
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 		UpperCamelCase__  (	unittest.TestCase   ):
   def 				lowerCAmelCase      (self			: Optional[Any] ,    snake_case_			: int ,    snake_case_			: int       ):
    __a			:  str		    =  jnp.ones((batch_size, length)       ) / length
    return scores
   def 				lowerCAmelCase      (self			: Optional[int]       ):
    __a			:  int		    =  None
    __a			:  List[str]		    =  2_0

    __a			:  Union[str, Any]		    =  self._get_uniform_logits(batch_size=2 ,    length=snake_case_       )

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

    # compute softmax
    __a			:  Any		    =  jax.nn.softmax(snake_case_ ,    axis=-1       )

    __a			:  str		    =  FlaxTemperatureLogitsWarper(temperature=0.5       )
    __a			:  Union[str, Any]		    =  FlaxTemperatureLogitsWarper(temperature=1.3       )

    __a			:  str		    =  jax.nn.softmax(temp_dist_warper_sharper(snake_case_ ,    scores.copy() ,    cur_len=snake_case_       ) ,    axis=-1       )
    __a			:  List[Any]		    =  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 				lowerCAmelCase      (self			: Tuple       ):
    __a			:  Any		    =  None
    __a			:  int		    =  1_0
    __a			:  Union[str, Any]		    =  2

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

    __a			:  Dict		    =  FlaxTopKLogitsWarper(3       )

    __a			:  Optional[Any]		    =  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			:  str		    =  5
    __a			:  int		    =  FlaxTopKLogitsWarper(top_k=1 ,    filter_value=0.0 ,    min_tokens_to_keep=3       )

    __a			:  str		    =  np.broadcast_to(np.arange(snake_case_       )[None, :] ,    (batch_size, length)       ).copy()
    __a			:  Tuple		    =  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 				lowerCAmelCase      (self			: Any       ):
    __a			:  Dict		    =  None
    __a			:  str		    =  1_0
    __a			:  Tuple		    =  2

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

    __a			:  Union[str, Any]		    =  FlaxTopPLogitsWarper(0.8       )
    __a			:  Tuple		    =  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			:  Optional[int]		    =  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			:  List[Any]		    =  np.broadcast_to(np.arange(snake_case_       )[None, :] ,    (batch_size, vocab_size)       ).copy() - (
        vocab_size // 2
    )

    # make ramp_logits more extreme
    __a			:  Optional[int]		    =  ramp_logits[1] * 100.0

    # make sure at least 2 tokens are kept
    __a			:  Any		    =  FlaxTopPLogitsWarper(0.9 ,    min_tokens_to_keep=2 ,    filter_value=0.0       )
    __a			:  Any		    =  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 				lowerCAmelCase      (self			: int       ):
    __a			:  int		    =  2_0
    __a			:  Union[str, Any]		    =  4
    __a			:  str		    =  0

    __a			:  Optional[Any]		    =  FlaxMinLengthLogitsProcessor(min_length=1_0 ,    eos_token_id=snake_case_       )

    # check that min length is applied at length 5
    __a			:  Union[str, Any]		    =  ids_tensor((batch_size, 2_0) ,    vocab_size=2_0       )
    __a			:  List[str]		    =  5
    __a			:  Any		    =  self._get_uniform_logits(snake_case_ ,    snake_case_       )
    __a			:  Optional[int]		    =  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			:  Optional[Any]		    =  self._get_uniform_logits(snake_case_ ,    snake_case_       )
    __a			:  Any		    =  1_5
    __a			:  Any		    =  min_dist_processor(snake_case_ ,    snake_case_ ,    cur_len=snake_case_       )
    self.assertFalse(jnp.isinf(snake_case_       ).any()       )
   def 				lowerCAmelCase      (self			: Optional[Any]       ):
    __a			:  Dict		    =  2_0
    __a			:  Union[str, Any]		    =  4
    __a			:  Optional[int]		    =  0

