import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel
def 		snake_case__       (		lowercase       ,							lowercase=1				):
   if n_shave_prefix_segments >= 0:
      return ".".join(path.split("."				)[n_shave_prefix_segments:]				)
   else:
      return ".".join(path.split("."				)[:n_shave_prefix_segments]				)
def 		snake_case__       (		lowercase       ,							lowercase=0				):
   lowerCAmelCase_:       List[Any]         =  []
   for old_item in old_list:
      lowerCAmelCase_:       Any         =  old_item
      lowerCAmelCase_:       Dict         =  new_item.replace("block."       ,							"resnets."				)
      lowerCAmelCase_:       Tuple         =  new_item.replace("conv_shorcut"       ,							"conv1"				)
      lowerCAmelCase_:       Optional[int]         =  new_item.replace("in_shortcut"       ,							"conv_shortcut"				)
      lowerCAmelCase_:       Any         =  new_item.replace("temb_proj"       ,							"time_emb_proj"				)

      lowerCAmelCase_:       Tuple         =  shave_segments(lowercase       ,							n_shave_prefix_segments=lowercase				)

      mapping.append({"old": old_item, "new": new_item}				)

   return mapping
def 		snake_case__       (		lowercase       ,							lowercase=0       ,							lowercase=False				):
   lowerCAmelCase_:       Any         =  []
   for old_item in old_list:
      lowerCAmelCase_:       List[Any]         =  old_item

      # In `model.mid`, the layer is called `attn`.
      if not in_mid:
         lowerCAmelCase_:       str         =  new_item.replace("attn"       ,							"attentions"				)
      lowerCAmelCase_:       str         =  new_item.replace(".k."       ,							".key."				)
      lowerCAmelCase_:       Tuple         =  new_item.replace(".v."       ,							".value."				)
      lowerCAmelCase_:       Any         =  new_item.replace(".q."       ,							".query."				)

      lowerCAmelCase_:       List[Any]         =  new_item.replace("proj_out"       ,							"proj_attn"				)
      lowerCAmelCase_:       List[str]         =  new_item.replace("norm"       ,							"group_norm"				)

      lowerCAmelCase_:       Dict         =  shave_segments(lowercase       ,							n_shave_prefix_segments=lowercase				)
      mapping.append({"old": old_item, "new": new_item}				)

   return mapping
def 		snake_case__       (		lowercase       ,							lowercase       ,							lowercase       ,							lowercase=None       ,							lowercase=None       ,							lowercase=None				):
   assert isinstance(lowercase       ,							lowercase				), "Paths should be a list of dicts containing 'old' and 'new' keys."

   if attention_paths_to_split is not None:
      if config is None:
         raise ValueError("Please specify the config if setting 'attention_paths_to_split' to 'True'."				)

      for path, path_map in attention_paths_to_split.items():
         lowerCAmelCase_:       List[Any]         =  old_checkpoint[path]
         lowerCAmelCase_:       Optional[Any]         =  old_tensor.shape[0] // 3

         lowerCAmelCase_:       Any         =  (-1, channels) if len(old_tensor.shape				) == 3 else (-1)

         lowerCAmelCase_:       Optional[int]         =  old_tensor.shape[0] // config.get("num_head_channels"       ,							1				) // 3

         lowerCAmelCase_:       Optional[int]         =  old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]				)
         lowerCAmelCase_:       Tuple         =  old_tensor.split(channels // num_heads       ,							dim=1				)

         lowerCAmelCase_:       List[str]         =  query.reshape(lowercase				).squeeze()
         lowerCAmelCase_:       str         =  key.reshape(lowercase				).squeeze()
         lowerCAmelCase_:       Tuple         =  value.reshape(lowercase				).squeeze()

   for path in paths:
      lowerCAmelCase_:       str         =  path["new"]

      if attention_paths_to_split is not None and new_path in attention_paths_to_split:
         continue

      lowerCAmelCase_:       str         =  new_path.replace("down."       ,							"down_blocks."				)
      lowerCAmelCase_:       Tuple         =  new_path.replace("up."       ,							"up_blocks."				)

      if additional_replacements is not None:
         for replacement in additional_replacements:
            lowerCAmelCase_:       Union[str, Any]         =  new_path.replace(replacement["old"]       ,							replacement["new"]				)

      if "attentions" in new_path:
         lowerCAmelCase_:       List[Any]         =  old_checkpoint[path["old"]].squeeze()
      else:
         lowerCAmelCase_:       Union[str, Any]         =  old_checkpoint[path["old"]]
def 		snake_case__       (		lowercase       ,							lowercase				):
   lowerCAmelCase_:       Any         =  {}

