





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    __snake_case	:





       '''simple docstring'''







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




       def 							UpperCAmelCase__			(  self			:   Union[str, Any]		):
         __snake_case:						str			  =						ids_tensor([self.batch_size, self.seq_length]							,    self.vocab_size		)

         __snake_case:						Union[str, Any]			  =						None
         if self.use_input_mask:
           __snake_case:						str			  =						random_attention_mask([self.batch_size, self.seq_length]		)

         __snake_case:						Dict			  =						None
         if self.use_labels:
           __snake_case:						Union[str, Any]			  =						ids_tensor([self.batch_size, self.seq_length]							,    self.num_labels		)

         __snake_case:						List[Any]			  =						self.get_config()

         return config, input_ids, input_mask, token_labels




       def 							UpperCAmelCase__			(  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=A							,    initializer_range=self.initializer_range							,    )




       def 							UpperCAmelCase__			(  self			:   Dict		):
         __snake_case:						str			  =						self.prepare_config_and_inputs()

         __snake_case:						Union[str, Any]			  =						True

         return config, input_ids, input_mask, token_labels




       def 							UpperCAmelCase__			(  self			:   Optional[Any]							,    A			:   List[Any]							,    A			:   int							,    A			:   Optional[int]		):
         __snake_case:						Optional[int]			  =						GPTNeoXJapaneseModel(config=A		)
         model.to(A		)
         model.eval()
         __snake_case:						Optional[Any]			  =						model(A							,    attention_mask=A		)
         __snake_case:						Optional[int]			  =						model(A		)
         self.parent.assertEqual(result.last_hidden_state.shape							,    (self.batch_size, self.seq_length, self.hidden_size)		)




       def 							UpperCAmelCase__			(  self			:   Optional[int]							,    A			:   int							,    A			:   List[str]							,    A			:   Dict		):
         __snake_case:						Optional[Any]			  =						True
         __snake_case:						int			  =						GPTNeoXJapaneseModel(A		)
         model.to(A		)
         model.eval()
         __snake_case:						Optional[int]			  =						model(A							,    attention_mask=A		)
         self.parent.assertEqual(result.last_hidden_state.shape							,    (self.batch_size, self.seq_length, self.hidden_size)		)




       def 							UpperCAmelCase__			(  self			:   Dict							,    A			:   Dict							,    A			:   int							,    A			:   Optional[Any]							,    A			:   Any		):
         __snake_case:						Optional[int]			  =						GPTNeoXJapaneseForCausalLM(config=A		)
         model.to(A		)
         model.eval()
         __snake_case:						List[str]			  =						model(A							,    attention_mask=A							,    labels=A		)
         self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.seq_length, self.vocab_size)		)




       def 							UpperCAmelCase__			(  self			:   str							,    A			:   Tuple							,    A			:   str							,    A			:   Dict		):
         __snake_case:						int			  =						True
         __snake_case:						Optional[Any]			  =						GPTNeoXJapaneseForCausalLM(config=A		)
         model.to(A		)
         model.eval()

         # first forward pass
         __snake_case:						Optional[Any]			  =						model(A							,    attention_mask=A							,    use_cache=A		)
         __snake_case:						Optional[int]			  =						outputs.past_key_values

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

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

         __snake_case:						str			  =						model(A							,    attention_mask=A							,    output_hidden_states=A		)
         __snake_case:						Union[str, Any]			  =						output_from_no_past["hidden_states"][0]
         __snake_case:						Union[str, Any]			  =						model(
             A							,    attention_mask=A							,    past_key_values=A							,    output_hidden_states=A							,    )["hidden_states"][0]

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






       def 							UpperCAmelCase__			(  self			:   Tuple		):
         __snake_case:						int			  =						self.prepare_config_and_inputs()
         __snake_case:						Tuple			  =						config_and_inputs
         __snake_case:						Optional[int]			  =						{"input_ids": input_ids, "attention_mask": input_mask}
         return config, inputs_dict







@require_torch
class    __snake_case	(							a__       ,      a__       ,      unittest.TestCase      ):





