





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 					__snake_case	(  unittest.TestCase    ):


    '''simple docstring'''







    def  lowercase_					(       self   ,				A_   ,				A_     ):





        '''simple docstring'''

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



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			None
        SCREAMING_SNAKE_CASE__											=			20

        SCREAMING_SNAKE_CASE__											=			self._get_uniform_logits(batch_size=2   ,				length=A_     )

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

        # compute softmax
        SCREAMING_SNAKE_CASE__											=			jax.nn.softmax(A_   ,				axis=-1     )

        SCREAMING_SNAKE_CASE__											=			FlaxTemperatureLogitsWarper(temperature=0.5     )
        SCREAMING_SNAKE_CASE__											=			FlaxTemperatureLogitsWarper(temperature=1.3     )

        SCREAMING_SNAKE_CASE__											=			jax.nn.softmax(temp_dist_warper_sharper(A_   ,				scores.copy()   ,				cur_len=A_     )   ,				axis=-1     )
        SCREAMING_SNAKE_CASE__											=			jax.nn.softmax(temp_dist_warper_smoother(A_   ,				scores.copy()   ,				cur_len=A_     )   ,				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  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			None
        SCREAMING_SNAKE_CASE__											=			10
        SCREAMING_SNAKE_CASE__											=			2

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

        SCREAMING_SNAKE_CASE__											=			FlaxTopKLogitsWarper(3     )

        SCREAMING_SNAKE_CASE__											=			top_k_warp(A_   ,				A_   ,				cur_len=A_     )

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

        SCREAMING_SNAKE_CASE__											=			np.broadcast_to(np.arange(A_     )[None, :]   ,				(batch_size, length)     ).copy()
        SCREAMING_SNAKE_CASE__											=			top_k_warp_safety_check(A_   ,				A_   ,				cur_len=A_     )

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



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			None
        SCREAMING_SNAKE_CASE__											=			10
        SCREAMING_SNAKE_CASE__											=			2

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

        SCREAMING_SNAKE_CASE__											=			FlaxTopPLogitsWarper(0.8     )
        SCREAMING_SNAKE_CASE__											=			np.exp(top_p_warp(A_   ,				A_   ,				cur_len=A_     )     )

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

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

        # make ramp_logits more extreme
        SCREAMING_SNAKE_CASE__											=			ramp_logits[1] * 100.0

        # make sure at least 2 tokens are kept
        SCREAMING_SNAKE_CASE__											=			FlaxTopPLogitsWarper(0.9   ,				min_tokens_to_keep=2   ,				filter_value=0.0     )
        SCREAMING_SNAKE_CASE__											=			top_p_warp(A_   ,				A_   ,				cur_len=A_     )

        # 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  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			20
        SCREAMING_SNAKE_CASE__											=			4
        SCREAMING_SNAKE_CASE__											=			0

        SCREAMING_SNAKE_CASE__											=			FlaxMinLengthLogitsProcessor(min_length=10   ,				eos_token_id=A_     )

        # check that min length is applied at length 5
        SCREAMING_SNAKE_CASE__											=			ids_tensor((batch_size, 20)   ,				vocab_size=20     )
        SCREAMING_SNAKE_CASE__											=			5
        SCREAMING_SNAKE_CASE__											=			self._get_uniform_logits(A_   ,				A_     )
        SCREAMING_SNAKE_CASE__											=			min_dist_processor(A_   ,				A_   ,				cur_len=A_     )
        self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist()   ,				4 * [-float('''inf'''     )]     )

        # check that min length is not applied anymore at length 15
        SCREAMING_SNAKE_CASE__											=			self._get_uniform_logits(A_   ,				A_     )
        SCREAMING_SNAKE_CASE__											=			15
        SCREAMING_SNAKE_CASE__											=			min_dist_processor(A_   ,				A_   ,				cur_len=A_     )
        self.assertFalse(jnp.isinf(A_     ).any()     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			20
        SCREAMING_SNAKE_CASE__											=			4
        SCREAMING_SNAKE_CASE__											=			0

        SCREAMING_SNAKE_CASE__											=			FlaxForcedBOSTokenLogitsProcessor(bos_token_id=A_     )

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



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			20
        SCREAMING_SNAKE_CASE__											=			4
        SCREAMING_SNAKE_CASE__											=			0
        SCREAMING_SNAKE_CASE__											=			5

        SCREAMING_SNAKE_CASE__											=			FlaxForcedEOSTokenLogitsProcessor(max_length=A_   ,				eos_token_id=A_     )

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



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			4
        SCREAMING_SNAKE_CASE__											=			10
        SCREAMING_SNAKE_CASE__											=			15
        SCREAMING_SNAKE_CASE__											=			2
        SCREAMING_SNAKE_CASE__											=			1
        SCREAMING_SNAKE_CASE__											=			15

