



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 			__snake_case   :







							'''simple docstring'''






							def __init__(				self	,	a_	,	a_=14	,	a_=7	,	a_=True	,	a_=True	,	a_=False	,	a_=True	,	a_=99	,	a_=32	,	a_=5	,	a_=4	,	a_=37	,	a_="gelu"	,	a_=0.1	,	a_=0.1	,	a_=512	,	a_=0.02	,	):
											a__					   =		parent
											a__					   =		batch_size
											a__					   =		seq_length
											a__					   =		is_training
											a__					   =		use_input_mask
											a__					   =		use_token_type_ids
											a__					   =		use_labels
											a__					   =		vocab_size
											a__					   =		hidden_size
											a__					   =		num_hidden_layers
											a__					   =		num_attention_heads
											a__					   =		intermediate_size
											a__					   =		hidden_act
											a__					   =		hidden_dropout_prob
											a__					   =		attention_probs_dropout_prob
											a__					   =		max_position_embeddings
											a__					   =		initializer_range
											a__					   =		None
											a__					   =		vocab_size - 1
											a__					   =		vocab_size - 1
											a__					   =		vocab_size - 1


							def _a   (				self		):
											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__					   =		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 _a   (				self		):
											a__					   =		self.prepare_config_and_inputs()
											a__   ,      a__   ,      a__					   =		config_and_inputs
											a__					   =		{"""input_ids""": input_ids, """attention_mask""": attention_mask}
											return config, inputs_dict


							def _a   (				self		):
											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,
											    attention_mask,
											    encoder_hidden_states,
											    encoder_attention_mask,
											)


							def _a   (				self	,	a_	,	a_	,	a_	,	a_		):
											a__					   =		20
											a__					   =		model_class_name(a_		)

											a__					   =		model.init_cache(input_ids.shape[0]	,	a_		)
											a__					   =		jnp.ones((input_ids.shape[0], max_decoder_length)	,	dtype="""i4"""		)

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

											a__					   =		jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]	,	dtype="""i4"""		)
											a__					   =		model(
											    input_ids[:, -1:]	,	attention_mask=a_	,	past_key_values=outputs_cache.past_key_values	,	position_ids=a_	,	)

											a__					   =		model(a_		)

											a__					   =		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 _a   (				self	,	a_	,	a_	,	a_	,	a_		):
											a__					   =		20
											a__					   =		model_class_name(a_		)

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

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

											a__					   =		model(
											    input_ids[:, :-1]	,	attention_mask=a_	,	past_key_values=a_	,	position_ids=a_	,	)
											a__					   =		jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]	,	dtype="""i4"""		)
											a__					   =		model(
											    input_ids[:, -1:]	,	past_key_values=outputs_cache.past_key_values	,	attention_mask=a_	,	position_ids=a_	,	)

											a__					   =		model(a_	,	attention_mask=a_		)

											a__					   =		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 			__snake_case   ( SCREAMING_SNAKE_CASE      ,SCREAMING_SNAKE_CASE      ,unittest.TestCase):







							'''simple docstring'''






							UpperCamelCase__					:   int       =       (FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
							UpperCamelCase__					:   Optional[int]       =       (FlaxGPTaLMHeadModel,) if is_flax_available() else ()


							def _a   (				self		):
											a__					   =		FlaxGPTaModelTester(self		)


							def _a   (				self		):
											for model_class_name in self.all_model_classes:
															a__   ,      a__   ,      a__					   =		self.model_tester.prepare_config_and_inputs()
															self.model_tester.check_use_cache_forward(a_	,	a_	,	a_	,	a_		)


							def _a   (				self		):
											for model_class_name in self.all_model_classes:
															a__   ,      a__   ,      a__					   =		self.model_tester.prepare_config_and_inputs()
															self.model_tester.check_use_cache_forward_with_attn_mask(
															    a_	,	a_	,	a_	,	a_		)


