


'''simple docstring'''



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__						:   Dict				=    """0.12"""  # assumed parallelism: 8


if is_torch_available():
      import torch



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Union[str, Any] ,       UpperCAmelCase_       :							List[str] ,       UpperCAmelCase_       :							str=None				)	->						List[Any]:
     if rng is None:
          __lowerCamelCase  :      str														=						random.Random()

     __lowerCamelCase  :      Union[str, Any]														=						1
     for dim in shape:
          total_dims *= dim

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

     __lowerCamelCase  :      Optional[Any]														=						np.array(UpperCAmelCase_ ,       dtype=jnp.intaa				).reshape(UpperCAmelCase_				)

     return output



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





@require_flax
class   UpperCAmelCase_    :





    """simple docstring"""






    lowerCamelCase    :   Union[str, Any]					=     None
    lowerCamelCase    :   Optional[Any]					=     ()







    def  lowercase_							(				self      )							->	Dict:
         __lowerCamelCase							,			__lowerCamelCase  :      Any														=						self.model_tester.prepare_config_and_inputs_for_common()

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

         __lowerCamelCase  :      Optional[int]														=						jnp.ones_like(SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Tuple														=						attention_mask[:max_batch_size, :sequence_length]

         # generate max 5 tokens
         __lowerCamelCase  :      Tuple														=						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()`
              __lowerCamelCase  :      List[str]														=						config.eos_token_id
         return config, input_ids, attention_mask, max_length







    @is_pt_flax_cross_test
    def  lowercase_							(				self      )							->	List[Any]:
         __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase  :      str														=						self._get_input_ids_and_config()
         __lowerCamelCase  :      Any														=						False
         __lowerCamelCase  :      Dict														=						max_length
         __lowerCamelCase  :      Union[str, Any]														=						0

         for model_class in self.all_generative_model_classes:
              __lowerCamelCase  :      List[Any]														=						model_class(SCREAMING_SNAKE_CASE_      )

              __lowerCamelCase  :      Union[str, Any]														=						model_class.__name__[4:]  # Skip the "Flax" at the beginning
              __lowerCamelCase  :      List[str]														=						getattr(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )
              __lowerCamelCase  :      List[str]														=						pt_model_class(SCREAMING_SNAKE_CASE_      ).eval()
              __lowerCamelCase  :      List[Any]														=						load_flax_weights_in_pytorch_model(SCREAMING_SNAKE_CASE_     ,	flax_model.params      )

              __lowerCamelCase  :      List[Any]														=						flax_model.generate(SCREAMING_SNAKE_CASE_      ).sequences
              __lowerCamelCase  :      Union[str, Any]														=						pt_model.generate(torch.tensor(SCREAMING_SNAKE_CASE_     ,	dtype=torch.long      )      )

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

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







    def  lowercase_							(				self      )							->	str:
         __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase  :      str														=						self._get_input_ids_and_config()
         __lowerCamelCase  :      Optional[Any]														=						False
         __lowerCamelCase  :      Optional[int]														=						max_length

         for model_class in self.all_generative_model_classes:
              __lowerCamelCase  :      List[str]														=						model_class(SCREAMING_SNAKE_CASE_      )

              __lowerCamelCase  :      Optional[int]														=						model.generate(SCREAMING_SNAKE_CASE_      ).sequences
              self.assertEqual(generation_outputs.shape[-1]     ,	SCREAMING_SNAKE_CASE_      )

              __lowerCamelCase  :      Optional[Any]														=						jit(model.generate      )
              __lowerCamelCase  :      Optional[Any]														=						jit_generate(SCREAMING_SNAKE_CASE_      ).sequences

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







    def  lowercase_							(				self      )							->	Union[str, Any]:
         __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase  :      List[Any]														=						self._get_input_ids_and_config()
         __lowerCamelCase  :      List[str]														=						True
         __lowerCamelCase  :      Dict														=						max_length

         for model_class in self.all_generative_model_classes:
              __lowerCamelCase  :      Optional[int]														=						model_class(SCREAMING_SNAKE_CASE_      )

