
"""simple docstring"""


import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

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


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

		from transformers.modeling_flax_pytorch_utils import (
		    convert_pytorch_state_dict_to_flax,
		    load_flax_weights_in_pytorch_model,
		)
		from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
		import torch







class 					lowerCAmelCase_					:



						'''simple docstring'''


						def __init__(			self  :   List[Any]	,A_  :   List[Any]	,A_  :   Optional[int]=14	,A_  :   int=7	,A_  :   Dict=True	,A_  :   str=True	,A_  :   List[str]=False	,A_  :   Optional[Any]=True	,A_  :   int=99	,A_  :   List[str]=32	,A_  :   Optional[Any]=4	,A_  :   int=4	,A_  :   int=4	,A_  :   Dict=37	,A_  :   Tuple="gelu"	,A_  :   Tuple=0.1	,A_  :   Union[str, Any]=0.1	,A_  :   Dict=512	,A_  :   Optional[int]=0.02	,)  -> Union[str, Any]:
							A     = parent
							A     = batch_size
							A     = seq_length
							A     = is_training
							A     = use_input_mask
							A     = use_token_type_ids
							A     = use_labels
							A     = vocab_size
							A     = hidden_size
							A     = rotary_dim
							A     = num_hidden_layers
							A     = num_attention_heads
							A     = intermediate_size
							A     = hidden_act
							A     = hidden_dropout_prob
							A     = attention_probs_dropout_prob
							A     = max_position_embeddings
							A     = initializer_range
							A     = None
							A     = vocab_size - 1
							A     = vocab_size - 1
							A     = vocab_size - 1


						def     _SCREAMING_SNAKE_CASE   (			self  :   int   )  -> Any:
							A     = ids_tensor([self.batch_size, self.seq_length]	,self.vocab_size   )

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

							A     = GPTJConfig(
							    vocab_size=self.vocab_size	,n_embd=self.hidden_size	,n_layer=self.num_hidden_layers	,n_head=self.num_attention_heads	,n_positions=self.max_position_embeddings	,use_cache=A_	,bos_token_id=self.bos_token_id	,eos_token_id=self.eos_token_id	,pad_token_id=self.pad_token_id	,rotary_dim=self.rotary_dim	,)

							return (config, input_ids, input_mask)


						def     _SCREAMING_SNAKE_CASE   (			self  :   int   )  -> Dict:
							A     = self.prepare_config_and_inputs()
							A ,   A ,   A     = config_and_inputs
							A     = {'input_ids': input_ids, 'attention_mask': attention_mask}
							return config, inputs_dict


						def     _SCREAMING_SNAKE_CASE   (			self  :   str	,A_  :   Optional[int]	,A_  :   int	,A_  :   Optional[int]	,A_  :   List[str]   )  -> List[Any]:
							A     = 20
							A     = model_class_name(A_   )

							A     = model.init_cache(input_ids.shape[0]	,A_   )
							A     = jnp.ones((input_ids.shape[0], max_decoder_length)	,dtype='i4'   )

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

							A     = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]	,dtype='i4'   )
							A     = model(
							    input_ids[:, -1:]	,attention_mask=A_	,past_key_values=outputs_cache.past_key_values	,position_ids=A_	,)

							A     = model(A_   )

							A     = 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  :   List[str]	,A_  :   int	,A_  :   Union[str, Any]	,A_  :   int	,A_  :   Optional[Any]   )  -> Optional[int]:
							A     = 20
							A     = model_class_name(A_   )

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

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

							A     = model(
							    input_ids[:, :-1]	,attention_mask=A_	,past_key_values=A_	,position_ids=A_	,)
							A     = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]	,dtype='i4'   )
							A     = model(
							    input_ids[:, -1:]	,past_key_values=outputs_cache.past_key_values	,attention_mask=A_	,position_ids=A_	,)

							A     = model(A_	,attention_mask=A_   )

							A     = 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_flax
class 					lowerCAmelCase_					(   _lowercase		,       _lowercase		,       unittest.TestCase     ):



						'''simple docstring'''


						_lowerCamelCase:     Optional[Any]   =     (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
						_lowerCamelCase:     Tuple   =     (FlaxGPTJForCausalLM,) if is_flax_available() else ()


