





"""simple docstring"""
import unittest
from unittest.util import safe_repr

from transformers import AutoTokenizer, RwkvConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RwkvForCausalLM,
		    RwkvModel,
		)
		from transformers.pytorch_utils import is_torch_greater_or_equal_than_2_0
else:
		_snake_case	:						str					=					False






class    _UpperCAmelCase  :


					def __init__(   self     :Any	,			__UpperCamelCase     :Optional[Any]	,			__UpperCamelCase     :Tuple=14	,			__UpperCamelCase     :Any=7	,			__UpperCamelCase     :Optional[int]=True	,			__UpperCamelCase     :Tuple=False	,			__UpperCamelCase     :Dict=True	,			__UpperCamelCase     :Any=True	,			__UpperCamelCase     :Dict=True	,			__UpperCamelCase     :Any=99	,			__UpperCamelCase     :Optional[Any]=32	,			__UpperCamelCase     :str=5	,			__UpperCamelCase     :List[str]=37	,			__UpperCamelCase     :Union[str, Any]="gelu"	,			__UpperCamelCase     :Optional[Any]=0.1	,			__UpperCamelCase     :List[Any]=0.1	,			__UpperCamelCase     :Optional[int]=5_12	,			__UpperCamelCase     :Tuple=16	,			__UpperCamelCase     :Any=2	,			__UpperCamelCase     :List[str]=3	,			__UpperCamelCase     :List[Any]=4	,			__UpperCamelCase     :Tuple=None	,			):
												A						=    parent
												A						=    batch_size
												A						=    seq_length
												A						=    is_training
												A						=    use_token_type_ids
												A						=    use_input_mask
												A						=    use_labels
												A						=    use_mc_token_ids
												A						=    vocab_size
												A						=    hidden_size
												A						=    num_hidden_layers
												A						=    intermediate_size
												A						=    hidden_act
												A						=    hidden_dropout_prob
												A						=    attention_probs_dropout_prob
												A						=    max_position_embeddings
												A						=    type_vocab_size
												A						=    type_sequence_label_size
												A						=    num_labels
												A						=    num_choices
												A						=    scope
												A						=    vocab_size - 1
												A						=    vocab_size - 1
												A						=    vocab_size - 1


					def       lowerCamelCase			(   self     :int						):
												return RwkvConfig.from_pretrained("sgugger/rwkv-4-pile-7b"						)


					def       lowerCamelCase			(   self     :str	,			__UpperCamelCase     :int=False	,			__UpperCamelCase     :Optional[int]=False	,			__UpperCamelCase     :str=False						):
												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						=    None
												if self.use_token_type_ids:
																			A						=    ids_tensor([self.batch_size, self.seq_length]	,			self.type_vocab_size						)

												A						=    None
												if self.use_mc_token_ids:
																			A						=    ids_tensor([self.batch_size, self.num_choices]	,			self.seq_length						)

												A						=    None
												A						=    None
												A						=    None
												if self.use_labels:
																			A						=    ids_tensor([self.batch_size]	,			self.type_sequence_label_size						)
																			A						=    ids_tensor([self.batch_size, self.seq_length]	,			self.num_labels						)
																			A						=    ids_tensor([self.batch_size]	,			self.num_choices						)

												A						=    self.get_config(
												    gradient_checkpointing=snake_case_	,			scale_attn_by_inverse_layer_idx=snake_case_	,			reorder_and_upcast_attn=snake_case_	,			)

												return (
												    config,
												    input_ids,
												    input_mask,
												    None,
												    token_type_ids,
												    mc_token_ids,
												    sequence_labels,
												    token_labels,
												    choice_labels,
												)