   lowerCAmelCase_:       List[str]         =  checkpoint["temb.dense.0.weight"]
   lowerCAmelCase_:       List[Any]         =  checkpoint["temb.dense.0.bias"]
   lowerCAmelCase_:       Any         =  checkpoint["temb.dense.1.weight"]
   lowerCAmelCase_:       Optional[Any]         =  checkpoint["temb.dense.1.bias"]

   lowerCAmelCase_:       Optional[Any]         =  checkpoint["norm_out.weight"]
   lowerCAmelCase_:       List[str]         =  checkpoint["norm_out.bias"]

   lowerCAmelCase_:       int         =  checkpoint["conv_in.weight"]
   lowerCAmelCase_:       int         =  checkpoint["conv_in.bias"]
   lowerCAmelCase_:       List[Any]         =  checkpoint["conv_out.weight"]
   lowerCAmelCase_:       Dict         =  checkpoint["conv_out.bias"]

   lowerCAmelCase_:       Dict         =  len({".".join(layer.split("."				)[:2]				) for layer in checkpoint if "down" in layer}				)
   lowerCAmelCase_:       Tuple         =  {
       layer_id: [key for key in checkpoint if F'''down.{layer_id}''' in key] for layer_id in range(lowercase				)
   }

   lowerCAmelCase_:       str         =  len({".".join(layer.split("."				)[:2]				) for layer in checkpoint if "up" in layer}				)
   lowerCAmelCase_:       Optional[int]         =  {layer_id: [key for key in checkpoint if F'''up.{layer_id}''' in key] for layer_id in range(lowercase				)}

   for i in range(lowercase				):
      lowerCAmelCase_:       List[str]         =  (i - 1) // (config["layers_per_block"] + 1)

      if any("downsample" in layer for layer in down_blocks[i]				):
         lowerCAmelCase_:       Any         =  checkpoint[
             F'''down.{i}.downsample.op.weight'''
         ]
         lowerCAmelCase_:       List[Any]         =  checkpoint[F'''down.{i}.downsample.op.bias''']
      #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
      #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

      if any("block" in layer for layer in down_blocks[i]				):
         lowerCAmelCase_:       Optional[Any]         =  len(
             {".".join(shave_segments(lowercase       ,							2				).split("."				)[:2]				) for layer in down_blocks[i] if "block" in layer}				)
         lowerCAmelCase_:       str         =  {
             layer_id: [key for key in down_blocks[i] if F'''block.{layer_id}''' in key]
             for layer_id in range(lowercase				)
         }

         if num_blocks > 0:
            for j in range(config["layers_per_block"]				):
               lowerCAmelCase_:       str         =  renew_resnet_paths(blocks[j]				)
               assign_to_checkpoint(lowercase       ,							lowercase       ,							lowercase				)

      if any("attn" in layer for layer in down_blocks[i]				):
         lowerCAmelCase_:       List[Any]         =  len(
             {".".join(shave_segments(lowercase       ,							2				).split("."				)[:2]				) for layer in down_blocks[i] if "attn" in layer}				)
         lowerCAmelCase_:       Optional[Any]         =  {
             layer_id: [key for key in down_blocks[i] if F'''attn.{layer_id}''' in key]
             for layer_id in range(lowercase				)
         }

         if num_attn > 0:
            for j in range(config["layers_per_block"]				):
               lowerCAmelCase_:       List[Any]         =  renew_attention_paths(attns[j]				)
               assign_to_checkpoint(lowercase       ,							lowercase       ,							lowercase       ,							config=lowercase				)

   lowerCAmelCase_:       List[str]         =  [key for key in checkpoint if "mid.block_1" in key]
   lowerCAmelCase_:       List[Any]         =  [key for key in checkpoint if "mid.block_2" in key]
   lowerCAmelCase_:       Any         =  [key for key in checkpoint if "mid.attn_1" in key]

   # Mid new 2
   lowerCAmelCase_:       Optional[int]         =  renew_resnet_paths(lowercase				)
   assign_to_checkpoint(
       lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}]       ,							)

   lowerCAmelCase_:       Dict         =  renew_resnet_paths(lowercase				)
   assign_to_checkpoint(
       lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}]       ,							)

   lowerCAmelCase_:       int         =  renew_attention_paths(lowercase       ,							in_mid=lowercase				)
   assign_to_checkpoint(
       lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}]       ,							)

   for i in range(lowercase				):
      lowerCAmelCase_:       Any         =  num_up_blocks - 1 - i