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]   )  -> str:
							A     = FlaxGPTJModelTester(self   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]   )  -> Optional[Any]:
							for model_class_name in self.all_model_classes:
								A ,   A ,   A     = self.model_tester.prepare_config_and_inputs()
								self.model_tester.check_use_cache_forward(A_	,A_	,A_	,A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]   )  -> List[str]:
							for model_class_name in self.all_model_classes:
								A ,   A ,   A     = self.model_tester.prepare_config_and_inputs()
								self.model_tester.check_use_cache_forward_with_attn_mask(
								    A_	,A_	,A_	,A_   )


						@tooslow
						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]   )  -> Optional[int]:
							A     = GPTaTokenizer.from_pretrained('gpt2'	,pad_token='<|endoftext|>'	,padding_side='left'   )
							A     = tokenizer(['Hello this is a long string', 'Hey']	,return_tensors='np'	,padding=A_	,truncation=A_   )

							A     = FlaxGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B'   )
							A     = False
							A     = model.config.eos_token_id

							A     = jax.jit(model.generate   )

							A     = jit_generate(
							    inputs['input_ids']	,attention_mask=inputs['attention_mask']	,pad_token_id=tokenizer.pad_token_id   ).sequences

							A     = tokenizer.batch_decode(A_	,skip_special_tokens=A_   )

							A     = [
							    'Hello this is a long string of text.\n\nI\'m trying to get the text of the',
							    'Hey, I\'m a little late to the party. I\'m going to',
							]

							self.assertListEqual(A_	,A_   )


						@is_pt_flax_cross_test
						def     _SCREAMING_SNAKE_CASE   (			self  :   int   )  -> Union[str, Any]:
							A ,   A     = 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
									A     = self._prepare_for_class(A_	,A_   )
									A     = {k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

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

									A ,   A     = pt_inputs['input_ids'].shape
									A     = np.random.randint(0	,seq_length - 1	,size=(batch_size,)   )
									for batch_idx, start_index in enumerate(A_   ):
										A     = 0
										A     = 1
										A     = 0
										A     = 1
									A     = pt_model_class(A_   ).eval()
									A     = model_class(A_	,dtype=jnp.floataa   )

									A     = convert_pytorch_state_dict_to_flax(pt_model.state_dict()	,A_   )
									A     = fx_state

									with torch.no_grad():
										A     = pt_model(**A_   ).to_tuple()

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

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

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


						@is_pt_flax_cross_test
						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]   )  -> Any:
							A ,   A     = 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
									A     = self._prepare_for_class(A_	,A_   )
									A     = {k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

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

									A     = pt_model_class(A_   ).eval()
									A     = model_class(A_	,dtype=jnp.floataa   )

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

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

									with torch.no_grad():
										A     = pt_model(**A_   ).to_tuple()

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

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

									with torch.no_grad():
										A     = pt_model_loaded(**A_   ).to_tuple()

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




						@tooslow
						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple   )  -> Dict:
							for model_class_name in self.all_model_classes:
								A     = model_class_name.from_pretrained('EleutherAI/gpt-j-6B'   )
								A     = model(np.ones((1, 1)   )   )
								self.assertIsNotNone(A_   )





class       a_       :





	def __init__(							self      ,     SCREAMING_SNAKE_CASE = ""      ,     SCREAMING_SNAKE_CASE = False      )  ->					None:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						{}

				# A node will be a leaf if the tree contains its word
				SCREAMING_SNAKE_CASE_								=						is_leaf

				SCREAMING_SNAKE_CASE_								=						prefix





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					tuple[str, str, str]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						0
				for q, w in zip(self.prefix      ,     SCREAMING_SNAKE_CASE      ):
							if q != w:
										break

							x += 1

				return self.prefix[:x], self.prefix[x:], word[x:]





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					None:




				"""simple docstring"""



				for word in words:
							self.insert(SCREAMING_SNAKE_CASE      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					None:




				"""simple docstring"""



				if self.prefix == word:
							SCREAMING_SNAKE_CASE_								=						True

				# Case 2: The node has no edges that have a prefix to the word
				# Solution: We create an edge from the current node to a new one
				# containing the word
				elif word[0] not in self.nodes:
							SCREAMING_SNAKE_CASE_								=						RadixNode(prefix=SCREAMING_SNAKE_CASE      ,     is_leaf=SCREAMING_SNAKE_CASE      )

				else:
							SCREAMING_SNAKE_CASE_								=						self.nodes[word[0]]
							SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						incoming_node.match(
							    SCREAMING_SNAKE_CASE      )

