




def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			list[list[float]]:



       """simple docstring"""

       A__ 					=       []
       for data in source_data:
              for i, el in enumerate(lowercase_		):
                     if len(lowercase_		) < i + 1:
                            data_lists.append([]		)
                     data_lists[i].append(float(lowercase_		)		)
       return data_lists







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_		)  ->			list[list[float]]:



       """simple docstring"""

       A__ 					=       []
       for dlist, weight in zip(lowercase_					,							lowercase_		):
              A__ 					=       min(lowercase_		)
              A__ 					=       max(lowercase_		)

              A__ 					=       []
              # for weight 0 score is 1 - actual score
              if weight == 0:
                     for item in dlist:
                            try:
                                   score.append(1 - ((item - mind) / (maxd - mind))		)
                            except ZeroDivisionError:
                                   score.append(1		)

              elif weight == 1:
                     for item in dlist:
                            try:
                                   score.append((item - mind) / (maxd - mind)		)
                            except ZeroDivisionError:
                                   score.append(0		)

        # weight not 0 or 1
              else:
                     A__ 					=       f"""Invalid weight of {weight:f} provided"""
                     raise ValueError(lowercase_		)

              score_lists.append(lowercase_		)

       return score_lists







def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			list[float]:



       """simple docstring"""

       A__ 					=       [0 for i in range(len(score_lists[0]		)		)]

       for slist in score_lists:
              for j, ele in enumerate(lowercase_		):
                     A__ 					=       final_scores[j] + ele

       return final_scores







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_		)  ->			list[list[float]]:



       """simple docstring"""

       A__ 					=       get_data(lowercase_		)
       A__ 					=       calculate_each_score(lowercase_					,							lowercase_		)
       A__ 					=       generate_final_scores(lowercase_		)

       # append scores to source data
       for i, ele in enumerate(lowercase_		):
              source_data[i].append(lowercase_		)

       return source_data




"""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  __lowerCamelCase  ( unittest.TestCase			):




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase   ):
							lowerCamelCase_					=  jnp.ones((batch_size, length)   ) / length
							return scores




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  None
							lowerCamelCase_					=  20

							lowerCamelCase_					=  self._get_uniform_logits(batch_size=2							,						length=UpperCAmelCase   )

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

							# compute softmax
							lowerCamelCase_					=  jax.nn.softmax(UpperCAmelCase							,						axis=-1   )

							lowerCamelCase_					=  FlaxTemperatureLogitsWarper(temperature=0.5   )
							lowerCamelCase_					=  FlaxTemperatureLogitsWarper(temperature=1.3   )

							lowerCamelCase_					=  jax.nn.softmax(temp_dist_warper_sharper(UpperCAmelCase							,						scores.copy()							,						cur_len=UpperCAmelCase   )							,						axis=-1   )
							lowerCamelCase_					=  jax.nn.softmax(temp_dist_warper_smoother(UpperCAmelCase							,						scores.copy()							,						cur_len=UpperCAmelCase   )							,						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        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  None
							lowerCamelCase_					=  10
							lowerCamelCase_					=  2

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

							lowerCamelCase_					=  FlaxTopKLogitsWarper(3   )

							lowerCamelCase_					=  top_k_warp(UpperCAmelCase							,						UpperCAmelCase							,						cur_len=UpperCAmelCase   )

							# 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(UpperCAmelCase   )[None, :]							,						(batch_size, length)   ).copy()
							lowerCamelCase_					=  top_k_warp_safety_check(UpperCAmelCase							,						UpperCAmelCase							,						cur_len=UpperCAmelCase   )

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




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  None
							lowerCamelCase_					=  10
							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], [0.1_5, 0.3, 0.3, 0.2_5]]   )   )

							lowerCamelCase_					=  FlaxTopPLogitsWarper(0.8   )
							lowerCamelCase_					=  np.exp(top_p_warp(UpperCAmelCase							,						UpperCAmelCase							,						cur_len=UpperCAmelCase   )   )

							# 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, 0.2_5]]   )
							self.assertTrue(np.allclose(UpperCAmelCase							,						UpperCAmelCase							,						atol=1e-3   )   )