    __a			:  int		    =  FlaxForcedBOSTokenLogitsProcessor(bos_token_id=snake_case_       )

    # check that all scores are -inf except the bos_token_id score
    __a			:  str		    =  ids_tensor((batch_size, 1) ,    vocab_size=2_0       )
    __a			:  str		    =  1
    __a			:  Tuple		    =  self._get_uniform_logits(snake_case_ ,    snake_case_       )
    __a			:  Optional[int]		    =  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			:  Optional[Any]		    =  3
    __a			:  Optional[Any]		    =  self._get_uniform_logits(snake_case_ ,    snake_case_       )
    __a			:  Union[str, Any]		    =  logits_processor(snake_case_ ,    snake_case_ ,    cur_len=snake_case_       )
    self.assertFalse(jnp.isinf(snake_case_       ).any()       )
   def 				lowerCAmelCase      (self			: Optional[int]       ):
    __a			:  Union[str, Any]		    =  2_0
    __a			:  str		    =  4
    __a			:  Optional[Any]		    =  0
    __a			:  int		    =  5

    __a			:  Union[str, Any]		    =  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			:  str		    =  ids_tensor((batch_size, 4) ,    vocab_size=2_0       )
    __a			:  int		    =  4
    __a			:  Dict		    =  self._get_uniform_logits(snake_case_ ,    snake_case_       )
    __a			:  List[Any]		    =  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			:  List[str]		    =  3
    __a			:  Union[str, Any]		    =  self._get_uniform_logits(snake_case_ ,    snake_case_       )
    __a			:  Union[str, Any]		    =  logits_processor(snake_case_ ,    snake_case_ ,    cur_len=snake_case_       )
    self.assertFalse(jnp.isinf(snake_case_       ).any()       )
   def 				lowerCAmelCase      (self			: int       ):
    __a			:  Dict		    =  4
    __a			:  Optional[int]		    =  1_0
    __a			:  int		    =  1_5
    __a			:  Optional[Any]		    =  2
    __a			:  Union[str, Any]		    =  1
    __a			:  Dict		    =  1_5

    # dummy input_ids and scores
    __a			:  Tuple		    =  ids_tensor((batch_size, sequence_length) ,    snake_case_       )
    __a			:  Dict		    =  input_ids.copy()

    __a			:  List[Any]		    =  self._get_uniform_logits(snake_case_ ,    snake_case_       )
    __a			:  Optional[Any]		    =  scores.copy()

    # instantiate all dist processors
    __a			:  Dict		    =  FlaxTemperatureLogitsWarper(temperature=0.5       )
    __a			:  Tuple		    =  FlaxTopKLogitsWarper(3       )
    __a			:  str		    =  FlaxTopPLogitsWarper(0.8       )

    # instantiate all logits processors
    __a			:  Optional[int]		    =  FlaxMinLengthLogitsProcessor(min_length=1_0 ,    eos_token_id=snake_case_       )
    __a			:  Any		    =  FlaxForcedBOSTokenLogitsProcessor(bos_token_id=snake_case_       )
    __a			:  Optional[Any]		    =  FlaxForcedEOSTokenLogitsProcessor(max_length=snake_case_ ,    eos_token_id=snake_case_       )

    __a			:  List[str]		    =  1_0

    # no processor list
    __a			:  Optional[Any]		    =  temp_dist_warp(snake_case_ ,    snake_case_ ,    cur_len=snake_case_       )
    __a			:  Dict		    =  top_k_warp(snake_case_ ,    snake_case_ ,    cur_len=snake_case_       )
    __a			:  Optional[int]		    =  top_p_warp(snake_case_ ,    snake_case_ ,    cur_len=snake_case_       )
    __a			:  Union[str, Any]		    =  min_dist_proc(snake_case_ ,    snake_case_ ,    cur_len=snake_case_       )
    __a			:  Union[str, Any]		    =  bos_dist_proc(snake_case_ ,    snake_case_ ,    cur_len=snake_case_       )
    __a			:  Union[str, Any]		    =  eos_dist_proc(snake_case_ ,    snake_case_ ,    cur_len=snake_case_       )

