




"""simple docstring"""

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 				lowercase_       (     unittest.TestCase							):



 '''simple docstring'''

 def 			lowerCAmelCase_							( self       :       Any    ,							_UpperCAmelCase       :       int    ,							_UpperCAmelCase       :       List[Any]		):
       _A					   =   jnp.ones((batch_size, length)		) / length
       return scores

 def 			lowerCAmelCase_							( self       :       List[Any]		):
       _A					   =   None
       _A					   =   20

       _A					   =   self._get_uniform_logits(batch_size=2    ,							length=_a		)

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

       # compute softmax
       _A					   =   jax.nn.softmax(_a    ,							axis=-1		)

       _A					   =   FlaxTemperatureLogitsWarper(temperature=0.5		)
       _A					   =   FlaxTemperatureLogitsWarper(temperature=1.3		)

       _A					   =   jax.nn.softmax(temp_dist_warper_sharper(_a    ,							scores.copy()    ,							cur_len=_a		)    ,							axis=-1		)
       _A					   =   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 			lowerCAmelCase_							( self       :       Dict		):
       _A					   =   None
       _A					   =   10
       _A					   =   2

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

       _A					   =   FlaxTopKLogitsWarper(3		)

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

       _A					   =   np.broadcast_to(np.arange(_a		)[None, :]    ,							(batch_size, length)		).copy()
       _A					   =   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 			lowerCAmelCase_							( self       :       Optional[Any]		):
       _A					   =   None
       _A					   =   10
       _A					   =   2

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

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

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

       # make sure at least 2 tokens are kept
       _A					   =   FlaxTopPLogitsWarper(0.9    ,							min_tokens_to_keep=2    ,							filter_value=0.0		)
       _A					   =   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 			lowerCAmelCase_							( self       :       Any		):
       _A					   =   20
       _A					   =   4
       _A					   =   0

       _A					   =   FlaxMinLengthLogitsProcessor(min_length=10    ,							eos_token_id=_a		)

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

 def 			lowerCAmelCase_							( self       :       List[Any]		):
       _A					   =   20
       _A					   =   4
       _A					   =   0

       _A					   =   FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_a		)

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

 def 			lowerCAmelCase_							( self       :       Optional[Any]		):
       _A					   =   20
       _A					   =   4
       _A					   =   0
       _A					   =   5

       _A					   =   FlaxForcedEOSTokenLogitsProcessor(max_length=_a    ,							eos_token_id=_a		)

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

 def 			lowerCAmelCase_							( self       :       Optional[Any]		):
       _A					   =   4
       _A					   =   10
       _A					   =   15
       _A					   =   2
       _A					   =   1
       _A					   =   15

       # dummy input_ids and scores
       _A					   =   ids_tensor((batch_size, sequence_length)    ,							_a		)
       _A					   =   input_ids.copy()

       _A					   =   self._get_uniform_logits(_a    ,							_a		)
       _A					   =   scores.copy()

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

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

       _A					   =   10

       # no processor list
       _A					   =   temp_dist_warp(_a    ,							_a    ,							cur_len=_a		)
       _A					   =   top_k_warp(_a    ,							_a    ,							cur_len=_a		)
       _A					   =   top_p_warp(_a    ,							_a    ,							cur_len=_a		)
       _A					   =   min_dist_proc(_a    ,							_a    ,							cur_len=_a		)
       _A					   =   bos_dist_proc(_a    ,							_a    ,							cur_len=_a		)
       _A					   =   eos_dist_proc(_a    ,							_a    ,							cur_len=_a		)

       # with processor list
       _A					   =   FlaxLogitsProcessorList(
           [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]		)
       _A					   =   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 			lowerCAmelCase_							( self       :       Optional[Any]		):
       _A					   =   4
       _A					   =   10
       _A					   =   15
       _A					   =   2
       _A					   =   1
       _A					   =   15

