





import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
    import os

    import jax.numpy as jnp
    from jax import jit

    from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
    from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

    snake_case_				=			'0.12'  # assumed parallelism: 8


if is_torch_available():
    import torch






def lowerCamelCase__    (							snake_case_	:  Dict       ,      snake_case_	:  List[str]       ,      snake_case_	:  str=None       )      ->				List[str]:
   if rng is None:
      __snake_case							       =   random.Random()

   __snake_case							       =   1
   for dim in shape:
      total_dims *= dim

   __snake_case							       =   []
   for _ in range(snake_case_       ):
      values.append(rng.randint(0       ,      vocab_size - 1       )       )

   __snake_case							       =   np.array(snake_case_       ,      dtype=jnp.intaa       ).reshape(snake_case_       )

   return output






def lowerCamelCase__    (							snake_case_	:  int       ,      snake_case_	:  List[str]=None       )      ->				Tuple:
   __snake_case							       =   ids_tensor(snake_case_       ,      vocab_size=2       ,      rng=snake_case_       )
   # make sure that at least one token is attended to for each batch
   __snake_case							       =   1
   return attn_mask

@require_flax
class 		SCREAMING_SNAKE_CASE__				:
      A_						:				Optional[int]							=    None
      A_						:				Union[str, Any]							=    ()


      def    a (self       :      int      ):





         """simple docstring"""



         __snake_case       ,	__snake_case							       =   self.model_tester.prepare_config_and_inputs_for_common()

         # cut to half length & take max batch_size 3
         __snake_case							       =   2
         __snake_case							       =   inputs['''input_ids'''].shape[-1] // 2
         __snake_case							       =   inputs['''input_ids'''][:max_batch_size, :sequence_length]

         __snake_case							       =   jnp.ones_like(a__      )
         __snake_case							       =   attention_mask[:max_batch_size, :sequence_length]

         # generate max 5 tokens
         __snake_case							       =   input_ids.shape[-1] + 5
         if config.eos_token_id is not None and config.pad_token_id is None:
            # hack to allow generate for models such as GPT2 as is done in `generate()`
            __snake_case							       =   config.eos_token_id
         return config, input_ids, attention_mask, max_length


      @is_pt_flax_cross_test
      def    a (self       :      Optional[int]      ):





         """simple docstring"""



         __snake_case       ,	__snake_case       ,	__snake_case       ,	__snake_case							       =   self._get_input_ids_and_config()
         __snake_case							       =   False
         __snake_case							       =   max_length
         __snake_case							       =   0

         for model_class in self.all_generative_model_classes:
            __snake_case							       =   model_class(a__      )

            __snake_case							       =   model_class.__name__[4:]  # Skip the "Flax" at the beginning
            __snake_case							       =   getattr(a__				,			a__      )
            __snake_case							       =   pt_model_class(a__      ).eval()
            __snake_case							       =   load_flax_weights_in_pytorch_model(a__				,			flax_model.params      )

            __snake_case							       =   flax_model.generate(a__      ).sequences
            __snake_case							       =   pt_model.generate(torch.tensor(a__				,			dtype=torch.long      )      )

            if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
               __snake_case							       =   flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

            self.assertListEqual(pt_generation_outputs.numpy().tolist()				,			flax_generation_outputs.tolist()      )


      def    a (self       :      Optional[int]      ):





         """simple docstring"""



         __snake_case       ,	__snake_case       ,	__snake_case       ,	__snake_case							       =   self._get_input_ids_and_config()
         __snake_case							       =   False
         __snake_case							       =   max_length

         for model_class in self.all_generative_model_classes:
            __snake_case							       =   model_class(a__      )

            __snake_case							       =   model.generate(a__      ).sequences
            self.assertEqual(generation_outputs.shape[-1]				,			a__      )

            __snake_case							       =   jit(model.generate      )
            __snake_case							       =   jit_generate(a__      ).sequences

            self.assertListEqual(generation_outputs.tolist()				,			jit_generation_outputs.tolist()      )


      def    a (self       :      List[Any]      ):





         """simple docstring"""



