

import unittest

import numpy as np

from transformers import is_flax_available
from transformers.testing_utils import require_flax

from ..test_modeling_flax_common import ids_tensor


if is_flax_available():
   import jax
   import jax.numpy as jnp

   from transformers.generation import (
       FlaxForcedBOSTokenLogitsProcessor,
       FlaxForcedEOSTokenLogitsProcessor,
       FlaxLogitsProcessorList,
       FlaxMinLengthLogitsProcessor,
       FlaxTemperatureLogitsWarper,
       FlaxTopKLogitsWarper,
       FlaxTopPLogitsWarper,
   )



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






  def 	SCREAMING_SNAKE_CASE   (      self			:   str	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   int   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						jnp.ones((batch_size, length)   ) / length
     return scores







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






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						None
     __lowerCAmelCase  :      int    =						20

     __lowerCAmelCase  :      Optional[Any]    =						self._get_uniform_logits(batch_size=2	,							length=lowerCAmelCase   )

     # tweak scores to not be uniform anymore
     __lowerCAmelCase  :      Dict    =						scores.at[1, 5].set((1 / length) + 0.1   )  # peak, 1st batch
     __lowerCAmelCase  :      Tuple    =						scores.at[1, 10].set((1 / length) - 0.4   )  # valley, 1st batch

     # compute softmax
     __lowerCAmelCase  :      Dict    =						jax.nn.softmax(lowerCAmelCase	,							axis=-1   )

     __lowerCAmelCase  :      int    =						FlaxTemperatureLogitsWarper(temperature=0.5   )
     __lowerCAmelCase  :      Optional[int]    =						FlaxTemperatureLogitsWarper(temperature=1.3   )

     __lowerCAmelCase  :      Union[str, Any]    =						jax.nn.softmax(temp_dist_warper_sharper(lowerCAmelCase	,							scores.copy()	,							cur_len=lowerCAmelCase   )	,							axis=-1   )
     __lowerCAmelCase  :      int    =						jax.nn.softmax(temp_dist_warper_smoother(lowerCAmelCase	,							scores.copy()	,							cur_len=lowerCAmelCase   )	,							axis=-1   )

     # uniform distribution stays uniform
     self.assertTrue(jnp.allclose(probs[0, :]	,							warped_prob_sharp[0, :]	,							atol=1e-3   )   )
     self.assertTrue(jnp.allclose(probs[0, :]	,							warped_prob_smooth[0, :]	,							atol=1e-3   )   )

     # sharp peaks get higher, valleys get lower
     self.assertLess(probs[1, :].max()	,							warped_prob_sharp[1, :].max()   )
     self.assertGreater(probs[1, :].min()	,							warped_prob_sharp[1, :].min()   )

     # smooth peaks get lower, valleys get higher
     self.assertGreater(probs[1, :].max()	,							warped_prob_smooth[1, :].max()   )
     self.assertLess(probs[1, :].min()	,							warped_prob_smooth[1, :].min()   )







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






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						None
     __lowerCAmelCase  :      Optional[Any]    =						10
     __lowerCAmelCase  :      Tuple    =						2

     # create ramp distribution
     __lowerCAmelCase  :      Tuple    =						np.broadcast_to(np.arange(lowerCAmelCase   )[None, :]	,							(batch_size, vocab_size)   ).copy()
     __lowerCAmelCase  :      List[Any]    =						ramp_logits[1:, : vocab_size // 2] + vocab_size

     __lowerCAmelCase  :      Dict    =						FlaxTopKLogitsWarper(3   )

     __lowerCAmelCase  :      str    =						top_k_warp(lowerCAmelCase	,							lowerCAmelCase	,							cur_len=lowerCAmelCase   )

     # check that correct tokens are filtered
     self.assertListEqual(jnp.isinf(scores[0]   ).tolist()	,							7 * [True] + 3 * [False]   )
     self.assertListEqual(jnp.isinf(scores[1]   ).tolist()	,							2 * [True] + 3 * [False] + 5 * [True]   )

     # check special case
     __lowerCAmelCase  :      Tuple    =						5
     __lowerCAmelCase  :      Tuple    =						FlaxTopKLogitsWarper(top_k=1	,							filter_value=0.0	,							min_tokens_to_keep=3   )

     __lowerCAmelCase  :      str    =						np.broadcast_to(np.arange(lowerCAmelCase   )[None, :]	,							(batch_size, length)   ).copy()
     __lowerCAmelCase  :      Any    =						top_k_warp_safety_check(lowerCAmelCase	,							lowerCAmelCase	,							cur_len=lowerCAmelCase   )

