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

    _lowercase				:   str   				='''0.12'''  # assumed parallelism: 8


if is_torch_available():
    import torch



def A__	(      lowercase:  Optional[int],		lowercase:  Dict,		lowercase:  int=None       )					->		int:
       if rng is None:
              A    :  Optional[Any]								=random.Random()

       A    :  str								=1
       for dim in shape:
              total_dims *= dim

       A    :  Optional[Any]								=[]
       for _ in range(__A       ):
              values.append(rng.randint(0,		vocab_size - 1       )       )

       A    :  Tuple								=np.array(__A,		dtype=jnp.intaa       ).reshape(__A       )

       return output



def A__	(      lowercase:  List[Any],		lowercase:  Any=None       )					->		Tuple:
       A    :  int								=ids_tensor(__A,		vocab_size=2,		rng=__A       )
       # make sure that at least one token is attended to for each batch
       A    :  Dict								=1
       return attn_mask



@require_flax
class 			SCREAMING_SNAKE_CASE_			:
  '''simple docstring'''


  lowercase		:  Optional[Any]							  =					None
  lowercase		:  Tuple							  =					()







  def 		SCREAMING_SNAKE_CASE_		(					self     :       Optional[Any]  )     ->					Union[str, Any]:
         A	,	A    :  Optional[int]								=self.model_tester.prepare_config_and_inputs_for_common()

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

         A    :  int								=jnp.ones_like(lowercase__  )
         A    :  Any								=attention_mask[:max_batch_size, :sequence_length]

         # generate max 5 tokens
         A    :  Dict								=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()`
                A    :  int								=config.eos_token_id
         return config, input_ids, attention_mask, max_length







  @is_pt_flax_cross_test
  def 		SCREAMING_SNAKE_CASE_		(					self     :       int  )     ->					str:
         A	,	A	,	A	,	A    :  Tuple								=self._get_input_ids_and_config()
         A    :  Optional[Any]								=False
         A    :  Optional[Any]								=max_length
         A    :  Any								=0

         for model_class in self.all_generative_model_classes:
                A    :  Any								=model_class(lowercase__  )

                A    :  Optional[Any]								=model_class.__name__[4:]  # Skip the "Flax" at the beginning
                A    :  Dict								=getattr(lowercase__   ,						lowercase__  )
                A    :  Tuple								=pt_model_class(lowercase__  ).eval()
                A    :  List[Any]								=load_flax_weights_in_pytorch_model(lowercase__   ,						flax_model.params  )

                A    :  List[Any]								=flax_model.generate(lowercase__  ).sequences
                A    :  Tuple								=pt_model.generate(torch.tensor(lowercase__   ,						dtype=torch.long  )  )

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

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







  def 		SCREAMING_SNAKE_CASE_		(					self     :       Tuple  )     ->					Dict:
         A	,	A	,	A	,	A    :  int								=self._get_input_ids_and_config()
         A    :  Optional[int]								=False
         A    :  Dict								=max_length

         for model_class in self.all_generative_model_classes:
                A    :  Optional[int]								=model_class(lowercase__  )

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

                A    :  Any								=jit(model.generate  )
                A    :  Any								=jit_generate(lowercase__  ).sequences

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







  def 		SCREAMING_SNAKE_CASE_		(					self     :       Union[str, Any]  )     ->					List[Any]:
         A	,	A	,	A	,	A    :  Union[str, Any]								=self._get_input_ids_and_config()
         A    :  int								=True
         A    :  str								=max_length

         for model_class in self.all_generative_model_classes:
                A    :  Optional[int]								=model_class(lowercase__  )

                A    :  Any								=model.generate(lowercase__  ).sequences
                self.assertEqual(generation_outputs.shape[-1]   ,						lowercase__  )

                A    :  Tuple								=jit(model.generate  )
                A    :  Dict								=jit_generate(lowercase__  ).sequences

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







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

         for model_class in self.all_generative_model_classes:
                A    :  Union[str, Any]								=model_class(lowercase__  )

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

                A    :  Union[str, Any]								=jit(model.generate  )
                A    :  Union[str, Any]								=jit_generate(lowercase__  ).sequences

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







  def 		SCREAMING_SNAKE_CASE_		(					self     :       int  )     ->					str:
         A	,	A	,	A	,	A    :  Union[str, Any]								=self._get_input_ids_and_config()
         A    :  Union[str, Any]								=False
         A    :  Optional[Any]								=max_length
         A    :  Dict								=2
         A    :  Any								=2

         for model_class in self.all_generative_model_classes:
                A    :  int								=model_class(lowercase__  )