              __lowerCamelCase  :      Optional[Any]														=						model.generate(SCREAMING_SNAKE_CASE_      ).sequences
              self.assertEqual(generation_outputs.shape[-1]     ,	SCREAMING_SNAKE_CASE_      )

              __lowerCamelCase  :      Optional[Any]														=						jit(model.generate      )
              __lowerCamelCase  :      Union[str, Any]														=						jit_generate(SCREAMING_SNAKE_CASE_      ).sequences

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







    def  lowercase_							(				self      )							->	List[Any]:
         __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase  :      Dict														=						self._get_input_ids_and_config()
         __lowerCamelCase  :      Optional[int]														=						False
         __lowerCamelCase  :      List[Any]														=						max_length
         __lowerCamelCase  :      List[str]														=						2

         for model_class in self.all_generative_model_classes:
              __lowerCamelCase  :      Optional[int]														=						model_class(SCREAMING_SNAKE_CASE_      )

              __lowerCamelCase  :      Union[str, Any]														=						model.generate(SCREAMING_SNAKE_CASE_      ).sequences
              self.assertEqual(generation_outputs.shape[-1]     ,	SCREAMING_SNAKE_CASE_      )

              __lowerCamelCase  :      List[Any]														=						jit(model.generate      )
              __lowerCamelCase  :      str														=						jit_generate(SCREAMING_SNAKE_CASE_      ).sequences

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







    def  lowercase_							(				self      )							->	Optional[int]:
         __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase  :      Tuple														=						self._get_input_ids_and_config()
         __lowerCamelCase  :      List[Any]														=						False
         __lowerCamelCase  :      Any														=						max_length
         __lowerCamelCase  :      Tuple														=						2
         __lowerCamelCase  :      List[str]														=						2

         for model_class in self.all_generative_model_classes:
              __lowerCamelCase  :      List[str]														=						model_class(SCREAMING_SNAKE_CASE_      )

              __lowerCamelCase  :      Optional[int]														=						model.generate(SCREAMING_SNAKE_CASE_      ).sequences
              self.assertEqual(generation_outputs.shape[0]     ,	input_ids.shape[0] * config.num_return_sequences      )







    def  lowercase_							(				self      )							->	Dict:
         __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase  :      Tuple														=						self._get_input_ids_and_config()
         __lowerCamelCase  :      List[str]														=						True
         __lowerCamelCase  :      List[str]														=						max_length
         __lowerCamelCase  :      Union[str, Any]														=						0.8
         __lowerCamelCase  :      Optional[Any]														=						10
         __lowerCamelCase  :      Dict														=						0.3
         __lowerCamelCase  :      int														=						1
         __lowerCamelCase  :      int														=						8
         __lowerCamelCase  :      List[str]														=						9

         for model_class in self.all_generative_model_classes:
              __lowerCamelCase  :      Optional[int]														=						model_class(SCREAMING_SNAKE_CASE_      )

              __lowerCamelCase  :      Union[str, Any]														=						model.generate(SCREAMING_SNAKE_CASE_      ).sequences
              self.assertEqual(generation_outputs.shape[-1]     ,	SCREAMING_SNAKE_CASE_      )

              __lowerCamelCase  :      List[Any]														=						jit(model.generate      )
              __lowerCamelCase  :      int														=						jit_generate(SCREAMING_SNAKE_CASE_      ).sequences

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







    def  lowercase_							(				self      )							->	Optional[int]:
         __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase  :      Tuple														=						self._get_input_ids_and_config()
         __lowerCamelCase  :      Optional[Any]														=						max_length
         __lowerCamelCase  :      Any														=						1
         __lowerCamelCase  :      List[str]														=						8
         __lowerCamelCase  :      List[Any]														=						9

         for model_class in self.all_generative_model_classes:
              __lowerCamelCase  :      Tuple														=						model_class(SCREAMING_SNAKE_CASE_      )

              __lowerCamelCase  :      int														=						model.generate(SCREAMING_SNAKE_CASE_      ).sequences
              self.assertEqual(generation_outputs.shape[-1]     ,	SCREAMING_SNAKE_CASE_      )