     # min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
     self.assertListEqual((scores == 0.0).sum(axis=-1   ).tolist()	,							[2, 2]   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Any   )							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Union[str, Any]    =						None
     __lowerCAmelCase  :      Optional[int]    =						10
     __lowerCAmelCase  :      Optional[Any]    =						2

     # create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
     __lowerCAmelCase  :      Optional[int]    =						np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]]   )   )

     __lowerCAmelCase  :      Optional[Any]    =						FlaxTopPLogitsWarper(0.8   )
     __lowerCAmelCase  :      str    =						np.exp(top_p_warp(lowerCAmelCase	,							lowerCAmelCase	,							cur_len=lowerCAmelCase   )   )

     # dist should be filtered to keep min num values so that sum is >= top_p
     # exp (-inf) => 0
     __lowerCAmelCase  :      str    =						np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]]   )
     self.assertTrue(np.allclose(lowerCAmelCase	,							lowerCAmelCase	,							atol=1e-3   )   )

     # check edge cases with negative and extreme logits
     __lowerCAmelCase  :      List[Any]    =						np.broadcast_to(np.arange(lowerCAmelCase   )[None, :]	,							(batch_size, vocab_size)   ).copy() - (
         vocab_size // 2
     )

     # make ramp_logits more extreme
     __lowerCAmelCase  :      Tuple    =						ramp_logits[1] * 100.0

     # make sure at least 2 tokens are kept
     __lowerCAmelCase  :      Optional[Any]    =						FlaxTopPLogitsWarper(0.9	,							min_tokens_to_keep=2	,							filter_value=0.0   )
     __lowerCAmelCase  :      Dict    =						top_p_warp(lowerCAmelCase	,							lowerCAmelCase	,							cur_len=lowerCAmelCase   )

     # first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
     self.assertListEqual((filtered_dist != 0.0).sum(axis=-1   ).tolist()	,							[3, 2]   )







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






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						20
     __lowerCAmelCase  :      List[str]    =						4
     __lowerCAmelCase  :      Dict    =						0

     __lowerCAmelCase  :      Tuple    =						FlaxMinLengthLogitsProcessor(min_length=10	,							eos_token_id=lowerCAmelCase   )

     # check that min length is applied at length 5
     __lowerCAmelCase  :      List[str]    =						ids_tensor((batch_size, 20)	,							vocab_size=20   )
     __lowerCAmelCase  :      List[str]    =						5
     __lowerCAmelCase  :      Optional[int]    =						self._get_uniform_logits(lowerCAmelCase	,							lowerCAmelCase   )
     __lowerCAmelCase  :      Dict    =						min_dist_processor(lowerCAmelCase	,							lowerCAmelCase	,							cur_len=lowerCAmelCase   )
     self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist()	,							4 * [-float("""inf"""   )]   )

     # check that min length is not applied anymore at length 15
     __lowerCAmelCase  :      Any    =						self._get_uniform_logits(lowerCAmelCase	,							lowerCAmelCase   )
     __lowerCAmelCase  :      Tuple    =						15
     __lowerCAmelCase  :      List[Any]    =						min_dist_processor(lowerCAmelCase	,							lowerCAmelCase	,							cur_len=lowerCAmelCase   )
     self.assertFalse(jnp.isinf(lowerCAmelCase   ).any()   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     str:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						20
     __lowerCAmelCase  :      int    =						4
     __lowerCAmelCase  :      Optional[int]    =						0

     __lowerCAmelCase  :      Optional[Any]    =						FlaxForcedBOSTokenLogitsProcessor(bos_token_id=lowerCAmelCase   )

     # check that all scores are -inf except the bos_token_id score
     __lowerCAmelCase  :      Any    =						ids_tensor((batch_size, 1)	,							vocab_size=20   )
     __lowerCAmelCase  :      List[str]    =						1
     __lowerCAmelCase  :      Any    =						self._get_uniform_logits(lowerCAmelCase	,							lowerCAmelCase   )
     __lowerCAmelCase  :      Tuple    =						logits_processor(lowerCAmelCase	,							lowerCAmelCase	,							cur_len=lowerCAmelCase   )
     self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :]   ).all()   )
     self.assertListEqual(scores[:, bos_token_id].tolist()	,							4 * [0]   )  # score for bos_token_id shold be zero

