





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   A							(						unittest.TestCase    ):



      def 						lowerCAmelCase__     (						self:    Dict		,	_lowerCAmelCase:    int		,	_lowerCAmelCase:    int							)    ->     List[str]:
         '''simple docstring'''

         UpperCAmelCase_           =jnp.ones((batch_size, length)							) / length
         return scores



      def 						lowerCAmelCase__     (						self:    List[str]							)    ->     Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_           =None
         UpperCAmelCase_           =20

         UpperCAmelCase_           =self._get_uniform_logits(batch_size=2		,	length=_lowerCAmelCase							)

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

         # compute softmax
         UpperCAmelCase_           =jax.nn.softmax(_lowerCAmelCase		,	axis=-1							)

         UpperCAmelCase_           =FlaxTemperatureLogitsWarper(temperature=0.5							)
         UpperCAmelCase_           =FlaxTemperatureLogitsWarper(temperature=1.3							)

         UpperCAmelCase_           =jax.nn.softmax(temp_dist_warper_sharper(_lowerCAmelCase		,	scores.copy()		,	cur_len=_lowerCAmelCase							)		,	axis=-1							)
         UpperCAmelCase_           =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 						lowerCAmelCase__     (						self:    Optional[Any]							)    ->     int:
         '''simple docstring'''

         UpperCAmelCase_           =None
         UpperCAmelCase_           =10
         UpperCAmelCase_           =2

         # create ramp distribution
         UpperCAmelCase_           =np.broadcast_to(np.arange(_lowerCAmelCase							)[None, :]		,	(batch_size, vocab_size)							).copy()
         UpperCAmelCase_           =ramp_logits[1:, : vocab_size // 2] + vocab_size

         UpperCAmelCase_           =FlaxTopKLogitsWarper(3							)

         UpperCAmelCase_           =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
         UpperCAmelCase_           =5
         UpperCAmelCase_           =FlaxTopKLogitsWarper(top_k=1		,	filter_value=0.0		,	min_tokens_to_keep=3							)

         UpperCAmelCase_           =np.broadcast_to(np.arange(_lowerCAmelCase							)[None, :]		,	(batch_size, length)							).copy()
         UpperCAmelCase_           =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 						lowerCAmelCase__     (						self:    Tuple							)    ->     List[Any]:
         '''simple docstring'''

         UpperCAmelCase_           =None
         UpperCAmelCase_           =10
         UpperCAmelCase_           =2

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

         UpperCAmelCase_           =FlaxTopPLogitsWarper(0.8							)
         UpperCAmelCase_           =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
         UpperCAmelCase_           =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
         UpperCAmelCase_           =np.broadcast_to(np.arange(_lowerCAmelCase							)[None, :]		,	(batch_size, vocab_size)							).copy() - (
             vocab_size // 2
         )

         # make ramp_logits more extreme
         UpperCAmelCase_           =ramp_logits[1] * 1_00.0

         # make sure at least 2 tokens are kept
         UpperCAmelCase_           =FlaxTopPLogitsWarper(0.9		,	min_tokens_to_keep=2		,	filter_value=0.0							)
         UpperCAmelCase_           =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 						lowerCAmelCase__     (						self:    Any							)    ->     Any:
         '''simple docstring'''

         UpperCAmelCase_           =20
         UpperCAmelCase_           =4
         UpperCAmelCase_           =0

         UpperCAmelCase_           =FlaxMinLengthLogitsProcessor(min_length=10		,	eos_token_id=_lowerCAmelCase							)

         # check that min length is applied at length 5
         UpperCAmelCase_           =ids_tensor((batch_size, 20)		,	vocab_size=20							)
         UpperCAmelCase_           =5
         UpperCAmelCase_           =self._get_uniform_logits(_lowerCAmelCase		,	_lowerCAmelCase							)
         UpperCAmelCase_           =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
         UpperCAmelCase_           =self._get_uniform_logits(_lowerCAmelCase		,	_lowerCAmelCase							)
         UpperCAmelCase_           =15
         UpperCAmelCase_           =min_dist_processor(_lowerCAmelCase		,	_lowerCAmelCase		,	cur_len=_lowerCAmelCase							)
         self.assertFalse(jnp.isinf(_lowerCAmelCase							).any()							)



      def 						lowerCAmelCase__     (						self:    Any							)    ->     Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_           =20
         UpperCAmelCase_           =4
         UpperCAmelCase_           =0

         UpperCAmelCase_           =FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_lowerCAmelCase							)

