import argparse
import json
import os

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextImageProcessor, ConvNextVaConfig, ConvNextVaForImageClassification
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
a     :			Dict					    =						logging.get_logger(__name__)
def 		snake_case__  (		lowercase				):
   lowerCAmelCase_:       Optional[Any]         =  ConvNextVaConfig()

   if "atto" in checkpoint_url:
      lowerCAmelCase_:       int         =  [2, 2, 6, 2]
      lowerCAmelCase_:       int         =  [40, 80, 160, 320]
   if "femto" in checkpoint_url:
      lowerCAmelCase_:       Dict         =  [2, 2, 6, 2]
      lowerCAmelCase_:       Optional[Any]         =  [48, 96, 192, 384]
   if "pico" in checkpoint_url:
      lowerCAmelCase_:       Any         =  [2, 2, 6, 2]
      lowerCAmelCase_:       List[Any]         =  [64, 128, 256, 512]
   if "nano" in checkpoint_url:
      lowerCAmelCase_:       Tuple         =  [2, 2, 8, 2]
      lowerCAmelCase_:       List[Any]         =  [80, 160, 320, 640]
   if "tiny" in checkpoint_url:
      lowerCAmelCase_:       Tuple         =  [3, 3, 9, 3]
      lowerCAmelCase_:       Union[str, Any]         =  [96, 192, 384, 768]
   if "base" in checkpoint_url:
      lowerCAmelCase_:       Dict         =  [3, 3, 27, 3]
      lowerCAmelCase_:       int         =  [128, 256, 512, 1024]
   if "large" in checkpoint_url:
      lowerCAmelCase_:       Optional[Any]         =  [3, 3, 27, 3]
      lowerCAmelCase_:       Optional[int]         =  [192, 384, 768, 1536]
   if "huge" in checkpoint_url:
      lowerCAmelCase_:       int         =  [3, 3, 27, 3]
      lowerCAmelCase_:       Tuple         =  [352, 704, 1408, 2816]

   lowerCAmelCase_:       List[str]         =  1000
   lowerCAmelCase_:       List[Any]         =  "imagenet-1k-id2label.json"
   lowerCAmelCase_:       Dict         =  (1, 1000)

   lowerCAmelCase_:       List[Any]         =  "huggingface/label-files"
   lowerCAmelCase_:       str         =  num_labels
   lowerCAmelCase_:       List[str]         =  json.load(open(hf_hub_download(lowercase       ,							lowercase       ,							repo_type="dataset"				)       ,							"r"				)				)
   lowerCAmelCase_:       Tuple         =  {int(lowercase				): v for k, v in idalabel.items()}

   lowerCAmelCase_:       Optional[Any]         =  idalabel
   lowerCAmelCase_:       int         =  {v: k for k, v in idalabel.items()}
   lowerCAmelCase_:       Dict         =  hidden_sizes
   lowerCAmelCase_:       Optional[Any]         =  depths

   return config, expected_shape
def 		snake_case__  (		lowercase				):
   if "downsample_layers.0.0" in name:
      lowerCAmelCase_:       Union[str, Any]         =  name.replace("downsample_layers.0.0"       ,							"embeddings.patch_embeddings"				)
   if "downsample_layers.0.1" in name:
      lowerCAmelCase_:       str         =  name.replace("downsample_layers.0.1"       ,							"embeddings.norm"				)  # we rename to layernorm later on
   if "downsample_layers.1.0" in name:
      lowerCAmelCase_:       Dict         =  name.replace("downsample_layers.1.0"       ,							"stages.1.downsampling_layer.0"				)
   if "downsample_layers.1.1" in name:
      lowerCAmelCase_:       List[Any]         =  name.replace("downsample_layers.1.1"       ,							"stages.1.downsampling_layer.1"				)
   if "downsample_layers.2.0" in name:
      lowerCAmelCase_:       Optional[int]         =  name.replace("downsample_layers.2.0"       ,							"stages.2.downsampling_layer.0"				)
   if "downsample_layers.2.1" in name:
      lowerCAmelCase_:       Optional[int]         =  name.replace("downsample_layers.2.1"       ,							"stages.2.downsampling_layer.1"				)
   if "downsample_layers.3.0" in name:
      lowerCAmelCase_:       List[Any]         =  name.replace("downsample_layers.3.0"       ,							"stages.3.downsampling_layer.0"				)
   if "downsample_layers.3.1" in name:
      lowerCAmelCase_:       List[Any]         =  name.replace("downsample_layers.3.1"       ,							"stages.3.downsampling_layer.1"				)
   if "stages" in name and "downsampling_layer" not in name:
      # stages.0.0. for instance should be renamed to stages.0.layers.0.
      lowerCAmelCase_:       int         =  name[: len("stages.0"				)] + ".layers" + name[len("stages.0"				) :]
   if "gamma" in name:
      lowerCAmelCase_:       Optional[Any]         =  name.replace("gamma"       ,							"weight"				)
   if "beta" in name:
      lowerCAmelCase_:       Dict         =  name.replace("beta"       ,							"bias"				)
   if "stages" in name:
      lowerCAmelCase_:       Optional[Any]         =  name.replace("stages"       ,							"encoder.stages"				)
   if "norm" in name:
      lowerCAmelCase_:       Tuple         =  name.replace("norm"       ,							"layernorm"				)
   if "head" in name:
      lowerCAmelCase_:       Dict         =  name.replace("head"       ,							"classifier"				)