                A    :  Union[str, Any]								=model.generate(lowercase__  ).sequences
                self.assertEqual(generation_outputs.shape[0]   ,						input_ids.shape[0] * config.num_return_sequences  )







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

         for model_class in self.all_generative_model_classes:
                A    :  Dict								=model_class(lowercase__  )

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

                A    :  Any								=jit(model.generate  )
                A    :  str								=jit_generate(lowercase__  ).sequences

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







  def 		SCREAMING_SNAKE_CASE_		(					self     :       List[str]  )     ->					Optional[Any]:
         A	,	A	,	A	,	A    :  Tuple								=self._get_input_ids_and_config()
         A    :  Tuple								=max_length
         A    :  Any								=1
         A    :  str								=8
         A    :  Dict								=9

         for model_class in self.all_generative_model_classes:
                A    :  Optional[Any]								=model_class(lowercase__  )

                A    :  Any								=model.generate(lowercase__  ).sequences
                self.assertEqual(generation_outputs.shape[-1]   ,						lowercase__  )

                A    :  List[str]								=jit(model.generate  )
                A    :  Any								=jit_generate(lowercase__  ).sequences

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







  def 		SCREAMING_SNAKE_CASE_		(					self     :       List[str]  )     ->					Optional[int]:
         A	,	A	,	A	,	A    :  List[str]								=self._get_input_ids_and_config()
         A    :  Any								=max_length
         A    :  int								=2
         A    :  Any								=1
         A    :  List[str]								=8
         A    :  Union[str, Any]								=9

         for model_class in self.all_generative_model_classes:
                A    :  Any								=model_class(lowercase__  )

                A    :  List[str]								=model.generate(lowercase__  ).sequences
                self.assertEqual(generation_outputs.shape[-1]   ,						lowercase__  )

                A    :  Optional[Any]								=jit(model.generate  )
                A    :  int								=jit_generate(lowercase__  ).sequences

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







  def 		SCREAMING_SNAKE_CASE_		(					self     :       str  )     ->					Any:
         A	,	A	,	A	,	A    :  List[Any]								=self._get_input_ids_and_config()

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

         A    :  str								=False
         A    :  Optional[Any]								=max_length

         for model_class in self.all_generative_model_classes:
                A    :  Union[str, Any]								=model_class(lowercase__  )

                A    :  List[str]								=model.generate(lowercase__   ,						attention_mask=lowercase__  ).sequences
                self.assertEqual(generation_outputs.shape[-1]   ,						lowercase__  )

                A    :  Tuple								=jit(model.generate  )
                A    :  Union[str, Any]								=jit_generate(lowercase__   ,						attention_mask=lowercase__  ).sequences

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







  def 		SCREAMING_SNAKE_CASE_		(					self     :       int  )     ->					Any:
         A	,	A	,	A	,	A    :  List[str]								=self._get_input_ids_and_config()

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

         A    :  Any								=True
         A    :  List[Any]								=max_length

         for model_class in self.all_generative_model_classes:
                A    :  Optional[int]								=model_class(lowercase__  )

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

                A    :  Dict								=jit(model.generate  )
                A    :  int								=jit_generate(lowercase__   ,						attention_mask=lowercase__  ).sequences

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







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

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

         A    :  str								=2
         A    :  Any								=max_length

         for model_class in self.all_generative_model_classes:
                A    :  Optional[Any]								=model_class(lowercase__  )

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

                A    :  str								=jit(model.generate  )
                A    :  Tuple								=jit_generate(lowercase__   ,						attention_mask=lowercase__  ).sequences

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







@require_flax
class 			SCREAMING_SNAKE_CASE_			(       unittest.TestCase      ):
  '''simple docstring'''


  def 		SCREAMING_SNAKE_CASE_		(					self     :       Dict  )     ->					List[Any]:
         A    :  int								=AutoTokenizer.from_pretrained('hf-internal-testing/tiny-bert'  )
         A    :  Optional[int]								=FlaxAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-bert-flax-only'  )

         A    :  str								='Hello world'
         A    :  List[str]								=tokenizer(lowercase__   ,						return_tensors='np'  ).input_ids

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

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









from typing import Dict, List, Optional

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_lowerCamelCase      :					Any					    =      logging.get_logger(__name__)


_lowerCamelCase      :					Tuple					    =      {
    '''nielsr/canine-s''': 2048,
}

# Unicode defines 1,114,112 total “codepoints”
_lowerCamelCase      :					List[str]					    =      111_4112

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
_lowerCamelCase      :					List[str]					    =      0

_lowerCamelCase      :					Any					    =      0xe_0_0_0
_lowerCamelCase      :					Union[str, Any]					    =      0xe_0_0_1
_lowerCamelCase      :					Any					    =      0xe_0_0_2
_lowerCamelCase      :					List[str]					    =      0xe_0_0_3
_lowerCamelCase      :					Any					    =      0xe_0_0_4

