import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

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.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
      import torch


class      lowerCamelCase    :
  """simple docstring"""







  def __init__(							self       :				Dict     ,   __magic_name__       :				Tuple     ,   __magic_name__       :				List[Any]=14     ,   __magic_name__       :				Tuple=7     ,   __magic_name__       :				Optional[int]=True     ,   __magic_name__       :				Tuple=True     ,   __magic_name__       :				Optional[int]=False     ,   __magic_name__       :				str=True     ,   __magic_name__       :				List[str]=99     ,   __magic_name__       :				Optional[int]=32     ,   __magic_name__       :				Dict=4     ,   __magic_name__       :				Optional[Any]=4     ,   __magic_name__       :				List[Any]=4     ,   __magic_name__       :				List[Any]=37     ,   __magic_name__       :				Union[str, Any]="gelu"     ,   __magic_name__       :				Optional[int]=0.1     ,   __magic_name__       :				List[Any]=0.1     ,   __magic_name__       :				str=512     ,   __magic_name__       :				List[Any]=0.02     ,   )     ->		List[Any]:
        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_      =     rotary_dim
        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       __A   (							self       :				List[Any]      )     ->		Optional[int]:
        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_      =     GPTJConfig(
            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=__magic_name__     ,   bos_token_id=self.bos_token_id     ,   eos_token_id=self.eos_token_id     ,   pad_token_id=self.pad_token_id     ,   rotary_dim=self.rotary_dim     ,   )

        return (config, input_ids, input_mask)







  def       __A   (							self       :				Any      )     ->		List[Any]:
        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       __A   (							self       :				Optional[int]     ,   __magic_name__       :				Any     ,   __magic_name__       :				Dict     ,   __magic_name__       :				Any     ,   __magic_name__       :				Dict      )     ->		List[Any]:
        SCREAMING_SNAKE_CASE_      =     20
        SCREAMING_SNAKE_CASE_      =     model_class_name(__magic_name__      )

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

        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=__magic_name__     ,   past_key_values=outputs_cache.past_key_values     ,   position_ids=__magic_name__     ,   )

        SCREAMING_SNAKE_CASE_      =     model(__magic_name__      )

        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       __A   (							self       :				Optional[Any]     ,   __magic_name__       :				int     ,   __magic_name__       :				Dict     ,   __magic_name__       :				Optional[Any]     ,   __magic_name__       :				Optional[int]      )     ->		Dict:
        SCREAMING_SNAKE_CASE_      =     20
        SCREAMING_SNAKE_CASE_      =     model_class_name(__magic_name__      )

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

        SCREAMING_SNAKE_CASE_      =     model(__magic_name__     ,   attention_mask=__magic_name__      )

        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      lowerCamelCase    (SCREAMING_SNAKE_CASE__     ,					SCREAMING_SNAKE_CASE__     ,					unittest.TestCase    ):
  """simple docstring"""







  lowerCamelCase__   =  (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
  lowerCamelCase__   =  (FlaxGPTJForCausalLM,) if is_flax_available() else ()







  def       __A   (							self       :				List[str]      )     ->		int:
        SCREAMING_SNAKE_CASE_      =     FlaxGPTJModelTester(self      )







  def       __A   (							self       :				int      )     ->		Optional[int]:
        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(__magic_name__     ,   __magic_name__     ,   __magic_name__     ,   __magic_name__      )







  def       __A   (							self       :				List[Any]      )     ->		Optional[int]:
        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(
                  __magic_name__     ,   __magic_name__     ,   __magic_name__     ,   __magic_name__      )







  @tooslow
  def       __A   (							self       :				Any      )     ->		str:
        SCREAMING_SNAKE_CASE_      =     GPTaTokenizer.from_pretrained("gpt2"     ,   pad_token="<|endoftext|>"     ,   padding_side="left"      )
        SCREAMING_SNAKE_CASE_      =     tokenizer(["Hello this is a long string", "Hey"]     ,   return_tensors="np"     ,   padding=__magic_name__     ,   truncation=__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     FlaxGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"      )
        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"]     ,   pad_token_id=tokenizer.pad_token_id      ).sequences

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

        SCREAMING_SNAKE_CASE_      =     [
            "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       __A   (							self       :				List[Any]      )     ->		List[str]:
        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(__magic_name__     ,   __magic_name__      )
                    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(__magic_name__     ,   __magic_name__      )

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

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

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

                    SCREAMING_SNAKE_CASE_      =     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__      )
                          SCREAMING_SNAKE_CASE_      =     model_class.from_pretrained(__magic_name__     ,   from_pt=__magic_name__      )

                    SCREAMING_SNAKE_CASE_      =     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       __A   (							self       :				int      )     ->		Dict:
        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(__magic_name__     ,   __magic_name__      )
                    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(__magic_name__     ,   __magic_name__      )

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

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

                    SCREAMING_SNAKE_CASE_      =     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__      )
                          SCREAMING_SNAKE_CASE_      =     pt_model_class.from_pretrained(__magic_name__     ,   from_flax=__magic_name__      )

                    with torch.no_grad():
                          SCREAMING_SNAKE_CASE_      =     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      )







  @tooslow
  def       __A   (							self       :				int      )     ->		Optional[int]:
        for model_class_name in self.all_model_classes:
              SCREAMING_SNAKE_CASE_      =     model_class_name.from_pretrained("EleutherAI/gpt-j-6B"      )
              SCREAMING_SNAKE_CASE_      =     model(np.ones((1, 1)      )      )
              self.assertIsNotNone(__magic_name__      )



def    a__       (						__UpperCamelCase = 1_0_0_0 ):
      SCREAMING_SNAKE_CASE_      =     -1
      SCREAMING_SNAKE_CASE_      =     0
      for a in range(1   ,					n // 3 ):
            # Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
            SCREAMING_SNAKE_CASE_      =     (n * n - 2 * a * n) // (2 * n - 2 * a)
            SCREAMING_SNAKE_CASE_      =     n - a - b
            if c * c == (a * a + b * b):
                  SCREAMING_SNAKE_CASE_      =     a * b * c
                  if candidate >= product:
                        SCREAMING_SNAKE_CASE_      =     candidate
      return product


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


