


import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, 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.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
						import torch

class   A__							:




						def __init__(    self   : Any			,      _a   : Union[str, Any]			,      _a   : Optional[int]=14			,      _a   : List[Any]=7			,      _a   : Union[str, Any]=True			,      _a   : Any=True			,      _a   : Union[str, Any]=False			,      _a   : Dict=True			,      _a   : str=99			,      _a   : str=32			,      _a   : Tuple=5			,      _a   : Dict=4			,      _a   : Union[str, Any]=37			,      _a   : int="gelu"			,      _a   : Optional[Any]=0.1			,      _a   : List[str]=0.1			,      _a   : Any=512			,      _a   : Optional[int]=0.02			,      )							->       Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=parent
										_SCREAMING_SNAKE_CASE     					=batch_size
										_SCREAMING_SNAKE_CASE     					=seq_length
										_SCREAMING_SNAKE_CASE     					=is_training
										_SCREAMING_SNAKE_CASE     					=use_input_mask
										_SCREAMING_SNAKE_CASE     					=use_token_type_ids
										_SCREAMING_SNAKE_CASE     					=use_labels
										_SCREAMING_SNAKE_CASE     					=vocab_size
										_SCREAMING_SNAKE_CASE     					=hidden_size
										_SCREAMING_SNAKE_CASE     					=num_hidden_layers
										_SCREAMING_SNAKE_CASE     					=num_attention_heads
										_SCREAMING_SNAKE_CASE     					=intermediate_size
										_SCREAMING_SNAKE_CASE     					=hidden_act
										_SCREAMING_SNAKE_CASE     					=hidden_dropout_prob
										_SCREAMING_SNAKE_CASE     					=attention_probs_dropout_prob
										_SCREAMING_SNAKE_CASE     					=max_position_embeddings
										_SCREAMING_SNAKE_CASE     					=initializer_range
										_SCREAMING_SNAKE_CASE     					=None
										_SCREAMING_SNAKE_CASE     					=vocab_size - 1
										_SCREAMING_SNAKE_CASE     					=vocab_size - 1
										_SCREAMING_SNAKE_CASE     					=vocab_size - 1




						def 							__UpperCamelCase			(    self   : Dict					)							->       Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=ids_tensor([self.batch_size, self.seq_length]			,      self.vocab_size					)

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

										_SCREAMING_SNAKE_CASE     					=GPTaConfig(
										    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			,      )

										return (config, input_ids, input_mask)




						def 							__UpperCamelCase			(    self   : Union[str, Any]					)							->       int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=self.prepare_config_and_inputs()
										_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=config_and_inputs
										_SCREAMING_SNAKE_CASE     					={'''input_ids''': input_ids, '''attention_mask''': attention_mask}
										return config, inputs_dict




						def 							__UpperCamelCase			(    self   : Optional[Any]					)							->       Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=self.prepare_config_and_inputs()

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

										return (
										    config,
										    input_ids,
										    attention_mask,
										    encoder_hidden_states,
										    encoder_attention_mask,
										)




						def 							__UpperCamelCase			(    self   : Tuple			,      _a   : Any			,      _a   : Any			,      _a   : Union[str, Any]			,      _a   : List[str]					)							->       Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=20
										_SCREAMING_SNAKE_CASE     					=model_class_name(_a					)

										_SCREAMING_SNAKE_CASE     					=model.init_cache(input_ids.shape[0]			,      _a					)
										_SCREAMING_SNAKE_CASE     					=jnp.ones((input_ids.shape[0], max_decoder_length)			,      dtype='''i4'''					)

										_SCREAMING_SNAKE_CASE     					=jnp.broadcast_to(
										    jnp.arange(input_ids.shape[-1] - 1					)[None, :]			,      (input_ids.shape[0], input_ids.shape[-1] - 1)					)
										_SCREAMING_SNAKE_CASE     					=model(
										    input_ids[:, :-1]			,      attention_mask=_a			,      past_key_values=_a			,      position_ids=_a			,      )

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

										_SCREAMING_SNAKE_CASE     					=model(_a					)

