

'''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

     lowerCamelCase			:				List[str] 			=					"""0.12"""  # assumed parallelism: 8


if is_torch_available():
     import torch


def 	_lowerCAmelCase (						_UpperCamelCase						:					str							,			_UpperCamelCase						:					str							,			_UpperCamelCase						:					List[Any]=None    )				->      Any:
     """simple docstring"""




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

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

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

     _SCREAMING_SNAKE_CASE							   =np.array(_UpperCamelCase							,			dtype=jnp.intaa    ).reshape(_UpperCamelCase    )

     return output


def 	_lowerCAmelCase (						_UpperCamelCase						:					int							,			_UpperCamelCase						:					Optional[int]=None    )				->      Union[str, Any]:
     """simple docstring"""




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







@require_flax
class 							A__   :
  A__					=    None
  A__					=    ()





  def 						A  (		self				:     str			)     ->   Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE							   =self.model_tester.prepare_config_and_inputs_for_common()

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

       _SCREAMING_SNAKE_CASE							   =jnp.ones_like(_a			)
       _SCREAMING_SNAKE_CASE							   =attention_mask[:max_batch_size, :sequence_length]

       # generate max 5 tokens
       _SCREAMING_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()`
            _SCREAMING_SNAKE_CASE							   =config.eos_token_id
       return config, input_ids, attention_mask, max_length





  @is_pt_flax_cross_test
  def 						A  (		self				:     Dict			)     ->   Optional[int]:





       '''simple docstring'''






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

       for model_class in self.all_generative_model_classes:
            _SCREAMING_SNAKE_CASE							   =model_class(_a			)

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

            _SCREAMING_SNAKE_CASE							   =flax_model.generate(_a			).sequences
            _SCREAMING_SNAKE_CASE							   =pt_model.generate(torch.tensor(_a							, dtype=torch.long			)			)

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

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





  def 						A  (		self				:     Any			)     ->   Optional[Any]:





       '''simple docstring'''






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

       for model_class in self.all_generative_model_classes:
            _SCREAMING_SNAKE_CASE							   =model_class(_a			)

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

            _SCREAMING_SNAKE_CASE							   =jit(model.generate			)
            _SCREAMING_SNAKE_CASE							   =jit_generate(_a			).sequences

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





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





       '''simple docstring'''






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

       for model_class in self.all_generative_model_classes:
            _SCREAMING_SNAKE_CASE							   =model_class(_a			)

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

            _SCREAMING_SNAKE_CASE							   =jit(model.generate			)
            _SCREAMING_SNAKE_CASE							   =jit_generate(_a			).sequences

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





  def 						A  (		self				:     str			)     ->   List[str]:





       '''simple docstring'''






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

       for model_class in self.all_generative_model_classes:
            _SCREAMING_SNAKE_CASE							   =model_class(_a			)

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

            _SCREAMING_SNAKE_CASE							   =jit(model.generate			)
            _SCREAMING_SNAKE_CASE							   =jit_generate(_a			).sequences

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





  def 						A  (		self				:     List[Any]			)     ->   List[str]:





       '''simple docstring'''






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

       for model_class in self.all_generative_model_classes:
            _SCREAMING_SNAKE_CASE							   =model_class(_a			)

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





  def 						A  (		self				:     Tuple			)     ->   List[str]:





       '''simple docstring'''






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

       for model_class in self.all_generative_model_classes:
            _SCREAMING_SNAKE_CASE							   =model_class(_a			)

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

            _SCREAMING_SNAKE_CASE							   =jit(model.generate			)
            _SCREAMING_SNAKE_CASE							   =jit_generate(_a			).sequences

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





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





       '''simple docstring'''






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

       for model_class in self.all_generative_model_classes:
            _SCREAMING_SNAKE_CASE							   =model_class(_a			)

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

            _SCREAMING_SNAKE_CASE							   =jit(model.generate			)
            _SCREAMING_SNAKE_CASE							   =jit_generate(_a			).sequences