    # with processor list
    __a			:  Dict		    =  FlaxLogitsProcessorList(
        [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]       )
    __a			:  Dict		    =  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 				lowerCAmelCase      (self			: int       ):
    __a			:  List[Any]		    =  4
    __a			:  Optional[Any]		    =  1_0
    __a			:  Optional[Any]		    =  1_5
    __a			:  int		    =  2
    __a			:  Tuple		    =  1
    __a			:  int		    =  1_5

    # dummy input_ids and scores
    __a			:  Union[str, Any]		    =  ids_tensor((batch_size, sequence_length) ,    snake_case_       )
    __a			:  List[str]		    =  input_ids.copy()

    __a			:  int		    =  self._get_uniform_logits(snake_case_ ,    snake_case_       )
    __a			:  Dict		    =  scores.copy()

    # instantiate all dist processors
    __a			:  List[Any]		    =  FlaxTemperatureLogitsWarper(temperature=0.5       )
    __a			:  str		    =  FlaxTopKLogitsWarper(3       )
    __a			:  Any		    =  FlaxTopPLogitsWarper(0.8       )

    # instantiate all logits processors
    __a			:  Optional[int]		    =  FlaxMinLengthLogitsProcessor(min_length=1_0 ,    eos_token_id=snake_case_       )
    __a			:  Optional[int]		    =  FlaxForcedBOSTokenLogitsProcessor(bos_token_id=snake_case_       )
    __a			:  Dict		    =  FlaxForcedEOSTokenLogitsProcessor(max_length=snake_case_ ,    eos_token_id=snake_case_       )

    __a			:  int		    =  1_0

    # no processor list
    def run_no_processor_list(snake_case_			: List[str] ,    snake_case_			: List[Any] ,    snake_case_			: Any       ):
     __a			:  Any		    =  temp_dist_warp(snake_case_ ,    snake_case_ ,    cur_len=snake_case_       )
     __a			:  Optional[Any]		    =  top_k_warp(snake_case_ ,    snake_case_ ,    cur_len=snake_case_       )
     __a			:  Optional[int]		    =  top_p_warp(snake_case_ ,    snake_case_ ,    cur_len=snake_case_       )
     __a			:  Optional[int]		    =  min_dist_proc(snake_case_ ,    snake_case_ ,    cur_len=snake_case_       )
     __a			:  List[str]		    =  bos_dist_proc(snake_case_ ,    snake_case_ ,    cur_len=snake_case_       )
     __a			:  int		    =  eos_dist_proc(snake_case_ ,    snake_case_ ,    cur_len=snake_case_       )
     return scores

    # with processor list
    def run_processor_list(snake_case_			: List[Any] ,    snake_case_			: Union[str, Any] ,    snake_case_			: Tuple       ):
     __a			:  List[Any]		    =  FlaxLogitsProcessorList(
         [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]       )
     __a			:  Dict		    =  processor(snake_case_ ,    snake_case_ ,    cur_len=snake_case_       )
     return scores

    __a			:  Dict		    =  jax.jit(snake_case_       )
    __a			:  Union[str, Any]		    =  jax.jit(snake_case_       )

    __a			:  List[str]		    =  jitted_run_no_processor_list(snake_case_ ,    snake_case_ ,    snake_case_       )
    __a			:  str		    =  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()       )


# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowercase__          ={
    'configuration_efficientnet': [
        'EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EfficientNetConfig',
        'EfficientNetOnnxConfig',
    ]
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__          =['EfficientNetImageProcessor']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__          =[
          'EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST',
          'EfficientNetForImageClassification',
          'EfficientNetModel',
          'EfficientNetPreTrainedModel',
      ]

if TYPE_CHECKING:
      from .configuration_efficientnet import (
          EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
          EfficientNetConfig,
          EfficientNetOnnxConfig,
      )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .image_processing_efficientnet import EfficientNetImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_efficientnet import (
                EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                EfficientNetForImageClassification,
                EfficientNetModel,
                EfficientNetPreTrainedModel,
            )

else:
      import sys

      lowercase__          =_LazyModule(__name__, globals()['__file__'], _import_structure)