     # check that bos_token_id is not forced if current length is greater than 1
     __lowerCAmelCase  :      List[Any]    =						3
     __lowerCAmelCase  :      Tuple    =						self._get_uniform_logits(lowerCAmelCase	,							lowerCAmelCase   )
     __lowerCAmelCase  :      Optional[int]    =						logits_processor(lowerCAmelCase	,							lowerCAmelCase	,							cur_len=lowerCAmelCase   )
     self.assertFalse(jnp.isinf(lowerCAmelCase   ).any()   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     Optional[int]:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						20
     __lowerCAmelCase  :      Any    =						4
     __lowerCAmelCase  :      Dict    =						0
     __lowerCAmelCase  :      List[str]    =						5

     __lowerCAmelCase  :      Any    =						FlaxForcedEOSTokenLogitsProcessor(max_length=lowerCAmelCase	,							eos_token_id=lowerCAmelCase   )

     # check that all scores are -inf except the eos_token_id when max_length is reached
     __lowerCAmelCase  :      List[str]    =						ids_tensor((batch_size, 4)	,							vocab_size=20   )
     __lowerCAmelCase  :      Dict    =						4
     __lowerCAmelCase  :      List[str]    =						self._get_uniform_logits(lowerCAmelCase	,							lowerCAmelCase   )
     __lowerCAmelCase  :      Dict    =						logits_processor(lowerCAmelCase	,							lowerCAmelCase	,							cur_len=lowerCAmelCase   )
     self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :]   ).all()   )
     self.assertListEqual(scores[:, eos_token_id].tolist()	,							4 * [0]   )  # score for eos_token_id should be zero

     # check that eos_token_id is not forced if max_length is not reached
     __lowerCAmelCase  :      Union[str, Any]    =						3
     __lowerCAmelCase  :      Tuple    =						self._get_uniform_logits(lowerCAmelCase	,							lowerCAmelCase   )
     __lowerCAmelCase  :      List[str]    =						logits_processor(lowerCAmelCase	,							lowerCAmelCase	,							cur_len=lowerCAmelCase   )
     self.assertFalse(jnp.isinf(lowerCAmelCase   ).any()   )







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






     """simple docstring"""







     __lowerCAmelCase  :      Tuple    =						4
     __lowerCAmelCase  :      Optional[Any]    =						10
     __lowerCAmelCase  :      List[Any]    =						15
     __lowerCAmelCase  :      List[str]    =						2
     __lowerCAmelCase  :      List[Any]    =						1
     __lowerCAmelCase  :      Any    =						15

     # dummy input_ids and scores
     __lowerCAmelCase  :      int    =						ids_tensor((batch_size, sequence_length)	,							lowerCAmelCase   )
     __lowerCAmelCase  :      str    =						input_ids.copy()

     __lowerCAmelCase  :      Optional[Any]    =						self._get_uniform_logits(lowerCAmelCase	,							lowerCAmelCase   )
     __lowerCAmelCase  :      List[Any]    =						scores.copy()

     # instantiate all dist processors
     __lowerCAmelCase  :      Tuple    =						FlaxTemperatureLogitsWarper(temperature=0.5   )
     __lowerCAmelCase  :      List[str]    =						FlaxTopKLogitsWarper(3   )
     __lowerCAmelCase  :      Tuple    =						FlaxTopPLogitsWarper(0.8   )

     # instantiate all logits processors
     __lowerCAmelCase  :      Optional[Any]    =						FlaxMinLengthLogitsProcessor(min_length=10	,							eos_token_id=lowerCAmelCase   )
     __lowerCAmelCase  :      Optional[int]    =						FlaxForcedBOSTokenLogitsProcessor(bos_token_id=lowerCAmelCase   )
     __lowerCAmelCase  :      int    =						FlaxForcedEOSTokenLogitsProcessor(max_length=lowerCAmelCase	,							eos_token_id=lowerCAmelCase   )

     __lowerCAmelCase  :      Optional[Any]    =						10

     # no processor list
     __lowerCAmelCase  :      Optional[int]    =						temp_dist_warp(lowerCAmelCase	,							lowerCAmelCase	,							cur_len=lowerCAmelCase   )
     __lowerCAmelCase  :      int    =						top_k_warp(lowerCAmelCase	,							lowerCAmelCase	,							cur_len=lowerCAmelCase   )
     __lowerCAmelCase  :      str    =						top_p_warp(lowerCAmelCase	,							lowerCAmelCase	,							cur_len=lowerCAmelCase   )
     __lowerCAmelCase  :      Union[str, Any]    =						min_dist_proc(lowerCAmelCase	,							lowerCAmelCase	,							cur_len=lowerCAmelCase   )
     __lowerCAmelCase  :      Any    =						bos_dist_proc(lowerCAmelCase	,							lowerCAmelCase	,							cur_len=lowerCAmelCase   )
     __lowerCAmelCase  :      List[Any]    =						eos_dist_proc(lowerCAmelCase	,							lowerCAmelCase	,							cur_len=lowerCAmelCase   )