							@slow
							def _a   (				self		):
											a__					   =		GPTaTokenizer.from_pretrained("""gpt2"""	,	pad_token="""</s>"""	,	padding_side="""left"""		)
											a__					   =		tokenizer(["""Hello this is a long string""", """Hey"""]	,	return_tensors="""np"""	,	padding=a_	,	truncation=a_		)

											a__					   =		FlaxGPTaLMHeadModel.from_pretrained("""gpt2"""		)
											a__					   =		False
											a__					   =		model.config.eos_token_id

											a__					   =		jax.jit(model.generate		)

											a__					   =		jit_generate(inputs["""input_ids"""]	,	attention_mask=inputs["""attention_mask"""]		).sequences

											a__					   =		tokenizer.batch_decode(a_	,	skip_special_tokens=a_		)

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

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

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

																			a__					   =		convert_pytorch_state_dict_to_flax(pt_model.state_dict()	,	a_		)
																			a__					   =		fx_state

																			with torch.no_grad():
																							a__					   =		pt_model(**a_		).to_tuple()

																			a__					   =		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_		)
																							a__					   =		model_class.from_pretrained(a_	,	from_pt=a_		)

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

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

																			a__					   =		pt_model_class(a_		).eval()
																			a__					   =		model_class(a_	,	dtype=jnp.floataa		)

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

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

																			with torch.no_grad():
																							a__					   =		pt_model(**a_		).to_tuple()

																			a__					   =		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_		)
																							a__					   =		pt_model_class.from_pretrained(a_	,	from_flax=a_		)

																			with torch.no_grad():
																							a__					   =		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 _a   (				self		):
											for model_class_name in self.all_model_classes:
															a__					   =		model_class_name.from_pretrained("""gpt2"""	,	from_pt=a_		)
															a__					   =		model(np.ones((1, 1)		)		)
															self.assertIsNotNone(a_		)



import sys

sys.set_int_max_str_digits(0)  # type: ignore





def        lowerCAmelCase       (				snake_case__   :      int )->			bool:
    A_								=					[0] * 11
    A_								=					[0] * 11

    # mark last 9 numbers
    for _ in range(9 ):
        A_								=					1
        A_								=					number // 10
    # flag
    A_								=					True

    # check last 9 numbers for pandigitality

    for x in range(9 ):
        if not check_last[x + 1]:
            A_								=					False
    if not f:
        return f

    # mark first 9 numbers
    A_								=					int(str(snake_case__ )[:9] )

    for _ in range(9 ):
        A_								=					1
        A_								=					number // 10

    # check first 9 numbers for pandigitality

    for x in range(9 ):
        if not check_front[x + 1]:
            A_								=					False
    return f





def        lowerCAmelCase       (				snake_case__   :      int )->			bool:
    A_								=					[0] * 11

    # mark last 9 numbers
    for _ in range(9 ):
        A_								=					1
        A_								=					number // 10
    # flag
    A_								=					True

    # check last 9 numbers for pandigitality

    for x in range(9 ):
        if not check_last[x + 1]:
            A_								=					False
    return f





def        lowerCAmelCase       (				)->			int:
    A_								=					1
    A_								=					1
    A_								=					2
    # temporary Fibonacci numbers

    A_								=					1
    A_								=					1
    A_								=					2
    # temporary Fibonacci numbers mod 1e9

    # mod m=1e9, done for fast optimisation
    A_								=					[0] * 1000000
    A_								=					1000000000

    for x in range(1000000 ):
        A_								=					(aa + ba) % m
        A_								=					ba % m
        A_								=					ca % m
        if checka(snake_case__ ):
            A_								=					1

    for x in range(1000000 ):
        A_								=					a + b
        A_								=					b
        A_								=					c
        # perform check only if in tocheck
        if tocheck[x + 3] and check(snake_case__ ):
            return x + 3  # first 2 already done
    return -1


if __name__ == "__main__":
     print(f"""{solution() = }""")