   return name
def 		snake_case__  (		):
   lowerCAmelCase_:       List[str]         =  "http://images.cocodataset.org/val2017/000000039769.jpg"
   lowerCAmelCase_:       Dict         =  Image.open(requests.get(lowercase       ,							stream=lowercase				).raw				)
   return im
def 		snake_case__  (		lowercase				):
   if "224" in checkpoint_url:
      lowerCAmelCase_:       Optional[int]         =  224
      lowerCAmelCase_:       str         =  224 / 256
   elif "384" in checkpoint_url:
      lowerCAmelCase_:       Tuple         =  384
      lowerCAmelCase_:       Tuple         =  None
   else:
      lowerCAmelCase_:       Optional[Any]         =  512
      lowerCAmelCase_:       int         =  None

   return ConvNextImageProcessor(
       size=lowercase       ,							crop_pct=lowercase       ,							image_mean=[0.485, 0.456, 0.406]       ,							image_std=[0.229, 0.224, 0.225]       ,							resample=PILImageResampling.BICUBIC       ,							)
@torch.no_grad()
def 		snake_case__  (		lowercase       ,							lowercase       ,							lowercase       ,							lowercase				):
   print("Downloading original model from checkpoint..."				)
   # define ConvNeXTV2 configuration based on URL
   lowerCAmelCase_     ,  lowerCAmelCase_:       List[Any]         =  get_convnextva_config(lowercase				)
   # load original state_dict from URL
   lowerCAmelCase_:       Optional[int]         =  torch.hub.load_state_dict_from_url(lowercase				)["model"]

   print("Converting model parameters..."				)
   # rename keys
   for key in state_dict.copy().keys():
      lowerCAmelCase_:       List[Any]         =  state_dict.pop(lowercase				)
      lowerCAmelCase_:       Optional[int]         =  val
   # add prefix to all keys expect classifier head
   for key in state_dict.copy().keys():
      lowerCAmelCase_:       Optional[Any]         =  state_dict.pop(lowercase				)
      if not key.startswith("classifier"				):
         lowerCAmelCase_:       str         =  "convnextv2." + key
      lowerCAmelCase_:       List[str]         =  val

   # load HuggingFace model
   lowerCAmelCase_:       List[str]         =  ConvNextVaForImageClassification(lowercase				)
   model.load_state_dict(lowercase				)
   model.eval()

   # Check outputs on an image, prepared by ConvNextImageProcessor
   lowerCAmelCase_:       Optional[Any]         =  convert_preprocessor(lowercase				)
   lowerCAmelCase_:       Any         =  preprocessor(images=prepare_img()       ,							return_tensors="pt"				)
   lowerCAmelCase_:       Dict         =  model(**lowercase				).logits