         __snake_case       ,	__snake_case       ,	__snake_case       ,	__snake_case							       =   self._get_input_ids_and_config()
         __snake_case							       =   True
         __snake_case							       =   max_length

         for model_class in self.all_generative_model_classes:
            __snake_case							       =   model_class(a__      )

            __snake_case							       =   model.generate(a__      ).sequences
            self.assertEqual(generation_outputs.shape[-1]				,			a__      )

            __snake_case							       =   jit(model.generate      )
            __snake_case							       =   jit_generate(a__      ).sequences

            self.assertListEqual(generation_outputs.tolist()				,			jit_generation_outputs.tolist()      )


      def    a (self       :      int      ):





         """simple docstring"""



         __snake_case       ,	__snake_case       ,	__snake_case       ,	__snake_case							       =   self._get_input_ids_and_config()
         __snake_case							       =   False
         __snake_case							       =   max_length
         __snake_case							       =   2

         for model_class in self.all_generative_model_classes:
            __snake_case							       =   model_class(a__      )

            __snake_case							       =   model.generate(a__      ).sequences
            self.assertEqual(generation_outputs.shape[-1]				,			a__      )

            __snake_case							       =   jit(model.generate      )
            __snake_case							       =   jit_generate(a__      ).sequences

            self.assertListEqual(generation_outputs.tolist()				,			jit_generation_outputs.tolist()      )


      def    a (self       :      Optional[int]      ):





         """simple docstring"""



         __snake_case       ,	__snake_case       ,	__snake_case       ,	__snake_case							       =   self._get_input_ids_and_config()
         __snake_case							       =   False
         __snake_case							       =   max_length
         __snake_case							       =   2
         __snake_case							       =   2

         for model_class in self.all_generative_model_classes:
            __snake_case							       =   model_class(a__      )

            __snake_case							       =   model.generate(a__      ).sequences
            self.assertEqual(generation_outputs.shape[0]				,			input_ids.shape[0] * config.num_return_sequences      )


      def    a (self       :      List[str]      ):





         """simple docstring"""



         __snake_case       ,	__snake_case       ,	__snake_case       ,	__snake_case							       =   self._get_input_ids_and_config()
         __snake_case							       =   True
         __snake_case							       =   max_length
         __snake_case							       =   0.8
         __snake_case							       =   10
         __snake_case							       =   0.3
         __snake_case							       =   1
         __snake_case							       =   8
         __snake_case							       =   9

         for model_class in self.all_generative_model_classes:
            __snake_case							       =   model_class(a__      )

            __snake_case							       =   model.generate(a__      ).sequences
            self.assertEqual(generation_outputs.shape[-1]				,			a__      )

            __snake_case							       =   jit(model.generate      )
            __snake_case							       =   jit_generate(a__      ).sequences

            self.assertListEqual(generation_outputs.tolist()				,			jit_generation_outputs.tolist()      )


      def    a (self       :      str      ):





         """simple docstring"""



         __snake_case       ,	__snake_case       ,	__snake_case       ,	__snake_case							       =   self._get_input_ids_and_config()
         __snake_case							       =   max_length
         __snake_case							       =   1
         __snake_case							       =   8
         __snake_case							       =   9

         for model_class in self.all_generative_model_classes:
            __snake_case							       =   model_class(a__      )

            __snake_case							       =   model.generate(a__      ).sequences
            self.assertEqual(generation_outputs.shape[-1]				,			a__      )

            __snake_case							       =   jit(model.generate      )
            __snake_case							       =   jit_generate(a__      ).sequences

            self.assertListEqual(generation_outputs.tolist()				,			jit_generation_outputs.tolist()      )


      def    a (self       :      Dict      ):





         """simple docstring"""



         __snake_case       ,	__snake_case       ,	__snake_case       ,	__snake_case							       =   self._get_input_ids_and_config()
         __snake_case							       =   max_length
         __snake_case							       =   2
         __snake_case							       =   1
         __snake_case							       =   8
         __snake_case							       =   9

         for model_class in self.all_generative_model_classes:
            __snake_case							       =   model_class(a__      )