     # with processor list
     __lowerCAmelCase  :      Optional[Any]    =						FlaxLogitsProcessorList(
         [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]   )
     __lowerCAmelCase  :      str    =						processor(lowerCAmelCase	,							lowerCAmelCase	,							cur_len=lowerCAmelCase   )

     # scores should be equal
     self.assertTrue(jnp.allclose(lowerCAmelCase	,							lowerCAmelCase	,							atol=1e-3   )   )

     # input_ids should never be changed
     self.assertListEqual(input_ids.tolist()	,							input_ids_comp.tolist()   )







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






     """simple docstring"""







     __lowerCAmelCase  :      Union[str, Any]    =						4
     __lowerCAmelCase  :      List[Any]    =						10
     __lowerCAmelCase  :      int    =						15
     __lowerCAmelCase  :      Dict    =						2
     __lowerCAmelCase  :      str    =						1
     __lowerCAmelCase  :      Optional[Any]    =						15

     # dummy input_ids and scores
     __lowerCAmelCase  :      Any    =						ids_tensor((batch_size, sequence_length)	,							lowerCAmelCase   )
     __lowerCAmelCase  :      Dict    =						input_ids.copy()

     __lowerCAmelCase  :      str    =						self._get_uniform_logits(lowerCAmelCase	,							lowerCAmelCase   )
     __lowerCAmelCase  :      List[str]    =						scores.copy()

     # instantiate all dist processors
     __lowerCAmelCase  :      List[str]    =						FlaxTemperatureLogitsWarper(temperature=0.5   )
     __lowerCAmelCase  :      Optional[int]    =						FlaxTopKLogitsWarper(3   )
     __lowerCAmelCase  :      str    =						FlaxTopPLogitsWarper(0.8   )

     # instantiate all logits processors
     __lowerCAmelCase  :      Any    =						FlaxMinLengthLogitsProcessor(min_length=10	,							eos_token_id=lowerCAmelCase   )
     __lowerCAmelCase  :      Any    =						FlaxForcedBOSTokenLogitsProcessor(bos_token_id=lowerCAmelCase   )
     __lowerCAmelCase  :      Union[str, Any]    =						FlaxForcedEOSTokenLogitsProcessor(max_length=lowerCAmelCase	,							eos_token_id=lowerCAmelCase   )

     __lowerCAmelCase  :      Dict    =						10

     # no processor list
     def run_no_processor_list(lowerCAmelCase			:   str	,							lowerCAmelCase			:   Dict	,							lowerCAmelCase			:   Optional[Any]   ):
        __lowerCAmelCase  :      Any    =						temp_dist_warp(lowerCAmelCase	,							lowerCAmelCase	,							cur_len=lowerCAmelCase   )
        __lowerCAmelCase  :      Optional[Any]    =						top_k_warp(lowerCAmelCase	,							lowerCAmelCase	,							cur_len=lowerCAmelCase   )
        __lowerCAmelCase  :      Dict    =						top_p_warp(lowerCAmelCase	,							lowerCAmelCase	,							cur_len=lowerCAmelCase   )
        __lowerCAmelCase  :      List[str]    =						min_dist_proc(lowerCAmelCase	,							lowerCAmelCase	,							cur_len=lowerCAmelCase   )
        __lowerCAmelCase  :      List[Any]    =						bos_dist_proc(lowerCAmelCase	,							lowerCAmelCase	,							cur_len=lowerCAmelCase   )
        __lowerCAmelCase  :      Optional[int]    =						eos_dist_proc(lowerCAmelCase	,							lowerCAmelCase	,							cur_len=lowerCAmelCase   )
        return scores

     # with processor list
     def run_processor_list(lowerCAmelCase			:   Dict	,							lowerCAmelCase			:   Optional[int]	,							lowerCAmelCase			:   Tuple   ):
        __lowerCAmelCase  :      Any    =						FlaxLogitsProcessorList(
            [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]   )
        __lowerCAmelCase  :      Any    =						processor(lowerCAmelCase	,							lowerCAmelCase	,							cur_len=lowerCAmelCase   )
        return scores

