




'''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 			_snake_case		(     unittest.TestCase							):




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__							)    ->    Tuple:

										'''simple docstring'''




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




			def        lowerCAmelCase__    ( self							)    ->    int:

										'''simple docstring'''




										snake_case_      =				None
										snake_case_      =				20

										snake_case_      =				self._get_uniform_logits(batch_size=2    ,							length=a__							)

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

										# compute softmax
										snake_case_      =				jax.nn.softmax(a__    ,							axis=-1							)

										snake_case_      =				FlaxTemperatureLogitsWarper(temperature=0.5							)
										snake_case_      =				FlaxTemperatureLogitsWarper(temperature=1.3							)

										snake_case_      =				jax.nn.softmax(temp_dist_warper_sharper(a__    ,							scores.copy()    ,							cur_len=a__							)    ,							axis=-1							)
										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        lowerCAmelCase__    ( self							)    ->    Optional[Any]:

										'''simple docstring'''




										snake_case_      =				None
										snake_case_      =				10
										snake_case_      =				2

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

										snake_case_      =				FlaxTopKLogitsWarper(3							)

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

										snake_case_      =				np.broadcast_to(np.arange(a__							)[None, :]    ,							(batch_size, length)							).copy()
										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        lowerCAmelCase__    ( self							)    ->    Optional[Any]:

										'''simple docstring'''




										snake_case_      =				None
										snake_case_      =				10
										snake_case_      =				2

										# create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
										snake_case_      =				np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.1_5, 0.3, 0.3, 0.2_5]]							)							)

										snake_case_      =				FlaxTopPLogitsWarper(0.8							)
										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
										snake_case_      =				np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.2_5]]							)
										self.assertTrue(np.allclose(a__    ,							a__    ,							atol=1e-3							)							)

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

										# make ramp_logits more extreme
										snake_case_      =				ramp_logits[1] * 1_0_0.0

										# make sure at least 2 tokens are kept
										snake_case_      =				FlaxTopPLogitsWarper(0.9    ,							min_tokens_to_keep=2    ,							filter_value=0.0							)
										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        lowerCAmelCase__    ( self							)    ->    Optional[Any]:

										'''simple docstring'''




										snake_case_      =				20
										snake_case_      =				4
										snake_case_      =				0

										snake_case_      =				FlaxMinLengthLogitsProcessor(min_length=10    ,							eos_token_id=a__							)

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




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

										'''simple docstring'''




										snake_case_      =				20
										snake_case_      =				4
										snake_case_      =				0

										snake_case_      =				FlaxForcedBOSTokenLogitsProcessor(bos_token_id=a__							)

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




			def        lowerCAmelCase__    ( self							)    ->    Optional[int]:

										'''simple docstring'''




										snake_case_      =				20
										snake_case_      =				4
										snake_case_      =				0
										snake_case_      =				5

										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
										snake_case_      =				ids_tensor((batch_size, 4)    ,							vocab_size=20							)
										snake_case_      =				4
										snake_case_      =				self._get_uniform_logits(a__    ,							a__							)
										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
										snake_case_      =				3
										snake_case_      =				self._get_uniform_logits(a__    ,							a__							)
										snake_case_      =				logits_processor(a__    ,							a__    ,							cur_len=a__							)
										self.assertFalse(jnp.isinf(a__							).any()							)




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

										'''simple docstring'''




										snake_case_      =				4
										snake_case_      =				10
										snake_case_      =				15
										snake_case_      =				2
										snake_case_      =				1
										snake_case_      =				15

										# dummy input_ids and scores
										snake_case_      =				ids_tensor((batch_size, sequence_length)    ,							a__							)
										snake_case_      =				input_ids.copy()

										snake_case_      =				self._get_uniform_logits(a__    ,							a__							)
										snake_case_      =				scores.copy()

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

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

										snake_case_      =				10

										# no processor list
										snake_case_      =				temp_dist_warp(a__    ,							a__    ,							cur_len=a__							)
										snake_case_      =				top_k_warp(a__    ,							a__    ,							cur_len=a__							)
										snake_case_      =				top_p_warp(a__    ,							a__    ,							cur_len=a__							)
										snake_case_      =				min_dist_proc(a__    ,							a__    ,							cur_len=a__							)
										snake_case_      =				bos_dist_proc(a__    ,							a__    ,							cur_len=a__							)
										snake_case_      =				eos_dist_proc(a__    ,							a__    ,							cur_len=a__							)

										# with processor list
										snake_case_      =				FlaxLogitsProcessorList(
										    [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]							)
										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        lowerCAmelCase__    ( self							)    ->    Tuple:

										'''simple docstring'''




										snake_case_      =				4
										snake_case_      =				10
										snake_case_      =				15
										snake_case_      =				2
										snake_case_      =				1
										snake_case_      =				15

										# dummy input_ids and scores
										snake_case_      =				ids_tensor((batch_size, sequence_length)    ,							a__							)
										snake_case_      =				input_ids.copy()

										snake_case_      =				self._get_uniform_logits(a__    ,							a__							)
										snake_case_      =				scores.copy()

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

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

										snake_case_      =				10

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

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

										snake_case_      =				jax.jit(a__							)
										snake_case_      =				jax.jit(a__							)

										snake_case_      =				jitted_run_no_processor_list(a__    ,							a__    ,							a__							)
										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'''





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


_SCREAMING_SNAKE_CASE							:       Any						=			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							:       int						=			{
    "alibaba-damo/mgp-str-base": "https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json",
}




class 			_snake_case		(     lowercase_							):
			lowerCAmelCase_					:				Union[str, Any]							=   "mgp-str"




			def __init__( self    ,							a__=[32, 128]    ,							a__=4    ,							a__=3    ,							a__=27    ,							a__=38    ,							a__=50_257    ,							a__=30_522    ,							a__=768    ,							a__=12    ,							a__=12    ,							a__=4.0    ,							a__=True    ,							a__=False    ,							a__=1e-5    ,							a__=0.0    ,							a__=0.0    ,							a__=0.0    ,							a__=False    ,							a__=0.0_2    ,							**a__    ,							)    ->    Union[str, Any]:

										'''simple docstring'''




										super().__init__(**a__							)

										snake_case_      =				image_size
										snake_case_      =				patch_size
										snake_case_      =				num_channels
										snake_case_      =				max_token_length
										snake_case_      =				num_character_labels
										snake_case_      =				num_bpe_labels
										snake_case_      =				num_wordpiece_labels
										snake_case_      =				hidden_size
										snake_case_      =				num_hidden_layers
										snake_case_      =				num_attention_heads
										snake_case_      =				mlp_ratio
										snake_case_      =				distilled
										snake_case_      =				layer_norm_eps
										snake_case_      =				drop_rate
										snake_case_      =				qkv_bias
										snake_case_      =				attn_drop_rate
										snake_case_      =				drop_path_rate
										snake_case_      =				output_aa_attentions
										snake_case_      =				initializer_range