              __lowerCamelCase  :      Optional[int]														=						jit(model.generate      )
              __lowerCamelCase  :      Union[str, Any]														=						jit_generate(SCREAMING_SNAKE_CASE_      ).sequences

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







    def  lowercase_							(				self      )							->	Optional[int]:
         __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase  :      Optional[Any]														=						self._get_input_ids_and_config()
         __lowerCamelCase  :      List[str]														=						max_length
         __lowerCamelCase  :      Dict														=						2
         __lowerCamelCase  :      List[str]														=						1
         __lowerCamelCase  :      Any														=						8
         __lowerCamelCase  :      Optional[int]														=						9

         for model_class in self.all_generative_model_classes:
              __lowerCamelCase  :      List[Any]														=						model_class(SCREAMING_SNAKE_CASE_      )

              __lowerCamelCase  :      Tuple														=						model.generate(SCREAMING_SNAKE_CASE_      ).sequences
              self.assertEqual(generation_outputs.shape[-1]     ,	SCREAMING_SNAKE_CASE_      )

              __lowerCamelCase  :      Optional[Any]														=						jit(model.generate      )
              __lowerCamelCase  :      Optional[int]														=						jit_generate(SCREAMING_SNAKE_CASE_      ).sequences

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







    def  lowercase_							(				self      )							->	str:
         __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase  :      int														=						self._get_input_ids_and_config()

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

         __lowerCamelCase  :      Tuple														=						False
         __lowerCamelCase  :      List[Any]														=						max_length

         for model_class in self.all_generative_model_classes:
              __lowerCamelCase  :      Optional[int]														=						model_class(SCREAMING_SNAKE_CASE_      )

              __lowerCamelCase  :      Any														=						model.generate(SCREAMING_SNAKE_CASE_     ,	attention_mask=SCREAMING_SNAKE_CASE_      ).sequences
              self.assertEqual(generation_outputs.shape[-1]     ,	SCREAMING_SNAKE_CASE_      )

              __lowerCamelCase  :      Union[str, Any]														=						jit(model.generate      )
              __lowerCamelCase  :      Any														=						jit_generate(SCREAMING_SNAKE_CASE_     ,	attention_mask=SCREAMING_SNAKE_CASE_      ).sequences

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







    def  lowercase_							(				self      )							->	List[Any]:
         __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase  :      Optional[int]														=						self._get_input_ids_and_config()

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

         __lowerCamelCase  :      int														=						True
         __lowerCamelCase  :      Dict														=						max_length

         for model_class in self.all_generative_model_classes:
              __lowerCamelCase  :      str														=						model_class(SCREAMING_SNAKE_CASE_      )

              __lowerCamelCase  :      int														=						model.generate(SCREAMING_SNAKE_CASE_     ,	attention_mask=SCREAMING_SNAKE_CASE_      ).sequences
              self.assertEqual(generation_outputs.shape[-1]     ,	SCREAMING_SNAKE_CASE_      )

              __lowerCamelCase  :      int														=						jit(model.generate      )
              __lowerCamelCase  :      Union[str, Any]														=						jit_generate(SCREAMING_SNAKE_CASE_     ,	attention_mask=SCREAMING_SNAKE_CASE_      ).sequences

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







    def  lowercase_							(				self      )							->	Optional[int]:
         __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase  :      Union[str, Any]														=						self._get_input_ids_and_config()

         # pad attention mask on the left
         __lowerCamelCase  :      List[str]														=						attention_mask.at[(0, 0)].set(0      )

         __lowerCamelCase  :      Optional[Any]														=						2
         __lowerCamelCase  :      Tuple														=						max_length

         for model_class in self.all_generative_model_classes:
              __lowerCamelCase  :      int														=						model_class(SCREAMING_SNAKE_CASE_      )

              __lowerCamelCase  :      Tuple														=						model.generate(SCREAMING_SNAKE_CASE_     ,	attention_mask=SCREAMING_SNAKE_CASE_      ).sequences
              self.assertEqual(generation_outputs.shape[-1]     ,	SCREAMING_SNAKE_CASE_      )

