
"""simple docstring"""




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     ,   _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=0.0_2     ,   ):
             lowercase   :    Union[str, Any]			=							parent
             lowercase   :    Optional[int]			=							batch_size
             lowercase   :    Union[str, Any]			=							seq_length
             lowercase   :    Union[str, Any]			=							is_training
             lowercase   :    List[Any]			=							use_input_mask
             lowercase   :    int			=							use_token_type_ids
             lowercase   :    int			=							use_labels
             lowercase   :    str			=							vocab_size
             lowercase   :    Optional[int]			=							hidden_size
             lowercase   :    List[str]			=							num_hidden_layers
             lowercase   :    Dict			=							num_attention_heads
             lowercase   :    str			=							intermediate_size
             lowercase   :    Optional[Any]			=							hidden_act
             lowercase   :    List[Any]			=							hidden_dropout_prob
             lowercase   :    List[str]			=							attention_probs_dropout_prob
             lowercase   :    int			=							max_position_embeddings
             lowercase   :    Tuple			=							initializer_range
             lowercase   :    str			=							None
             lowercase   :    Optional[int]			=							vocab_size - 1
             lowercase   :    List[str]			=							vocab_size - 1
             lowercase   :    Any			=							vocab_size - 1
      def       __magic_name__      (      self    ):
             lowercase   :    Optional[Any]			=							ids_tensor([self.batch_size, self.seq_length]     ,   self.vocab_size    )

             lowercase   :    List[Any]			=							None
             if self.use_input_mask:
                    lowercase   :    Tuple			=							random_attention_mask([self.batch_size, self.seq_length]    )

             lowercase   :    Dict			=							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=__UpperCamelCase     ,   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       __magic_name__      (      self    ):
             lowercase   :    List[Any]			=							self.prepare_config_and_inputs()
             lowercase		,   lowercase		,   lowercase   :    Tuple			=							config_and_inputs
             lowercase   :    str			=							{"input_ids": input_ids, "attention_mask": attention_mask}
             return config, inputs_dict
      def       __magic_name__      (      self    ):
             lowercase		,   lowercase		,   lowercase   :    Optional[Any]			=							self.prepare_config_and_inputs()

             lowercase   :    Dict			=							floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
             lowercase   :    Dict			=							ids_tensor([self.batch_size, self.seq_length]     ,   vocab_size=2    )

             return (
                 config,
                 input_ids,
                 attention_mask,
                 encoder_hidden_states,
                 encoder_attention_mask,
             )
      def       __magic_name__      (      self     ,   _a     ,   _a     ,   _a     ,   _a    ):
             lowercase   :    Dict			=							20
             lowercase   :    Union[str, Any]			=							model_class_name(__UpperCamelCase    )

             lowercase   :    List[str]			=							model.init_cache(input_ids.shape[0]     ,   __UpperCamelCase    )
             lowercase   :    Tuple			=							jnp.ones((input_ids.shape[0], max_decoder_length)     ,   dtype="i4"    )

             lowercase   :    Dict			=							jnp.broadcast_to(
                 jnp.arange(input_ids.shape[-1] - 1    )[None, :]     ,   (input_ids.shape[0], input_ids.shape[-1] - 1)    )
             lowercase   :    Optional[Any]			=							model(
                 input_ids[:, :-1]     ,   attention_mask=__UpperCamelCase     ,   past_key_values=__UpperCamelCase     ,   position_ids=__UpperCamelCase     ,   )

             lowercase   :    Optional[Any]			=							jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]     ,   dtype="i4"    )
             lowercase   :    Tuple			=							model(
                 input_ids[:, -1:]     ,   attention_mask=__UpperCamelCase     ,   past_key_values=outputs_cache.past_key_values     ,   position_ids=__UpperCamelCase     ,   )

             lowercase   :    Union[str, Any]			=							model(__UpperCamelCase    )

             lowercase   :    str			=							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       __magic_name__      (      self     ,   _a     ,   _a     ,   _a     ,   _a    ):
             lowercase   :    List[Any]			=							20
             lowercase   :    Dict			=							model_class_name(__UpperCamelCase    )

