
"""simple docstring"""




import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, 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, 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.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
					import torch
class SCREAMING_SNAKE_CASE       :






	'''simple docstring'''







	def __init__(  self :List[str]       ,    __magic_name__ :Optional[Any]       ,    __magic_name__ :Tuple=14       ,    __magic_name__ :str=7       ,    __magic_name__ :Optional[Any]=True       ,    __magic_name__ :int=True       ,    __magic_name__ :Optional[int]=False       ,    __magic_name__ :List[Any]=True       ,    __magic_name__ :List[Any]=99       ,    __magic_name__ :Tuple=32       ,    __magic_name__ :Tuple=4       ,    __magic_name__ :Any=4       ,    __magic_name__ :Any=[[["global", "local"], 2]]       ,    __magic_name__ :Optional[Any]=37       ,    __magic_name__ :Tuple="gelu"       ,    __magic_name__ :str=0.1       ,    __magic_name__ :Optional[Any]=0.1       ,    __magic_name__ :str=512       ,    __magic_name__ :List[Any]=7       ,    __magic_name__ :Optional[Any]=0.02       ,    )			->   List[str]:

			'''simple docstring'''






			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__   		=     attention_types
			a__   		=     intermediate_size
			a__   		=     hidden_act
			a__   		=     hidden_dropout_prob
			a__   		=     attention_probs_dropout_prob
			a__   		=     max_position_embeddings
			a__   		=     window_size
			a__   		=     initializer_range
			a__   		=     None
			a__   		=     vocab_size - 1
			a__   		=     vocab_size - 1
			a__   		=     vocab_size - 1


	def    _UpperCamelCase							(  self :List[str]			)			->   str:

			'''simple docstring'''






			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__   		=     GPTNeoConfig(
			    vocab_size=self.vocab_size       ,    hidden_size=self.hidden_size       ,    num_layers=self.num_hidden_layers       ,    num_heads=self.num_attention_heads       ,    max_position_embeddings=self.max_position_embeddings       ,    use_cache=__magic_name__       ,    bos_token_id=self.bos_token_id       ,    eos_token_id=self.eos_token_id       ,    pad_token_id=self.pad_token_id       ,    window_size=self.window_size       ,    attention_types=self.attention_types       ,    )

			return (config, input_ids, input_mask)


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

			'''simple docstring'''






			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    _UpperCamelCase							(  self :str       ,    __magic_name__ :Optional[int]       ,    __magic_name__ :List[Any]       ,    __magic_name__ :Optional[Any]       ,    __magic_name__ :Dict			)			->   Tuple:

			'''simple docstring'''






			a__   		=     20
			a__   		=     model_class_name(__magic_name__			)

			a__   		=     model.init_cache(input_ids.shape[0]       ,    __magic_name__			)
			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=__magic_name__       ,    past_key_values=__magic_name__       ,    position_ids=__magic_name__       ,    )

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

			a__   		=     model(__magic_name__			)

			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    _UpperCamelCase							(  self :Optional[int]       ,    __magic_name__ :List[Any]       ,    __magic_name__ :Union[str, Any]       ,    __magic_name__ :Dict       ,    __magic_name__ :Any			)			->   int:

			'''simple docstring'''






			a__   		=     20
			a__   		=     model_class_name(__magic_name__			)

			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]       ,    __magic_name__			)
			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=__magic_name__       ,    past_key_values=__magic_name__       ,    position_ids=__magic_name__       ,    )
			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=__magic_name__       ,    position_ids=__magic_name__       ,    )

			a__   		=     model(__magic_name__       ,    attention_mask=__magic_name__			)

			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 SCREAMING_SNAKE_CASE       (      lowerCAmelCase					,   lowerCAmelCase					,   unittest.TestCase					):






	'''simple docstring'''







	snake_case__							:		str         =		(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
	snake_case__							:		int         =		(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()


	def    _UpperCamelCase							(  self :Dict			)			->   List[str]:

			'''simple docstring'''






			a__   		=     FlaxGPTNeoModelTester(self			)


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

			'''simple docstring'''






			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(__magic_name__       ,    __magic_name__       ,    __magic_name__       ,    __magic_name__			)


	def    _UpperCamelCase							(  self :Any			)			->   List[Any]:

			'''simple docstring'''






			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(
					    __magic_name__       ,    __magic_name__       ,    __magic_name__       ,    __magic_name__			)


	@slow
	def    _UpperCamelCase							(  self :Any			)			->   Any:

			'''simple docstring'''






			a__   		=     GPTaTokenizer.from_pretrained('''gpt2'''       ,    pad_token='''<|endoftext|>'''       ,    padding_side='''left'''			)
			a__   		=     tokenizer(['''Hello this is a long string''', '''Hey''']       ,    return_tensors='''np'''       ,    padding=__magic_name__       ,    truncation=__magic_name__			)

			a__   		=     FlaxGPTNeoForCausalLM.from_pretrained('''EleutherAI/gpt-neo-125M'''			)
			a__   		=     False
			a__   		=     model.config.eos_token_id

			a__   		=     jax.jit(model.generate			)

			a__   		=     jit_generate(
			    inputs['''input_ids''']       ,    attention_mask=inputs['''attention_mask''']       ,    pad_token_id=tokenizer.pad_token_id			).sequences

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

			a__   		=     [
			    '''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(__magic_name__       ,    __magic_name__			)