              __lowerCamelCase  :      List[str]														=						jit(model.generate      )
              __lowerCamelCase  :      int														=						jit_generate(SCREAMING_SNAKE_CASE_     ,	attention_mask=SCREAMING_SNAKE_CASE_      ).sequences

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







@require_flax
class   UpperCAmelCase_    (unittest.TestCase    ):





    """simple docstring"""






    def  lowercase_							(				self      )							->	List[Any]:
         __lowerCamelCase  :      Optional[int]														=						AutoTokenizer.from_pretrained('hf-internal-testing/tiny-bert'      )
         __lowerCamelCase  :      Union[str, Any]														=						FlaxAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-bert-flax-only'      )

         __lowerCamelCase  :      Tuple														=						'Hello world'
         __lowerCamelCase  :      int														=						tokenizer(SCREAMING_SNAKE_CASE_     ,	return_tensors='np'      ).input_ids

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

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










'''simple docstring'''



# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union


A__						:   Any				=    re.compile(R"""^(?P<major>\d+)""" R"""\.(?P<minor>\d+)""" R"""\.(?P<patch>\d+)$""")





@total_ordering
@dataclass
class   UpperCAmelCase_    :





    """simple docstring"""






    lowerCamelCase    :   str
    lowerCamelCase    :   Optional[str]					=     None
    lowerCamelCase    :   Optional[Union[str, int]]					=     None
    lowerCamelCase    :   Optional[Union[str, int]]					=     None
    lowerCamelCase    :   Optional[Union[str, int]]					=     None







    def  lowercase_							(				self      )							->	List[str]:
         __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase  :      List[Any]														=						_str_to_version_tuple(self.version_str      )







    def __repr__(				self      )							->	Any:
         return f'{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}'







    @property
    def  lowercase_							(				self      )							->	int:
         return self.major, self.minor, self.patch







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_      )							->	List[Any]:
         if isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ):
              return Version(SCREAMING_SNAKE_CASE_      )
         elif isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ):
              return other
         raise TypeError(f'{other} (type {type(SCREAMING_SNAKE_CASE_      )}) cannot be compared to version.'      )







    def __eq__(				self     ,	SCREAMING_SNAKE_CASE_      )							->	Optional[Any]:
         try:
              __lowerCamelCase  :      Union[str, Any]														=						self._validate_operand(SCREAMING_SNAKE_CASE_      )
         except (TypeError, ValueError):
              return False
         else:
              return self.tuple == other.tuple







    def __lt__(				self     ,	SCREAMING_SNAKE_CASE_      )							->	List[Any]:
         __lowerCamelCase  :      List[Any]														=						self._validate_operand(SCREAMING_SNAKE_CASE_      )
         return self.tuple < other.tuple







    def __hash__(				self      )							->	List[str]:
         return hash(_version_tuple_to_str(self.tuple      )      )







    @classmethod
    def  lowercase_							(				cls     ,	SCREAMING_SNAKE_CASE_      )							->	List[str]:
         __lowerCamelCase  :      str														=						{f.name for f in dataclasses.fields(cls      )}
         return cls(**{k: v for k, v in dic.items() if k in field_names}      )







    def  lowercase_							(				self      )							->	str:
         return self.version_str







def      UpperCAmelCase__   (      UpperCAmelCase_       :							Union[str, Any]				)	->						str:
     __lowerCamelCase  :      str														=						_VERSION_REG.match(UpperCAmelCase_				)
     if not res:
          raise ValueError(F'Invalid version \'{version_str}\'. Format should be x.y.z with {{x,y,z}} being digits.'				)
     return tuple(int(UpperCAmelCase_				) for v in [res.group('major'				), res.group('minor'				), res.group('patch'				)]				)



def      UpperCAmelCase__   (      UpperCAmelCase_       :							List[str]				)	->						Dict:
     return ".".join(str(UpperCAmelCase_				) for v in version_tuple				)