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

             lowercase   :    Union[str, Any]			=							model.init_cache(input_ids.shape[0]     ,   __UpperCamelCase    )
             lowercase   :    Any			=							jnp.broadcast_to(
                 jnp.arange(input_ids.shape[-1] - 1    )[None, :]     ,   (input_ids.shape[0], input_ids.shape[-1] - 1)    )

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

             lowercase   :    List[Any]			=							model(__UpperCamelCase     ,   attention_mask=__UpperCamelCase    )

             lowercase   :    str			=							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						):
      __lowerCAmelCase						=    (FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
      __lowerCAmelCase						=    (FlaxGPTaLMHeadModel,) if is_flax_available() else ()
      def       __magic_name__      (      self    ):
             lowercase   :    List[Any]			=							FlaxGPTaModelTester(self    )
      def       __magic_name__      (      self    ):
             for model_class_name in self.all_model_classes:
                    lowercase		,   lowercase		,   lowercase   :    List[Any]			=							self.model_tester.prepare_config_and_inputs()
                    self.model_tester.check_use_cache_forward(__UpperCamelCase     ,   __UpperCamelCase     ,   __UpperCamelCase     ,   __UpperCamelCase    )
      def       __magic_name__      (      self    ):
             for model_class_name in self.all_model_classes:
                    lowercase		,   lowercase		,   lowercase   :    List[str]			=							self.model_tester.prepare_config_and_inputs()
                    self.model_tester.check_use_cache_forward_with_attn_mask(
                        __UpperCamelCase     ,   __UpperCamelCase     ,   __UpperCamelCase     ,   __UpperCamelCase    )
      @slow
      def       __magic_name__      (      self    ):
             lowercase   :    Tuple			=							GPTaTokenizer.from_pretrained("gpt2"     ,   pad_token="</s>"     ,   padding_side="left"    )
             lowercase   :    List[Any]			=							tokenizer(["Hello this is a long string", "Hey"]     ,   return_tensors="np"     ,   padding=__UpperCamelCase     ,   truncation=__UpperCamelCase    )

             lowercase   :    Tuple			=							FlaxGPTaLMHeadModel.from_pretrained("gpt2"    )
             lowercase   :    Any			=							False
             lowercase   :    Dict			=							model.config.eos_token_id

             lowercase   :    Optional[int]			=							jax.jit(model.generate    )

             lowercase   :    int			=							jit_generate(inputs["input_ids"]     ,   attention_mask=inputs["attention_mask"]    ).sequences

             lowercase   :    str			=							tokenizer.batch_decode(__UpperCamelCase     ,   skip_special_tokens=__UpperCamelCase    )

             lowercase   :    List[Any]			=							[
                 "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(__UpperCamelCase     ,   __UpperCamelCase    )
      @is_pt_flax_cross_test
      def       __magic_name__      (      self    ):
             lowercase		,   lowercase   :    List[str]			=							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
                           lowercase   :    Tuple			=							self._prepare_for_class(__UpperCamelCase     ,   __UpperCamelCase    )
                           lowercase   :    List[str]			=							{k: torch.tensor(v.tolist()    ) for k, v in prepared_inputs_dict.items()}

                           # load corresponding PyTorch class
                           lowercase   :    List[Any]			=							model_class.__name__[4:]  # Skip the "Flax" at the beginning
                           lowercase   :    Union[str, Any]			=							getattr(__UpperCamelCase     ,   __UpperCamelCase    )

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

                           lowercase   :    List[str]			=							convert_pytorch_state_dict_to_flax(pt_model.state_dict()     ,   __UpperCamelCase    )
                           lowercase   :    List[Any]			=							fx_state

                           with torch.no_grad():
                                  lowercase   :    Optional[Any]			=							pt_model(**__UpperCamelCase    ).to_tuple()

                           lowercase   :    Optional[Any]			=							fx_model(**__UpperCamelCase    ).to_tuple()
                           self.assertEqual(len(__UpperCamelCase    )     ,   len(__UpperCamelCase    )     ,   "Output lengths differ between Flax and PyTorch"    )
                           for fx_output, pt_output in zip(__UpperCamelCase     ,   __UpperCamelCase    ):
                                  self.assert_almost_equals(fx_output[:, -1]     ,   pt_output[:, -1].numpy()     ,   4E-2    )

