
'''simple docstring'''





import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

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


if is_flax_available():
      import os

      # The slow tests are often failing with OOM error on GPU
      # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
      # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
      SCREAMING_SNAKE_CASE     =       """platform"""

      import jax
      import jax.numpy as jnp

      from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
          FlaxBlenderbotSmallForConditionalGeneration,
          FlaxBlenderbotSmallModel,
          shift_tokens_right,
      )







def snake_case_     (					lowercase__   ,       lowercase__   ,       lowercase__=None   ,       lowercase__=None   ,       lowercase__=None   ,       lowercase__=None   ,       lowercase__=None   ,       lowercase__=None   ,       ):
  if attention_mask is None:
    UpperCAmelCase__      :					Any				       =							np.where(input_ids != config.pad_token_id   ,       1   ,       0		)
  if decoder_attention_mask is None:
    UpperCAmelCase__      :					Optional[Any]				       =							np.where(decoder_input_ids != config.pad_token_id   ,       1   ,       0		)
  if head_mask is None:
    UpperCAmelCase__      :					int				       =							np.ones((config.encoder_layers, config.encoder_attention_heads)		)
  if decoder_head_mask is None:
    UpperCAmelCase__      :					Any				       =							np.ones((config.decoder_layers, config.decoder_attention_heads)		)
  if cross_attn_head_mask is None:
    UpperCAmelCase__      :					List[Any]				       =							np.ones((config.decoder_layers, config.decoder_attention_heads)		)
  return {
      "input_ids": input_ids,
      "decoder_input_ids": decoder_input_ids,
      "attention_mask": attention_mask,
      "decoder_attention_mask": attention_mask,
  }






class 							UpperCAmelCase_							:

 '''simple docstring'''







 def __init__(					self		:  List[str]			,				snake_case__		:  Optional[int]			,				snake_case__		:  Union[str, Any]=13			,				snake_case__		:  Dict=7			,				snake_case__		:  Union[str, Any]=True			,				snake_case__		:  Optional[int]=False			,				snake_case__		:  List[str]=99			,				snake_case__		:  Union[str, Any]=16			,				snake_case__		:  Tuple=2			,				snake_case__		:  List[Any]=4			,				snake_case__		:  Tuple=4			,				snake_case__		:  Optional[Any]="gelu"			,				snake_case__		:  str=0.1			,				snake_case__		:  Optional[int]=0.1			,				snake_case__		:  Dict=32			,				snake_case__		:  Dict=2			,				snake_case__		:  Optional[int]=1			,				snake_case__		:  Tuple=0			,				snake_case__		:  str=0.02			,				):



   '''simple docstring'''




   UpperCAmelCase__      :					Union[str, Any]				       =							parent
   UpperCAmelCase__      :					Tuple				       =							batch_size
   UpperCAmelCase__      :					int				       =							seq_length
   UpperCAmelCase__      :					Optional[int]				       =							is_training
   UpperCAmelCase__      :					str				       =							use_labels
   UpperCAmelCase__      :					Union[str, Any]				       =							vocab_size
   UpperCAmelCase__      :					List[Any]				       =							hidden_size
   UpperCAmelCase__      :					Dict				       =							num_hidden_layers
   UpperCAmelCase__      :					List[str]				       =							num_attention_heads
   UpperCAmelCase__      :					List[Any]				       =							intermediate_size
   UpperCAmelCase__      :					Optional[int]				       =							hidden_act
   UpperCAmelCase__      :					Any				       =							hidden_dropout_prob
   UpperCAmelCase__      :					List[Any]				       =							attention_probs_dropout_prob
   UpperCAmelCase__      :					Union[str, Any]				       =							max_position_embeddings
   UpperCAmelCase__      :					Tuple				       =							eos_token_id
   UpperCAmelCase__      :					int				       =							pad_token_id
   UpperCAmelCase__      :					Any				       =							bos_token_id
   UpperCAmelCase__      :					Tuple				       =							initializer_range







 def 							UpperCamelCase  (					self		:  Optional[int] ):