					def       lowerCamelCase			(   self     :Optional[int]	,			__UpperCamelCase     :str=False	,			__UpperCamelCase     :List[Any]=False	,			__UpperCamelCase     :List[Any]=False						):
												return RwkvConfig(
												    vocab_size=self.vocab_size	,			hidden_size=self.hidden_size	,			num_hidden_layers=self.num_hidden_layers	,			intermediate_size=self.intermediate_size	,			activation_function=self.hidden_act	,			resid_pdrop=self.hidden_dropout_prob	,			attn_pdrop=self.attention_probs_dropout_prob	,			n_positions=self.max_position_embeddings	,			type_vocab_size=self.type_vocab_size	,			use_cache=snake_case_	,			bos_token_id=self.bos_token_id	,			eos_token_id=self.eos_token_id	,			pad_token_id=self.pad_token_id	,			gradient_checkpointing=snake_case_	,			scale_attn_by_inverse_layer_idx=snake_case_	,			reorder_and_upcast_attn=snake_case_	,			)


					def       lowerCamelCase			(   self     :Union[str, Any]						):
												A						=    self.get_config()
												A						=    3_00
												return config


					def       lowerCamelCase			(   self     :str						):
												(
												    (
												    A 
												),      (
												    A 
												),      (
												    A 
												),      (
												    A 
												),      (
												    A 
												),      (
												    A 
												),      (
												    A 
												),      (
												    A 
												),      (
												    A 
												),       
												)						=    self.prepare_config_and_inputs()

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

												return (
												    config,
												    input_ids,
												    input_mask,
												    head_mask,
												    token_type_ids,
												    sequence_labels,
												    token_labels,
												    choice_labels,
												    encoder_hidden_states,
												    encoder_attention_mask,
												)


					def       lowerCamelCase			(   self     :Optional[int]	,			__UpperCamelCase     :str	,			__UpperCamelCase     :Dict	,			__UpperCamelCase     :Union[str, Any]	,			__UpperCamelCase     :List[Any]	,			__UpperCamelCase     :str	,			*__UpperCamelCase     :Optional[int]						):
												A						=    True
												A						=    RwkvModel(config=snake_case_						)
												model.to(snake_case_						)
												model.eval()

												A						=    model(snake_case_						)

												self.parent.assertEqual(result.last_hidden_state.shape	,			(self.batch_size, self.seq_length, self.hidden_size)						)
												self.parent.assertEqual(len(result.hidden_states						)	,			config.num_hidden_layers + 1						)


					def       lowerCamelCase			(   self     :Dict	,			__UpperCamelCase     :int	,			__UpperCamelCase     :List[str]	,			__UpperCamelCase     :List[Any]	,			__UpperCamelCase     :List[str]	,			__UpperCamelCase     :Union[str, Any]	,			*__UpperCamelCase     :List[Any]						):
												A						=    RwkvForCausalLM(snake_case_						)
												model.to(snake_case_						)
												model.eval()

												A						=    model(snake_case_	,			labels=snake_case_						)
												self.parent.assertEqual(result.loss.shape	,			()						)
												self.parent.assertEqual(result.logits.shape	,			(self.batch_size, self.seq_length, self.vocab_size)						)


					def       lowerCamelCase			(   self     :str	,			__UpperCamelCase     :List[str]	,			__UpperCamelCase     :List[Any]	,			__UpperCamelCase     :List[str]	,			__UpperCamelCase     :List[Any]	,			__UpperCamelCase     :List[str]	,			*__UpperCamelCase     :int						):
												A						=    RwkvModel(config=snake_case_						)
												model.to(snake_case_						)
												model.eval()

												A						=    model(snake_case_						)
												A						=    outputs.last_hidden_state

												A						=    model(input_ids[:, :2]						)
												A						=    outputs.last_hidden_state

												# Using the state computed on the first inputs, we will get the same output
												A						=    model(input_ids[:, 2:]	,			state=outputs.state						)
												A						=    outputs.last_hidden_state

												self.parent.assertTrue(torch.allclose(torch.cat([output_one, output_two]	,			dim=1						)	,			snake_case_	,			atol=1e-5						)						)


					def       lowerCamelCase			(   self     :Tuple	,			__UpperCamelCase     :str	,			__UpperCamelCase     :Tuple	,			__UpperCamelCase     :Any	,			__UpperCamelCase     :Optional[int]	,			__UpperCamelCase     :List[Any]	,			*__UpperCamelCase     :Union[str, Any]	,			__UpperCamelCase     :str=False						):
												A						=    RwkvForCausalLM(snake_case_						)
												model.to(snake_case_						)
												if gradient_checkpointing:
																			model.gradient_checkpointing_enable()

