
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

   _A						      =				"0.12"  # assumed parallelism: 8


if is_torch_available():
   import torch



def 		lowercase_    (     A__				,	A__				,	A__=None	)							->							Optional[int]:
    """simple docstring"""

    if rng is None:
        snake_case				  =      random.Random()

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

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

    snake_case				  =      np.array(A__				,	dtype=jnp.intaa	).reshape(A__	)

    return output



def 		lowercase_    (     A__				,	A__=None	)							->							Optional[int]:
    """simple docstring"""

    snake_case				  =      ids_tensor(A__				,	vocab_size=2				,	rng=A__	)
    # make sure that at least one token is attended to for each batch
    snake_case				  =      1
    return attn_mask







@require_flax
class   lowerCamelCase	:
      UpperCAmelCase__       :							Union[str, Any]     =	None
      UpperCAmelCase__       :							List[str]     =	()





      def 	UpperCAmelCase(self   :					int     )		->		Union[str, Any]:
          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 	UpperCAmelCase(self   :					str     )		->		List[Any]:
          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 	UpperCAmelCase(self   :					Union[str, Any]     )		->		Optional[int]:
          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 	UpperCAmelCase(self   :					Dict     )		->		Union[str, Any]:
          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 	UpperCAmelCase(self   :					Union[str, Any]     )		->		Any:
          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 	UpperCAmelCase(self   :					Dict     )		->		Tuple:
          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 	UpperCAmelCase(self   :					Any     )		->		Tuple:
          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				  =      1_0
          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 	UpperCAmelCase(self   :					Union[str, Any]     )		->		List[str]:
          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 	UpperCAmelCase(self   :					Dict     )		->		int:
          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 	UpperCAmelCase(self   :					Dict     )		->		Tuple:
          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 	UpperCAmelCase(self   :					Union[str, Any]     )		->		Union[str, Any]:
          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 	UpperCAmelCase(self   :					Optional[int]     )		->		Any:
          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   lowerCamelCase	(						unittest.TestCase						):





      def 	UpperCAmelCase(self   :					Optional[Any]     )		->		int:
          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     )



_A						      =				0  # The first color of the flag.
_A						      =				1  # The second color of the flag.
_A						      =				2  # The third color of the flag.
_A						      =				(red, white, blue)



def 		lowercase_    (     A__	)							->							list:
    """simple docstring"""

    if not sequence:
        return []
    if len(A__	) == 1:
        return list(A__	)
    snake_case				  =      0
    snake_case				  =      len(A__	) - 1
    snake_case				  =      0
    while mid <= high:
        if sequence[mid] == colors[0]:
            snake_case  ,							snake_case				  =      sequence[mid], sequence[low]
            low += 1
            mid += 1
        elif sequence[mid] == colors[1]:
            mid += 1
        elif sequence[mid] == colors[2]:
            snake_case  ,							snake_case				  =      sequence[high], sequence[mid]
            high -= 1
        else:
            snake_case				  =      F'The elements inside the sequence must contains only {colors} values'
            raise ValueError(A__	)
    return sequence


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   _A						      =				input("Enter numbers separated by commas:\n").strip()
   _A						      =				[int(item.strip()) for item in user_input.split(",")]
   print(f"{dutch_national_flag_sort(unsorted)}")