                           with tempfile.TemporaryDirectory() as tmpdirname:
                                  pt_model.save_pretrained(__UpperCamelCase    )
                                  lowercase   :    Union[str, Any]			=							model_class.from_pretrained(__UpperCamelCase     ,   from_pt=__UpperCamelCase    )

                           lowercase   :    Union[str, Any]			=							fx_model_loaded(**__UpperCamelCase    ).to_tuple()
                           self.assertEqual(
                               len(__UpperCamelCase    )     ,   len(__UpperCamelCase    )     ,   "Output lengths differ between Flax and PyTorch"    )
                           for fx_output_loaded, pt_output in zip(__UpperCamelCase     ,   __UpperCamelCase    ):
                                  self.assert_almost_equals(fx_output_loaded[:, -1]     ,   pt_output[:, -1].numpy()     ,   4E-2    )
      @is_pt_flax_cross_test
      def       __magic_name__      (      self    ):
             lowercase		,   lowercase   :    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
                           lowercase   :    Tuple			=							self._prepare_for_class(__UpperCamelCase     ,   __UpperCamelCase    )
                           lowercase   :    Union[str, Any]			=							{k: torch.tensor(v.tolist()    ) for k, v in prepared_inputs_dict.items()}

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

                           lowercase   :    Union[str, Any]			=							pt_model_class(__UpperCamelCase    ).eval()
                           lowercase   :    Optional[int]			=							model_class(__UpperCamelCase     ,   dtype=jnp.floataa    )

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

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

                           with torch.no_grad():
                                  lowercase   :    Optional[Any]			=							pt_model(**__UpperCamelCase    ).to_tuple()

                           lowercase   :    int			=							fx_model(**__UpperCamelCase    ).to_tuple()
                           self.assertEqual(len(__UpperCamelCase    )     ,   len(__UpperCamelCase    )     ,   "Output lengths differ between Flax and PyTorch"    )
                           for fx_output, pt_output in zip(__UpperCamelCase     ,   __UpperCamelCase    ):
                                  self.assert_almost_equals(fx_output[:, -1]     ,   pt_output[:, -1].numpy()     ,   4E-2    )

                           with tempfile.TemporaryDirectory() as tmpdirname:
                                  fx_model.save_pretrained(__UpperCamelCase    )
                                  lowercase   :    Optional[Any]			=							pt_model_class.from_pretrained(__UpperCamelCase     ,   from_flax=__UpperCamelCase    )

                           with torch.no_grad():
                                  lowercase   :    Tuple			=							pt_model_loaded(**__UpperCamelCase    ).to_tuple()

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

      @slow
      def       __magic_name__      (      self    ):
             for model_class_name in self.all_model_classes:
                    lowercase   :    int			=							model_class_name.from_pretrained("gpt2"     ,   from_pt=__UpperCamelCase    )
                    lowercase   :    List[str]			=							model(np.ones((1, 1)    )    )
                    self.assertIsNotNone(__UpperCamelCase    )




"""simple docstring"""




from __future__ import annotations

_A      :	List[str]       		=	"""Alexander Joslin"""


def   __magic_name__     (				__snake_case    :	int  )			->    int:

       if number <= 0:
              lowercase   :    Optional[Any]			=							f"""n must be greater than 0. Got n = {number}"""
              raise ValueError(__snake_case  )

       lowercase   :    List[str]			=							[number + 1] * (number + 1)

       # starting position
       lowercase   :    Optional[Any]			=							0
       for i in range(1					,			__snake_case  ):
              lowercase   :    str			=							min(table[i + 1]					,			table[i] + 1  )
              # check if out of bounds
              if i * 2 <= number:
                     lowercase   :    int			=							min(table[i * 2]					,			table[i] + 1  )
              # check if out of bounds
              if i * 3 <= number:
                     lowercase   :    Optional[int]			=							min(table[i * 3]					,			table[i] + 1  )
       return table[number]


if __name__ == "__main__":
   import doctest

   doctest.testmod()