        # dummy input_ids and scores
        SCREAMING_SNAKE_CASE__											=			ids_tensor((batch_size, sequence_length)   ,				A_     )
        SCREAMING_SNAKE_CASE__											=			input_ids.copy()

        SCREAMING_SNAKE_CASE__											=			self._get_uniform_logits(A_   ,				A_     )
        SCREAMING_SNAKE_CASE__											=			scores.copy()

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

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

        SCREAMING_SNAKE_CASE__											=			10

        # no processor list
        SCREAMING_SNAKE_CASE__											=			temp_dist_warp(A_   ,				A_   ,				cur_len=A_     )
        SCREAMING_SNAKE_CASE__											=			top_k_warp(A_   ,				A_   ,				cur_len=A_     )
        SCREAMING_SNAKE_CASE__											=			top_p_warp(A_   ,				A_   ,				cur_len=A_     )
        SCREAMING_SNAKE_CASE__											=			min_dist_proc(A_   ,				A_   ,				cur_len=A_     )
        SCREAMING_SNAKE_CASE__											=			bos_dist_proc(A_   ,				A_   ,				cur_len=A_     )
        SCREAMING_SNAKE_CASE__											=			eos_dist_proc(A_   ,				A_   ,				cur_len=A_     )

        # with processor list
        SCREAMING_SNAKE_CASE__											=			FlaxLogitsProcessorList(
            [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]     )
        SCREAMING_SNAKE_CASE__											=			processor(A_   ,				A_   ,				cur_len=A_     )

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

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




    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			4
        SCREAMING_SNAKE_CASE__											=			10
        SCREAMING_SNAKE_CASE__											=			15
        SCREAMING_SNAKE_CASE__											=			2
        SCREAMING_SNAKE_CASE__											=			1
        SCREAMING_SNAKE_CASE__											=			15

        # dummy input_ids and scores
        SCREAMING_SNAKE_CASE__											=			ids_tensor((batch_size, sequence_length)   ,				A_     )
        SCREAMING_SNAKE_CASE__											=			input_ids.copy()

        SCREAMING_SNAKE_CASE__											=			self._get_uniform_logits(A_   ,				A_     )
        SCREAMING_SNAKE_CASE__											=			scores.copy()

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

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

        SCREAMING_SNAKE_CASE__											=			10

        # no processor list
        def run_no_processor_list(A_   ,				A_   ,				A_     ):
            SCREAMING_SNAKE_CASE__											=			temp_dist_warp(A_   ,				A_   ,				cur_len=A_     )
            SCREAMING_SNAKE_CASE__											=			top_k_warp(A_   ,				A_   ,				cur_len=A_     )
            SCREAMING_SNAKE_CASE__											=			top_p_warp(A_   ,				A_   ,				cur_len=A_     )
            SCREAMING_SNAKE_CASE__											=			min_dist_proc(A_   ,				A_   ,				cur_len=A_     )
            SCREAMING_SNAKE_CASE__											=			bos_dist_proc(A_   ,				A_   ,				cur_len=A_     )
            SCREAMING_SNAKE_CASE__											=			eos_dist_proc(A_   ,				A_   ,				cur_len=A_     )
            return scores

        # with processor list
        def run_processor_list(A_   ,				A_   ,				A_     ):
            SCREAMING_SNAKE_CASE__											=			FlaxLogitsProcessorList(
                [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]     )
            SCREAMING_SNAKE_CASE__											=			processor(A_   ,				A_   ,				cur_len=A_     )
            return scores

        SCREAMING_SNAKE_CASE__											=			jax.jit(A_     )
        SCREAMING_SNAKE_CASE__											=			jax.jit(A_     )

        SCREAMING_SNAKE_CASE__											=			jitted_run_no_processor_list(A_   ,				A_   ,				A_     )
        SCREAMING_SNAKE_CASE__											=			jitted_run_processor_list(A_   ,				A_   ,				A_     )

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

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





"""simple docstring"""






import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case				:  Tuple =       logging.get_logger(__name__)

