

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 	__magic_name__    (			unittest.TestCase      ):





			def 		__magic_name__    (		self					,  __snake_case					,  __snake_case					)   -> List[str]:




						'''simple docstring'''
						__a        =jnp.ones((batch_size, length)					) / length
						return scores





			def 		__magic_name__    (		self					)   -> Union[str, Any]:




						'''simple docstring'''
						__a        =None
						__a        =20

						__a        =self._get_uniform_logits(batch_size=2					,  length=__snake_case					)

						# 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(__snake_case					,  axis=-1					)

						__a        =FlaxTemperatureLogitsWarper(temperature=0.5					)
						__a        =FlaxTemperatureLogitsWarper(temperature=1.3					)

						__a        =jax.nn.softmax(temp_dist_warper_sharper(__snake_case					,  scores.copy()					,  cur_len=__snake_case					)					,  axis=-1					)
						__a        =jax.nn.softmax(temp_dist_warper_smoother(__snake_case					,  scores.copy()					,  cur_len=__snake_case					)					,  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 		__magic_name__    (		self					)   -> List[Any]:




						'''simple docstring'''
						__a        =None
						__a        =10
						__a        =2

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

						__a        =FlaxTopKLogitsWarper(3					)

						__a        =top_k_warp(__snake_case					,  __snake_case					,  cur_len=__snake_case					)

						# 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(__snake_case					)[None, :]					,  (batch_size, length)					).copy()
						__a        =top_k_warp_safety_check(__snake_case					,  __snake_case					,  cur_len=__snake_case					)

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





			def 		__magic_name__    (		self					)   -> Tuple:




						'''simple docstring'''
						__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(__snake_case					,  __snake_case					,  cur_len=__snake_case					)					)

						# 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(__snake_case					,  __snake_case					,  atol=1e-3					)					)

						# check edge cases with negative and extreme logits
						__a        =np.broadcast_to(np.arange(__snake_case					)[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(__snake_case					,  __snake_case					,  cur_len=__snake_case					)

						# 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 		__magic_name__    (		self					)   -> Optional[Any]:




						'''simple docstring'''
						__a        =20
						__a        =4
						__a        =0

						__a        =FlaxMinLengthLogitsProcessor(min_length=10					,  eos_token_id=__snake_case					)

						# 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(__snake_case					,  __snake_case					)
						__a        =min_dist_processor(__snake_case					,  __snake_case					,  cur_len=__snake_case					)
						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(__snake_case					,  __snake_case					)
						__a        =15
						__a        =min_dist_processor(__snake_case					,  __snake_case					,  cur_len=__snake_case					)
						self.assertFalse(jnp.isinf(__snake_case					).any()					)





			def 		__magic_name__    (		self					)   -> Union[str, Any]:




						'''simple docstring'''
						__a        =20
						__a        =4
						__a        =0

						__a        =FlaxForcedBOSTokenLogitsProcessor(bos_token_id=__snake_case					)

						# 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(__snake_case					,  __snake_case					)
						__a        =logits_processor(__snake_case					,  __snake_case					,  cur_len=__snake_case					)
						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(__snake_case					,  __snake_case					)
						__a        =logits_processor(__snake_case					,  __snake_case					,  cur_len=__snake_case					)
						self.assertFalse(jnp.isinf(__snake_case					).any()					)





			def 		__magic_name__    (		self					)   -> Any:




						'''simple docstring'''
						__a        =20
						__a        =4
						__a        =0
						__a        =5

						__a        =FlaxForcedEOSTokenLogitsProcessor(max_length=__snake_case					,  eos_token_id=__snake_case					)

						# 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(__snake_case					,  __snake_case					)
						__a        =logits_processor(__snake_case					,  __snake_case					,  cur_len=__snake_case					)
						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(__snake_case					,  __snake_case					)
						__a        =logits_processor(__snake_case					,  __snake_case					,  cur_len=__snake_case					)
						self.assertFalse(jnp.isinf(__snake_case					).any()					)





			def 		__magic_name__    (		self					)   -> Optional[Any]:




						'''simple docstring'''
						__a        =4
						__a        =10
						__a        =15
						__a        =2
						__a        =1
						__a        =15

						# dummy input_ids and scores
						__a        =ids_tensor((batch_size, sequence_length)					,  __snake_case					)
						__a        =input_ids.copy()

						__a        =self._get_uniform_logits(__snake_case					,  __snake_case					)
						__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=__snake_case					)
						__a        =FlaxForcedBOSTokenLogitsProcessor(bos_token_id=__snake_case					)
						__a        =FlaxForcedEOSTokenLogitsProcessor(max_length=__snake_case					,  eos_token_id=__snake_case					)

						__a        =10

						# no processor list
						__a        =temp_dist_warp(__snake_case					,  __snake_case					,  cur_len=__snake_case					)
						__a        =top_k_warp(__snake_case					,  __snake_case					,  cur_len=__snake_case					)
						__a        =top_p_warp(__snake_case					,  __snake_case					,  cur_len=__snake_case					)
						__a        =min_dist_proc(__snake_case					,  __snake_case					,  cur_len=__snake_case					)
						__a        =bos_dist_proc(__snake_case					,  __snake_case					,  cur_len=__snake_case					)
						__a        =eos_dist_proc(__snake_case					,  __snake_case					,  cur_len=__snake_case					)

						# 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(__snake_case					,  __snake_case					,  cur_len=__snake_case					)

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

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





			def 		__magic_name__    (		self					)   -> Any:




						'''simple docstring'''
						__a        =4
						__a        =10
						__a        =15
						__a        =2
						__a        =1
						__a        =15

						# dummy input_ids and scores
						__a        =ids_tensor((batch_size, sequence_length)					,  __snake_case					)
						__a        =input_ids.copy()

						__a        =self._get_uniform_logits(__snake_case					,  __snake_case					)
						__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=__snake_case					)
						__a        =FlaxForcedBOSTokenLogitsProcessor(bos_token_id=__snake_case					)
						__a        =FlaxForcedEOSTokenLogitsProcessor(max_length=__snake_case					,  eos_token_id=__snake_case					)

						__a        =10

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

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

						__a        =jax.jit(__snake_case					)
						__a        =jax.jit(__snake_case					)

						__a        =jitted_run_no_processor_list(__snake_case					,  __snake_case					,  __snake_case					)
						__a        =jitted_run_processor_list(__snake_case					,  __snake_case					,  __snake_case					)

						# scores should be equal
						self.assertTrue(jnp.allclose(__snake_case					,  __snake_case					,  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


_lowerCAmelCase					:			Tuple  				=			logging.get_logger(__name__)

_lowerCAmelCase					:			Union[str, Any]  				=			{"ctrl": "https://huggingface.co/ctrl/resolve/main/config.json"}






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						'ctrl'
			SCREAMING_SNAKE_CASE							   =						['past_key_values']
			SCREAMING_SNAKE_CASE							   =						{
			    'max_position_embeddings': 'n_positions',
			    'hidden_size': 'n_embd',
			    'num_attention_heads': 'n_head',
			    'num_hidden_layers': 'n_layer',
			}





			def __init__(		self					,  __snake_case=24_6534					,  __snake_case=256					,  __snake_case=1280					,  __snake_case=8192					,  __snake_case=48					,  __snake_case=16					,  __snake_case=0.1					,  __snake_case=0.1					,  __snake_case=1e-6					,  __snake_case=0.02					,  __snake_case=True					,  **__snake_case					,  )   -> Union[str, Any]:




						'''simple docstring'''
						__a        =vocab_size
						__a        =n_positions
						__a        =n_embd
						__a        =n_layer
						__a        =n_head
						__a        =dff
						__a        =resid_pdrop
						__a        =embd_pdrop
						__a        =layer_norm_epsilon
						__a        =initializer_range

						__a        =use_cache

						super().__init__(**__snake_case					)






