


import unittest

from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseModel



class        a					:





     def __init__(    self					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				str=13    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=7    ,	SCREAMING_SNAKE_CASE_					:				int=True    ,	SCREAMING_SNAKE_CASE_					:				Dict=True    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=True    ,	SCREAMING_SNAKE_CASE_					:				int=True    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=99    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=32    ,	SCREAMING_SNAKE_CASE_					:				int=5    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=4    ,	SCREAMING_SNAKE_CASE_					:				Tuple=4    ,	SCREAMING_SNAKE_CASE_					:				Any="gelu"    ,	SCREAMING_SNAKE_CASE_					:				str=0.0    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=0.1    ,	SCREAMING_SNAKE_CASE_					:				List[str]=True    ,	SCREAMING_SNAKE_CASE_					:				Dict=512    ,	SCREAMING_SNAKE_CASE_					:				int=16    ,	SCREAMING_SNAKE_CASE_					:				Tuple=2    ,	SCREAMING_SNAKE_CASE_					:				Any=0.02    ,	SCREAMING_SNAKE_CASE_					:				List[str]=3    ,	SCREAMING_SNAKE_CASE_					:				List[str]=4    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]=None    ,	):
        __lowerCamelCase:				str     =	parent
        __lowerCamelCase:				str     =	batch_size
        __lowerCamelCase:				Tuple     =	seq_length
        __lowerCamelCase:				Tuple     =	is_training
        __lowerCamelCase:				str     =	use_input_mask
        __lowerCamelCase:				List[str]     =	use_token_type_ids
        __lowerCamelCase:				List[str]     =	use_labels
        __lowerCamelCase:				Any     =	vocab_size
        __lowerCamelCase:				Union[str, Any]     =	hidden_size
        __lowerCamelCase:				Optional[Any]     =	num_hidden_layers
        __lowerCamelCase:				Optional[Any]     =	num_attention_heads
        __lowerCamelCase:				Optional[Any]     =	intermediate_multiple_size
        __lowerCamelCase:				Any     =	hidden_act
        __lowerCamelCase:				Dict     =	hidden_dropout
        __lowerCamelCase:				Dict     =	attention_dropout
        __lowerCamelCase:				Dict     =	weight_tying
        __lowerCamelCase:				Tuple     =	max_position_embeddings
        __lowerCamelCase:				List[str]     =	type_vocab_size
        __lowerCamelCase:				Tuple     =	type_sequence_label_size
        __lowerCamelCase:				Union[str, Any]     =	initializer_range
        __lowerCamelCase:				Dict     =	num_labels
        __lowerCamelCase:				List[str]     =	num_choices
        __lowerCamelCase:				Union[str, Any]     =	scope





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]							):
        __lowerCamelCase:				Union[str, Any]     =	ids_tensor([self.batch_size, self.seq_length]    ,	self.vocab_size							)

        __lowerCamelCase:				Dict     =	None
        if self.use_input_mask:
           __lowerCamelCase:				List[Any]     =	random_attention_mask([self.batch_size, self.seq_length]							)

        __lowerCamelCase:				int     =	None
        if self.use_labels:
           __lowerCamelCase:				List[Any]     =	ids_tensor([self.batch_size, self.seq_length]    ,	self.num_labels							)

        __lowerCamelCase:				Tuple     =	self.get_config()

        return config, input_ids, input_mask, token_labels





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]							):
        return GPTNeoXJapaneseConfig(
            vocab_size=self.vocab_size    ,	hidden_size=self.hidden_size    ,	num_hidden_layers=self.num_hidden_layers    ,	num_attention_heads=self.num_attention_heads    ,	intermediate_multiple_size=self.intermediate_multiple_size    ,	hidden_act=self.hidden_act    ,	hidden_dropout=self.hidden_dropout    ,	attention_dropout=self.attention_dropout    ,	weight_tying=self.weight_tying    ,	max_position_embeddings=self.max_position_embeddings    ,	type_vocab_size=self.type_vocab_size    ,	is_decoder=SCREAMING_SNAKE_CASE_    ,	initializer_range=self.initializer_range    ,	)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        __lowerCamelCase   ,     __lowerCamelCase   ,     __lowerCamelCase   ,     __lowerCamelCase:				Union[str, Any]     =	self.prepare_config_and_inputs()

