





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    ):


    '''simple docstring'''







    def  lowercase_					(       self   ,				A_   ,				A_     ):





        '''simple docstring'''

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



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			None
        SCREAMING_SNAKE_CASE__											=			20

        SCREAMING_SNAKE_CASE__											=			self._get_uniform_logits(batch_size=2   ,				length=A_     )

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

        # compute softmax
        SCREAMING_SNAKE_CASE__											=			jax.nn.softmax(A_   ,				axis=-1     )

        SCREAMING_SNAKE_CASE__											=			FlaxTemperatureLogitsWarper(temperature=0.5     )
        SCREAMING_SNAKE_CASE__											=			FlaxTemperatureLogitsWarper(temperature=1.3     )

        SCREAMING_SNAKE_CASE__											=			jax.nn.softmax(temp_dist_warper_sharper(A_   ,				scores.copy()   ,				cur_len=A_     )   ,				axis=-1     )
        SCREAMING_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  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			None
        SCREAMING_SNAKE_CASE__											=			10
        SCREAMING_SNAKE_CASE__											=			2

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

        SCREAMING_SNAKE_CASE__											=			FlaxTopKLogitsWarper(3     )

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

        SCREAMING_SNAKE_CASE__											=			np.broadcast_to(np.arange(A_     )[None, :]   ,				(batch_size, length)     ).copy()
        SCREAMING_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  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			None
        SCREAMING_SNAKE_CASE__											=			10
        SCREAMING_SNAKE_CASE__											=			2

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

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

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

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





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			20
        SCREAMING_SNAKE_CASE__											=			4
        SCREAMING_SNAKE_CASE__											=			0

        SCREAMING_SNAKE_CASE__											=			FlaxMinLengthLogitsProcessor(min_length=10   ,				eos_token_id=A_     )

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



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			20
        SCREAMING_SNAKE_CASE__											=			4
        SCREAMING_SNAKE_CASE__											=			0

        SCREAMING_SNAKE_CASE__											=			FlaxForcedBOSTokenLogitsProcessor(bos_token_id=A_     )

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



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			20
        SCREAMING_SNAKE_CASE__											=			4
        SCREAMING_SNAKE_CASE__											=			0
        SCREAMING_SNAKE_CASE__											=			5

        SCREAMING_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
        SCREAMING_SNAKE_CASE__											=			ids_tensor((batch_size, 4)   ,				vocab_size=20     )
        SCREAMING_SNAKE_CASE__											=			4
        SCREAMING_SNAKE_CASE__											=			self._get_uniform_logits(A_   ,				A_     )
        SCREAMING_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
        SCREAMING_SNAKE_CASE__											=			3
        SCREAMING_SNAKE_CASE__											=			self._get_uniform_logits(A_   ,				A_     )
        SCREAMING_SNAKE_CASE__											=			logits_processor(A_   ,				A_   ,				cur_len=A_     )
        self.assertFalse(jnp.isinf(A_     ).any()     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			4
        SCREAMING_SNAKE_CASE__											=			10
        SCREAMING_SNAKE_CASE__											=			15
        SCREAMING_SNAKE_CASE__											=			2
        SCREAMING_SNAKE_CASE__											=			1
        SCREAMING_SNAKE_CASE__											=			15

        # dummy input_ids and scores
        SCREAMING_SNAKE_CASE__											=			ids_tensor((batch_size, sequence_length)   ,				A_     )
        SCREAMING_SNAKE_CASE__											=			input_ids.copy()

        SCREAMING_SNAKE_CASE__											=			self._get_uniform_logits(A_   ,				A_     )
        SCREAMING_SNAKE_CASE__											=			scores.copy()

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

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

        SCREAMING_SNAKE_CASE__											=			10

        # no processor list
        SCREAMING_SNAKE_CASE__											=			temp_dist_warp(A_   ,				A_   ,				cur_len=A_     )
        SCREAMING_SNAKE_CASE__											=			top_k_warp(A_   ,				A_   ,				cur_len=A_     )
        SCREAMING_SNAKE_CASE__											=			top_p_warp(A_   ,				A_   ,				cur_len=A_     )
        SCREAMING_SNAKE_CASE__											=			min_dist_proc(A_   ,				A_   ,				cur_len=A_     )
        SCREAMING_SNAKE_CASE__											=			bos_dist_proc(A_   ,				A_   ,				cur_len=A_     )
        SCREAMING_SNAKE_CASE__											=			eos_dist_proc(A_   ,				A_   ,				cur_len=A_     )

