





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:						List[str]		,  _lowerCAmelCase:						int		,  _lowerCAmelCase:						int       )						->    Union[str, Any]:
         '''simple docstring'''

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



      def 	lowerCAmelCase__    (	self:						str       )						->    Union[str, 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:						Tuple       )						->    Union[str, Any]:
         '''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:						Dict       )						->    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] * 100.0

         # 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:						int       )						->    Optional[int]:
         '''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[Any]:
         '''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:						Tuple       )						->    Tuple:
         '''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:						Optional[Any]       )						->    List[str]:
         '''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:						Optional[int]       )						->    List[str]:
         '''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:						List[str]		,  _lowerCAmelCase:						Any		,  _lowerCAmelCase:						Tuple       ):
            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:						List[Any]		,  _lowerCAmelCase:						str		,  _lowerCAmelCase:						Optional[Any]       ):
            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()       )











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


__lowercase  :   Union[str, Any]						      =logging.get_logger(__name__)

__lowercase  :   List[Any]						      ={
    """tanreinama/GPTSAN-2.8B-spout_is_uniform""": (
        """https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json"""
    ),
}

class      A       (	__lowercase						):
      _snake_case							     ='''gptsan-japanese'''
      _snake_case							     =[
          '''past_key_values''',
      ]
      _snake_case							     ={
          '''hidden_size''': '''d_model''',
          '''num_attention_heads''': '''num_heads''',
          '''num_hidden_layers''': '''num_layers''',
      }



      def __init__(	self:						Optional[Any]		,  _lowerCAmelCase:						List[Any]=3_6000		,  _lowerCAmelCase:						List[Any]=1280		,  _lowerCAmelCase:						str=1024		,  _lowerCAmelCase:						Any=8192		,  _lowerCAmelCase:						str=4096		,  _lowerCAmelCase:						int=128		,  _lowerCAmelCase:						int=10		,  _lowerCAmelCase:						Dict=0		,  _lowerCAmelCase:						Any=16		,  _lowerCAmelCase:						Optional[int]=16		,  _lowerCAmelCase:						List[Any]=128		,  _lowerCAmelCase:						Tuple=0.0		,  _lowerCAmelCase:						Optional[Any]=1e-5		,  _lowerCAmelCase:						int=False		,  _lowerCAmelCase:						Optional[Any]=0.0		,  _lowerCAmelCase:						str="float32"		,  _lowerCAmelCase:						Dict=False		,  _lowerCAmelCase:						Any=False		,  _lowerCAmelCase:						int=False		,  _lowerCAmelCase:						Union[str, Any]=0.0_02		,  _lowerCAmelCase:						Optional[int]=False		,  _lowerCAmelCase:						int=True		,  _lowerCAmelCase:						List[str]=3_5998		,  _lowerCAmelCase:						Optional[int]=3_5995		,  _lowerCAmelCase:						Dict=3_5999		,  **_lowerCAmelCase:						Optional[Any]		,  )						->    List[Any]:
         '''simple docstring'''

         UpperCAmelCase_											=vocab_size
         UpperCAmelCase_											=max_position_embeddings
         UpperCAmelCase_											=d_model
         UpperCAmelCase_											=d_ff
         UpperCAmelCase_											=d_ext
         UpperCAmelCase_											=d_spout
         UpperCAmelCase_											=num_switch_layers
         UpperCAmelCase_											=num_ext_layers
         UpperCAmelCase_											=num_switch_layers + num_ext_layers
         UpperCAmelCase_											=num_heads
         UpperCAmelCase_											=num_experts
         UpperCAmelCase_											=expert_capacity
         UpperCAmelCase_											=dropout_rate
         UpperCAmelCase_											=layer_norm_epsilon
         UpperCAmelCase_											=router_bias
         UpperCAmelCase_											=router_jitter_noise
         UpperCAmelCase_											=router_dtype
         UpperCAmelCase_											=router_ignore_padding_tokens
         UpperCAmelCase_											=output_hidden_states
         UpperCAmelCase_											=output_attentions
         UpperCAmelCase_											=initializer_factor
         UpperCAmelCase_											=output_router_logits
         UpperCAmelCase_											=use_cache

         super().__init__(
             separator_token_id=_lowerCAmelCase		,  pad_token_id=_lowerCAmelCase		,  eos_token_id=_lowerCAmelCase		,  **_lowerCAmelCase		,  )