     __lowerCAmelCase  :      str    =						jax.jit(lowerCAmelCase   )
     __lowerCAmelCase  :      List[str]    =						jax.jit(lowerCAmelCase   )

     __lowerCAmelCase  :      Any    =						jitted_run_no_processor_list(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase   )
     __lowerCAmelCase  :      Tuple    =						jitted_run_processor_list(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase   )

     # scores should be equal
     self.assertTrue(jnp.allclose(lowerCAmelCase	,							lowerCAmelCase	,							atol=1e-3   )   )

     # input_ids should never be changed
     self.assertListEqual(input_ids.tolist()	,							input_ids_comp.tolist()   )





import unittest

from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       MODEL_FOR_PRETRAINING_MAPPING,
       MobileBertForMaskedLM,
       MobileBertForMultipleChoice,
       MobileBertForNextSentencePrediction,
       MobileBertForPreTraining,
       MobileBertForQuestionAnswering,
       MobileBertForSequenceClassification,
       MobileBertForTokenClassification,
       MobileBertModel,
   )



class SCREAMING_SNAKE_CASE     :
  """simple docstring"""






  def __init__(      self			:   Union[str, Any]	,							lowerCAmelCase			:   Dict	,							lowerCAmelCase			:   Dict=13	,							lowerCAmelCase			:   int=7	,							lowerCAmelCase			:   Any=True	,							lowerCAmelCase			:   str=True	,							lowerCAmelCase			:   Optional[Any]=True	,							lowerCAmelCase			:   str=True	,							lowerCAmelCase			:   Tuple=99	,							lowerCAmelCase			:   int=64	,							lowerCAmelCase			:   Any=32	,							lowerCAmelCase			:   str=5	,							lowerCAmelCase			:   List[str]=4	,							lowerCAmelCase			:   str=37	,							lowerCAmelCase			:   Union[str, Any]="gelu"	,							lowerCAmelCase			:   Dict=0.1	,							lowerCAmelCase			:   Dict=0.1	,							lowerCAmelCase			:   Optional[int]=5_12	,							lowerCAmelCase			:   List[str]=16	,							lowerCAmelCase			:   str=2	,							lowerCAmelCase			:   Union[str, Any]=0.02	,							lowerCAmelCase			:   Dict=3	,							lowerCAmelCase			:   int=4	,							lowerCAmelCase			:   Union[str, Any]=None	,							)							->     Optional[int]:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						parent
     __lowerCAmelCase  :      Tuple    =						batch_size
     __lowerCAmelCase  :      Dict    =						seq_length
     __lowerCAmelCase  :      List[str]    =						is_training
     __lowerCAmelCase  :      Dict    =						use_input_mask
     __lowerCAmelCase  :      Optional[int]    =						use_token_type_ids
     __lowerCAmelCase  :      List[str]    =						use_labels
     __lowerCAmelCase  :      Dict    =						vocab_size
     __lowerCAmelCase  :      List[str]    =						hidden_size
     __lowerCAmelCase  :      Optional[int]    =						embedding_size
     __lowerCAmelCase  :      Optional[int]    =						num_hidden_layers
     __lowerCAmelCase  :      Optional[Any]    =						num_attention_heads
     __lowerCAmelCase  :      Optional[Any]    =						intermediate_size
     __lowerCAmelCase  :      Optional[int]    =						hidden_act
     __lowerCAmelCase  :      Any    =						hidden_dropout_prob
     __lowerCAmelCase  :      Optional[int]    =						attention_probs_dropout_prob
     __lowerCAmelCase  :      List[str]    =						max_position_embeddings
     __lowerCAmelCase  :      Optional[Any]    =						type_vocab_size
     __lowerCAmelCase  :      Optional[Any]    =						type_sequence_label_size
     __lowerCAmelCase  :      Optional[Any]    =						initializer_range
     __lowerCAmelCase  :      Optional[Any]    =						num_labels
     __lowerCAmelCase  :      Union[str, Any]    =						num_choices
     __lowerCAmelCase  :      Union[str, Any]    =						scope







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     List[str]:






     """simple docstring"""







     __lowerCAmelCase  :      Union[str, Any]    =						ids_tensor([self.batch_size, self.seq_length]	,							self.vocab_size   )

     __lowerCAmelCase  :      Dict    =						None
     if self.use_input_mask:
        __lowerCAmelCase  :      Union[str, Any]    =						random_attention_mask([self.batch_size, self.seq_length]   )