							# Case 3: The node prefix is equal to the matching
							# Solution: We insert remaining word on the next node
							if remaining_prefix == "":
										self.nodes[matching_string[0]].insert(SCREAMING_SNAKE_CASE      )

							# Case 4: The word is greater equal to the matching
							# Solution: Create a node in between both nodes, change
							# prefixes and add the new node for the remaining word
							else:
										SCREAMING_SNAKE_CASE_								=						remaining_prefix

										SCREAMING_SNAKE_CASE_								=						self.nodes[matching_string[0]]
										SCREAMING_SNAKE_CASE_								=						RadixNode(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )
										SCREAMING_SNAKE_CASE_								=						aux_node

										if remaining_word == "":
													SCREAMING_SNAKE_CASE_								=						True
										else:
													self.nodes[matching_string[0]].insert(SCREAMING_SNAKE_CASE      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					bool:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.nodes.get(word[0]      ,     SCREAMING_SNAKE_CASE      )
				if not incoming_node:
							return False
				else:
							SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						incoming_node.match(
							    SCREAMING_SNAKE_CASE      )
							# If there is remaining prefix, the word can't be on the tree
							if remaining_prefix != "":
										return False
							# This applies when the word and the prefix are equal
							elif remaining_word == "":
										return incoming_node.is_leaf
							# We have word remaining so we check the next node
							else:
										return incoming_node.find(SCREAMING_SNAKE_CASE      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					bool:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.nodes.get(word[0]      ,     SCREAMING_SNAKE_CASE      )
				if not incoming_node:
							return False
				else:
							SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						incoming_node.match(
							    SCREAMING_SNAKE_CASE      )
							# If there is remaining prefix, the word can't be on the tree
							if remaining_prefix != "":
										return False
							# We have word remaining so we check the next node
							elif remaining_word != "":
										return incoming_node.delete(SCREAMING_SNAKE_CASE      )
							else:
										# If it is not a leaf, we don't have to delete
										if not incoming_node.is_leaf:
													return False
										else:
													# We delete the nodes if no edges go from it
													if len(incoming_node.nodes      ) == 0:
																del self.nodes[word[0]]
																# We merge the current node with its only child
																if len(self.nodes      ) == 1 and not self.is_leaf:
																			SCREAMING_SNAKE_CASE_								=						list(self.nodes.values()      )[0]
																			SCREAMING_SNAKE_CASE_								=						merging_node.is_leaf
																			self.prefix += merging_node.prefix
																			SCREAMING_SNAKE_CASE_								=						merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
													elif len(incoming_node.nodes      ) > 1:
																SCREAMING_SNAKE_CASE_								=						False
													# If there is 1 edge, we merge it with its child
													else:
																SCREAMING_SNAKE_CASE_								=						list(incoming_node.nodes.values()      )[0]
																SCREAMING_SNAKE_CASE_								=						merging_node.is_leaf
																incoming_node.prefix += merging_node.prefix
																SCREAMING_SNAKE_CASE_								=						merging_node.nodes

													return True





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE = 0      )  ->					None:




				"""simple docstring"""



				if self.prefix != "":
							print('-' * height      ,     self.prefix      ,     '  (leaf)' if self.is_leaf else ''      )

				for value in self.nodes.values():
							value.print_tree(height + 1      )




def lowercase    (  )			->							bool:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						'banana bananas bandana band apple all beast'.split()
			SCREAMING_SNAKE_CASE_								=						RadixNode()
			root.insert_many(SCREAMING_SNAKE_CASE )

			assert all(root.find(SCREAMING_SNAKE_CASE ) for word in words )
			assert not root.find('bandanas' )
			assert not root.find('apps' )
			root.delete('all' )
			assert not root.find('all' )
			root.delete('banana' )
			assert not root.find('banana' )
			assert root.find('bananas' )

			return True

def lowercase    (  )			->							None:


			'''simple docstring'''







			assert test_trie()

def lowercase    (  )			->							None:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						RadixNode()
			SCREAMING_SNAKE_CASE_								=						'banana bananas bandanas bandana band apple all beast'.split()
			root.insert_many(SCREAMING_SNAKE_CASE )

			print('Words:'       ,    SCREAMING_SNAKE_CASE )
			print('Tree:' )
			root.print_tree()


if __name__ == "__main__":
				main()