# Maps special codepoints to human-readable names.
_lowerCamelCase      :					Dict[int, str]					    =      {
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: "[CLS]",
    SEP: "[SEP]",
    BOS: "[BOS]",
    MASK: "[MASK]",
    PAD: "[PAD]",
    RESERVED: "[RESERVED]",
}

# Maps special codepoint human-readable names to their codepoint values.
_lowerCamelCase      :					Dict[str, int]					    =      {name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}





class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       lowercase_						=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES

       def __init__(							self ,   lowercase__=chr(lowercase__     ) ,   lowercase__=chr(lowercase__     ) ,   lowercase__=chr(lowercase__     ) ,   lowercase__=chr(lowercase__     ) ,   lowercase__=chr(lowercase__     ) ,   lowercase__=chr(lowercase__     ) ,   lowercase__=False ,   lowercase__=2_0_4_8 ,   **lowercase__ ,   ):







             '''simple docstring'''



             __A    =AddedToken(lowercase__ ,   lstrip=lowercase__ ,   rstrip=lowercase__     ) if isinstance(lowercase__ ,   lowercase__     ) else bos_token
             __A    =AddedToken(lowercase__ ,   lstrip=lowercase__ ,   rstrip=lowercase__     ) if isinstance(lowercase__ ,   lowercase__     ) else eos_token
             __A    =AddedToken(lowercase__ ,   lstrip=lowercase__ ,   rstrip=lowercase__     ) if isinstance(lowercase__ ,   lowercase__     ) else sep_token
             __A    =AddedToken(lowercase__ ,   lstrip=lowercase__ ,   rstrip=lowercase__     ) if isinstance(lowercase__ ,   lowercase__     ) else cls_token
             __A    =AddedToken(lowercase__ ,   lstrip=lowercase__ ,   rstrip=lowercase__     ) if isinstance(lowercase__ ,   lowercase__     ) else pad_token

             # Mask token behave like a normal word, i.e. include the space before it
             __A    =AddedToken(lowercase__ ,   lstrip=lowercase__ ,   rstrip=lowercase__     ) if isinstance(lowercase__ ,   lowercase__     ) else mask_token

             super().__init__(
                 bos_token=lowercase__ ,   eos_token=lowercase__ ,   sep_token=lowercase__ ,   cls_token=lowercase__ ,   pad_token=lowercase__ ,   mask_token=lowercase__ ,   add_prefix_space=lowercase__ ,   model_max_length=lowercase__ ,   **lowercase__ ,   )

             # Creates a mapping for looking up the IDs of special symbols.
             __A    ={}
             for codepoint, name in SPECIAL_CODEPOINTS.items():
                   __A    =codepoint

             # Creates a mapping for looking up the string forms of special symbol IDs.
             __A    ={
                 codepoint: name for name, codepoint in self._special_codepoints.items()
             }

             __A    =UNICODE_VOCAB_SIZE
             __A    =len(self._special_codepoints     )

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return self._unicode_vocab_size

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             return list(lowercase__     )

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             try:
                   return ord(lowercase__     )
             except TypeError:
                   raise ValueError(f'''invalid token: \'{token}\''''     )

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             try:
                   if index in SPECIAL_CODEPOINTS:
                         return SPECIAL_CODEPOINTS[index]
                   return chr(lowercase__     )
             except TypeError:
                   raise ValueError(f'''invalid id: {index}'''     )

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             return "".join(lowercase__     )

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = None     ):







             '''simple docstring'''



             __A    =[self.sep_token_id]
             __A    =[self.cls_token_id]

             __A    =cls + token_ids_a + sep
             if token_ids_a is not None:
                   result += token_ids_a + sep
             return result

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = None ,   lowercase__ = False     ):







             '''simple docstring'''



             if already_has_special_tokens:
                   return super().get_special_tokens_mask(
                       token_ids_a=lowercase__ ,   token_ids_a=lowercase__ ,   already_has_special_tokens=lowercase__     )

             __A    =[1] + ([0] * len(lowercase__     )) + [1]
             if token_ids_a is not None:
                   result += ([0] * len(lowercase__     )) + [1]
             return result

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = None     ):







             '''simple docstring'''



             __A    =[self.sep_token_id]
             __A    =[self.cls_token_id]

             __A    =len(cls + token_ids_a + sep     ) * [0]
             if token_ids_a is not None:
                   result += len(token_ids_a + sep     ) * [1]
             return result


       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = None     ):







             '''simple docstring'''



             return ()


