





import tempfile
import unittest

import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
      import jax
      import jax.numpy as jnp
      import numpy as np

      from transformers.modeling_flax_pytorch_utils import (
          convert_pytorch_state_dict_to_flax,
          load_flax_weights_in_pytorch_model,
      )
      from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel


if is_torch_available():
      import torch

@require_flax
class 						UpperCamelCase__					:



     '''simple docstring'''

     def __init__(				self		:       List[str]		,lowerCamelCase__		:       str		,lowerCamelCase__		:       List[str]=14		,lowerCamelCase__		:       List[str]=7		,lowerCamelCase__		:       List[Any]=True		,lowerCamelCase__		:       Dict=True		,lowerCamelCase__		:       Tuple=True		,lowerCamelCase__		:       Optional[int]=99		,lowerCamelCase__		:       List[Any]=32		,lowerCamelCase__		:       Tuple=5		,lowerCamelCase__		:       Dict=4		,lowerCamelCase__		:       Optional[Any]=37		,lowerCamelCase__		:       str="gelu"		,lowerCamelCase__		:       Any=0.1		,lowerCamelCase__		:       List[str]=0.1		,lowerCamelCase__		:       int=512		,lowerCamelCase__		:       Optional[Any]=0.02		,lowerCamelCase__		:       List[str]=None		,)	->						List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		parent
           SCREAMING_SNAKE_CASE						=		batch_size
           SCREAMING_SNAKE_CASE						=		seq_length
           SCREAMING_SNAKE_CASE						=		is_training
           SCREAMING_SNAKE_CASE						=		use_input_mask
           SCREAMING_SNAKE_CASE						=		use_labels
           SCREAMING_SNAKE_CASE						=		vocab_size
           SCREAMING_SNAKE_CASE						=		d_model
           SCREAMING_SNAKE_CASE						=		num_hidden_layers
           SCREAMING_SNAKE_CASE						=		num_attention_heads
           SCREAMING_SNAKE_CASE						=		ffn_dim
           SCREAMING_SNAKE_CASE						=		activation_function
           SCREAMING_SNAKE_CASE						=		activation_dropout
           SCREAMING_SNAKE_CASE						=		attention_dropout
           SCREAMING_SNAKE_CASE						=		max_position_embeddings
           SCREAMING_SNAKE_CASE						=		initializer_range
           SCREAMING_SNAKE_CASE						=		None
           SCREAMING_SNAKE_CASE						=		0
           SCREAMING_SNAKE_CASE						=		2
           SCREAMING_SNAKE_CASE						=		1






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Dict			)	->						Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		np.clip(ids_tensor([self.batch_size, self.seq_length]		,self.vocab_size			)		,3		,self.vocab_size			)

           SCREAMING_SNAKE_CASE						=		None
           if self.use_input_mask:
                 SCREAMING_SNAKE_CASE						=		random_attention_mask([self.batch_size, self.seq_length]			)

           SCREAMING_SNAKE_CASE						=		XGLMConfig(
               vocab_size=self.vocab_size		,d_model=self.hidden_size		,num_layers=self.num_hidden_layers		,attention_heads=self.num_attention_heads		,ffn_dim=self.ffn_dim		,activation_function=self.activation_function		,activation_dropout=self.activation_dropout		,attention_dropout=self.attention_dropout		,max_position_embeddings=self.max_position_embeddings		,initializer_range=self.initializer_range		,use_cache=_lowerCamelCase		,bos_token_id=self.bos_token_id		,eos_token_id=self.eos_token_id		,pad_token_id=self.pad_token_id		,)

           return (config, input_ids, input_mask)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Optional[Any]			)	->						Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self.prepare_config_and_inputs()
           SCREAMING_SNAKE_CASE						=		config_and_inputs
           SCREAMING_SNAKE_CASE						=		{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
           return config, inputs_dict






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Union[str, Any]			)	->						Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self.prepare_config_and_inputs()

           SCREAMING_SNAKE_CASE						=		floats_tensor([self.batch_size, self.seq_length, self.hidden_size]			)
           SCREAMING_SNAKE_CASE						=		ids_tensor([self.batch_size, self.seq_length]		,vocab_size=2			)

           return (
               config,
               input_ids,
               attention_mask,
               encoder_hidden_states,
               encoder_attention_mask,
           )