										_SCREAMING_SNAKE_CASE     					=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   : Optional[Any]			,      _a   : List[str]			,      _a   : str			,      _a   : Tuple			,      _a   : Union[str, Any]					)							->       Union[str, Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=20
										_SCREAMING_SNAKE_CASE     					=model_class_name(_a					)

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

										_SCREAMING_SNAKE_CASE     					=model.init_cache(input_ids.shape[0]			,      _a					)
										_SCREAMING_SNAKE_CASE     					=jnp.broadcast_to(
										    jnp.arange(input_ids.shape[-1] - 1					)[None, :]			,      (input_ids.shape[0], input_ids.shape[-1] - 1)					)

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

										_SCREAMING_SNAKE_CASE     					=model(_a			,      attention_mask=_a					)

										_SCREAMING_SNAKE_CASE     					=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   A__							(						UpperCamelCase__				,       UpperCamelCase__				,       unittest.TestCase					):
						UpperCAmelCase     =  (FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
						UpperCAmelCase     =  (FlaxGPTaLMHeadModel,) if is_flax_available() else ()




						def 							__UpperCamelCase			(    self   : List[Any]					)							->       Union[str, Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=FlaxGPTaModelTester(self					)




						def 							__UpperCamelCase			(    self   : Any					)							->       int:


										"""simple docstring"""





										for model_class_name in self.all_model_classes:
														_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=self.model_tester.prepare_config_and_inputs()
														self.model_tester.check_use_cache_forward(_a			,      _a			,      _a			,      _a					)




						def 							__UpperCamelCase			(    self   : Optional[Any]					)							->       Optional[int]:


										"""simple docstring"""





										for model_class_name in self.all_model_classes:
														_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=self.model_tester.prepare_config_and_inputs()
														self.model_tester.check_use_cache_forward_with_attn_mask(
														    _a			,      _a			,      _a			,      _a					)




						@slow
						def 							__UpperCamelCase			(    self   : Tuple					)							->       str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=GPTaTokenizer.from_pretrained('''gpt2'''			,      pad_token='''</s>'''			,      padding_side='''left'''					)
										_SCREAMING_SNAKE_CASE     					=tokenizer(['''Hello this is a long string''', '''Hey''']			,      return_tensors='''np'''			,      padding=_a			,      truncation=_a					)

										_SCREAMING_SNAKE_CASE     					=FlaxGPTaLMHeadModel.from_pretrained('''gpt2'''					)
										_SCREAMING_SNAKE_CASE     					=False
										_SCREAMING_SNAKE_CASE     					=model.config.eos_token_id

										_SCREAMING_SNAKE_CASE     					=jax.jit(model.generate					)

										_SCREAMING_SNAKE_CASE     					=jit_generate(inputs['''input_ids''']			,      attention_mask=inputs['''attention_mask''']					).sequences

										_SCREAMING_SNAKE_CASE     					=tokenizer.batch_decode(_a			,      skip_special_tokens=_a					)

										_SCREAMING_SNAKE_CASE     					=[
										    '''Hello this is a long string of words. I\'m going to start with the first one.\n''',
										    '''Hey, I\'m not sure if I\'m going to be able to do''',
										]

										self.assertListEqual(_a			,      _a					)




						@is_pt_flax_cross_test
						def 							__UpperCamelCase			(    self   : Dict					)							->       str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=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
																		_SCREAMING_SNAKE_CASE     					=self._prepare_for_class(_a			,      _a					)
																		_SCREAMING_SNAKE_CASE     					={k: torch.tensor(v.tolist()					) for k, v in prepared_inputs_dict.items()}

																		# load corresponding PyTorch class
																		_SCREAMING_SNAKE_CASE     					=model_class.__name__[4:]  # Skip the "Flax" at the beginning
																		_SCREAMING_SNAKE_CASE     					=getattr(_a			,      _a					)

																		_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=pt_inputs['''input_ids'''].shape
																		_SCREAMING_SNAKE_CASE     					=np.random.randint(0			,      seq_length - 1			,      size=(batch_size,)					)
																		for batch_idx, start_index in enumerate(_a					):
																						_SCREAMING_SNAKE_CASE     					=0
																						_SCREAMING_SNAKE_CASE     					=1
																						_SCREAMING_SNAKE_CASE     					=0
																						_SCREAMING_SNAKE_CASE     					=1
																		_SCREAMING_SNAKE_CASE     					=pt_model_class(_a					).eval()
																		_SCREAMING_SNAKE_CASE     					=model_class(_a			,      dtype=jnp.floataa					)