												A						=    model(snake_case_	,			labels=snake_case_						)
												self.parent.assertEqual(result.loss.shape	,			()						)
												self.parent.assertEqual(result.logits.shape	,			(self.batch_size, self.seq_length, self.vocab_size)						)
												result.loss.backward()


					def       lowerCamelCase			(   self     :Tuple						):
												A						=    self.prepare_config_and_inputs()

												(
												    (
												    A 
												),      (
												    A 
												),      (
												    A 
												),      (
												    A 
												),      (
												    A 
												),      (
												    A 
												),      (
												    A 
												),      (
												    A 
												),      (
												    A 
												),       
												)						=    config_and_inputs

												A						=    {"input_ids": input_ids}

												return config, inputs_dict







@unittest.skipIf(
    not is_torch_greater_or_equal_than_2_0	,				reason='''See https://github.com/huggingface/transformers/pull/24204'''					)
@require_torch
class    _UpperCAmelCase  (      A_	,				A_	,				A_	,				unittest.TestCase					):
					UpperCamelCase						=				(RwkvModel, RwkvForCausalLM) if is_torch_available() else ()
					UpperCamelCase						=				(
					    {'''feature-extraction''': RwkvModel, '''text-generation''': RwkvForCausalLM} if is_torch_available() else {}
					)
					# all_generative_model_classes = (RwkvForCausalLM,) if is_torch_available() else ()
					UpperCamelCase						=				False
					UpperCamelCase						=				False
					UpperCamelCase						=				False
					UpperCamelCase						=				False
					UpperCamelCase						=				False  # Rwkv does not support head masking


					def       lowerCamelCase			(   self     :Any						):
												A						=    RwkvModelTester(self						)
												A						=    ConfigTester(
												    self	,			config_class=snake_case_	,			n_embd=37	,			common_properties=["hidden_size", "num_hidden_layers"]						)


					def       lowerCamelCase			(   self     :Dict	,			__UpperCamelCase     :Optional[Any]	,			__UpperCamelCase     :Dict	,			__UpperCamelCase     :Tuple=None						):

												if isinstance(snake_case_	,			torch.Tensor						):
																			A,      A						=    member.max().item(), member.min().item()
												elif isinstance(snake_case_	,			snake_case_						) or isinstance(snake_case_	,			snake_case_						):
																			A,      A						=    max(snake_case_						), min(snake_case_						)

												if not isinstance(snake_case_	,			snake_case_						):
																			raise TypeError("container should be a list or tuple"						)
												elif len(snake_case_						) != 2:
																			raise ValueError("container should have 2 elements"						)

												A,      A						=    container

												A						=    (min_value >= expected_min) and (max_value <= expected_max)

												if not is_inside_interval:
																			A						=    "%s not found in %s" % (safe_repr(snake_case_						), safe_repr(snake_case_						))
																			self.fail(self._formatMessage(snake_case_	,			snake_case_						)						)


					def       lowerCamelCase			(   self     :int						):
												self.config_tester.run_common_tests()


					def       lowerCamelCase			(   self     :Union[str, Any]						):
												A						=    self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_rwkv_model(*snake_case_						)


					def       lowerCamelCase			(   self     :List[str]						):
												A						=    self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_causl_lm(*snake_case_						)


					def       lowerCamelCase			(   self     :Tuple						):
												A						=    self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_state_equivalency(*snake_case_						)