            __snake_case							       =   model.generate(a__      ).sequences
            self.assertEqual(generation_outputs.shape[-1]				,			a__      )

            __snake_case							       =   jit(model.generate      )
            __snake_case							       =   jit_generate(a__      ).sequences

            self.assertListEqual(generation_outputs.tolist()				,			jit_generation_outputs.tolist()      )


      def    a (self       :      Optional[Any]      ):





         """simple docstring"""



         __snake_case       ,	__snake_case       ,	__snake_case       ,	__snake_case							       =   self._get_input_ids_and_config()

         # pad attention mask on the left
         __snake_case							       =   attention_mask.at[(0, 0)].set(0      )

         __snake_case							       =   False
         __snake_case							       =   max_length

         for model_class in self.all_generative_model_classes:
            __snake_case							       =   model_class(a__      )

            __snake_case							       =   model.generate(a__				,			attention_mask=a__      ).sequences
            self.assertEqual(generation_outputs.shape[-1]				,			a__      )

            __snake_case							       =   jit(model.generate      )
            __snake_case							       =   jit_generate(a__				,			attention_mask=a__      ).sequences

            self.assertListEqual(generation_outputs.tolist()				,			jit_generation_outputs.tolist()      )


      def    a (self       :      Any      ):





         """simple docstring"""



         __snake_case       ,	__snake_case       ,	__snake_case       ,	__snake_case							       =   self._get_input_ids_and_config()

         # pad attention mask on the left
         __snake_case							       =   attention_mask.at[(0, 0)].set(0      )

         __snake_case							       =   True
         __snake_case							       =   max_length

         for model_class in self.all_generative_model_classes:
            __snake_case							       =   model_class(a__      )

            __snake_case							       =   model.generate(a__				,			attention_mask=a__      ).sequences
            self.assertEqual(generation_outputs.shape[-1]				,			a__      )

            __snake_case							       =   jit(model.generate      )
            __snake_case							       =   jit_generate(a__				,			attention_mask=a__      ).sequences

            self.assertListEqual(generation_outputs.tolist()				,			jit_generation_outputs.tolist()      )


      def    a (self       :      str      ):





         """simple docstring"""



         __snake_case       ,	__snake_case       ,	__snake_case       ,	__snake_case							       =   self._get_input_ids_and_config()

         # pad attention mask on the left
         __snake_case							       =   attention_mask.at[(0, 0)].set(0      )

         __snake_case							       =   2
         __snake_case							       =   max_length

         for model_class in self.all_generative_model_classes:
            __snake_case							       =   model_class(a__      )

            __snake_case							       =   model.generate(a__				,			attention_mask=a__      ).sequences
            self.assertEqual(generation_outputs.shape[-1]				,			a__      )

            __snake_case							       =   jit(model.generate      )
            __snake_case							       =   jit_generate(a__				,			attention_mask=a__      ).sequences

            self.assertListEqual(generation_outputs.tolist()				,			jit_generation_outputs.tolist()      )






@require_flax
class 		SCREAMING_SNAKE_CASE__				(       unittest.TestCase  ):


      def    a (self       :      Any      ):





         """simple docstring"""



         __snake_case							       =   AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-bert'''      )
         __snake_case							       =   FlaxAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''      )

         __snake_case							       =   '''Hello world'''
         __snake_case							       =   tokenizer(a__				,			return_tensors='''np'''      ).input_ids

         # typos are quickly detected (the correct argument is `do_sample`)
         with self.assertRaisesRegex(a__				,			'''do_samples'''      ):
            model.generate(a__				,			do_samples=a__      )

         # arbitrary arguments that will not be used anywhere are also not accepted
         with self.assertRaisesRegex(a__				,			'''foo'''      ):
            __snake_case							       =   {'''foo''': '''bar'''}
            model.generate(a__				,			**a__      )








def lowerCamelCase__    (							)      ->				int:
   return [
       a * b * (1000 - a - b)
       for a in range(1       ,      999       )
       for b in range(snake_case_       ,      999       )
       if (a * a + b * b == (1000 - a - b) ** 2)
   ][0]


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