   # note: the logits below were obtained without center cropping
   if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt":
      lowerCAmelCase_:       Dict         =  torch.tensor([-0.3930, 0.1747, -0.5246, 0.4177, 0.4295]				)
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
      lowerCAmelCase_:       List[Any]         =  torch.tensor([-0.1727, -0.5341, -0.7818, -0.4745, -0.6566]				)
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
      lowerCAmelCase_:       int         =  torch.tensor([-0.0333, 0.1563, -0.9137, 0.1054, 0.0381]				)
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
      lowerCAmelCase_:       str         =  torch.tensor([-0.1744, -0.1555, -0.0713, 0.0950, -0.1431]				)
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
      lowerCAmelCase_:       Optional[int]         =  torch.tensor([0.9996, 0.1966, -0.4386, -0.3472, 0.6661]				)
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
      lowerCAmelCase_:       Dict         =  torch.tensor([-0.2553, -0.6708, -0.1359, 0.2518, -0.2488]				)
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
      lowerCAmelCase_:       Union[str, Any]         =  torch.tensor([-0.0673, -0.5627, -0.3753, -0.2722, 0.0178]				)
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
      lowerCAmelCase_:       List[Any]         =  torch.tensor([-0.6377, -0.7458, -0.2150, 0.1184, -0.0597]				)
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
      lowerCAmelCase_:       Union[str, Any]         =  torch.tensor([1.0799, 0.2322, -0.8860, 1.0219, 0.6231]				)
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
      lowerCAmelCase_:       str         =  torch.tensor([0.3766, 0.4917, -1.1426, 0.9942, 0.6024]				)
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
      lowerCAmelCase_:       Optional[Any]         =  torch.tensor([0.4220, -0.6919, -0.4317, -0.2881, -0.6609]				)
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
      lowerCAmelCase_:       List[Any]         =  torch.tensor([0.1082, -0.8286, -0.5095, 0.4681, -0.8085]				)
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
      lowerCAmelCase_:       List[str]         =  torch.tensor([-0.2419, -0.6221, 0.2176, -0.0980, -0.7527]				)
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
      lowerCAmelCase_:       Tuple         =  torch.tensor([0.0391, -0.4371, 0.3786, 0.1251, -0.2784]				)
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
      lowerCAmelCase_:       List[Any]         =  torch.tensor([-0.0504, 0.5636, -0.1729, -0.6507, -0.3949]				)
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
      lowerCAmelCase_:       str         =  torch.tensor([0.3560, 0.9486, 0.3149, -0.2667, -0.5138]				)
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
      lowerCAmelCase_:       int         =  torch.tensor([-0.2469, -0.4550, -0.5853, -0.0810, 0.0309]				)
   elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
      lowerCAmelCase_:       Union[str, Any]         =  torch.tensor([-0.3090, 0.0802, -0.0682, -0.1979, -0.2826]				)
   else:
      raise ValueError(F'''Unknown URL: {checkpoint_url}'''				)

   assert torch.allclose(logits[0, :5]       ,							lowercase       ,							atol=1E-3				)
   assert logits.shape == expected_shape
   print("Model outputs match the original results!"				)

   if save_model:
      print("Saving model to local..."				)
      # Create folder to save model
      if not os.path.isdir(lowercase				):
         os.mkdir(lowercase				)

      model.save_pretrained(lowercase				)
      preprocessor.save_pretrained(lowercase				)

   lowerCAmelCase_:       Optional[Any]         =  "convnextv2"
   if "atto" in checkpoint_url:
      model_name += "-atto"
   if "femto" in checkpoint_url:
      model_name += "-femto"
   if "pico" in checkpoint_url:
      model_name += "-pico"
   if "nano" in checkpoint_url:
      model_name += "-nano"
   elif "tiny" in checkpoint_url:
      model_name += "-tiny"
   elif "base" in checkpoint_url:
      model_name += "-base"
   elif "large" in checkpoint_url:
      model_name += "-large"
   elif "huge" in checkpoint_url:
      model_name += "-huge"
   if "22k" in checkpoint_url and "1k" not in checkpoint_url:
      model_name += "-22k"
   elif "22k" in checkpoint_url and "1k" in checkpoint_url:
      model_name += "-22k-1k"
   elif "1k" in checkpoint_url:
      model_name += "-1k"
   if "224" in checkpoint_url:
      model_name += "-224"
   elif "384" in checkpoint_url:
      model_name += "-384"
   elif "512" in checkpoint_url:
      model_name += "-512"

   if push_to_hub:
      print(F'''Pushing {model_name} to the hub...'''				)
      model.push_to_hub(lowercase				)
      preprocessor.push_to_hub(lowercase				)


if __name__ == "__main__":
 a     :			List[str]					    =						argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--checkpoint_url""",
     default="""https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt""",
     type=str,
     help="""URL of the original ConvNeXTV2 checkpoint you'd like to convert.""",
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""",
     default="""model""",
     type=str,
     help="""Path to the output PyTorch model directory.""",
 )
 parser.add_argument("""--save_model""", action="""store_true""", help="""Save model to local""")
 parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image preprocessor to the hub""")

 a     :			Tuple					    =						parser.parse_args()
 convert_convnextva_checkpoint(
     args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
 )
from typing import TYPE_CHECKING

