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     :  Tuple					     =       "0.12"  # assumed parallelism: 8


if is_torch_available():
      import torch


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase=None				):
      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      a__ (		__UpperCamelCase     ,      __UpperCamelCase=None				):
      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   lowerCamelCase					:
  """simple docstring"""







  lowerCamelCase__     =						None
  lowerCamelCase__     =						()







  def    __A     (							self :       Tuple   )  ->	int:
        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(__magic_name__   )
        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 :       Optional[int]   )  ->	Any:
        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(__magic_name__   )

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

              SCREAMING_SNAKE_CASE_          =  flax_model.generate(__magic_name__   ).sequences
              SCREAMING_SNAKE_CASE_          =  pt_model.generate(torch.tensor(__magic_name__  ,     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 :       List[Any]   )  ->	Optional[Any]:
        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(__magic_name__   )

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

              SCREAMING_SNAKE_CASE_          =  jit(model.generate   )
              SCREAMING_SNAKE_CASE_          =  jit_generate(__magic_name__   ).sequences

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







  def    __A     (							self :       Optional[int]   )  ->	int:
        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(__magic_name__   )

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

              SCREAMING_SNAKE_CASE_          =  jit(model.generate   )
              SCREAMING_SNAKE_CASE_          =  jit_generate(__magic_name__   ).sequences

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







  def    __A     (							self :       Optional[Any]   )  ->	List[str]:
        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(__magic_name__   )

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

              SCREAMING_SNAKE_CASE_          =  jit(model.generate   )
              SCREAMING_SNAKE_CASE_          =  jit_generate(__magic_name__   ).sequences

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







  def    __A     (							self :       Optional[Any]   )  ->	Union[str, Any]:
        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(__magic_name__   )

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







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

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

              SCREAMING_SNAKE_CASE_          =  jit(model.generate   )
              SCREAMING_SNAKE_CASE_          =  jit_generate(__magic_name__   ).sequences

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







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

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

              SCREAMING_SNAKE_CASE_          =  jit(model.generate   )
              SCREAMING_SNAKE_CASE_          =  jit_generate(__magic_name__   ).sequences

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







  def    __A     (							self :       int   )  ->	Tuple:
        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(__magic_name__   )

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

              SCREAMING_SNAKE_CASE_          =  jit(model.generate   )
              SCREAMING_SNAKE_CASE_          =  jit_generate(__magic_name__   ).sequences

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







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

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

              SCREAMING_SNAKE_CASE_          =  jit(model.generate   )
              SCREAMING_SNAKE_CASE_          =  jit_generate(__magic_name__  ,     attention_mask=__magic_name__   ).sequences

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







  def    __A     (							self :       Any   )  ->	Tuple:
        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(__magic_name__   )

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

              SCREAMING_SNAKE_CASE_          =  jit(model.generate   )
              SCREAMING_SNAKE_CASE_          =  jit_generate(__magic_name__  ,     attention_mask=__magic_name__   ).sequences

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







  def    __A     (							self :       Dict   )  ->	str:
        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(__magic_name__   )

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

              SCREAMING_SNAKE_CASE_          =  jit(model.generate   )
              SCREAMING_SNAKE_CASE_          =  jit_generate(__magic_name__  ,     attention_mask=__magic_name__   ).sequences

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



@require_flax
class   lowerCamelCase					(unittest.TestCase					):
  """simple docstring"""







  def    __A     (							self :       str   )  ->	str:
        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(__magic_name__  ,     return_tensors="np"   ).input_ids

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

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



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A     :  str					     =       {
    "configuration_rembert": ["REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RemBertConfig", "RemBertOnnxConfig"]
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Tuple					     =       ["RemBertTokenizer"]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Tuple					     =       ["RemBertTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Tuple					     =       [
          "REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "RemBertForCausalLM",
          "RemBertForMaskedLM",
          "RemBertForMultipleChoice",
          "RemBertForQuestionAnswering",
          "RemBertForSequenceClassification",
          "RemBertForTokenClassification",
          "RemBertLayer",
          "RemBertModel",
          "RemBertPreTrainedModel",
          "load_tf_weights_in_rembert",
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  List[str]					     =       [
          "TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFRemBertForCausalLM",
          "TFRemBertForMaskedLM",
          "TFRemBertForMultipleChoice",
          "TFRemBertForQuestionAnswering",
          "TFRemBertForSequenceClassification",
          "TFRemBertForTokenClassification",
          "TFRemBertLayer",
          "TFRemBertModel",
          "TFRemBertPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_rembert import RemBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_rembert_fast import RemBertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_rembert import (
                REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                RemBertForCausalLM,
                RemBertForMaskedLM,
                RemBertForMultipleChoice,
                RemBertForQuestionAnswering,
                RemBertForSequenceClassification,
                RemBertForTokenClassification,
                RemBertLayer,
                RemBertModel,
                RemBertPreTrainedModel,
                load_tf_weights_in_rembert,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_rembert import (
                TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRemBertForCausalLM,
                TFRemBertForMaskedLM,
                TFRemBertForMultipleChoice,
                TFRemBertForQuestionAnswering,
                TFRemBertForSequenceClassification,
                TFRemBertForTokenClassification,
                TFRemBertLayer,
                TFRemBertModel,
                TFRemBertPreTrainedModel,
            )


else:
      import sys

      A     :  Tuple					     =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