       '''simple docstring'''







       lowerCAmelCase__						=					(GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
       lowerCAmelCase__						=					(GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
       lowerCAmelCase__						=					(
           {"""feature-extraction""": GPTNeoXJapaneseModel, """text-generation""": GPTNeoXJapaneseForCausalLM}
           if is_torch_available()
           else {}
       )
       lowerCAmelCase__						=					False
       lowerCAmelCase__						=					False
       lowerCAmelCase__						=					False
       lowerCAmelCase__						=					False




       def 							UpperCAmelCase__			(  self			:   int		):
         __snake_case:						Union[str, Any]			  =						GPTNeoXJapaneseModelTester(self		)
         __snake_case:						Optional[int]			  =						ConfigTester(self							,    config_class=A							,    hidden_size=37		)




       def 							UpperCAmelCase__			(  self			:   Any		):
         self.config_tester.run_common_tests()




       def 							UpperCAmelCase__			(  self			:   str		):
         __snake_case:						Optional[Any]			  =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(A							,    A							,    A		)




       def 							UpperCAmelCase__			(  self			:   Tuple		):
         __snake_case:						Dict			  =						self.model_tester.prepare_config_and_inputs_for_decoder()
         self.model_tester.create_and_check_model_as_decoder(A							,    A							,    A		)




       def 							UpperCAmelCase__			(  self			:   List[str]		):
         # This regression test was failing with PyTorch < 1.3
         __snake_case:						Optional[Any]			  =						self.model_tester.prepare_config_and_inputs_for_decoder()

         __snake_case:						Optional[int]			  =						None

         self.model_tester.create_and_check_model_as_decoder(A							,    A							,    A		)




       def 							UpperCAmelCase__			(  self			:   Union[str, Any]		):
         __snake_case:						Optional[int]			  =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_decoder_model_past_large_inputs(A							,    A							,    A		)




       def 							UpperCAmelCase__			(  self			:   Optional[int]		):
         __snake_case:						Tuple			  =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_causal_lm(*A		)






       @slow
       def 							UpperCAmelCase__			(  self			:   List[Any]		):
         __snake_case:						Any			  =						"abeja/gpt-neox-japanese-2.7b"

         __snake_case:						Dict			  =						["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、"]

         __snake_case:						List[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。",
         ]

         __snake_case:						int			  =						GPTNeoXJapaneseTokenizer.from_pretrained(A		)
         __snake_case:						Optional[Any]			  =						GPTNeoXJapaneseForCausalLM.from_pretrained(A		)

         __snake_case:						Optional[Any]			  =						[]
         for prompt in prompts:
           __snake_case:						List[str]			  =						tokenizer(A							,    return_tensors="""pt"""		).input_ids
           __snake_case:						List[str]			  =						model.generate(A							,    max_length=50		)
           __snake_case:						Union[str, Any]			  =						tokenizer.batch_decode(A							,    skip_special_tokens=A		)
           predicted_outputs += generated_string
         self.assertListEqual(A							,    A		)








import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute







def       A__							(   SCREAMING_SNAKE_CASE__ = 3)       ->   qiskit.result.counts.Counts:
  if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__):
    raise TypeError("""number of qubits must be a integer.""")
  if number_of_qubits <= 0:
    raise ValueError("""number of qubits must be > 0.""")
  if math.floor(SCREAMING_SNAKE_CASE__) != number_of_qubits:
    raise ValueError("""number of qubits must be exact integer.""")
  if number_of_qubits > 10:
    raise ValueError("""number of qubits too large to simulate(>10).""")

  __snake_case:						int			  =						QuantumRegister(SCREAMING_SNAKE_CASE__ , """qr""")
  __snake_case:						List[str]			  =						ClassicalRegister(SCREAMING_SNAKE_CASE__ , """cr""")

  __snake_case:						Optional[Any]			  =						QuantumCircuit(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)

  __snake_case:						Tuple			  =						number_of_qubits

  for i in range(SCREAMING_SNAKE_CASE__):
    quantum_circuit.h(number_of_qubits - i - 1)
    counter -= 1
    for j in range(SCREAMING_SNAKE_CASE__):
      quantum_circuit.cp(np.pi / 2 ** (counter - j) , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)

  for k in range(number_of_qubits // 2):
    quantum_circuit.swap(SCREAMING_SNAKE_CASE__ , number_of_qubits - k - 1)

  # measure all the qubits
  quantum_circuit.measure(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)
  # simulate with 10000 shots
  __snake_case:						Union[str, Any]			  =						Aer.get_backend("""qasm_simulator""")
  __snake_case:						Optional[Any]			  =						execute(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , shots=1_0000)

  return job.result().get_counts(SCREAMING_SNAKE_CASE__)


if __name__ == "__main__":
      print(
          f'Total count for quantum fourier transform state is: \
    {quantum_fourier_transform(3)}'
      )