   '''simple docstring'''




   UpperCAmelCase__      :					Tuple				       =							np.clip(ids_tensor([self.batch_size, self.seq_length - 1]			,				self.vocab_size )			,				3			,				self.vocab_size )
   UpperCAmelCase__      :					List[str]				       =							np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)			,				dtype=np.intaa ))			,				-1 )

   UpperCAmelCase__      :					str				       =							shift_tokens_right(snake_case__			,				1			,				2 )

   UpperCAmelCase__      :					Dict				       =							BlenderbotSmallConfig(
       vocab_size=self.vocab_size			,				d_model=self.hidden_size			,				encoder_layers=self.num_hidden_layers			,				decoder_layers=self.num_hidden_layers			,				encoder_attention_heads=self.num_attention_heads			,				decoder_attention_heads=self.num_attention_heads			,				encoder_ffn_dim=self.intermediate_size			,				decoder_ffn_dim=self.intermediate_size			,				dropout=self.hidden_dropout_prob			,				attention_dropout=self.attention_probs_dropout_prob			,				max_position_embeddings=self.max_position_embeddings			,				eos_token_id=self.eos_token_id			,				bos_token_id=self.bos_token_id			,				pad_token_id=self.pad_token_id			,				initializer_range=self.initializer_range			,				use_cache=snake_case__			,				)
   UpperCAmelCase__      :					int				       =							prepare_blenderbot_inputs_dict(snake_case__			,				snake_case__			,				snake_case__ )
   return config, inputs_dict







 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   UpperCAmelCase__       ,			UpperCAmelCase__      :					int				       =							self.prepare_config_and_inputs()
   return config, inputs_dict







 def 							UpperCamelCase  (					self		:  List[str]			,				snake_case__		:  Optional[Any]			,				snake_case__		:  str			,				snake_case__		:  Optional[Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					str				       =							20
   UpperCAmelCase__      :					Optional[Any]				       =							model_class_name(snake_case__ )

   UpperCAmelCase__      :					Any				       =							model.encode(inputs_dict["input_ids"] )

   UpperCAmelCase__       ,			UpperCAmelCase__      :					Any				       =							(
       inputs_dict["decoder_input_ids"],
       inputs_dict["decoder_attention_mask"],
   )

   UpperCAmelCase__      :					Optional[Any]				       =							model.init_cache(decoder_input_ids.shape[0]			,				snake_case__			,				snake_case__ )
   UpperCAmelCase__      :					Tuple				       =							jnp.ones((decoder_input_ids.shape[0], max_decoder_length)			,				dtype="i4" )

   UpperCAmelCase__      :					Union[str, Any]				       =							jnp.broadcast_to(
       jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :]			,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)			,				)
   UpperCAmelCase__      :					Optional[int]				       =							model.decode(
       decoder_input_ids[:, :-1]			,				snake_case__			,				decoder_attention_mask=snake_case__			,				past_key_values=snake_case__			,				decoder_position_ids=snake_case__			,				)

   UpperCAmelCase__      :					Optional[int]				       =							jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]			,				dtype="i4" )
   UpperCAmelCase__      :					str				       =							model.decode(
       decoder_input_ids[:, -1:]			,				snake_case__			,				decoder_attention_mask=snake_case__			,				past_key_values=outputs_cache.past_key_values			,				decoder_position_ids=snake_case__			,				)

   UpperCAmelCase__      :					Dict				       =							model.decode(snake_case__			,				snake_case__ )

   UpperCAmelCase__      :					str				       =							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 							UpperCamelCase  (					self		:  int			,				snake_case__		:  Optional[int]			,				snake_case__		:  Union[str, Any]			,				snake_case__		:  Optional[Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					Dict				       =							20
   UpperCAmelCase__      :					Optional[Any]				       =							model_class_name(snake_case__ )