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Optional[Any]		,lowerCamelCase__		:       List[Any]		,lowerCamelCase__		:       Dict		,lowerCamelCase__		:       List[Any]		,lowerCamelCase__		:       List[Any]			)	->						Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		20
           SCREAMING_SNAKE_CASE						=		model_class_name(_lowerCamelCase			)

           SCREAMING_SNAKE_CASE						=		model.init_cache(input_ids.shape[0]		,_lowerCamelCase			)
           SCREAMING_SNAKE_CASE						=		jnp.ones((input_ids.shape[0], max_decoder_length)		,dtype="""i4"""			)

           SCREAMING_SNAKE_CASE						=		jnp.broadcast_to(
               jnp.arange(input_ids.shape[-1] - 1			)[None, :]		,(input_ids.shape[0], input_ids.shape[-1] - 1)			)
           SCREAMING_SNAKE_CASE						=		model(
               input_ids[:, :-1]		,attention_mask=_lowerCamelCase		,past_key_values=_lowerCamelCase		,position_ids=_lowerCamelCase		,)

           SCREAMING_SNAKE_CASE						=		jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]		,dtype="""i4"""			)
           SCREAMING_SNAKE_CASE						=		model(
               input_ids[:, -1:]		,attention_mask=_lowerCamelCase		,past_key_values=outputs_cache.past_key_values		,position_ids=_lowerCamelCase		,)

           SCREAMING_SNAKE_CASE						=		model(_lowerCamelCase			)

           SCREAMING_SNAKE_CASE						=		np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])			)			)
           self.parent.assertTrue(diff < 1e-3		,msg=F"""Max diff is {diff}"""			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Dict		,lowerCamelCase__		:       Any		,lowerCamelCase__		:       Optional[Any]		,lowerCamelCase__		:       Optional[int]		,lowerCamelCase__		:       List[Any]			)	->						List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		20
           SCREAMING_SNAKE_CASE						=		model_class_name(_lowerCamelCase			)

           SCREAMING_SNAKE_CASE						=		jnp.concatenate(
               [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])			)]		,axis=-1		,)

           SCREAMING_SNAKE_CASE						=		model.init_cache(input_ids.shape[0]		,_lowerCamelCase			)
           SCREAMING_SNAKE_CASE						=		jnp.broadcast_to(
               jnp.arange(input_ids.shape[-1] - 1			)[None, :]		,(input_ids.shape[0], input_ids.shape[-1] - 1)			)

           SCREAMING_SNAKE_CASE						=		model(
               input_ids[:, :-1]		,attention_mask=_lowerCamelCase		,past_key_values=_lowerCamelCase		,position_ids=_lowerCamelCase		,)
           SCREAMING_SNAKE_CASE						=		jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]		,dtype="""i4"""			)
           SCREAMING_SNAKE_CASE						=		model(
               input_ids[:, -1:]		,past_key_values=outputs_cache.past_key_values		,attention_mask=_lowerCamelCase		,position_ids=_lowerCamelCase		,)

           SCREAMING_SNAKE_CASE						=		model(_lowerCamelCase		,attention_mask=_lowerCamelCase			)
           SCREAMING_SNAKE_CASE						=		np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])			)			)
           self.parent.assertTrue(diff < 1e-3		,msg=F"""Max diff is {diff}"""			)

@require_sentencepiece
@require_flax
class 						UpperCamelCase__					( __UpperCAmelCase				, __UpperCAmelCase				, unittest.TestCase  ):



     '''simple docstring'''

     __snake_case       :      List[Any]						      =     (FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
     __snake_case       :      Optional[int]						      =     (FlaxXGLMForCausalLM,) if is_flax_available() else ()






     def 							SCREAMING_SNAKE_CASE__    (				self		:       List[Any]			)	->						Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		FlaxXGLMModelTester(self			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       List[Any]			)	->						Optional[int]:




           '''simple docstring'''


           for model_class_name in self.all_model_classes:
                 SCREAMING_SNAKE_CASE						=		self.model_tester.prepare_config_and_inputs()
                 self.model_tester.check_use_cache_forward(_lowerCamelCase		,_lowerCamelCase		,_lowerCamelCase		,_lowerCamelCase			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       List[Any]			)	->						Tuple:




           '''simple docstring'''


           for model_class_name in self.all_model_classes:
                 SCREAMING_SNAKE_CASE						=		self.model_tester.prepare_config_and_inputs()
                 self.model_tester.check_use_cache_forward_with_attn_mask(
                     _lowerCamelCase		,_lowerCamelCase		,_lowerCamelCase		,_lowerCamelCase			)