         # check that all scores are -inf except the bos_token_id score
         UpperCAmelCase_           =ids_tensor((batch_size, 1)		,	vocab_size=20							)
         UpperCAmelCase_           =1
         UpperCAmelCase_           =self._get_uniform_logits(_lowerCAmelCase		,	_lowerCAmelCase							)
         UpperCAmelCase_           =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
         UpperCAmelCase_           =3
         UpperCAmelCase_           =self._get_uniform_logits(_lowerCAmelCase		,	_lowerCAmelCase							)
         UpperCAmelCase_           =logits_processor(_lowerCAmelCase		,	_lowerCAmelCase		,	cur_len=_lowerCAmelCase							)
         self.assertFalse(jnp.isinf(_lowerCAmelCase							).any()							)



      def 						lowerCAmelCase__     (						self:    str							)    ->     Any:
         '''simple docstring'''

         UpperCAmelCase_           =20
         UpperCAmelCase_           =4
         UpperCAmelCase_           =0
         UpperCAmelCase_           =5

         UpperCAmelCase_           =FlaxForcedEOSTokenLogitsProcessor(max_length=_lowerCAmelCase		,	eos_token_id=_lowerCAmelCase							)

         # check that all scores are -inf except the eos_token_id when max_length is reached
         UpperCAmelCase_           =ids_tensor((batch_size, 4)		,	vocab_size=20							)
         UpperCAmelCase_           =4
         UpperCAmelCase_           =self._get_uniform_logits(_lowerCAmelCase		,	_lowerCAmelCase							)
         UpperCAmelCase_           =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
         UpperCAmelCase_           =3
         UpperCAmelCase_           =self._get_uniform_logits(_lowerCAmelCase		,	_lowerCAmelCase							)
         UpperCAmelCase_           =logits_processor(_lowerCAmelCase		,	_lowerCAmelCase		,	cur_len=_lowerCAmelCase							)
         self.assertFalse(jnp.isinf(_lowerCAmelCase							).any()							)



      def 						lowerCAmelCase__     (						self:    List[str]							)    ->     Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_           =4
         UpperCAmelCase_           =10
         UpperCAmelCase_           =15
         UpperCAmelCase_           =2
         UpperCAmelCase_           =1
         UpperCAmelCase_           =15

         # dummy input_ids and scores
         UpperCAmelCase_           =ids_tensor((batch_size, sequence_length)		,	_lowerCAmelCase							)
         UpperCAmelCase_           =input_ids.copy()

         UpperCAmelCase_           =self._get_uniform_logits(_lowerCAmelCase		,	_lowerCAmelCase							)
         UpperCAmelCase_           =scores.copy()

         # instantiate all dist processors
         UpperCAmelCase_           =FlaxTemperatureLogitsWarper(temperature=0.5							)
         UpperCAmelCase_           =FlaxTopKLogitsWarper(3							)
         UpperCAmelCase_           =FlaxTopPLogitsWarper(0.8							)

         # instantiate all logits processors
         UpperCAmelCase_           =FlaxMinLengthLogitsProcessor(min_length=10		,	eos_token_id=_lowerCAmelCase							)
         UpperCAmelCase_           =FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_lowerCAmelCase							)
         UpperCAmelCase_           =FlaxForcedEOSTokenLogitsProcessor(max_length=_lowerCAmelCase		,	eos_token_id=_lowerCAmelCase							)

         UpperCAmelCase_           =10

         # no processor list
         UpperCAmelCase_           =temp_dist_warp(_lowerCAmelCase		,	_lowerCAmelCase		,	cur_len=_lowerCAmelCase							)
         UpperCAmelCase_           =top_k_warp(_lowerCAmelCase		,	_lowerCAmelCase		,	cur_len=_lowerCAmelCase							)
         UpperCAmelCase_           =top_p_warp(_lowerCAmelCase		,	_lowerCAmelCase		,	cur_len=_lowerCAmelCase							)
         UpperCAmelCase_           =min_dist_proc(_lowerCAmelCase		,	_lowerCAmelCase		,	cur_len=_lowerCAmelCase							)
         UpperCAmelCase_           =bos_dist_proc(_lowerCAmelCase		,	_lowerCAmelCase		,	cur_len=_lowerCAmelCase							)
         UpperCAmelCase_           =eos_dist_proc(_lowerCAmelCase		,	_lowerCAmelCase		,	cur_len=_lowerCAmelCase							)

         # with processor list
         UpperCAmelCase_           =FlaxLogitsProcessorList(
             [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]							)
         UpperCAmelCase_           =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 						lowerCAmelCase__     (						self:    str							)    ->     Tuple:
         '''simple docstring'''