					def       lowerCamelCase			(   self     :List[Any]						):
												A,      A						=    self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			A						=    model_class(config=snake_case_						)
																			for name, param in model.named_parameters():
																										if "time_decay" in name:
																																	if param.requires_grad:
																																								self.assertTrue(param.data.max().item() == 3.0						)
																																								self.assertTrue(param.data.min().item() == -5.0						)
																										elif "time_first" in name:
																																	if param.requires_grad:
																																								# check if it's a ones like
																																								self.assertTrue(torch.allclose(param.data	,			torch.ones_like(param.data						)	,			atol=1e-5	,			rtol=1e-5						)						)
																										elif any(x in name for x in ["time_mix_key", "time_mix_receptance"]						):
																																	if param.requires_grad:
																																								self.assertInterval(
																																								    param.data	,			[0.0, 1.0]	,			msg=f"Parameter {name} of model {model_class} seems not properly initialized"	,			)
																										elif "time_mix_value" in name:
																																	if param.requires_grad:
																																								self.assertInterval(
																																								    param.data	,			[0.0, 1.3]	,			msg=f"Parameter {name} of model {model_class} seems not properly initialized"	,			)


					def       lowerCamelCase			(   self     :List[str]						):
												A,      A						=    self.model_tester.prepare_config_and_inputs_for_common()
												A						=    True

												A						=    getattr(self.model_tester	,			"seq_length"	,			snake_case_						)

												for model_class in self.all_model_classes:
																			A						=    True
																			A						=    False
																			A						=    True
																			A						=    model_class(snake_case_						)
																			model.to(snake_case_						)
																			model.eval()

																			A						=    self._prepare_for_class(snake_case_	,			snake_case_						)
																			A						=    inputs["input_ids"].shape[0]
																			with torch.no_grad():
																										A						=    model(**snake_case_						)
																			A						=    outputs.attentions
																			self.assertEqual(len(snake_case_						)	,			self.model_tester.num_hidden_layers						)

																			# check that output_attentions also work using config
																			del inputs_dict["output_attentions"]
																			A						=    True
																			A						=    model_class(snake_case_						)
																			model.to(snake_case_						)
																			model.eval()

																			A						=    self._prepare_for_class(snake_case_	,			snake_case_						)
																			A						=    inputs["input_ids"].shape[0]
																			with torch.no_grad():
																										A						=    model(**snake_case_						)
																			A						=    outputs.attentions
																			self.assertEqual(len(snake_case_						)	,			self.model_tester.num_hidden_layers						)

																			self.assertListEqual(
																			    list(attentions[0].shape[-3:]						)	,			[batch_size, seq_len, config.hidden_size]	,			)
																			A						=    len(snake_case_						)

																			# Check attention is always last and order is fine
																			A						=    True
																			A						=    True
																			A						=    model_class(snake_case_						)
																			model.to(snake_case_						)
																			model.eval()

																			A						=    self._prepare_for_class(snake_case_	,			snake_case_						)
																			A						=    inputs["input_ids"].shape[0]
																			with torch.no_grad():
																										A						=    model(**snake_case_						)

																			A						=    1
																			self.assertEqual(out_len + added_hidden_states	,			len(snake_case_						)						)

																			A						=    outputs.attentions

																			self.assertEqual(len(snake_case_						)	,			self.model_tester.num_hidden_layers						)
																			self.assertListEqual(
																			    list(self_attentions[0].shape[-3:]						)	,			[batch_size, seq_len, config.hidden_size]	,			)


					@slow
					def       lowerCamelCase			(   self     :Tuple						):

												for model_name in RWKV_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			A						=    RwkvModel.from_pretrained(snake_case_						)
																			self.assertIsNotNone(snake_case_						)







@unittest.skipIf(
    not is_torch_greater_or_equal_than_2_0	,				reason='''See https://github.com/huggingface/transformers/pull/24204'''					)
@slow
class    _UpperCAmelCase  (      unittest.TestCase					):


					def       lowerCamelCase			(   self     :Optional[Any]						):
												A						=    "RWKV/rwkv-4-169m-pile"
												A						=    AutoTokenizer.from_pretrained(self.model_id						)


					def       lowerCamelCase			(   self     :int						):
												A						=    "Hello my name is Jasmine and I am a newbie to the"
												A						=    RwkvForCausalLM.from_pretrained(self.model_id						).to(snake_case_						)