   UpperCAmelCase__      :					Union[str, Any]				       =							model.encode(inputs_dict["input_ids"] )

   UpperCAmelCase__       ,			UpperCAmelCase__      :					Tuple				       =							(
       inputs_dict["decoder_input_ids"],
       inputs_dict["decoder_attention_mask"],
   )

   UpperCAmelCase__      :					List[str]				       =							jnp.concatenate(
       [
           decoder_attention_mask,
           jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
       ]			,				axis=-1			,				)

   UpperCAmelCase__      :					Tuple				       =							model.init_cache(decoder_input_ids.shape[0]			,				snake_case__			,				snake_case__ )
   UpperCAmelCase__      :					Optional[Any]				       =							jnp.broadcast_to(
       jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :]			,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)			,				)

   UpperCAmelCase__      :					Union[str, Any]				       =							model.decode(
       decoder_input_ids[:, :-1]			,				snake_case__			,				decoder_attention_mask=snake_case__			,				past_key_values=snake_case__			,				decoder_position_ids=snake_case__			,				)
   UpperCAmelCase__      :					Any				       =							jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]			,				dtype="i4" )
   UpperCAmelCase__      :					Union[str, Any]				       =							model.decode(
       decoder_input_ids[:, -1:]			,				snake_case__			,				past_key_values=outputs_cache.past_key_values			,				decoder_attention_mask=snake_case__			,				decoder_position_ids=snake_case__			,				)

   UpperCAmelCase__      :					List[str]				       =							model.decode(snake_case__			,				snake_case__			,				decoder_attention_mask=snake_case__ )

   UpperCAmelCase__      :					List[str]				       =							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 							UpperCAmelCase_							(    unittest.TestCase				):

 '''simple docstring'''
 lowercase_					:    List[Any]				=							99







 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					List[str]				       =							np.array(
       [
           [71, 82, 18, 33, 46, 91, 2],
           [68, 34, 26, 58, 30, 82, 2],
           [5, 97, 17, 39, 94, 40, 2],
           [76, 83, 94, 25, 70, 78, 2],
           [87, 59, 41, 35, 48, 66, 2],
           [55, 13, 16, 58, 5, 2, 1],  # note padding
           [64, 27, 31, 51, 12, 75, 2],
           [52, 64, 86, 17, 83, 39, 2],
           [48, 61, 9, 24, 71, 82, 2],
           [26, 1, 60, 48, 22, 13, 2],
           [21, 5, 62, 28, 14, 76, 2],
           [45, 98, 37, 86, 59, 48, 2],
           [70, 70, 50, 9, 28, 0, 2],
       ]			,				dtype=np.intaa			,				)

   UpperCAmelCase__      :					List[Any]				       =							input_ids.shape[0]
   UpperCAmelCase__      :					int				       =							BlenderbotSmallConfig(
       vocab_size=self.vocab_size			,				d_model=24			,				encoder_layers=2			,				decoder_layers=2			,				encoder_attention_heads=2			,				decoder_attention_heads=2			,				encoder_ffn_dim=32			,				decoder_ffn_dim=32			,				max_position_embeddings=48			,				eos_token_id=2			,				pad_token_id=1			,				bos_token_id=0			,				)
   return config, input_ids, batch_size







 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   UpperCAmelCase__       ,			UpperCAmelCase__       ,			UpperCAmelCase__      :					Optional[int]				       =							self._get_config_and_data()
   UpperCAmelCase__      :					Dict				       =							FlaxBlenderbotSmallForConditionalGeneration(snake_case__ )
   UpperCAmelCase__      :					Optional[int]				       =							lm_model(input_ids=snake_case__ )
   UpperCAmelCase__      :					List[str]				       =							(batch_size, input_ids.shape[1], config.vocab_size)
   self.assertEqual(outputs["logits"].shape			,				snake_case__ )







