple docstring"""

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

				SCREAMING_SNAKE_CASE   :      int				     =       TFAutoModelWithLMHead.from_pretrained(a      ,      from_pt=a	)
				self.assertIsNotNone(a	)
				self.assertIsInstance(a      ,      a	)

				SCREAMING_SNAKE_CASE   :      int				     =       AutoModelWithLMHead.from_pretrained(a      ,      from_tf=a	)
				self.assertIsNotNone(a	)
				self.assertIsInstance(a      ,      a	)
		@slow
		def 							__UpperCamelCase (			self							: Any	)							->       int:

			"""simple docstring"""

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

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       TFAutoModelForMaskedLM.from_pretrained(a      ,      from_pt=a	)
				SCREAMING_SNAKE_CASE   :      str				     =       TFAutoModelForMaskedLM.from_pretrained(
				    a      ,      output_loading_info=a      ,      from_pt=a	)
				self.assertIsNotNone(a	)
				self.assertIsInstance(a      ,      a	)

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       AutoModelForMaskedLM.from_pretrained(a      ,      from_tf=a	)
				SCREAMING_SNAKE_CASE   :      Tuple				     =       AutoModelForMaskedLM.from_pretrained(
				    a      ,      output_loading_info=a      ,      from_tf=a	)
				self.assertIsNotNone(a	)
				self.assertIsInstance(a      ,      a	)
		@slow
		def 							__UpperCamelCase (			self							: Tuple	)							->       List[str]:

			"""simple docstring"""

			for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
				SCREAMING_SNAKE_CASE   :      Dict				     =       AutoConfig.from_pretrained(a	)
				self.assertIsNotNone(a	)
				self.assertIsInstance(a      ,      a	)

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       TFAutoModelForSeqaSeqLM.from_pretrained(a      ,      from_pt=a	)
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       TFAutoModelForSeqaSeqLM.from_pretrained(
				    a      ,      output_loading_info=a      ,      from_pt=a	)
				self.assertIsNotNone(a	)
				self.assertIsInstance(a      ,      a	)

				SCREAMING_SNAKE_CASE   :      List[str]				     =       AutoModelForSeqaSeqLM.from_pretrained(a      ,      from_tf=a	)
				SCREAMING_SNAKE_CASE   :      List[str]				     =       AutoModelForSeqaSeqLM.from_pretrained(
				    a      ,      output_loading_info=a      ,      from_tf=a	)
				self.assertIsNotNone(a	)
				self.assertIsInstance(a      ,      a	)
		@slow
		def 							__UpperCamelCase (			self							: List[Any]	)							->       Union[str, Any]:

			"""simple docstring"""

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

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       TFAutoModelForSequenceClassification.from_pretrained(a      ,      from_pt=a	)
				self.assertIsNotNone(a	)
				self.assertIsInstance(a      ,      a	)

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       AutoModelForSequenceClassification.from_pretrained(a      ,      from_tf=a	)
				self.assertIsNotNone(a	)
				self.assertIsInstance(a      ,      a	)
		@slow
		def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       Any:

			"""simple docstring"""

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

				SCREAMING_SNAKE_CASE   :      str				     =       TFAutoModelForQuestionAnswering.from_pretrained(a      ,      from_pt=a	)
				self.assertIsNotNone(a	)
				self.assertIsInstance(a      ,      a	)

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       AutoModelForQuestionAnswering.from_pretrained(a      ,      from_tf=a	)
				self.assertIsNotNone(a	)
				self.assertIsInstance(a      ,      a	)
		def 							__UpperCamelCase (			self							: Any	)							->       int:

			"""simple docstring"""

			SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       TFAutoModelWithLMHead.from_pretrained(a      ,      from_pt=a	)
			self.assertIsInstance(a      ,      a	)
			self.assertEqual(model.num_parameters()      ,      1_4410	)
			self.assertEqual(model.num_parameters(only_trainable=a	)      ,      1_4410	)

			SCREAMING_SNAKE_CASE   :      Dict				     =       AutoModelWithLMHead.from_pretrained(a      ,      from_tf=a	)
			self.assertIsInstance(a      ,      a	)
			self.assertEqual(model.num_parameters()      ,      1_4410	)
			self.assertEqual(model.num_parameters(only_trainable=a	)      ,      1_4410	)






		def 							__UpperCamelCase (			self							: Tuple	)							->       str:

			"""simple docstring"""

			SCREAMING_SNAKE_CASE   :      Dict				     =       TFAutoModelWithLMHead.from_pretrained(a      ,      from_pt=a	)
			self.assertIsInstance(a      ,      a	)
			self.assertEqual(model.num_parameters()      ,      1_4410	)
			self.assertEqual(model.num_parameters(only_trainable=a	)      ,      1_4410	)

			SCREAMING_SNAKE_CASE   :      Dict				     =       AutoModelWithLMHead.from_pretrained(a      ,      from_tf=a	)
			self.assertIsInstance(a      ,      a	)
			self.assertEqual(model.num_parameters()      ,      1_4410	)
			self.assertEqual(model.num_parameters(only_trainable=a	)      ,      1_4410	)


def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a):
	global f  # a global dp table for knapsack
	if f[i][j] < 0:
		if j < wt[i - 1]:
			SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       mf_knapsack(i - 1							,			_a							,			_a							,			_a)
		else:
			SCREAMING_SNAKE_CASE   :      Any				     =       max(
			    mf_knapsack(i - 1							,			_a							,			_a							,			_a)							,			mf_knapsack(i - 1							,			_a							,			_a							,			j - wt[i - 1]) + val[i - 1]							,			)
		SCREAMING_SNAKE_CASE   :      Tuple				     =       val
	return f[i][j]

def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a):
	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       [[0] * (w + 1) for _ in range(n + 1)]

	for i in range(1							,			n + 1):
		for w_ in range(1							,			w + 1):
			if wt[i - 1] <= w_:
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]							,			dp[i - 1][w_])
			else:
				SCREAMING_SNAKE_CASE   :      int				     =       dp[i - 1][w_]

	return dp[n][w_], dp

def  lowerCamelCase__ (					_a							,			_a							,			_a):
	if not (isinstance(_a							,			(list, tuple)) and isinstance(_a							,			(list, tuple))):
		raise ValueError(
		    "Both the weights and values vectors must be either lists or tuples")

	SCREAMING_SNAKE_CASE   :      int				     =       len(_a)
	if num_items != len(_a):
		SCREAMING_SNAKE_CASE   :      List[str]				     =       (
		    "The number of weights must be the same as the number of values.\n"
		    f"But got {num_items} weights and {len(_a)} values"
		)
		raise ValueError(_a)
	for i in range(_a):
		if not isinstance(wt[i]							,			_a):
			SCREAMING_SNAKE_CASE   :      int				     =       (
			    "All weights must be integers but got weight of "
			    f"type {type(wt[i])} at index {i}"
			)
			raise TypeError(_a)

	SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Tuple				     =       knapsack(_a							,			_a							,			_a							,			_a)
	SCREAMING_SNAKE_CASE   :      set				     =       set()
	_construct_solution(_a							,			_a							,			_a							,			_a							,			_a)

	return optimal_val, example_optional_set

def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a							,			_a):
	# for the current item i at a maximum weight j to be part of an optimal subset,
	# the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
	# where i - 1 means considering only the previous items at the given maximum weight
	if i > 0 and j > 0:
		if dp[i - 1][j] == dp[i][j]:
			_construct_solution(_a							,			_a							,			i - 1							,			_a							,			_a)
		else:
			optimal_set.add(_a)
			_construct_solution(_a							,			_a							,			i - 1							,			j - wt[i - 1]							,			_a)


if __name__ == "__main__":
			a_						     =    [3, 2, 4, 4]
			a_						     =    [4, 3, 2, 3]
			a_						     =    4
			a_						     =    6
			a_						     =    [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
			a_     ,	a_						     =    knapsack(w, wt, val, n)
			print(optimal_solution)
			print(mf_knapsack(n, wt, val, w))  # switched the n and w

			# testing the dynamic programming problem with example
			# the optimal subset for the above example are items 3 and 4
			a_     ,	a_						     =    knapsack_with_example_solution(w, wt, val)
			assert optimal_solution == 8
			assert optimal_subset == {3, 4}
			print('optimal_value = ', optimal_solution)
			print('An optimal subset corresponding to the optimal value', optimal_subset)