
'''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 	__lowercase				:

			def __init__(self			,   A			,   A=1_4			,   A=7			,   A=True			,   A=True			,   A=False			,   A=True			,   A=9_9			,   A=3_2			,   A=4			,   A=4			,   A=4			,   A=3_7			,   A="gelu"			,   A=0.1			,   A=0.1			,   A=5_1_2			,   A=0.02			,   ):
								lowerCamelCase_					:      List[Any]     =		parent
								lowerCamelCase_					:      List[str]     =		batch_size
								lowerCamelCase_					:      List[str]     =		seq_length
								lowerCamelCase_					:      Union[str, Any]     =		is_training
								lowerCamelCase_					:      List[str]     =		use_input_mask
								lowerCamelCase_					:      Tuple     =		use_token_type_ids
								lowerCamelCase_					:      Any     =		use_labels
								lowerCamelCase_					:      List[str]     =		vocab_size
								lowerCamelCase_					:      Any     =		hidden_size
								lowerCamelCase_					:      Optional[Any]     =		rotary_dim
								lowerCamelCase_					:      List[str]     =		num_hidden_layers
								lowerCamelCase_					:      List[Any]     =		num_attention_heads
								lowerCamelCase_					:      Optional[Any]     =		intermediate_size
								lowerCamelCase_					:      List[str]     =		hidden_act
								lowerCamelCase_					:      List[str]     =		hidden_dropout_prob
								lowerCamelCase_					:      Optional[Any]     =		attention_probs_dropout_prob
								lowerCamelCase_					:      Optional[Any]     =		max_position_embeddings
								lowerCamelCase_					:      Optional[int]     =		initializer_range
								lowerCamelCase_					:      Tuple     =		None
								lowerCamelCase_					:      List[Any]     =		vocab_size - 1
								lowerCamelCase_					:      Union[str, Any]     =		vocab_size - 1
								lowerCamelCase_					:      int     =		vocab_size - 1

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Dict     =		ids_tensor([self.batch_size, self.seq_length]			,   self.vocab_size      )

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

								lowerCamelCase_					:      int     =		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        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      List[Any]     =		self.prepare_config_and_inputs()
								lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_					:      List[str]     =		config_and_inputs
								lowerCamelCase_					:      Tuple     =		{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
								return config, inputs_dict

			def        UpperCAmelCase__ (self			,   A			,   A			,   A			,   A      ):
								lowerCamelCase_					:      Tuple     =		2_0
								lowerCamelCase_					:      Optional[Any]     =		model_class_name(A      )

								lowerCamelCase_					:      List[str]     =		model.init_cache(input_ids.shape[0]			,   A      )
								lowerCamelCase_					:      Union[str, Any]     =		jnp.ones((input_ids.shape[0], max_decoder_length)			,   dtype='''i4'''      )

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

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

								lowerCamelCase_					:      Any     =		model(A      )

								lowerCamelCase_					:      Dict     =		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			,   A			,   A			,   A			,   A      ):
								lowerCamelCase_					:      Optional[int]     =		2_0
								lowerCamelCase_					:      str     =		model_class_name(A      )

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

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

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

								lowerCamelCase_					:      Union[str, Any]     =		model(A			,   attention_mask=A      )

								lowerCamelCase_					:      int     =		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 	__lowercase				(  _lowercase      ,     _lowercase      ,     unittest.TestCase    ):
			lowerCamelCase			:		Optional[Any]	      =			(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
			lowerCamelCase			:		Union[str, Any]	      =			(FlaxGPTJForCausalLM,) if is_flax_available() else ()

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      List[str]     =		FlaxGPTJModelTester(self      )

			def        UpperCAmelCase__ (self      ):
								for model_class_name in self.all_model_classes:
													lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_					:      int     =		self.model_tester.prepare_config_and_inputs()
													self.model_tester.check_use_cache_forward(A			,   A			,   A			,   A      )

			def        UpperCAmelCase__ (self      ):
								for model_class_name in self.all_model_classes:
													lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_					:      int     =		self.model_tester.prepare_config_and_inputs()
													self.model_tester.check_use_cache_forward_with_attn_mask(
													    A			,   A			,   A			,   A      )

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

								lowerCamelCase_					:      Any     =		FlaxGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B'''      )
								lowerCamelCase_					:      str     =		False
								lowerCamelCase_					:      str     =		model.config.eos_token_id

								lowerCamelCase_					:      Optional[Any]     =		jax.jit(model.generate      )

								lowerCamelCase_					:      Tuple     =		jit_generate(
								    inputs['''input_ids''']			,   attention_mask=inputs['''attention_mask''']			,   pad_token_id=tokenizer.pad_token_id      ).sequences

								lowerCamelCase_					:      Union[str, Any]     =		tokenizer.batch_decode(A			,   skip_special_tokens=A      )

								lowerCamelCase_					:      List[str]     =		[
								    '''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        UpperCAmelCase__ (self      ):
								lowerCamelCase_,      lowerCamelCase_					:      Union[str, Any]     =		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
																		lowerCamelCase_					:      Union[str, Any]     =		self._prepare_for_class(A			,   A      )
																		lowerCamelCase_					:      Tuple     =		{k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