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





  def 						A  (		self				:     Tuple			)     ->   Tuple:





       '''simple docstring'''






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

       for model_class in self.all_generative_model_classes:
            _SCREAMING_SNAKE_CASE							   =model_class(_a			)

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

            _SCREAMING_SNAKE_CASE							   =jit(model.generate			)
            _SCREAMING_SNAKE_CASE							   =jit_generate(_a			).sequences

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





  def 						A  (		self				:     Union[str, Any]			)     ->   Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE							   =self._get_input_ids_and_config()

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

       _SCREAMING_SNAKE_CASE							   =False
       _SCREAMING_SNAKE_CASE							   =max_length

       for model_class in self.all_generative_model_classes:
            _SCREAMING_SNAKE_CASE							   =model_class(_a			)

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

            _SCREAMING_SNAKE_CASE							   =jit(model.generate			)
            _SCREAMING_SNAKE_CASE							   =jit_generate(_a							, attention_mask=_a			).sequences

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





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





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE							   =self._get_input_ids_and_config()

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

       _SCREAMING_SNAKE_CASE							   =True
       _SCREAMING_SNAKE_CASE							   =max_length

       for model_class in self.all_generative_model_classes:
            _SCREAMING_SNAKE_CASE							   =model_class(_a			)

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

            _SCREAMING_SNAKE_CASE							   =jit(model.generate			)
            _SCREAMING_SNAKE_CASE							   =jit_generate(_a							, attention_mask=_a			).sequences

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





  def 						A  (		self				:     Tuple			)     ->   Union[str, Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE							   =self._get_input_ids_and_config()

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

       _SCREAMING_SNAKE_CASE							   =2
       _SCREAMING_SNAKE_CASE							   =max_length

       for model_class in self.all_generative_model_classes:
            _SCREAMING_SNAKE_CASE							   =model_class(_a			)

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

            _SCREAMING_SNAKE_CASE							   =jit(model.generate			)
            _SCREAMING_SNAKE_CASE							   =jit_generate(_a							, attention_mask=_a			).sequences

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







@require_flax
class 							A__   (	unittest.TestCase  ):





  def 						A  (		self				:     Union[str, Any]			)     ->   Optional[int]:





       '''simple docstring'''






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

       _SCREAMING_SNAKE_CASE							   ='Hello world'
       _SCREAMING_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'			):
            _SCREAMING_SNAKE_CASE							   ={'foo': 'bar'}
            model.generate(_a							, **_a			)



'''simple docstring'''
import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
lowerCamelCase			:				int 			=					logging.getLogger(__name__)

if __name__ == "__main__":
     lowerCamelCase			:				Optional[Any] 			=					argparse.ArgumentParser(
         description="Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)"
     )
     parser.add_argument(
         "--data_file", type=str, default="data/dump.bert-base-uncased.pickle", help="The binarized dataset."
     )
     parser.add_argument(
         "--token_counts_dump", type=str, default="data/token_counts.bert-base-uncased.pickle", help="The dump file."
     )
     parser.add_argument("--vocab_size", default=3_0_5_2_2, type=int)
     lowerCamelCase			:				Optional[Any] 			=					parser.parse_args()

     logger.info(f'''Loading data from {args.data_file}''')
     with open(args.data_file, "rb") as fp:
          lowerCamelCase			:				Optional[int] 			=					pickle.load(fp)

     logger.info("Counting occurrences for MLM.")
     lowerCamelCase			:				Dict 			=					Counter()
     for tk_ids in data:
          counter.update(tk_ids)
     lowerCamelCase			:				Tuple 			=					[0] * args.vocab_size
     for k, v in counter.items():
          lowerCamelCase			:				Any 			=					v

     logger.info(f'''Dump to {args.token_counts_dump}''')
     with open(args.token_counts_dump, "wb") as handle:
          pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)
