




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 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A			):




											"""simple docstring"""



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






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       None
											__lowerCAmelCase				   =       2_0

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

											# tweak scores to not be uniform anymore
											__lowerCAmelCase				   =       scores.at[1, 5].set((1 / length) + 0.1			)  # peak, 1st batch
											__lowerCAmelCase				   =       scores.at[1, 1_0].set((1 / length) - 0.4			)  # valley, 1st batch

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

											__lowerCAmelCase				   =       FlaxTemperatureLogitsWarper(temperature=0.5			)
											__lowerCAmelCase				   =       FlaxTemperatureLogitsWarper(temperature=1.3			)

											__lowerCAmelCase				   =       jax.nn.softmax(temp_dist_warper_sharper(_A		,			scores.copy()		,			cur_len=_A			)		,			axis=-1			)
											__lowerCAmelCase				   =       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 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       None
											__lowerCAmelCase				   =       1_0
											__lowerCAmelCase				   =       2

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

											__lowerCAmelCase				   =       FlaxTopKLogitsWarper(3			)

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

											__lowerCAmelCase				   =       np.broadcast_to(np.arange(_A			)[None, :]		,			(batch_size, length)			).copy()
											__lowerCAmelCase				   =       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 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       None
											__lowerCAmelCase				   =       1_0
											__lowerCAmelCase				   =       2

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

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

											# make ramp_logits more extreme
											__lowerCAmelCase				   =       ramp_logits[1] * 1_00.0

											# make sure at least 2 tokens are kept
											__lowerCAmelCase				   =       FlaxTopPLogitsWarper(0.9		,			min_tokens_to_keep=2		,			filter_value=0.0			)
											__lowerCAmelCase				   =       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 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       2_0
											__lowerCAmelCase				   =       4
											__lowerCAmelCase				   =       0

											__lowerCAmelCase				   =       FlaxMinLengthLogitsProcessor(min_length=1_0		,			eos_token_id=_A			)

											# check that min length is applied at length 5
											__lowerCAmelCase				   =       ids_tensor((batch_size, 2_0)		,			vocab_size=2_0			)
											__lowerCAmelCase				   =       5
											__lowerCAmelCase				   =       self._get_uniform_logits(_A		,			_A			)
											__lowerCAmelCase				   =       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
											__lowerCAmelCase				   =       self._get_uniform_logits(_A		,			_A			)
											__lowerCAmelCase				   =       1_5
											__lowerCAmelCase				   =       min_dist_processor(_A		,			_A		,			cur_len=_A			)
											self.assertFalse(jnp.isinf(_A			).any()			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       2_0
											__lowerCAmelCase				   =       4
											__lowerCAmelCase				   =       0

											__lowerCAmelCase				   =       FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_A			)

											# check that all scores are -inf except the bos_token_id score
											__lowerCAmelCase				   =       ids_tensor((batch_size, 1)		,			vocab_size=2_0			)
											__lowerCAmelCase				   =       1
											__lowerCAmelCase				   =       self._get_uniform_logits(_A		,			_A			)
											__lowerCAmelCase				   =       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
											__lowerCAmelCase				   =       3
											__lowerCAmelCase				   =       self._get_uniform_logits(_A		,			_A			)
											__lowerCAmelCase				   =       logits_processor(_A		,			_A		,			cur_len=_A			)
											self.assertFalse(jnp.isinf(_A			).any()			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       2_0
											__lowerCAmelCase				   =       4
											__lowerCAmelCase				   =       0
											__lowerCAmelCase				   =       5

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

											# check that all scores are -inf except the eos_token_id when max_length is reached
											__lowerCAmelCase				   =       ids_tensor((batch_size, 4)		,			vocab_size=2_0			)
											__lowerCAmelCase				   =       4
											__lowerCAmelCase				   =       self._get_uniform_logits(_A		,			_A			)
											__lowerCAmelCase				   =       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
											__lowerCAmelCase				   =       3
											__lowerCAmelCase				   =       self._get_uniform_logits(_A		,			_A			)
											__lowerCAmelCase				   =       logits_processor(_A		,			_A		,			cur_len=_A			)
											self.assertFalse(jnp.isinf(_A			).any()			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       4
											__lowerCAmelCase				   =       1_0
											__lowerCAmelCase				   =       1_5
											__lowerCAmelCase				   =       2
											__lowerCAmelCase				   =       1
											__lowerCAmelCase				   =       1_5

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

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

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

											# instantiate all logits processors
											__lowerCAmelCase				   =       FlaxMinLengthLogitsProcessor(min_length=1_0		,			eos_token_id=_A			)
											__lowerCAmelCase				   =       FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_A			)
											__lowerCAmelCase				   =       FlaxForcedEOSTokenLogitsProcessor(max_length=_A		,			eos_token_id=_A			)

											__lowerCAmelCase				   =       1_0

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

											# with processor list
											__lowerCAmelCase				   =       FlaxLogitsProcessorList(
											    [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]			)
											__lowerCAmelCase				   =       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 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       4
											__lowerCAmelCase				   =       1_0
											__lowerCAmelCase				   =       1_5
											__lowerCAmelCase				   =       2
											__lowerCAmelCase				   =       1
											__lowerCAmelCase				   =       1_5

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

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

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

											# instantiate all logits processors
											__lowerCAmelCase				   =       FlaxMinLengthLogitsProcessor(min_length=1_0		,			eos_token_id=_A			)
											__lowerCAmelCase				   =       FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_A			)
											__lowerCAmelCase				   =       FlaxForcedEOSTokenLogitsProcessor(max_length=_A		,			eos_token_id=_A			)

											__lowerCAmelCase				   =       1_0

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

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

											__lowerCAmelCase				   =       jax.jit(_A			)
											__lowerCAmelCase				   =       jax.jit(_A			)

											__lowerCAmelCase				   =       jitted_run_no_processor_list(_A		,			_A		,			_A			)
											__lowerCAmelCase				   =       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"""




UpperCAmelCase__      :    Any    							= '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

UpperCAmelCase__      :    Any    							= [{'type': 'code', 'content': INSTALL_CONTENT}]
UpperCAmelCase__      :    Optional[int]    							= {
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}




