



"""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 			UpperCAmelCase_						:



      def __init__(  self  :			str  ,			A  :			Union[str, Any]  ,			A  :			List[Any]=1_4  ,			A  :			List[str]=7  ,			A  :			Union[str, Any]=True  ,			A  :			Any=True  ,			A  :			int=False  ,			A  :			Dict=True  ,			A  :			Optional[int]=9_9  ,			A  :			List[Any]=3_2  ,			A  :			Union[str, Any]=4  ,			A  :			Tuple=4  ,			A  :			Optional[int]=[[["global", "local"], 2]]  ,			A  :			Any=3_7  ,			A  :			str="gelu"  ,			A  :			List[Any]=0.1  ,			A  :			Union[str, Any]=0.1  ,			A  :			Union[str, Any]=5_1_2  ,			A  :			Dict=7  ,			A  :			Tuple=0.02  ,			):
           _UpperCAmelCase    :			Optional[Any]              =					parent
           _UpperCAmelCase    :			Optional[int]              =					batch_size
           _UpperCAmelCase    :			Optional[Any]              =					seq_length
           _UpperCAmelCase    :			List[str]              =					is_training
           _UpperCAmelCase    :			Union[str, Any]              =					use_input_mask
           _UpperCAmelCase    :			List[Any]              =					use_token_type_ids
           _UpperCAmelCase    :			str              =					use_labels
           _UpperCAmelCase    :			Dict              =					vocab_size
           _UpperCAmelCase    :			int              =					hidden_size
           _UpperCAmelCase    :			int              =					num_hidden_layers
           _UpperCAmelCase    :			Optional[int]              =					num_attention_heads
           _UpperCAmelCase    :			Optional[Any]              =					attention_types
           _UpperCAmelCase    :			int              =					intermediate_size
           _UpperCAmelCase    :			str              =					hidden_act
           _UpperCAmelCase    :			Optional[int]              =					hidden_dropout_prob
           _UpperCAmelCase    :			Optional[int]              =					attention_probs_dropout_prob
           _UpperCAmelCase    :			Any              =					max_position_embeddings
           _UpperCAmelCase    :			List[str]              =					window_size
           _UpperCAmelCase    :			List[Any]              =					initializer_range
           _UpperCAmelCase    :			Dict              =					None
           _UpperCAmelCase    :			Tuple              =					vocab_size - 1
           _UpperCAmelCase    :			List[str]              =					vocab_size - 1
           _UpperCAmelCase    :			Union[str, Any]              =					vocab_size - 1



      def 		snake_case_      (  self  :			Tuple		):
           _UpperCAmelCase    :			Optional[Any]              =					ids_tensor([self.batch_size, self.seq_length]  ,			self.vocab_size		)

           _UpperCAmelCase    :			List[Any]              =					None
           if self.use_input_mask:
                _UpperCAmelCase    :			Union[str, Any]              =					random_attention_mask([self.batch_size, self.seq_length]		)

           _UpperCAmelCase    :			int              =					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=A  ,			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 		snake_case_      (  self  :			Any		):
           _UpperCAmelCase    :			List[str]              =					self.prepare_config_and_inputs()
           _UpperCAmelCase    :			Dict              =					config_and_inputs
           _UpperCAmelCase    :			Tuple              =					{"input_ids": input_ids, "attention_mask": attention_mask}
           return config, inputs_dict



      def 		snake_case_      (  self  :			Optional[Any]  ,			A  :			Union[str, Any]  ,			A  :			Any  ,			A  :			Any  ,			A  :			List[str]		):
           _UpperCAmelCase    :			List[Any]              =					2_0
           _UpperCAmelCase    :			Optional[Any]              =					model_class_name(A		)

           _UpperCAmelCase    :			Dict              =					model.init_cache(input_ids.shape[0]  ,			A		)
           _UpperCAmelCase    :			Union[str, Any]              =					jnp.ones((input_ids.shape[0], max_decoder_length)  ,			dtype="i4"		)

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

           _UpperCAmelCase    :			str              =					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,			dtype="i4"		)
           _UpperCAmelCase    :			Union[str, Any]              =					model(
               input_ids[:, -1:]  ,			attention_mask=A  ,			past_key_values=outputs_cache.past_key_values  ,			position_ids=A  ,			)

           _UpperCAmelCase    :			str              =					model(A		)

           _UpperCAmelCase    :			Union[str, Any]              =					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 		snake_case_      (  self  :			str  ,			A  :			Optional[Any]  ,			A  :			str  ,			A  :			List[Any]  ,			A  :			List[Any]		):
           _UpperCAmelCase    :			List[str]              =					2_0
           _UpperCAmelCase    :			str              =					model_class_name(A		)

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

           _UpperCAmelCase    :			Optional[int]              =					model.init_cache(input_ids.shape[0]  ,			A		)
           _UpperCAmelCase    :			Any              =					jnp.broadcast_to(
               jnp.arange(input_ids.shape[-1] - 1		)[None, :]  ,			(input_ids.shape[0], input_ids.shape[-1] - 1)		)

           _UpperCAmelCase    :			Union[str, Any]              =					model(
               input_ids[:, :-1]  ,			attention_mask=A  ,			past_key_values=A  ,			position_ids=A  ,			)
           _UpperCAmelCase    :			List[Any]              =					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,			dtype="i4"		)
           _UpperCAmelCase    :			Optional[Any]              =					model(
               input_ids[:, -1:]  ,			past_key_values=outputs_cache.past_key_values  ,			attention_mask=A  ,			position_ids=A  ,			)

           _UpperCAmelCase    :			List[str]              =					model(A  ,			attention_mask=A		)