																		_SCREAMING_SNAKE_CASE     					=convert_pytorch_state_dict_to_flax(pt_model.state_dict()			,      _a					)
																		_SCREAMING_SNAKE_CASE     					=fx_state

																		with torch.no_grad():
																						_SCREAMING_SNAKE_CASE     					=pt_model(**_a					).to_tuple()

																		_SCREAMING_SNAKE_CASE     					=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					)
																						_SCREAMING_SNAKE_CASE     					=model_class.from_pretrained(_a			,      from_pt=_a					)

																		_SCREAMING_SNAKE_CASE     					=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   : Any					)							->       Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=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
																		_SCREAMING_SNAKE_CASE     					=self._prepare_for_class(_a			,      _a					)
																		_SCREAMING_SNAKE_CASE     					={k: torch.tensor(v.tolist()					) for k, v in prepared_inputs_dict.items()}

																		# load corresponding PyTorch class
																		_SCREAMING_SNAKE_CASE     					=model_class.__name__[4:]  # Skip the "Flax" at the beginning
																		_SCREAMING_SNAKE_CASE     					=getattr(_a			,      _a					)

																		_SCREAMING_SNAKE_CASE     					=pt_model_class(_a					).eval()
																		_SCREAMING_SNAKE_CASE     					=model_class(_a			,      dtype=jnp.floataa					)

																		_SCREAMING_SNAKE_CASE     					=load_flax_weights_in_pytorch_model(_a			,      fx_model.params					)
																		_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=pt_inputs['''input_ids'''].shape
																		_SCREAMING_SNAKE_CASE     					=np.random.randint(0			,      seq_length - 1			,      size=(batch_size,)					)
																		for batch_idx, start_index in enumerate(_a					):
																						_SCREAMING_SNAKE_CASE     					=0
																						_SCREAMING_SNAKE_CASE     					=1
																						_SCREAMING_SNAKE_CASE     					=0
																						_SCREAMING_SNAKE_CASE     					=1

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

																		with torch.no_grad():
																						_SCREAMING_SNAKE_CASE     					=pt_model(**_a					).to_tuple()

																		_SCREAMING_SNAKE_CASE     					=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					)
																						_SCREAMING_SNAKE_CASE     					=pt_model_class.from_pretrained(_a			,      from_flax=_a					)

																		with torch.no_grad():
																						_SCREAMING_SNAKE_CASE     					=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					)




						@slow
						def 							__UpperCamelCase			(    self   : str					)							->       Any:


										"""simple docstring"""





										for model_class_name in self.all_model_classes:
														_SCREAMING_SNAKE_CASE     					=model_class_name.from_pretrained('''gpt2'''			,      from_pt=_a					)
														_SCREAMING_SNAKE_CASE     					=model(np.ones((1, 1)					)					)
														self.assertIsNotNone(_a					)



import math







def   lowerCamelCase(				a__):
				_SCREAMING_SNAKE_CASE     					=math.log(2		,10) * number
				_SCREAMING_SNAKE_CASE     					=round((log_number - int(a__))		,15)

				return difference







def   lowerCamelCase(				a__ = 67_8910):
				_SCREAMING_SNAKE_CASE     					=90
				_SCREAMING_SNAKE_CASE     					=0

				_SCREAMING_SNAKE_CASE     					=math.log(1.23		,10)
				_SCREAMING_SNAKE_CASE     					=math.log(1.24		,10)
				_SCREAMING_SNAKE_CASE     					=0

				while position < number:
								_SCREAMING_SNAKE_CASE     					=log_difference(a__)

								if difference >= upper_limit:
												power_iterator += 93

								elif difference < lower_limit:
												power_iterator += 196

								else:
												_SCREAMING_SNAKE_CASE     					=power_iterator
												power_iterator += 196
												position += 1

				return previous_power


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							print(f"""{solution() = }""")