 def 							UpperCamelCase  (					self		:  Union[str, Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					Tuple				       =							BlenderbotSmallConfig(
       vocab_size=self.vocab_size			,				d_model=14			,				encoder_layers=2			,				decoder_layers=2			,				encoder_attention_heads=2			,				decoder_attention_heads=2			,				encoder_ffn_dim=8			,				decoder_ffn_dim=8			,				max_position_embeddings=48			,				)
   UpperCAmelCase__      :					int				       =							FlaxBlenderbotSmallForConditionalGeneration(snake_case__ )
   UpperCAmelCase__      :					Tuple				       =							np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]			,				dtype=np.intaa )
   UpperCAmelCase__      :					List[str]				       =							np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]			,				dtype=np.intaa )
   UpperCAmelCase__      :					List[str]				       =							lm_model(input_ids=snake_case__			,				decoder_input_ids=snake_case__ )
   UpperCAmelCase__      :					str				       =							(*summary.shape, config.vocab_size)
   self.assertEqual(outputs["logits"].shape			,				snake_case__ )







 def 							UpperCamelCase  (					self		:  List[str] ):



   '''simple docstring'''




   UpperCAmelCase__      :					int				       =							np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]			,				dtype=np.intaa )
   UpperCAmelCase__      :					Any				       =							shift_tokens_right(snake_case__			,				1			,				2 )
   UpperCAmelCase__      :					Optional[int]				       =							np.equal(snake_case__			,				1 ).astype(np.floataa ).sum()
   UpperCAmelCase__      :					Optional[int]				       =							np.equal(snake_case__			,				1 ).astype(np.floataa ).sum()
   self.assertEqual(shifted.shape			,				input_ids.shape )
   self.assertEqual(snake_case__			,				n_pad_before - 1 )
   self.assertTrue(np.equal(shifted[:, 0]			,				2 ).all() )






@require_flax
class 							UpperCAmelCase_							(    A					,				unittest.TestCase					,				A				):

 '''simple docstring'''
 lowercase_					:    Optional[int]				=							True
 lowercase_					:    str				=							(
     (
         FlaxBlenderbotSmallModel,
         FlaxBlenderbotSmallForConditionalGeneration,
     )
     if is_flax_available()
     else ()
 )
 lowercase_					:    List[str]				=							(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()







 def 							UpperCamelCase  (					self		:  Optional[int] ):



   '''simple docstring'''




   UpperCAmelCase__      :					Union[str, Any]				       =							FlaxBlenderbotSmallModelTester(self )







 def 							UpperCamelCase  (					self		:  int ):



   '''simple docstring'''




   UpperCAmelCase__       ,			UpperCAmelCase__      :					Dict				       =							self.model_tester.prepare_config_and_inputs()
   for model_class in self.all_model_classes:
     self.model_tester.check_use_cache_forward(snake_case__			,				snake_case__			,				snake_case__ )







 def 							UpperCamelCase  (					self		:  Optional[Any] ):



   '''simple docstring'''




   UpperCAmelCase__       ,			UpperCAmelCase__      :					Tuple				       =							self.model_tester.prepare_config_and_inputs()
   for model_class in self.all_model_classes:
     self.model_tester.check_use_cache_forward_with_attn_mask(snake_case__			,				snake_case__			,				snake_case__ )







 def 							UpperCamelCase  (					self		:  Tuple ):



   '''simple docstring'''




   UpperCAmelCase__       ,			UpperCAmelCase__      :					str				       =							self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     with self.subTest(model_class.__name__ ):
       UpperCAmelCase__      :					str				       =							self._prepare_for_class(snake_case__			,				snake_case__ )
       UpperCAmelCase__      :					Dict				       =							model_class(snake_case__ )