     @slow
     def 							SCREAMING_SNAKE_CASE__    (				self		:       List[str]			)	->						Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		XGLMTokenizer.from_pretrained("""XGLM"""		,padding_side="""left"""			)
           SCREAMING_SNAKE_CASE						=		tokenizer(["""Hello this is a long string""", """Hey"""]		,return_tensors="""np"""		,padding=_lowerCamelCase		,truncation=_lowerCamelCase			)

           SCREAMING_SNAKE_CASE						=		FlaxXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""			)
           SCREAMING_SNAKE_CASE						=		1
           SCREAMING_SNAKE_CASE						=		False

           SCREAMING_SNAKE_CASE						=		jax.jit(model.generate			)

           SCREAMING_SNAKE_CASE						=		jit_generate(inputs["""input_ids"""]		,attention_mask=inputs["""attention_mask"""]			).sequences

           SCREAMING_SNAKE_CASE						=		tokenizer.batch_decode(_lowerCamelCase		,skip_special_tokens=_lowerCamelCase			)

           SCREAMING_SNAKE_CASE						=		[
               '''Hello this is a long string of questions, but I\'m not sure if I\'m''',
               '''Hey, I\'m a newbie to the forum and I\'''',
           ]

           self.assertListEqual(_lowerCamelCase		,_lowerCamelCase			)






     @is_pt_flax_cross_test
     def 							SCREAMING_SNAKE_CASE__    (				self		:       Tuple			)	->						str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__			):
                       # prepare inputs
                       SCREAMING_SNAKE_CASE						=		self._prepare_for_class(_lowerCamelCase		,_lowerCamelCase			)
                       SCREAMING_SNAKE_CASE						=		{k: torch.tensor(v.tolist()			) for k, v in prepared_inputs_dict.items()}

                       # load corresponding PyTorch class
                       SCREAMING_SNAKE_CASE						=		model_class.__name__[4:]  # Skip the "Flax" at the beginning
                       SCREAMING_SNAKE_CASE						=		getattr(_lowerCamelCase		,_lowerCamelCase			)

                       SCREAMING_SNAKE_CASE						=		pt_inputs['''input_ids'''].shape
                       SCREAMING_SNAKE_CASE						=		np.random.randint(0		,seq_length - 1		,size=(batch_size,)			)
                       for batch_idx, start_index in enumerate(_lowerCamelCase			):
                             SCREAMING_SNAKE_CASE						=		0
                             SCREAMING_SNAKE_CASE						=		1
                             SCREAMING_SNAKE_CASE						=		0
                             SCREAMING_SNAKE_CASE						=		1
                       SCREAMING_SNAKE_CASE						=		pt_model_class(_lowerCamelCase			).eval()
                       # Flax models don't use the `use_cache` option and cache is not returned as a default.
                       # So we disable `use_cache` here for PyTorch model.
                       SCREAMING_SNAKE_CASE						=		False
                       SCREAMING_SNAKE_CASE						=		model_class(_lowerCamelCase		,dtype=jnp.floataa			)
                       SCREAMING_SNAKE_CASE						=		convert_pytorch_state_dict_to_flax(pt_model.state_dict()		,_lowerCamelCase			)
                       SCREAMING_SNAKE_CASE						=		fx_state

                       with torch.no_grad():
                             SCREAMING_SNAKE_CASE						=		pt_model(**_lowerCamelCase			).to_tuple()

                       SCREAMING_SNAKE_CASE						=		fx_model(**_lowerCamelCase			).to_tuple()
                       self.assertEqual(len(_lowerCamelCase			)		,len(_lowerCamelCase			)		,"""Output lengths differ between Flax and PyTorch"""			)
                       for fx_output, pt_output in zip(_lowerCamelCase		,_lowerCamelCase			):
                             self.assert_almost_equals(fx_output[:, -1]		,pt_output[:, -1].numpy()		,4e-2			)

                       with tempfile.TemporaryDirectory() as tmpdirname:
                             pt_model.save_pretrained(_lowerCamelCase			)
                             SCREAMING_SNAKE_CASE						=		model_class.from_pretrained(_lowerCamelCase		,from_pt=_lowerCamelCase			)