        __lowerCamelCase:				int     =	True

        return config, input_ids, input_mask, token_labels





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]							):
        __lowerCamelCase:				Any     =	GPTNeoXJapaneseModel(config=SCREAMING_SNAKE_CASE_							)
        model.to(SCREAMING_SNAKE_CASE_							)
        model.eval()
        __lowerCamelCase:				List[Any]     =	model(SCREAMING_SNAKE_CASE_    ,	attention_mask=SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				List[Any]     =	model(SCREAMING_SNAKE_CASE_							)
        self.parent.assertEqual(result.last_hidden_state.shape    ,	(self.batch_size, self.seq_length, self.hidden_size)							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Any							):
        __lowerCamelCase:				Optional[Any]     =	True
        __lowerCamelCase:				Optional[Any]     =	GPTNeoXJapaneseModel(SCREAMING_SNAKE_CASE_							)
        model.to(SCREAMING_SNAKE_CASE_							)
        model.eval()
        __lowerCamelCase:				str     =	model(SCREAMING_SNAKE_CASE_    ,	attention_mask=SCREAMING_SNAKE_CASE_							)
        self.parent.assertEqual(result.last_hidden_state.shape    ,	(self.batch_size, self.seq_length, self.hidden_size)							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				str    ,	SCREAMING_SNAKE_CASE_					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				str							):
        __lowerCamelCase:				Union[str, Any]     =	GPTNeoXJapaneseForCausalLM(config=SCREAMING_SNAKE_CASE_							)
        model.to(SCREAMING_SNAKE_CASE_							)
        model.eval()
        __lowerCamelCase:				int     =	model(SCREAMING_SNAKE_CASE_    ,	attention_mask=SCREAMING_SNAKE_CASE_    ,	labels=SCREAMING_SNAKE_CASE_							)
        self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.seq_length, self.vocab_size)							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				Tuple							):
        __lowerCamelCase:				List[Any]     =	True
        __lowerCamelCase:				List[Any]     =	GPTNeoXJapaneseForCausalLM(config=SCREAMING_SNAKE_CASE_							)
        model.to(SCREAMING_SNAKE_CASE_							)
        model.eval()

        # first forward pass
        __lowerCamelCase:				Dict     =	model(SCREAMING_SNAKE_CASE_    ,	attention_mask=SCREAMING_SNAKE_CASE_    ,	use_cache=SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				List[str]     =	outputs.past_key_values

        # create hypothetical multiple next token and extent to next_input_ids
        __lowerCamelCase:				List[Any]     =	ids_tensor((self.batch_size, 3)    ,	config.vocab_size							)
        __lowerCamelCase:				Optional[int]     =	ids_tensor((self.batch_size, 3)    ,	vocab_size=2							)

        # append to next input_ids and
        __lowerCamelCase:				Tuple     =	torch.cat([input_ids, next_tokens]    ,	dim=-1							)
        __lowerCamelCase:				Union[str, Any]     =	torch.cat([input_mask, next_mask]    ,	dim=-1							)

        __lowerCamelCase:				Any     =	model(SCREAMING_SNAKE_CASE_    ,	attention_mask=SCREAMING_SNAKE_CASE_    ,	output_hidden_states=SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Any     =	output_from_no_past["""hidden_states"""][0]
        __lowerCamelCase:				Optional[Any]     =	model(
            SCREAMING_SNAKE_CASE_    ,	attention_mask=SCREAMING_SNAKE_CASE_    ,	past_key_values=SCREAMING_SNAKE_CASE_    ,	output_hidden_states=SCREAMING_SNAKE_CASE_    ,	)["""hidden_states"""][0]

        # select random slice
        __lowerCamelCase:				List[Any]     =	ids_tensor((1,)    ,	output_from_past.shape[-1]							).item()
        __lowerCamelCase:				Optional[int]     =	output_from_no_past[:, -3:, random_slice_idx].detach()
        __lowerCamelCase:				Any     =	output_from_past[:, :, random_slice_idx].detach()

        self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]							)