        # with processor list
        SCREAMING_SNAKE_CASE__											=			FlaxLogitsProcessorList(
            [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]     )
        SCREAMING_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  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			4
        SCREAMING_SNAKE_CASE__											=			10
        SCREAMING_SNAKE_CASE__											=			15
        SCREAMING_SNAKE_CASE__											=			2
        SCREAMING_SNAKE_CASE__											=			1
        SCREAMING_SNAKE_CASE__											=			15

        # dummy input_ids and scores
        SCREAMING_SNAKE_CASE__											=			ids_tensor((batch_size, sequence_length)   ,				A_     )
        SCREAMING_SNAKE_CASE__											=			input_ids.copy()

        SCREAMING_SNAKE_CASE__											=			self._get_uniform_logits(A_   ,				A_     )
        SCREAMING_SNAKE_CASE__											=			scores.copy()

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

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

        SCREAMING_SNAKE_CASE__											=			10

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

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

        SCREAMING_SNAKE_CASE__											=			jax.jit(A_     )
        SCREAMING_SNAKE_CASE__											=			jax.jit(A_     )

        SCREAMING_SNAKE_CASE__											=			jitted_run_no_processor_list(A_   ,				A_   ,				A_     )
        SCREAMING_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()     )







import os
import unittest

from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin


_A					:				Dict				=			get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")



class 					__snake_case	(  __SCREAMING_SNAKE_CASE			,     unittest.TestCase    ):


    '''simple docstring'''







    lowerCamelCase__    :		Any				=	BartphoTokenizer
    lowerCamelCase__    :		Tuple				=	False
    lowerCamelCase__    :		Dict				=	True



    def  lowercase_					(       self     ):





        '''simple docstring'''

        super().setUp()

        SCREAMING_SNAKE_CASE__											=			['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']
        SCREAMING_SNAKE_CASE__											=			dict(zip(A_   ,				range(len(A_     )     )     )     )
        SCREAMING_SNAKE_CASE__											=			{'''unk_token''': '''<unk>'''}

        SCREAMING_SNAKE_CASE__											=			os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES['''monolingual_vocab_file''']     )
        with open(self.monolingual_vocab_file   ,				'''w'''   ,				encoding='''utf-8'''     ) as fp:
            for token in vocab_tokens:
                fp.write(f'''{token} {vocab_tokens[token]}\n'''     )

        SCREAMING_SNAKE_CASE__											=			BartphoTokenizer(A_   ,				self.monolingual_vocab_file   ,				**self.special_tokens_map     )
        tokenizer.save_pretrained(self.tmpdirname     )



    def  lowercase_					(       self   ,				**A_     ):





        '''simple docstring'''

        kwargs.update(self.special_tokens_map     )
        return BartphoTokenizer.from_pretrained(self.tmpdirname   ,				**A_     )



    def  lowercase_					(       self   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			'''This is a là test'''
        SCREAMING_SNAKE_CASE__											=			'''This is a<unk><unk> test'''
        return input_text, output_text




    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			BartphoTokenizer(A_   ,				self.monolingual_vocab_file   ,				**self.special_tokens_map     )
        SCREAMING_SNAKE_CASE__											=			'''This is a là test'''
        SCREAMING_SNAKE_CASE__											=			'''▁This ▁is ▁a ▁l à ▁t est'''.split()
        SCREAMING_SNAKE_CASE__											=			tokenizer.tokenize(A_     )
        self.assertListEqual(A_   ,				A_     )

        SCREAMING_SNAKE_CASE__											=			tokens + [tokenizer.unk_token]
        SCREAMING_SNAKE_CASE__											=			[4, 5, 6, 3, 3, 7, 8, 3]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(A_     )   ,				A_     )