                       SCREAMING_SNAKE_CASE						=		fx_model_loaded(**_lowerCamelCase			).to_tuple()
                       self.assertEqual(
                           len(_lowerCamelCase			)		,len(_lowerCamelCase			)		,"""Output lengths differ between Flax and PyTorch"""			)
                       for fx_output_loaded, pt_output in zip(_lowerCamelCase		,_lowerCamelCase			):
                             self.assert_almost_equals(fx_output_loaded[:, -1]		,pt_output[:, -1].numpy()		,4e-2			)






     @is_pt_flax_cross_test
     def 							SCREAMING_SNAKE_CASE__    (				self		:       int			)	->						Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self.model_tester.prepare_config_and_inputs_for_common()
           for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__			):
                       # prepare inputs
                       SCREAMING_SNAKE_CASE						=		self._prepare_for_class(_lowerCamelCase		,_lowerCamelCase			)
                       SCREAMING_SNAKE_CASE						=		{k: torch.tensor(v.tolist()			) for k, v in prepared_inputs_dict.items()}

                       # load corresponding PyTorch class
                       SCREAMING_SNAKE_CASE						=		model_class.__name__[4:]  # Skip the "Flax" at the beginning
                       SCREAMING_SNAKE_CASE						=		getattr(_lowerCamelCase		,_lowerCamelCase			)

                       SCREAMING_SNAKE_CASE						=		pt_model_class(_lowerCamelCase			).eval()
                       SCREAMING_SNAKE_CASE						=		False
                       SCREAMING_SNAKE_CASE						=		model_class(_lowerCamelCase		,dtype=jnp.floataa			)

                       SCREAMING_SNAKE_CASE						=		load_flax_weights_in_pytorch_model(_lowerCamelCase		,fx_model.params			)
                       SCREAMING_SNAKE_CASE						=		pt_inputs['''input_ids'''].shape
                       SCREAMING_SNAKE_CASE						=		np.random.randint(0		,seq_length - 1		,size=(batch_size,)			)
                       for batch_idx, start_index in enumerate(_lowerCamelCase			):
                             SCREAMING_SNAKE_CASE						=		0
                             SCREAMING_SNAKE_CASE						=		1
                             SCREAMING_SNAKE_CASE						=		0
                             SCREAMING_SNAKE_CASE						=		1

                       # make sure weights are tied in PyTorch
                       pt_model.tie_weights()

                       with torch.no_grad():
                             SCREAMING_SNAKE_CASE						=		pt_model(**_lowerCamelCase			).to_tuple()

                       SCREAMING_SNAKE_CASE						=		fx_model(**_lowerCamelCase			).to_tuple()
                       self.assertEqual(len(_lowerCamelCase			)		,len(_lowerCamelCase			)		,"""Output lengths differ between Flax and PyTorch"""			)
                       for fx_output, pt_output in zip(_lowerCamelCase		,_lowerCamelCase			):
                             self.assert_almost_equals(fx_output[:, -1]		,pt_output[:, -1].numpy()		,4e-2			)

                       with tempfile.TemporaryDirectory() as tmpdirname:
                             fx_model.save_pretrained(_lowerCamelCase			)
                             SCREAMING_SNAKE_CASE						=		pt_model_class.from_pretrained(_lowerCamelCase		,from_flax=_lowerCamelCase			)

                       with torch.no_grad():
                             SCREAMING_SNAKE_CASE						=		pt_model_loaded(**_lowerCamelCase			).to_tuple()

                       self.assertEqual(
                           len(_lowerCamelCase			)		,len(_lowerCamelCase			)		,"""Output lengths differ between Flax and PyTorch"""			)
                       for fx_output, pt_output in zip(_lowerCamelCase		,_lowerCamelCase			):
                             self.assert_almost_equals(fx_output[:, -1]		,pt_output[:, -1].numpy()		,4e-2			)






     @slow
     def 							SCREAMING_SNAKE_CASE__    (				self		:       List[str]			)	->						Any:




           '''simple docstring'''


           for model_class_name in self.all_model_classes:
                 SCREAMING_SNAKE_CASE						=		model_class_name.from_pretrained("""facebook/xglm-564M"""			)
                 SCREAMING_SNAKE_CASE						=		model(np.ones((1, 1)			)			)
                 self.assertIsNotNone(_lowerCamelCase			)








"""simple docstring"""