       @jax.jit
       def encode_jitted(snake_case__		:  Union[str, Any]			,				snake_case__		:  Any=None			,				**snake_case__		:  List[str] ):
         return model.encode(input_ids=snake_case__			,				attention_mask=snake_case__ )

       with self.subTest("JIT Enabled" ):
         UpperCAmelCase__      :					Optional[int]				       =							encode_jitted(**snake_case__ ).to_tuple()

       with self.subTest("JIT Disabled" ):
         with jax.disable_jit():
           UpperCAmelCase__      :					int				       =							encode_jitted(**snake_case__ ).to_tuple()

       self.assertEqual(len(snake_case__ )			,				len(snake_case__ ) )
       for jitted_output, output in zip(snake_case__			,				snake_case__ ):
         self.assertEqual(jitted_output.shape			,				output.shape )







 def 							UpperCamelCase  (					self		:  Union[str, Any] ):



   '''simple docstring'''




   UpperCAmelCase__       ,			UpperCAmelCase__      :					List[Any]				       =							self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     with self.subTest(model_class.__name__ ):
       UpperCAmelCase__      :					Union[str, Any]				       =							model_class(snake_case__ )
       UpperCAmelCase__      :					Optional[Any]				       =							model.encode(inputs_dict["input_ids"]			,				inputs_dict["attention_mask"] )

       UpperCAmelCase__      :					Tuple				       =							{
           "decoder_input_ids": inputs_dict["decoder_input_ids"],
           "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
           "encoder_outputs": encoder_outputs,
       }

       @jax.jit
       def decode_jitted(snake_case__		:  Optional[Any]			,				snake_case__		:  Tuple			,				snake_case__		:  List[str] ):
         return model.decode(
             decoder_input_ids=snake_case__			,				decoder_attention_mask=snake_case__			,				encoder_outputs=snake_case__			,				)

       with self.subTest("JIT Enabled" ):
         UpperCAmelCase__      :					List[str]				       =							decode_jitted(**snake_case__ ).to_tuple()

       with self.subTest("JIT Disabled" ):
         with jax.disable_jit():
           UpperCAmelCase__      :					Union[str, Any]				       =							decode_jitted(**snake_case__ ).to_tuple()

       self.assertEqual(len(snake_case__ )			,				len(snake_case__ ) )
       for jitted_output, output in zip(snake_case__			,				snake_case__ ):
         self.assertEqual(jitted_output.shape			,				output.shape )







 @slow
 def 							UpperCamelCase  (					self		:  int ):



   '''simple docstring'''




   for model_class_name in self.all_model_classes:
     UpperCAmelCase__      :					Dict				       =							model_class_name.from_pretrained("facebook/blenderbot_small-90M" )
     # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
     UpperCAmelCase__      :					Tuple				       =							np.ones((1, 1) ) * model.config.eos_token_id
     UpperCAmelCase__      :					str				       =							model(snake_case__ )
     self.assertIsNotNone(snake_case__ )





'''simple docstring'''





import math

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







def snake_case_     (					lowercase__ = 3		):
  if isinstance(lowercase__   ,       lowercase__		):
    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(lowercase__		) != number_of_qubits:
    raise ValueError("number of qubits must be exact integer."		)
  if number_of_qubits > 1_0:
    raise ValueError("number of qubits too large to simulate(>10)."		)

  UpperCAmelCase__      :					Optional[int]				       =							QuantumRegister(lowercase__   ,       "qr"		)
  UpperCAmelCase__      :					List[Any]				       =							ClassicalRegister(lowercase__   ,       "cr"		)

  UpperCAmelCase__      :					List[str]				       =							QuantumCircuit(lowercase__   ,       lowercase__		)

  UpperCAmelCase__      :					Union[str, Any]				       =							number_of_qubits

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

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

  # measure all the qubits
  quantum_circuit.measure(lowercase__   ,       lowercase__		)
  # simulate with 10000 shots
  UpperCAmelCase__      :					Union[str, Any]				       =							Aer.get_backend("qasm_simulator"		)
  UpperCAmelCase__      :					Optional[int]				       =							execute(lowercase__   ,       lowercase__   ,       shots=1_0_0_0_0		)

  return job.result().get_counts(lowercase__		)


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