        # test that outputs are equal for slice
        self.parent.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	atol=1E-3							)							)







     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]							):
        __lowerCamelCase:				List[Any]     =	self.prepare_config_and_inputs()
        __lowerCamelCase   ,     __lowerCamelCase   ,     __lowerCamelCase   ,     __lowerCamelCase:				List[str]     =	config_and_inputs
        __lowerCamelCase:				List[Any]     =	{"""input_ids""": input_ids, """attention_mask""": input_mask}
        return config, inputs_dict



@require_torch
class        a					(			_UpperCAmelCase     ,_UpperCAmelCase     ,unittest.TestCase   ):
     UpperCAmelCase__ :  Union[str, Any]						      =	(GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
     UpperCAmelCase__ :  Union[str, Any]						      =	(GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
     UpperCAmelCase__ :  Any						      =	(
         {"feature-extraction": GPTNeoXJapaneseModel, "text-generation": GPTNeoXJapaneseForCausalLM}
         if is_torch_available()
         else {}
     )
     UpperCAmelCase__ :  Any						      =	False
     UpperCAmelCase__ :  Optional[int]						      =	False
     UpperCAmelCase__ :  Dict						      =	False
     UpperCAmelCase__ :  Any						      =	False





     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]							):
        __lowerCamelCase:				Optional[int]     =	GPTNeoXJapaneseModelTester(self							)
        __lowerCamelCase:				Optional[Any]     =	ConfigTester(self    ,	config_class=SCREAMING_SNAKE_CASE_    ,	hidden_size=37							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]							):
        self.config_tester.run_common_tests()





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]							):
        __lowerCamelCase   ,     __lowerCamelCase   ,     __lowerCamelCase   ,     __lowerCamelCase:				Any     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        __lowerCamelCase   ,     __lowerCamelCase   ,     __lowerCamelCase   ,     __lowerCamelCase:				Optional[int]     =	self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_model_as_decoder(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]							):
        # This regression test was failing with PyTorch < 1.3
        __lowerCamelCase   ,     __lowerCamelCase   ,     __lowerCamelCase   ,     __lowerCamelCase:				str     =	self.model_tester.prepare_config_and_inputs_for_decoder()

        __lowerCamelCase:				Union[str, Any]     =	None

        self.model_tester.create_and_check_model_as_decoder(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]							):
        __lowerCamelCase   ,     __lowerCamelCase   ,     __lowerCamelCase   ,     __lowerCamelCase:				Optional[Any]     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_decoder_model_past_large_inputs(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        __lowerCamelCase:				List[Any]     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_causal_lm(*SCREAMING_SNAKE_CASE_							)







     @slow
     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        __lowerCamelCase:				int     =	"""abeja/gpt-neox-japanese-2.7b"""

        __lowerCamelCase:				int     =	["""データサイエンティストとは、""", """100年後に必要とされる会社は、""", """フルリモートの環境で働くために必要なことは、""", """国境の長いトンネルを抜けると""", """美味しい日本食といえば、"""]

        __lowerCamelCase:				Optional[Any]     =	[
            """データサイエンティストとは、データを分析し、ビジネスに役立つ知見を導き出す専門家のことです。""",
            """100年後に必要とされる会社は、「人」が中心の会社です。""",
            """フルリモートの環境で働くために必要なことは、「自分の時間をコントロールする」ことです。""",
            """国境の長いトンネルを抜けると、そこは雪国だった。""",
            """美味しい日本食といえば、やっぱりお寿司ですよね。""",
        ]

        __lowerCamelCase:				List[Any]     =	GPTNeoXJapaneseTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Optional[Any]     =	GPTNeoXJapaneseForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Any     =	[]
        for prompt in prompts:
           __lowerCamelCase:				Any     =	tokenizer(SCREAMING_SNAKE_CASE_    ,	return_tensors="""pt"""							).input_ids
           __lowerCamelCase:				Any     =	model.generate(SCREAMING_SNAKE_CASE_    ,	max_length=50							)
           __lowerCamelCase:				Dict     =	tokenizer.batch_decode(SCREAMING_SNAKE_CASE_    ,	skip_special_tokens=SCREAMING_SNAKE_CASE_							)
           predicted_outputs += generated_string
        self.assertListEqual(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)






class        a					:





     def __init__(    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				str = ""    ,	SCREAMING_SNAKE_CASE_					:				bool = False							):
        # Mapping from the first character of the prefix of the node
        __lowerCamelCase:				dict[str, RadixNode]     =	{}