	@is_pt_flax_cross_test
	def    _UpperCamelCase							(  self :Optional[Any]			)			->   List[str]:

			'''simple docstring'''






			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(__magic_name__       ,    __magic_name__			)
							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(__magic_name__       ,    __magic_name__			)

							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(__magic_name__			):
									a__   		=     0
									a__   		=     1
									a__   		=     0
									a__   		=     1
							a__   		=     pt_model_class(__magic_name__			).eval()
							a__   		=     model_class(__magic_name__       ,    dtype=jnp.floataa			)

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

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

							a__   		=     fx_model(**__magic_name__			).to_tuple()
							self.assertEqual(len(__magic_name__			)       ,    len(__magic_name__			)       ,    '''Output lengths differ between Flax and PyTorch'''			)
							for fx_output, pt_output in zip(__magic_name__       ,    __magic_name__			):
									self.assert_almost_equals(fx_output[:, -1]       ,    pt_output[:, -1].numpy()       ,    4e-2			)

							with tempfile.TemporaryDirectory() as tmpdirname:
									pt_model.save_pretrained(__magic_name__			)
									a__   		=     model_class.from_pretrained(__magic_name__       ,    from_pt=__magic_name__			)

							a__   		=     fx_model_loaded(**__magic_name__			).to_tuple()
							self.assertEqual(
							    len(__magic_name__			)       ,    len(__magic_name__			)       ,    '''Output lengths differ between Flax and PyTorch'''			)
							for fx_output_loaded, pt_output in zip(__magic_name__       ,    __magic_name__			):
									self.assert_almost_equals(fx_output_loaded[:, -1]       ,    pt_output[:, -1].numpy()       ,    4e-2			)


	@is_pt_flax_cross_test
	def    _UpperCamelCase							(  self :Optional[Any]			)			->   str:

			'''simple docstring'''






			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(__magic_name__       ,    __magic_name__			)
							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(__magic_name__       ,    __magic_name__			)

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

							a__   		=     load_flax_weights_in_pytorch_model(__magic_name__       ,    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(__magic_name__			):
									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(**__magic_name__			).to_tuple()

							a__   		=     fx_model(**__magic_name__			).to_tuple()
							self.assertEqual(len(__magic_name__			)       ,    len(__magic_name__			)       ,    '''Output lengths differ between Flax and PyTorch'''			)
							for fx_output, pt_output in zip(__magic_name__       ,    __magic_name__			):
									self.assert_almost_equals(fx_output[:, -1]       ,    pt_output[:, -1].numpy()       ,    4e-2			)

							with tempfile.TemporaryDirectory() as tmpdirname:
									fx_model.save_pretrained(__magic_name__			)
									a__   		=     pt_model_class.from_pretrained(__magic_name__       ,    from_flax=__magic_name__			)

							with torch.no_grad():
									a__   		=     pt_model_loaded(**__magic_name__			).to_tuple()

							self.assertEqual(
							    len(__magic_name__			)       ,    len(__magic_name__			)       ,    '''Output lengths differ between Flax and PyTorch'''			)
							for fx_output, pt_output in zip(__magic_name__       ,    __magic_name__			):
									self.assert_almost_equals(fx_output[:, -1]       ,    pt_output[:, -1].numpy()       ,    4e-2			)






	@slow
	def    _UpperCamelCase							(  self :int			)			->   Optional[int]:

			'''simple docstring'''






			for model_class_name in self.all_model_classes:
					a__   		=     model_class_name.from_pretrained('''EleutherAI/gpt-neo-125M'''			)
					a__   		=     model(np.ones((1, 1)			)			)
					self.assertIsNotNone(__magic_name__			)






"""simple docstring"""




__lowerCAmelCase							:  Any 				=		{
    '''0''': 1,
    '''1''': 1,
    '''2''': 2,
    '''3''': 6,
    '''4''': 24,
    '''5''': 120,
    '''6''': 720,
    '''7''': 5040,
    '''8''': 40320,
    '''9''': 362880,
}

__lowerCAmelCase							:  List[Any] 				=		{145: 145}

__lowerCAmelCase							:  Any 				=		{
    145: 0,
    169: 3,
    36301: 3,
    1454: 3,
    871: 2,
    45361: 2,
    872: 2,
}

def __snake_case (      UpperCamelCase    )       ->    int:


		"""simple docstring"""





		if n in CACHE_SUM_DIGIT_FACTORIALS:
				return CACHE_SUM_DIGIT_FACTORIALS[n]
		a__   		=     sum(DIGIT_FACTORIALS[let] for let in str(UpperCamelCase    )    )
		a__   		=     ret
		return ret

def __snake_case (      UpperCamelCase						,					UpperCamelCase = None    )       ->    int:


		"""simple docstring"""





		a__   		=     previous or set()
		if n in CHAIN_LENGTH_CACHE:
				return CHAIN_LENGTH_CACHE[n]
		a__   		=     sum_digit_factorials(UpperCamelCase    )
		if next_number in previous:
				a__   		=     0
				return 0
		else:
				previous.add(UpperCamelCase    )
				a__   		=     1 + chain_length(UpperCamelCase						,					UpperCamelCase    )
				a__   		=     ret
				return ret

def __snake_case (      UpperCamelCase = 60						,					UpperCamelCase = 1_000_000    )       ->    int:


		"""simple docstring"""





		return sum(1 for i in range(1						,					UpperCamelCase    ) if chain_length(UpperCamelCase    ) == num_terms    )


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