# This Python program implements an optimal binary search tree (abbreviated BST)
# building dynamic programming algorithm that delivers O(n^2) performance.
#
# The goal of the optimal BST problem is to build a low-cost BST for a
# given set of nodes, each with its own key and frequency. The frequency
# of the node is defined as how many time the node is being searched.
# The search cost of binary search tree is given by this formula:
#
# cost(1, n) = sum{i = 1 to n}((depth(node_i) + 1) * node_i_freq)
#
# where n is number of nodes in the BST. The characteristic of low-cost
# BSTs is having a faster overall search time than other implementations.
# The reason for their fast search time is that the nodes with high
# frequencies will be placed near the root of the tree while the nodes
# with low frequencies will be placed near the leaves of the tree thus
# reducing search time in the most frequent instances.
import sys
from random import randint

class 							lowercase   :







				def __init__(				self :       Optional[int]   ,  _lowerCamelCase :       str   ,  _lowerCamelCase :       Optional[Any]							):



					"""simple docstring"""

					A_	:					Union[str, Any]						 =		key
					A_	:					str						 =		freq







				def __str__(				self :       Optional[Any]							):



					"""simple docstring"""

					return F"""Node(key={self.key}, freq={self.freq})"""

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






	if i > j or i < 0 or j > len(_UpperCAmelCase						) - 1:
		return

	A_	:					Union[str, Any]						 =		root[i][j]
	if parent == -1:  # root does not have a parent
		print(f"""{key[node]} is the root of the binary search tree."""						)
	elif is_left:
		print(f"""{key[node]} is the left child of key {parent}."""						)
	else:
		print(f"""{key[node]} is the right child of key {parent}."""						)

	print_binary_search_tree(_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   node - 1	,   key[node]	,   _UpperCAmelCase						)
	print_binary_search_tree(_UpperCAmelCase	,   _UpperCAmelCase	,   node + 1	,   _UpperCAmelCase	,   key[node]	,   _UpperCAmelCase						)

def 		lowercase_   (		_UpperCAmelCase						):




	"""simple docstring"""






	nodes.sort(key=lambda _UpperCAmelCase						: node.key						)

	A_	:					Any						 =		len(_UpperCAmelCase						)

	A_	:					int						 =		[nodes[i].key for i in range(_UpperCAmelCase						)]
	A_	:					List[str]						 =		[nodes[i].freq for i in range(_UpperCAmelCase						)]

	# This 2D array stores the overall tree cost (which's as minimized as possible);
	# for a single key, cost is equal to frequency of the key.
	A_	:					List[str]						 =		[[freqs[i] if i == j else 0 for j in range(_UpperCAmelCase						)] for i in range(_UpperCAmelCase						)]
	# sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
	# array
	A_	:					Optional[int]						 =		[[freqs[i] if i == j else 0 for j in range(_UpperCAmelCase						)] for i in range(_UpperCAmelCase						)]
	# stores tree roots that will be used later for constructing binary search tree
	A_	:					List[Any]						 =		[[i if i == j else 0 for j in range(_UpperCAmelCase						)] for i in range(_UpperCAmelCase						)]

	for interval_length in range(2	,   n + 1						):
		for i in range(n - interval_length + 1						):
			A_	:					List[Any]						 =		i + interval_length - 1

			A_	:					List[Any]						 =		sys.maxsize  # set the value to "infinity"
			A_	:					Union[str, Any]						 =		total[i][j - 1] + freqs[j]

			# Apply Knuth's optimization
			# Loop without optimization: for r in range(i, j + 1):
			for r in range(root[i][j - 1]	,   root[i + 1][j] + 1						):  # r is a temporal root
				A_	:					Tuple						 =		dp[i][r - 1] if r != i else 0  # optimal cost for left subtree
				A_	:					List[str]						 =		dp[r + 1][j] if r != j else 0  # optimal cost for right subtree
				A_	:					Optional[int]						 =		left + total[i][j] + right

				if dp[i][j] > cost:
					A_	:					Dict						 =		cost
					A_	:					List[str]						 =		r

	print('''Binary search tree nodes:'''						)
	for node in nodes:
		print(_UpperCAmelCase						)

	print(f"""\nThe cost of optimal BST for given tree nodes is {dp[0][n - 1]}."""						)
	print_binary_search_tree(_UpperCAmelCase	,   _UpperCAmelCase	,   0	,   n - 1	,   -1	,   _UpperCAmelCase						)

def 		lowercase_   (		):




	"""simple docstring"""






	A_	:					List[Any]						 =		[Node(_UpperCAmelCase	,   randint(1	,   50						)						) for i in range(10	,   0	,   -1						)]
	find_optimal_binary_search_tree(_UpperCAmelCase						)


if __name__ == "__main__":
		main()