         UpperCAmelCase_           =4
         UpperCAmelCase_           =10
         UpperCAmelCase_           =15
         UpperCAmelCase_           =2
         UpperCAmelCase_           =1
         UpperCAmelCase_           =15

         # dummy input_ids and scores
         UpperCAmelCase_           =ids_tensor((batch_size, sequence_length)		,	_lowerCAmelCase							)
         UpperCAmelCase_           =input_ids.copy()

         UpperCAmelCase_           =self._get_uniform_logits(_lowerCAmelCase		,	_lowerCAmelCase							)
         UpperCAmelCase_           =scores.copy()

         # instantiate all dist processors
         UpperCAmelCase_           =FlaxTemperatureLogitsWarper(temperature=0.5							)
         UpperCAmelCase_           =FlaxTopKLogitsWarper(3							)
         UpperCAmelCase_           =FlaxTopPLogitsWarper(0.8							)

         # instantiate all logits processors
         UpperCAmelCase_           =FlaxMinLengthLogitsProcessor(min_length=10		,	eos_token_id=_lowerCAmelCase							)
         UpperCAmelCase_           =FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_lowerCAmelCase							)
         UpperCAmelCase_           =FlaxForcedEOSTokenLogitsProcessor(max_length=_lowerCAmelCase		,	eos_token_id=_lowerCAmelCase							)

         UpperCAmelCase_           =10

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

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

         UpperCAmelCase_           =jax.jit(_lowerCAmelCase							)
         UpperCAmelCase_           =jax.jit(_lowerCAmelCase							)

         UpperCAmelCase_           =jitted_run_no_processor_list(_lowerCAmelCase		,	_lowerCAmelCase		,	_lowerCAmelCase							)
         UpperCAmelCase_           =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()							)





'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


__lowercase        =    {
    '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',
    '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/config.json''',
    '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/config.json''',
    '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json''',
    '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/config.json''',
    '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/config.json''',
    '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/config.json''',
    '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json''',
}






class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	List[str]						=			'''albert'''



			def __init__(  self			,       __lowerCAmelCase=30000			,       __lowerCAmelCase=128			,       __lowerCAmelCase=4096			,       __lowerCAmelCase=12			,       __lowerCAmelCase=1			,       __lowerCAmelCase=64			,       __lowerCAmelCase=16384			,       __lowerCAmelCase=1			,       __lowerCAmelCase="gelu_new"			,       __lowerCAmelCase=0			,       __lowerCAmelCase=0			,       __lowerCAmelCase=512			,       __lowerCAmelCase=2			,       __lowerCAmelCase=0.02			,       __lowerCAmelCase=1E-1_2			,       __lowerCAmelCase=0.1			,       __lowerCAmelCase="absolute"			,       __lowerCAmelCase=0			,       __lowerCAmelCase=2			,       __lowerCAmelCase=3			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					super().__init__(pad_token_id=__lowerCAmelCase			,       bos_token_id=__lowerCAmelCase			,       eos_token_id=__lowerCAmelCase			,       **__lowerCAmelCase)

					lowerCAmelCase     =    vocab_size
					lowerCAmelCase     =    embedding_size
					lowerCAmelCase     =    hidden_size
					lowerCAmelCase     =    num_hidden_layers
					lowerCAmelCase     =    num_hidden_groups
					lowerCAmelCase     =    num_attention_heads
					lowerCAmelCase     =    inner_group_num
					lowerCAmelCase     =    hidden_act
					lowerCAmelCase     =    intermediate_size
					lowerCAmelCase     =    hidden_dropout_prob
					lowerCAmelCase     =    attention_probs_dropout_prob
					lowerCAmelCase     =    max_position_embeddings
					lowerCAmelCase     =    type_vocab_size
					lowerCAmelCase     =    initializer_range
					lowerCAmelCase     =    layer_norm_eps
					lowerCAmelCase     =    classifier_dropout_prob
					lowerCAmelCase     =    position_embedding_type







class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			@property
			def 				a_			(  self):




					"""simple docstring"""


					if self.task == "multiple-choice":
							lowerCAmelCase     =    {0: """batch""", 1: """choice""", 2: """sequence"""}
					else:
							lowerCAmelCase     =    {0: """batch""", 1: """sequence"""}
					return OrderedDict(
					    [
					        ("""input_ids""", dynamic_axis),
					        ("""attention_mask""", dynamic_axis),
					        ("""token_type_ids""", dynamic_axis),
					    ])