     __lowerCAmelCase  :      List[str]    =						None
     if self.use_token_type_ids:
        __lowerCAmelCase  :      Tuple    =						ids_tensor([self.batch_size, self.seq_length]	,							self.type_vocab_size   )

     __lowerCAmelCase  :      List[Any]    =						None
     __lowerCAmelCase  :      Tuple    =						None
     __lowerCAmelCase  :      int    =						None
     if self.use_labels:
        __lowerCAmelCase  :      Tuple    =						ids_tensor([self.batch_size]	,							self.type_sequence_label_size   )
        __lowerCAmelCase  :      List[str]    =						ids_tensor([self.batch_size, self.seq_length]	,							self.num_labels   )
        __lowerCAmelCase  :      str    =						ids_tensor([self.batch_size]	,							self.num_choices   )

     __lowerCAmelCase  :      Optional[int]    =						self.get_config()

     return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







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






     """simple docstring"""







     return MobileBertConfig(
         vocab_size=self.vocab_size	,							hidden_size=self.hidden_size	,							num_hidden_layers=self.num_hidden_layers	,							num_attention_heads=self.num_attention_heads	,							intermediate_size=self.intermediate_size	,							embedding_size=self.embedding_size	,							hidden_act=self.hidden_act	,							hidden_dropout_prob=self.hidden_dropout_prob	,							attention_probs_dropout_prob=self.attention_probs_dropout_prob	,							max_position_embeddings=self.max_position_embeddings	,							type_vocab_size=self.type_vocab_size	,							is_decoder=lowerCAmelCase	,							initializer_range=self.initializer_range	,							)







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]	,							lowerCAmelCase			:   Dict	,							lowerCAmelCase			:   Optional[Any]	,							lowerCAmelCase			:   Optional[int]	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   Tuple	,							lowerCAmelCase			:   List[Any]   )							->     Tuple:






     """simple docstring"""







     __lowerCAmelCase  :      Any    =						MobileBertModel(config=lowerCAmelCase   )
     model.to(lowerCAmelCase   )
     model.eval()
     __lowerCAmelCase  :      Union[str, Any]    =						model(lowerCAmelCase	,							attention_mask=lowerCAmelCase	,							token_type_ids=lowerCAmelCase   )
     __lowerCAmelCase  :      List[Any]    =						model(lowerCAmelCase	,							token_type_ids=lowerCAmelCase   )
     __lowerCAmelCase  :      Tuple    =						model(lowerCAmelCase   )

     self.parent.assertEqual(result.last_hidden_state.shape	,							(self.batch_size, self.seq_length, self.hidden_size)   )
     self.parent.assertEqual(result.pooler_output.shape	,							(self.batch_size, self.hidden_size)   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   str	,							lowerCAmelCase			:   List[Any]	,							lowerCAmelCase			:   Dict	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   Optional[int]   )							->     Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Any    =						MobileBertForMaskedLM(config=lowerCAmelCase   )
     model.to(lowerCAmelCase   )
     model.eval()
     __lowerCAmelCase  :      List[Any]    =						model(lowerCAmelCase	,							attention_mask=lowerCAmelCase	,							token_type_ids=lowerCAmelCase	,							labels=lowerCAmelCase   )
     self.parent.assertEqual(result.logits.shape	,							(self.batch_size, self.seq_length, self.vocab_size)   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict	,							lowerCAmelCase			:   List[str]	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   str	,							lowerCAmelCase			:   Optional[Any]	,							lowerCAmelCase			:   Dict	,							lowerCAmelCase			:   Optional[int]   )							->     List[str]:






     """simple docstring"""