												A						=    self.tokenizer("Hello my name is"	,			return_tensors="pt"						).input_ids.to(snake_case_						)
												A						=    model.generate(snake_case_	,			max_new_tokens=10						)
												A						=    self.tokenizer.decode(output[0].tolist()						)

												self.assertEqual(snake_case_	,			snake_case_						)


					def       lowerCamelCase			(   self     :str						):
												A						=    "Hello my name is Jasmine and I am a newbie to the"

												A						=    self.tokenizer("Hello my name is"	,			return_tensors="pt"						).input_ids.to(snake_case_						)
												A						=    RwkvForCausalLM.from_pretrained(self.model_id	,			torch_dtype=torch.bfloataa						).to(snake_case_						)

												A						=    model.generate(snake_case_	,			max_new_tokens=10						)
												A						=    self.tokenizer.decode(output[0].tolist()						)

												self.assertEqual(snake_case_	,			snake_case_						)








"""simple docstring"""



import numpy as np
import qiskit







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_		)		->						qiskit.QuantumCircuit:
 A__			=							qiskit.QuantumCircuit(num_qubits + 1		)

 # First, let's deal with the case in which oracle is balanced
 if case == "balanced":
  # First generate a random number that tells us which CNOTs to
  # wrap in X-gates:
  A__			=							np.random.randint(1     ,					2**num_qubits		)
  # Next, format 'b' as a binary string of length 'n', padded with zeros:
  A__			=							format(lowercase_     ,					f"""0{num_qubits}b"""		)
  # Next, we place the first X-gates. Each digit in our binary string
  # corresponds to a qubit, if the digit is 0, we do nothing, if it's 1
  # we apply an X-gate to that qubit:
  for index, bit in enumerate(lowercase_		):
   if bit == "1":
    oracle_qc.x(lowercase_		)
        # Do the controlled-NOT gates for each qubit, using the output qubit
        # as the target:
  for index in range(lowercase_		):
   oracle_qc.cx(lowercase_     ,					lowercase_		)
  # Next, place the final X-gates
  for index, bit in enumerate(lowercase_		):
   if bit == "1":
    oracle_qc.x(lowercase_		)

    # Case in which oracle is constant
 if case == "constant":
  # First decide what the fixed output of the oracle will be
  # (either always 0 or always 1)
  A__			=							np.random.randint(2		)
  if output == 1:
   oracle_qc.x(lowercase_		)

 A__			=							oracle_qc.to_gate()
 A__			=							"Oracle"  # To show when we display the circuit
 return oracle_gate







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_		)		->						qiskit.QuantumCircuit:
 A__			=							qiskit.QuantumCircuit(num_qubits + 1     ,					lowercase_		)
 # Set up the output qubit:
 dj_circuit.x(lowercase_		)
 dj_circuit.h(lowercase_		)
 # And set up the input register:
 for qubit in range(lowercase_		):
  dj_circuit.h(lowercase_		)
 # Let's append the oracle gate to our circuit:
 dj_circuit.append(lowercase_     ,					range(num_qubits + 1		)		)
 # Finally, perform the H-gates again and measure:
 for qubit in range(lowercase_		):
  dj_circuit.h(lowercase_		)

 for i in range(lowercase_		):
  dj_circuit.measure(lowercase_     ,					lowercase_		)

 return dj_circuit







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_		)		->						qiskit.result.counts.Counts:
 A__			=							qiskit.Aer.get_backend("aer_simulator"		)

 A__			=							dj_oracle(lowercase_     ,					lowercase_		)
 A__			=							dj_algorithm(lowercase_     ,					lowercase_		)

 # Execute the circuit on the simulator
 A__			=							qiskit.execute(lowercase_     ,					lowercase_     ,					shots=10_00		)

 # Return the histogram data of the results of the experiment.
 return job.result().get_counts(lowercase_		)


if __name__ == "__main__":
      print(f'Deutsch Jozsa - Constant Oracle: {deutsch_jozsa("constant", 3)}')
      print(f'Deutsch Jozsa - Balanced Oracle: {deutsch_jozsa("balanced", 3)}')