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

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

							# 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(UpperCAmelCase							,						UpperCAmelCase							,						cur_len=UpperCAmelCase   )

							# 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        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  20
							lowerCamelCase_					=  4
							lowerCamelCase_					=  0

							lowerCamelCase_					=  FlaxMinLengthLogitsProcessor(min_length=10							,						eos_token_id=UpperCAmelCase   )

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




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  20
							lowerCamelCase_					=  4
							lowerCamelCase_					=  0

							lowerCamelCase_					=  FlaxForcedBOSTokenLogitsProcessor(bos_token_id=UpperCAmelCase   )

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




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  20
							lowerCamelCase_					=  4
							lowerCamelCase_					=  0
							lowerCamelCase_					=  5

							lowerCamelCase_					=  FlaxForcedEOSTokenLogitsProcessor(max_length=UpperCAmelCase							,						eos_token_id=UpperCAmelCase   )

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




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  4
							lowerCamelCase_					=  10
							lowerCamelCase_					=  15
							lowerCamelCase_					=  2
							lowerCamelCase_					=  1
							lowerCamelCase_					=  15

							# dummy input_ids and scores
							lowerCamelCase_					=  ids_tensor((batch_size, sequence_length)							,						UpperCAmelCase   )
							lowerCamelCase_					=  input_ids.copy()

							lowerCamelCase_					=  self._get_uniform_logits(UpperCAmelCase							,						UpperCAmelCase   )
							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=10							,						eos_token_id=UpperCAmelCase   )
							lowerCamelCase_					=  FlaxForcedBOSTokenLogitsProcessor(bos_token_id=UpperCAmelCase   )
							lowerCamelCase_					=  FlaxForcedEOSTokenLogitsProcessor(max_length=UpperCAmelCase							,						eos_token_id=UpperCAmelCase   )

							lowerCamelCase_					=  10

							# no processor list
							lowerCamelCase_					=  temp_dist_warp(UpperCAmelCase							,						UpperCAmelCase							,						cur_len=UpperCAmelCase   )
							lowerCamelCase_					=  top_k_warp(UpperCAmelCase							,						UpperCAmelCase							,						cur_len=UpperCAmelCase   )
							lowerCamelCase_					=  top_p_warp(UpperCAmelCase							,						UpperCAmelCase							,						cur_len=UpperCAmelCase   )
							lowerCamelCase_					=  min_dist_proc(UpperCAmelCase							,						UpperCAmelCase							,						cur_len=UpperCAmelCase   )
							lowerCamelCase_					=  bos_dist_proc(UpperCAmelCase							,						UpperCAmelCase							,						cur_len=UpperCAmelCase   )
							lowerCamelCase_					=  eos_dist_proc(UpperCAmelCase							,						UpperCAmelCase							,						cur_len=UpperCAmelCase   )

							# 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(UpperCAmelCase							,						UpperCAmelCase							,						cur_len=UpperCAmelCase   )

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

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





				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  4
							lowerCamelCase_					=  10
							lowerCamelCase_					=  15
							lowerCamelCase_					=  2
							lowerCamelCase_					=  1
							lowerCamelCase_					=  15

							# dummy input_ids and scores
							lowerCamelCase_					=  ids_tensor((batch_size, sequence_length)							,						UpperCAmelCase   )
							lowerCamelCase_					=  input_ids.copy()

							lowerCamelCase_					=  self._get_uniform_logits(UpperCAmelCase							,						UpperCAmelCase   )
							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=10							,						eos_token_id=UpperCAmelCase   )
							lowerCamelCase_					=  FlaxForcedBOSTokenLogitsProcessor(bos_token_id=UpperCAmelCase   )
							lowerCamelCase_					=  FlaxForcedEOSTokenLogitsProcessor(max_length=UpperCAmelCase							,						eos_token_id=UpperCAmelCase   )

							lowerCamelCase_					=  10

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

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

							lowerCamelCase_					=  jax.jit(UpperCAmelCase   )
							lowerCamelCase_					=  jax.jit(UpperCAmelCase   )

							lowerCamelCase_					=  jitted_run_no_processor_list(UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   )
							lowerCamelCase_					=  jitted_run_processor_list(UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   )

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

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