           _UpperCAmelCase    :			Optional[Any]              =					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 			UpperCAmelCase_						(       _UpperCamelCase ,    _UpperCamelCase ,    unittest.TestCase		):
      __SCREAMING_SNAKE_CASE		:     Any				  =				(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
      __SCREAMING_SNAKE_CASE		:     str				  =				(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()



      def 		snake_case_      (  self  :			Optional[int]		):
           _UpperCAmelCase    :			List[str]              =					FlaxGPTNeoModelTester(self		)



      def 		snake_case_      (  self  :			Any		):
           for model_class_name in self.all_model_classes:
                _UpperCAmelCase    :			List[str]              =					self.model_tester.prepare_config_and_inputs()
                self.model_tester.check_use_cache_forward(A  ,			A  ,			A  ,			A		)



      def 		snake_case_      (  self  :			Tuple		):
           for model_class_name in self.all_model_classes:
                _UpperCAmelCase    :			List[str]              =					self.model_tester.prepare_config_and_inputs()
                self.model_tester.check_use_cache_forward_with_attn_mask(
                    A  ,			A  ,			A  ,			A		)



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

           _UpperCAmelCase    :			Optional[Any]              =					FlaxGPTNeoForCausalLM.from_pretrained("EleutherAI/gpt-neo-125M"		)
           _UpperCAmelCase    :			Optional[int]              =					False
           _UpperCAmelCase    :			Optional[Any]              =					model.config.eos_token_id

           _UpperCAmelCase    :			str              =					jax.jit(model.generate		)

           _UpperCAmelCase    :			Any              =					jit_generate(
               inputs["input_ids"]  ,			attention_mask=inputs["attention_mask"]  ,			pad_token_id=tokenizer.pad_token_id		).sequences

           _UpperCAmelCase    :			Tuple              =					tokenizer.batch_decode(A  ,			skip_special_tokens=A		)

           _UpperCAmelCase    :			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 		snake_case_      (  self  :			Optional[int]		):
           _UpperCAmelCase    :			Optional[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
                     _UpperCAmelCase    :			str              =					self._prepare_for_class(A  ,			A		)
                     _UpperCAmelCase    :			List[Any]              =					{k: torch.tensor(v.tolist()		) for k, v in prepared_inputs_dict.items()}

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

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

                     _UpperCAmelCase    :			Any              =					convert_pytorch_state_dict_to_flax(pt_model.state_dict()  ,			A		)
                     _UpperCAmelCase    :			Any              =					fx_state

                     with torch.no_grad():
                          _UpperCAmelCase    :			str              =					pt_model(**A		).to_tuple()

                     _UpperCAmelCase    :			str              =					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		)
                          _UpperCAmelCase    :			str              =					model_class.from_pretrained(A  ,			from_pt=A		)

                     _UpperCAmelCase    :			Tuple              =					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 		snake_case_      (  self  :			Tuple		):
           _UpperCAmelCase    :			int              =					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
                     _UpperCAmelCase    :			Any              =					self._prepare_for_class(A  ,			A		)
                     _UpperCAmelCase    :			int              =					{k: torch.tensor(v.tolist()		) for k, v in prepared_inputs_dict.items()}

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

                     _UpperCAmelCase    :			List[str]              =					pt_model_class(A		).eval()
                     _UpperCAmelCase    :			Union[str, Any]              =					model_class(A  ,			dtype=jnp.floataa		)

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

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

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

                     _UpperCAmelCase    :			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		)
                          _UpperCAmelCase    :			Any              =					pt_model_class.from_pretrained(A  ,			from_flax=A		)

                     with torch.no_grad():
                          _UpperCAmelCase    :			Union[str, Any]              =					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 		snake_case_      (  self  :			Tuple		):
           for model_class_name in self.all_model_classes:
                _UpperCAmelCase    :			Dict              =					model_class_name.from_pretrained("EleutherAI/gpt-neo-125M"		)
                _UpperCAmelCase    :			Optional[Any]              =					model(np.ones((1, 1)		)		)
                self.assertIsNotNone(A		)





"""simple docstring"""




_lowerCAmelCase       :      str							=							{
    "0": 1,
    "1": 1,
    "2": 2,
    "3": 6,
    "4": 24,
    "5": 1_20,
    "6": 7_20,
    "7": 50_40,
    "8": 4_03_20,
    "9": 36_28_80,
}

_lowerCAmelCase       :      Optional[int]							=							{1_45: 1_45}

_lowerCAmelCase       :      int							=							{
    1_45: 0,
    1_69: 3,
    3_63_01: 3,
    14_54: 3,
    8_71: 2,
    4_53_61: 2,
    8_72: 2,
}

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		int       )						->     int:





     '''simple docstring'''

     if n in CACHE_SUM_DIGIT_FACTORIALS:
          return CACHE_SUM_DIGIT_FACTORIALS[n]
     _UpperCAmelCase    :			Dict              =					sum(DIGIT_FACTORIALS[let] for let in str(SCREAMING_SNAKE_CASE__       )       )
     _UpperCAmelCase    :			Any              =					ret
     return ret

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		int		,						SCREAMING_SNAKE_CASE__			:		set | None = None       )						->     int:





     '''simple docstring'''

     _UpperCAmelCase    :			Dict              =					previous or set()
     if n in CHAIN_LENGTH_CACHE:
          return CHAIN_LENGTH_CACHE[n]
     _UpperCAmelCase    :			Dict              =					sum_digit_factorials(SCREAMING_SNAKE_CASE__       )
     if next_number in previous:
          _UpperCAmelCase    :			List[str]              =					0
          return 0
     else:
          previous.add(SCREAMING_SNAKE_CASE__       )
          _UpperCAmelCase    :			str              =					1 + chain_length(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )
          _UpperCAmelCase    :			str              =					ret
          return ret

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





     '''simple docstring'''

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


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