     __lowerCAmelCase  :      Tuple    =						MobileBertForNextSentencePrediction(config=lowerCAmelCase   )
     model.to(lowerCAmelCase   )
     model.eval()
     __lowerCAmelCase  :      List[Any]    =						model(
         lowerCAmelCase	,							attention_mask=lowerCAmelCase	,							token_type_ids=lowerCAmelCase	,							labels=lowerCAmelCase	,							)
     self.parent.assertEqual(result.logits.shape	,							(self.batch_size, 2)   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   Optional[int]	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   Tuple	,							lowerCAmelCase			:   List[Any]	,							lowerCAmelCase			:   List[Any]   )							->     Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Any    =						MobileBertForPreTraining(config=lowerCAmelCase   )
     model.to(lowerCAmelCase   )
     model.eval()
     __lowerCAmelCase  :      Optional[int]    =						model(
         lowerCAmelCase	,							attention_mask=lowerCAmelCase	,							token_type_ids=lowerCAmelCase	,							labels=lowerCAmelCase	,							next_sentence_label=lowerCAmelCase	,							)
     self.parent.assertEqual(result.prediction_logits.shape	,							(self.batch_size, self.seq_length, self.vocab_size)   )
     self.parent.assertEqual(result.seq_relationship_logits.shape	,							(self.batch_size, 2)   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict	,							lowerCAmelCase			:   Tuple	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   Dict	,							lowerCAmelCase			:   Optional[int]	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   Any   )							->     Any:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[int]    =						MobileBertForQuestionAnswering(config=lowerCAmelCase   )
     model.to(lowerCAmelCase   )
     model.eval()
     __lowerCAmelCase  :      int    =						model(
         lowerCAmelCase	,							attention_mask=lowerCAmelCase	,							token_type_ids=lowerCAmelCase	,							start_positions=lowerCAmelCase	,							end_positions=lowerCAmelCase	,							)
     self.parent.assertEqual(result.start_logits.shape	,							(self.batch_size, self.seq_length)   )
     self.parent.assertEqual(result.end_logits.shape	,							(self.batch_size, self.seq_length)   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict	,							lowerCAmelCase			:   List[Any]	,							lowerCAmelCase			:   str	,							lowerCAmelCase			:   str	,							lowerCAmelCase			:   List[str]	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   Union[str, Any]   )							->     Tuple:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						self.num_labels
     __lowerCAmelCase  :      int    =						MobileBertForSequenceClassification(lowerCAmelCase   )
     model.to(lowerCAmelCase   )
     model.eval()
     __lowerCAmelCase  :      Optional[int]    =						model(lowerCAmelCase	,							attention_mask=lowerCAmelCase	,							token_type_ids=lowerCAmelCase	,							labels=lowerCAmelCase   )
     self.parent.assertEqual(result.logits.shape	,							(self.batch_size, self.num_labels)   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]	,							lowerCAmelCase			:   Optional[Any]	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   Tuple	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   Optional[int]   )							->     Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						self.num_labels
     __lowerCAmelCase  :      Dict    =						MobileBertForTokenClassification(config=lowerCAmelCase   )
     model.to(lowerCAmelCase   )
     model.eval()
     __lowerCAmelCase  :      Tuple    =						model(lowerCAmelCase	,							attention_mask=lowerCAmelCase	,							token_type_ids=lowerCAmelCase	,							labels=lowerCAmelCase   )
     self.parent.assertEqual(result.logits.shape	,							(self.batch_size, self.seq_length, self.num_labels)   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]	,							lowerCAmelCase			:   List[Any]	,							lowerCAmelCase			:   Dict	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   Optional[Any]	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   List[Any]   )							->     Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Any    =						self.num_choices
     __lowerCAmelCase  :      List[Any]    =						MobileBertForMultipleChoice(config=lowerCAmelCase   )
     model.to(lowerCAmelCase   )
     model.eval()
     __lowerCAmelCase  :      Dict    =						input_ids.unsqueeze(1   ).expand(-1	,							self.num_choices	,							-1   ).contiguous()
     __lowerCAmelCase  :      Union[str, Any]    =						token_type_ids.unsqueeze(1   ).expand(-1	,							self.num_choices	,							-1   ).contiguous()
     __lowerCAmelCase  :      Optional[int]    =						input_mask.unsqueeze(1   ).expand(-1	,							self.num_choices	,							-1   ).contiguous()
     __lowerCAmelCase  :      Optional[int]    =						model(
         lowerCAmelCase	,							attention_mask=lowerCAmelCase	,							token_type_ids=lowerCAmelCase	,							labels=lowerCAmelCase	,							)
     self.parent.assertEqual(result.logits.shape	,							(self.batch_size, self.num_choices)   )







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






     """simple docstring"""







     __lowerCAmelCase  :      Tuple    =						self.prepare_config_and_inputs()
     (
         (
         __lowerCAmelCase
     )   ,(
         __lowerCAmelCase
     )   ,(
         __lowerCAmelCase
     )   ,(
         __lowerCAmelCase
     )   ,(
         __lowerCAmelCase
     )   ,(
         __lowerCAmelCase
     )   ,(
         __lowerCAmelCase
     )   ,
     )  :      List[Any]    =						config_and_inputs
     __lowerCAmelCase  :      List[str]    =						{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
     return config, inputs_dict