from ..utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available


a     :			Optional[Any]					    =						{
    """configuration_utils""": ["""GenerationConfig"""],
    """streamers""": ["""TextIteratorStreamer""", """TextStreamer"""],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a     :			Union[str, Any]					    =						[
     """Constraint""",
     """ConstraintListState""",
     """DisjunctiveConstraint""",
     """PhrasalConstraint""",
 ]
 a     :			Optional[int]					    =						[
     """BeamHypotheses""",
     """BeamScorer""",
     """BeamSearchScorer""",
     """ConstrainedBeamSearchScorer""",
 ]
 a     :			Any					    =						[
     """EpsilonLogitsWarper""",
     """EtaLogitsWarper""",
     """ForcedBOSTokenLogitsProcessor""",
     """ForcedEOSTokenLogitsProcessor""",
     """HammingDiversityLogitsProcessor""",
     """InfNanRemoveLogitsProcessor""",
     """LogitsProcessor""",
     """LogitsProcessorList""",
     """LogitsWarper""",
     """MinLengthLogitsProcessor""",
     """MinNewTokensLengthLogitsProcessor""",
     """NoBadWordsLogitsProcessor""",
     """NoRepeatNGramLogitsProcessor""",
     """PrefixConstrainedLogitsProcessor""",
     """RepetitionPenaltyLogitsProcessor""",
     """SequenceBiasLogitsProcessor""",
     """EncoderRepetitionPenaltyLogitsProcessor""",
     """TemperatureLogitsWarper""",
     """TopKLogitsWarper""",
     """TopPLogitsWarper""",
     """TypicalLogitsWarper""",
     """EncoderNoRepeatNGramLogitsProcessor""",
     """ExponentialDecayLengthPenalty""",
     """LogitNormalization""",
 ]
 a     :			int					    =						[
     """MaxNewTokensCriteria""",
     """MaxLengthCriteria""",
     """MaxTimeCriteria""",
     """StoppingCriteria""",
     """StoppingCriteriaList""",
     """validate_stopping_criteria""",
 ]
 a     :			Dict					    =						[
     """GenerationMixin""",
     """top_k_top_p_filtering""",
     """GreedySearchEncoderDecoderOutput""",
     """GreedySearchDecoderOnlyOutput""",
     """SampleEncoderDecoderOutput""",
     """SampleDecoderOnlyOutput""",
     """BeamSearchEncoderDecoderOutput""",
     """BeamSearchDecoderOnlyOutput""",
     """BeamSampleEncoderDecoderOutput""",
     """BeamSampleDecoderOnlyOutput""",
     """ContrastiveSearchEncoderDecoderOutput""",
     """ContrastiveSearchDecoderOnlyOutput""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a     :			Optional[Any]					    =						[
     """TFForcedBOSTokenLogitsProcessor""",
     """TFForcedEOSTokenLogitsProcessor""",
     """TFLogitsProcessor""",
     """TFLogitsProcessorList""",
     """TFLogitsWarper""",
     """TFMinLengthLogitsProcessor""",
     """TFNoBadWordsLogitsProcessor""",
     """TFNoRepeatNGramLogitsProcessor""",
     """TFRepetitionPenaltyLogitsProcessor""",
     """TFTemperatureLogitsWarper""",
     """TFTopKLogitsWarper""",
     """TFTopPLogitsWarper""",
     """TFForceTokensLogitsProcessor""",
     """TFSuppressTokensAtBeginLogitsProcessor""",
     """TFSuppressTokensLogitsProcessor""",
 ]
 a     :			Any					    =						[
     """TFGenerationMixin""",
     """tf_top_k_top_p_filtering""",
     """TFGreedySearchDecoderOnlyOutput""",
     """TFGreedySearchEncoderDecoderOutput""",
     """TFSampleEncoderDecoderOutput""",
     """TFSampleDecoderOnlyOutput""",
     """TFBeamSearchEncoderDecoderOutput""",
     """TFBeamSearchDecoderOnlyOutput""",
     """TFBeamSampleEncoderDecoderOutput""",
     """TFBeamSampleDecoderOnlyOutput""",
     """TFContrastiveSearchEncoderDecoderOutput""",
     """TFContrastiveSearchDecoderOnlyOutput""",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a     :			Union[str, Any]					    =						[
     """FlaxForcedBOSTokenLogitsProcessor""",
     """FlaxForcedEOSTokenLogitsProcessor""",
     """FlaxLogitsProcessor""",
     """FlaxLogitsProcessorList""",
     """FlaxLogitsWarper""",
     """FlaxMinLengthLogitsProcessor""",
     """FlaxTemperatureLogitsWarper""",
     """FlaxTopKLogitsWarper""",
     """FlaxTopPLogitsWarper""",
 ]
 a     :			Dict					    =						[
     """FlaxGenerationMixin""",
     """FlaxGreedySearchOutput""",
     """FlaxSampleOutput""",
     """FlaxBeamSearchOutput""",
 ]