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

																		lowerCamelCase_,      lowerCamelCase_					:      Any     =		pt_inputs['''input_ids'''].shape
																		lowerCamelCase_					:      Tuple     =		np.random.randint(0			,   seq_length - 1			,   size=(batch_size,)      )
																		for batch_idx, start_index in enumerate(A      ):
																							lowerCamelCase_					:      Optional[int]     =		0
																							lowerCamelCase_					:      Optional[Any]     =		1
																							lowerCamelCase_					:      Optional[Any]     =		0
																							lowerCamelCase_					:      str     =		1
																		lowerCamelCase_					:      List[str]     =		pt_model_class(A      ).eval()
																		lowerCamelCase_					:      Union[str, Any]     =		model_class(A			,   dtype=jnp.floataa      )

																		lowerCamelCase_					:      Tuple     =		convert_pytorch_state_dict_to_flax(pt_model.state_dict()			,   A      )
																		lowerCamelCase_					:      List[str]     =		fx_state

																		with torch.no_grad():
																							lowerCamelCase_					:      Optional[Any]     =		pt_model(**A      ).to_tuple()

																		lowerCamelCase_					:      Any     =		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      )
																							lowerCamelCase_					:      List[Any]     =		model_class.from_pretrained(A			,   from_pt=A      )

																		lowerCamelCase_					:      Dict     =		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        UpperCAmelCase__ (self      ):
								lowerCamelCase_,      lowerCamelCase_					:      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__      ):
																		# prepare inputs
																		lowerCamelCase_					:      Tuple     =		self._prepare_for_class(A			,   A      )
																		lowerCamelCase_					:      int     =		{k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

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

																		lowerCamelCase_					:      Tuple     =		pt_model_class(A      ).eval()
																		lowerCamelCase_					:      Optional[Any]     =		model_class(A			,   dtype=jnp.floataa      )

																		lowerCamelCase_					:      List[str]     =		load_flax_weights_in_pytorch_model(A			,   fx_model.params      )
																		lowerCamelCase_,      lowerCamelCase_					:      List[Any]     =		pt_inputs['''input_ids'''].shape
																		lowerCamelCase_					:      Union[str, Any]     =		np.random.randint(0			,   seq_length - 1			,   size=(batch_size,)      )
																		for batch_idx, start_index in enumerate(A      ):
																							lowerCamelCase_					:      int     =		0
																							lowerCamelCase_					:      List[str]     =		1
																							lowerCamelCase_					:      Optional[int]     =		0
																							lowerCamelCase_					:      Tuple     =		1

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

																		with torch.no_grad():
																							lowerCamelCase_					:      List[Any]     =		pt_model(**A      ).to_tuple()

																		lowerCamelCase_					:      List[Any]     =		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      )
																							lowerCamelCase_					:      Union[str, Any]     =		pt_model_class.from_pretrained(A			,   from_flax=A      )

																		with torch.no_grad():
																							lowerCamelCase_					:      Tuple     =		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        UpperCAmelCase__ (self      ):
								for model_class_name in self.all_model_classes:
													lowerCamelCase_					:      List[str]     =		model_class_name.from_pretrained('''EleutherAI/gpt-j-6B'''      )
													lowerCamelCase_					:      Any     =		model(np.ones((1, 1)      )      )
													self.assertIsNotNone(A      )




'''simple docstring'''


import qiskit




def lowercase_	(   _lowercase			,       _lowercase				)     ->							qiskit.result.counts.Counts:
					'''simple docstring'''

					lowerCamelCase_					:      Optional[Any]     =		qiskit.Aer.get_backend('''aer_simulator'''				)

					# Create a Quantum Circuit acting on the q register
					lowerCamelCase_					:      Optional[int]     =		qiskit.QuantumCircuit(_lowercase			,       _lowercase				)

					# Apply X (NOT) Gate to Qubits 0 & 1
					circuit.x(0				)
					circuit.x(1				)

					# Map the quantum measurement to the classical bits
					circuit.measure([0, 1]			,       [0, 1]				)

					# Execute the circuit on the qasm simulator
					lowerCamelCase_					:      Optional[int]     =		qiskit.execute(_lowercase			,       _lowercase			,       shots=1_000				)

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


if __name__ == "__main__":
					__lowercase							:   List[Any]					=     single_qubit_measure(2, 2)
					print(f'Total count for various states are: {counts}')