__snake_case				:  Tuple =       {
    'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/config.json',
    'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/config.json',
}



class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      'xlnet'
	SCREAMING_SNAKE_CASE        =      ['mems']
	SCREAMING_SNAKE_CASE        =      {
	    'n_token': 'vocab_size',  # Backward compatibility
	    'hidden_size': 'd_model',
	    'num_attention_heads': 'n_head',
	    'num_hidden_layers': 'n_layer',
	}

	def __init__(	self:						Tuple			,     _SCREAMING_SNAKE_CASE:						List[str]=3_2000			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=1024			,     _SCREAMING_SNAKE_CASE:						Dict=24			,     _SCREAMING_SNAKE_CASE:						Optional[int]=16			,     _SCREAMING_SNAKE_CASE:						List[str]=4096			,     _SCREAMING_SNAKE_CASE:						Tuple="gelu"			,     _SCREAMING_SNAKE_CASE:						int=True			,     _SCREAMING_SNAKE_CASE:						List[Any]="bi"			,     _SCREAMING_SNAKE_CASE:						Any=0.02			,     _SCREAMING_SNAKE_CASE:						List[str]=1e-12			,     _SCREAMING_SNAKE_CASE:						Any=0.1			,     _SCREAMING_SNAKE_CASE:						Any=512			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=None			,     _SCREAMING_SNAKE_CASE:						Any=True			,     _SCREAMING_SNAKE_CASE:						Any=False			,     _SCREAMING_SNAKE_CASE:						Tuple=False			,     _SCREAMING_SNAKE_CASE:						List[Any]=-1			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=False			,     _SCREAMING_SNAKE_CASE:						Optional[int]="last"			,     _SCREAMING_SNAKE_CASE:						Tuple=True			,     _SCREAMING_SNAKE_CASE:						Dict="tanh"			,     _SCREAMING_SNAKE_CASE:						int=0.1			,     _SCREAMING_SNAKE_CASE:						Tuple=5			,     _SCREAMING_SNAKE_CASE:						List[str]=5			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=5			,     _SCREAMING_SNAKE_CASE:						Optional[int]=1			,     _SCREAMING_SNAKE_CASE:						Tuple=2			,     **_SCREAMING_SNAKE_CASE:						str			,     )							->			Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    Dict				=			vocab_size
				__lowerCAmelCase :    Optional[int]				=			d_model
				__lowerCAmelCase :    Union[str, Any]				=			n_layer
				__lowerCAmelCase :    List[Any]				=			n_head
				if d_model % n_head != 0:
							raise ValueError(F"""'d_model % n_head' ({d_model % n_head}) should be equal to 0""")
				if "d_head" in kwargs:
							if kwargs["d_head"] != d_model // n_head:
										raise ValueError(
										    F"""`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})""")
				__lowerCAmelCase :    Optional[Any]				=			d_model // n_head
				__lowerCAmelCase :    Tuple				=			ff_activation
				__lowerCAmelCase :    Union[str, Any]				=			d_inner
				__lowerCAmelCase :    Any				=			untie_r
				__lowerCAmelCase :    int				=			attn_type

				__lowerCAmelCase :    Any				=			initializer_range
				__lowerCAmelCase :    List[Any]				=			layer_norm_eps

				__lowerCAmelCase :    int				=			dropout
				__lowerCAmelCase :    List[Any]				=			mem_len
				__lowerCAmelCase :    str				=			reuse_len
				__lowerCAmelCase :    Any				=			bi_data
				__lowerCAmelCase :    Optional[int]				=			clamp_len
				__lowerCAmelCase :    Union[str, Any]				=			same_length

				__lowerCAmelCase :    List[str]				=			summary_type
				__lowerCAmelCase :    Optional[int]				=			summary_use_proj
				__lowerCAmelCase :    Tuple				=			summary_activation
				__lowerCAmelCase :    Dict				=			summary_last_dropout
				__lowerCAmelCase :    str				=			start_n_top
				__lowerCAmelCase :    List[str]				=			end_n_top

				__lowerCAmelCase :    str				=			bos_token_id
				__lowerCAmelCase :    Optional[Any]				=			pad_token_id
				__lowerCAmelCase :    Optional[Any]				=			eos_token_id

				if "use_cache" in kwargs:
							warnings.warn(
							    "The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`"
							    " instead."			,     _SCREAMING_SNAKE_CASE			,     )
							__lowerCAmelCase :    Optional[int]				=			kwargs["use_cache"]

				__lowerCAmelCase :    Optional[int]				=			use_mems_eval
				__lowerCAmelCase :    Any				=			use_mems_train
				super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE			,     bos_token_id=_SCREAMING_SNAKE_CASE			,     eos_token_id=_SCREAMING_SNAKE_CASE			,     **_SCREAMING_SNAKE_CASE)

	@property
	def 				_SCREAMING_SNAKE_CASE  (	self:						Any)							->			int:

				"""simple docstring"""

				logger.info(F"""The model {self.model_type} is one of the few models that has no sequence length limit.""")
				return -1







	@max_position_embeddings.setter
	def 				_SCREAMING_SNAKE_CASE  (	self:						List[str]			,     _SCREAMING_SNAKE_CASE:						Optional[int])							->			Optional[int]:

				"""simple docstring"""

				raise NotImplementedError(
				    F"""The model {self.model_type} is one of the few models that has no sequence length limit.""")