if TYPE_CHECKING:
 from .configuration_utils import GenerationConfig
 from .streamers import TextIteratorStreamer, TextStreamer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
  from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
  from .logits_process import (
      EncoderNoRepeatNGramLogitsProcessor,
      EncoderRepetitionPenaltyLogitsProcessor,
      EpsilonLogitsWarper,
      EtaLogitsWarper,
      ExponentialDecayLengthPenalty,
      ForcedBOSTokenLogitsProcessor,
      ForcedEOSTokenLogitsProcessor,
      HammingDiversityLogitsProcessor,
      InfNanRemoveLogitsProcessor,
      LogitNormalization,
      LogitsProcessor,
      LogitsProcessorList,
      LogitsWarper,
      MinLengthLogitsProcessor,
      MinNewTokensLengthLogitsProcessor,
      NoBadWordsLogitsProcessor,
      NoRepeatNGramLogitsProcessor,
      PrefixConstrainedLogitsProcessor,
      RepetitionPenaltyLogitsProcessor,
      SequenceBiasLogitsProcessor,
      TemperatureLogitsWarper,
      TopKLogitsWarper,
      TopPLogitsWarper,
      TypicalLogitsWarper,
  )
  from .stopping_criteria import (
      MaxLengthCriteria,
      MaxNewTokensCriteria,
      MaxTimeCriteria,
      StoppingCriteria,
      StoppingCriteriaList,
      validate_stopping_criteria,
  )
  from .utils import (
      BeamSampleDecoderOnlyOutput,
      BeamSampleEncoderDecoderOutput,
      BeamSearchDecoderOnlyOutput,
      BeamSearchEncoderDecoderOutput,
      ContrastiveSearchDecoderOnlyOutput,
      ContrastiveSearchEncoderDecoderOutput,
      GenerationMixin,
      GreedySearchDecoderOnlyOutput,
      GreedySearchEncoderDecoderOutput,
      SampleDecoderOnlyOutput,
      SampleEncoderDecoderOutput,
      top_k_top_p_filtering,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tf_logits_process import (
      TFForcedBOSTokenLogitsProcessor,
      TFForcedEOSTokenLogitsProcessor,
      TFForceTokensLogitsProcessor,
      TFLogitsProcessor,
      TFLogitsProcessorList,
      TFLogitsWarper,
      TFMinLengthLogitsProcessor,
      TFNoBadWordsLogitsProcessor,
      TFNoRepeatNGramLogitsProcessor,
      TFRepetitionPenaltyLogitsProcessor,
      TFSuppressTokensAtBeginLogitsProcessor,
      TFSuppressTokensLogitsProcessor,
      TFTemperatureLogitsWarper,
      TFTopKLogitsWarper,
      TFTopPLogitsWarper,
  )
  from .tf_utils import (
      TFBeamSampleDecoderOnlyOutput,
      TFBeamSampleEncoderDecoderOutput,
      TFBeamSearchDecoderOnlyOutput,
      TFBeamSearchEncoderDecoderOutput,
      TFContrastiveSearchDecoderOnlyOutput,
      TFContrastiveSearchEncoderDecoderOutput,
      TFGenerationMixin,
      TFGreedySearchDecoderOnlyOutput,
      TFGreedySearchEncoderDecoderOutput,
      TFSampleDecoderOnlyOutput,
      TFSampleEncoderDecoderOutput,
      tf_top_k_top_p_filtering,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .flax_logits_process import (
      FlaxForcedBOSTokenLogitsProcessor,
      FlaxForcedEOSTokenLogitsProcessor,
      FlaxLogitsProcessor,
      FlaxLogitsProcessorList,
      FlaxLogitsWarper,
      FlaxMinLengthLogitsProcessor,
      FlaxTemperatureLogitsWarper,
      FlaxTopKLogitsWarper,
      FlaxTopPLogitsWarper,
  )
  from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
else:
 import sys

 a     :			str					    =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)