        # A node will be a leaf if the tree contains its word
        __lowerCamelCase:				str     =	is_leaf

        __lowerCamelCase:				Optional[int]     =	prefix





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				str							):
        __lowerCamelCase:				Optional[Any]     =	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  SCREAMING_SNAKE_CASE__       (    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				list[str]							):
        for word in words:
           self.insert(SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				str							):
        # Case 1: If the word is the prefix of the node
        # Solution: We set the current node as leaf
        if self.prefix == word:
           __lowerCamelCase:				Union[str, Any]     =	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:
           __lowerCamelCase:				Any     =	RadixNode(prefix=SCREAMING_SNAKE_CASE_    ,	is_leaf=SCREAMING_SNAKE_CASE_							)

        else:
           __lowerCamelCase:				Union[str, Any]     =	self.nodes[word[0]]
           __lowerCamelCase   ,     __lowerCamelCase   ,     __lowerCamelCase:				List[str]     =	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:
              __lowerCamelCase:				List[Any]     =	remaining_prefix

              __lowerCamelCase:				Optional[Any]     =	self.nodes[matching_string[0]]
              __lowerCamelCase:				Optional[int]     =	RadixNode(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)
              __lowerCamelCase:				Union[str, Any]     =	aux_node

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





     def  SCREAMING_SNAKE_CASE__       (    self					:				int    ,	SCREAMING_SNAKE_CASE_					:				str							):
        __lowerCamelCase:				int     =	self.nodes.get(word[0]    ,	SCREAMING_SNAKE_CASE_							)
        if not incoming_node:
           return False
        else:
           __lowerCamelCase   ,     __lowerCamelCase   ,     __lowerCamelCase:				Any     =	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  SCREAMING_SNAKE_CASE__       (    self					:				str    ,	SCREAMING_SNAKE_CASE_					:				str							):
        __lowerCamelCase:				str     =	self.nodes.get(word[0]    ,	SCREAMING_SNAKE_CASE_							)
        if not incoming_node:
           return False
        else:
           __lowerCamelCase   ,     __lowerCamelCase   ,     __lowerCamelCase:				Dict     =	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:
                       __lowerCamelCase:				List[Any]     =	list(self.nodes.values()							)[0]
                       __lowerCamelCase:				Any     =	merging_node.is_leaf
                       self.prefix += merging_node.prefix
                       __lowerCamelCase:				Tuple     =	merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
                 elif len(incoming_node.nodes							) > 1:
                    __lowerCamelCase:				int     =	False
                 # If there is 1 edge, we merge it with its child
                 else:
                    __lowerCamelCase:				Union[str, Any]     =	list(incoming_node.nodes.values()							)[0]
                    __lowerCamelCase:				List[str]     =	merging_node.is_leaf
                    incoming_node.prefix += merging_node.prefix
                    __lowerCamelCase:				Union[str, Any]     =	merging_node.nodes

                 return True







     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				int = 0							):
        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     __lowerCAmelCase						(							)       ->							bool:
   __lowerCamelCase:				Optional[int]     =	"""banana bananas bandana band apple all beast""".split()
   __lowerCamelCase:				Optional[Any]     =	RadixNode()
   root.insert_many(snake_case )

   assert all(root.find(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     __lowerCAmelCase						(							)       ->							None:
   assert test_trie()



def     __lowerCAmelCase						(							)       ->							None:
   __lowerCamelCase:				int     =	RadixNode()
   __lowerCamelCase:				str     =	"""banana bananas bandanas bandana band apple all beast""".split()
   root.insert_many(snake_case )

   print("""Words:"""						,      snake_case )
   print("""Tree:""" )
   root.print_tree()


if __name__ == "__main__":
   main()