       # dummy input_ids and scores
       _A					   =   ids_tensor((batch_size, sequence_length)    ,							_a		)
       _A					   =   input_ids.copy()

       _A					   =   self._get_uniform_logits(_a    ,							_a		)
       _A					   =   scores.copy()

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

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

       _A					   =   10

       # no processor list
       def run_no_processor_list(_UpperCAmelCase       :       Union[str, Any]    ,							_UpperCAmelCase       :       List[str]    ,							_UpperCAmelCase       :       Optional[Any]		):
             _A					   =   temp_dist_warp(_a    ,							_a    ,							cur_len=_a		)
             _A					   =   top_k_warp(_a    ,							_a    ,							cur_len=_a		)
             _A					   =   top_p_warp(_a    ,							_a    ,							cur_len=_a		)
             _A					   =   min_dist_proc(_a    ,							_a    ,							cur_len=_a		)
             _A					   =   bos_dist_proc(_a    ,							_a    ,							cur_len=_a		)
             _A					   =   eos_dist_proc(_a    ,							_a    ,							cur_len=_a		)
             return scores

       # with processor list
       def run_processor_list(_UpperCAmelCase       :       Dict    ,							_UpperCAmelCase       :       Union[str, Any]    ,							_UpperCAmelCase       :       int		):
             _A					   =   FlaxLogitsProcessorList(
                 [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]		)
             _A					   =   processor(_a    ,							_a    ,							cur_len=_a		)
             return scores

       _A					   =   jax.jit(_a		)
       _A					   =   jax.jit(_a		)

       _A					   =   jitted_run_no_processor_list(_a    ,							_a    ,							_a		)
       _A					   =   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()		)




from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


a__				=		logging.get_logger(__name__)

a__				=		{
    '''google/bit-50''': '''https://huggingface.co/google/bit-50/resolve/main/config.json''',
}

class 				UpperCAmelCase_		(			__lowercase				,       __lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Any	    =    "bit"
							UpperCAmelCase__       :						Optional[int]	    =    ["preactivation", "bottleneck"]
							UpperCAmelCase__       :						Optional[Any]	    =    ["SAME", "VALID"]


							def __init__(			self       ,							_a=3       ,							_a=6_4       ,							_a=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]       ,							_a=[3, 4, 6, 3]       ,							_a="preactivation"       ,							_a="relu"       ,							_a=None       ,							_a=3_2       ,							_a=0.0       ,							_a=False       ,							_a=3_2       ,							_a=1       ,							_a=None       ,							_a=None       ,							**_a       ,							)    ->    Union[str, Any]:
												super().__init__(**_a       )
												if layer_type not in self.layer_types:
																	raise ValueError(F"""layer_type={layer_type} is not one of {','.join(self.layer_types       )}"""       )
												if global_padding is not None:
																	if global_padding.upper() in self.supported_padding:
																						_a	:    Any         =		global_padding.upper()
																	else:
																						raise ValueError(F"""Padding strategy {global_padding} not supported"""       )
												_a	:    Optional[int]         =		num_channels
												_a	:    List[Any]         =		embedding_size
												_a	:    Any         =		hidden_sizes
												_a	:    int         =		depths
												_a	:    Dict         =		layer_type
												_a	:    int         =		hidden_act
												_a	:    Optional[Any]         =		global_padding
												_a	:    Optional[Any]         =		num_groups
												_a	:    Union[str, Any]         =		drop_path_rate
												_a	:    Tuple         =		embedding_dynamic_padding
												_a	:    Union[str, Any]         =		output_stride
												_a	:    Any         =		width_factor

												_a	:    Any         =		['''stem'''] + [F"""stage{idx}""" for idx in range(1       ,							len(_a       ) + 1       )]
												_a      ,					_a	:    List[str]         =		get_aligned_output_features_output_indices(
												    out_features=_a       ,							out_indices=_a       ,							stage_names=self.stage_names       )