@require_torch
class SCREAMING_SNAKE_CASE     (  a_       ,							a_       ,							unittest.TestCase			):
  """simple docstring"""






  lowerCamelCase   :      str										=(
      (
          MobileBertModel,
          MobileBertForMaskedLM,
          MobileBertForMultipleChoice,
          MobileBertForNextSentencePrediction,
          MobileBertForPreTraining,
          MobileBertForQuestionAnswering,
          MobileBertForSequenceClassification,
          MobileBertForTokenClassification,
      )
      if is_torch_available()
      else ()
  )
  lowerCamelCase   :      Optional[int]										=(
      {
          "feature-extraction": MobileBertModel,
          "fill-mask": MobileBertForMaskedLM,
          "question-answering": MobileBertForQuestionAnswering,
          "text-classification": MobileBertForSequenceClassification,
          "token-classification": MobileBertForTokenClassification,
          "zero-shot": MobileBertForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  lowerCamelCase   :      Union[str, Any]										=True







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]	,							lowerCAmelCase			:   str	,							lowerCAmelCase			:   List[str]	,							lowerCAmelCase			:   List[Any]=False   )							->     List[str]:






     """simple docstring"""







     __lowerCAmelCase  :      Union[str, Any]    =						super()._prepare_for_class(lowerCAmelCase	,							lowerCAmelCase	,							return_labels=lowerCAmelCase   )

     if return_labels:
        if model_class in get_values(lowerCAmelCase   ):
           __lowerCAmelCase  :      Tuple    =						torch.zeros(
               (self.model_tester.batch_size, self.model_tester.seq_length)	,							dtype=torch.long	,							device=lowerCAmelCase   )
           __lowerCAmelCase  :      Union[str, Any]    =						torch.zeros(
               self.model_tester.batch_size	,							dtype=torch.long	,							device=lowerCAmelCase   )
     return inputs_dict







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






     """simple docstring"""







     __lowerCAmelCase  :      int    =						MobileBertModelTester(self   )
     __lowerCAmelCase  :      Optional[Any]    =						ConfigTester(self	,							config_class=lowerCAmelCase	,							hidden_size=37   )







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






     """simple docstring"""







     self.config_tester.run_common_tests()







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






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mobilebert_model(*lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     Tuple:






     """simple docstring"""







     __lowerCAmelCase  :      str    =						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mobilebert_for_masked_lm(*lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      str    =						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mobilebert_for_multiple_choice(*lowerCAmelCase   )







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






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     Optional[int]:






     """simple docstring"""







     __lowerCAmelCase  :      Tuple    =						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mobilebert_for_pretraining(*lowerCAmelCase   )







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






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mobilebert_for_question_answering(*lowerCAmelCase   )







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






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mobilebert_for_sequence_classification(*lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     Tuple:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mobilebert_for_token_classification(*lowerCAmelCase   )






def      snake_case_				(__A					:						Any					)      ->     Optional[Any]:
   return torch.tensor(
       __A ,       dtype=torch.long ,       device=__A ,       )


__UpperCAmelCase								=			1e-3



@require_torch
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE     (  unittest.TestCase			):
  """simple docstring"""






  @slow
  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						MobileBertModel.from_pretrained("""google/mobilebert-uncased"""   ).to(lowerCAmelCase   )
     __lowerCAmelCase  :      int    =						_long_tensor([[1_01, 71_10, 10_05, 10_56, 20_23, 1_13_33, 1_74_13, 10_29, 1_02]]   )
     with torch.no_grad():
        __lowerCAmelCase  :      List[str]    =						model(lowerCAmelCase   )[0]
     __lowerCAmelCase  :      List[Any]    =						torch.Size((1, 9, 5_12)   )
     self.assertEqual(output.shape	,							lowerCAmelCase   )
     __lowerCAmelCase  :      int    =						torch.tensor(
         [
             [
                 [-2.473_6526e07, 8.269_1656e04, 1.652_1838e05],
                 [-5.754_1704e-01, 3.905_6022e00, 4.401_1507e00],
                 [2.604_7359e00, 1.567_7652e00, -1.732_4188e-01],
             ]
         ]	,							device=lowerCAmelCase	,							)

     # MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
     # ~1 difference, it's therefore not a good idea to measure using addition.
     # Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
     # result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
     __lowerCAmelCase  :      Union[str, Any]    =						torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE   )
     __lowerCAmelCase  :      Union[str, Any]    =						torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE   )

     self.assertTrue(lower_bound and upper_bound   )