      if any("upsample" in layer for layer in up_blocks[i]				):
         lowerCAmelCase_:       str         =  checkpoint[
             F'''up.{i}.upsample.conv.weight'''
         ]
         lowerCAmelCase_:       Union[str, Any]         =  checkpoint[F'''up.{i}.upsample.conv.bias''']

      if any("block" in layer for layer in up_blocks[i]				):
         lowerCAmelCase_:       List[str]         =  len(
             {".".join(shave_segments(lowercase       ,							2				).split("."				)[:2]				) for layer in up_blocks[i] if "block" in layer}				)
         lowerCAmelCase_:       List[str]         =  {
             layer_id: [key for key in up_blocks[i] if F'''block.{layer_id}''' in key] for layer_id in range(lowercase				)
         }

         if num_blocks > 0:
            for j in range(config["layers_per_block"] + 1				):
               lowerCAmelCase_:       List[Any]         =  {"old": F'''up_blocks.{i}''', "new": F'''up_blocks.{block_id}'''}
               lowerCAmelCase_:       List[Any]         =  renew_resnet_paths(blocks[j]				)
               assign_to_checkpoint(lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[replace_indices]				)

      if any("attn" in layer for layer in up_blocks[i]				):
         lowerCAmelCase_:       Optional[int]         =  len(
             {".".join(shave_segments(lowercase       ,							2				).split("."				)[:2]				) for layer in up_blocks[i] if "attn" in layer}				)
         lowerCAmelCase_:       List[str]         =  {
             layer_id: [key for key in up_blocks[i] if F'''attn.{layer_id}''' in key] for layer_id in range(lowercase				)
         }

         if num_attn > 0:
            for j in range(config["layers_per_block"] + 1				):
               lowerCAmelCase_:       Optional[int]         =  {"old": F'''up_blocks.{i}''', "new": F'''up_blocks.{block_id}'''}
               lowerCAmelCase_:       Dict         =  renew_attention_paths(attns[j]				)
               assign_to_checkpoint(lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[replace_indices]				)

   lowerCAmelCase_:       Tuple         =  {k.replace("mid_new_2"       ,							"mid_block"				): v for k, v in new_checkpoint.items()}
   return new_checkpoint
def 		snake_case__       (		lowercase       ,							lowercase				):
   lowerCAmelCase_:       Optional[int]         =  {}

   lowerCAmelCase_:       Any         =  checkpoint["encoder.norm_out.weight"]
   lowerCAmelCase_:       Tuple         =  checkpoint["encoder.norm_out.bias"]

   lowerCAmelCase_:       Optional[int]         =  checkpoint["encoder.conv_in.weight"]
   lowerCAmelCase_:       List[Any]         =  checkpoint["encoder.conv_in.bias"]
   lowerCAmelCase_:       List[str]         =  checkpoint["encoder.conv_out.weight"]
   lowerCAmelCase_:       List[Any]         =  checkpoint["encoder.conv_out.bias"]

   lowerCAmelCase_:       Dict         =  checkpoint["decoder.norm_out.weight"]
   lowerCAmelCase_:       List[str]         =  checkpoint["decoder.norm_out.bias"]

   lowerCAmelCase_:       Dict         =  checkpoint["decoder.conv_in.weight"]
   lowerCAmelCase_:       List[Any]         =  checkpoint["decoder.conv_in.bias"]
   lowerCAmelCase_:       List[Any]         =  checkpoint["decoder.conv_out.weight"]
   lowerCAmelCase_:       List[str]         =  checkpoint["decoder.conv_out.bias"]

   lowerCAmelCase_:       List[str]         =  len({".".join(layer.split("."				)[:3]				) for layer in checkpoint if "down" in layer}				)
   lowerCAmelCase_:       Any         =  {
       layer_id: [key for key in checkpoint if F'''down.{layer_id}''' in key] for layer_id in range(lowercase				)
   }

   lowerCAmelCase_:       List[str]         =  len({".".join(layer.split("."				)[:3]				) for layer in checkpoint if "up" in layer}				)
   lowerCAmelCase_:       Union[str, Any]         =  {layer_id: [key for key in checkpoint if F'''up.{layer_id}''' in key] for layer_id in range(lowercase				)}

   for i in range(lowercase				):
      lowerCAmelCase_:       Any         =  (i - 1) // (config["layers_per_block"] + 1)

      if any("downsample" in layer for layer in down_blocks[i]				):
         lowerCAmelCase_:       Optional[Any]         =  checkpoint[
             F'''encoder.down.{i}.downsample.conv.weight'''
         ]
         lowerCAmelCase_:       Any         =  checkpoint[
             F'''encoder.down.{i}.downsample.conv.bias'''
         ]

      if any("block" in layer for layer in down_blocks[i]				):
         lowerCAmelCase_:       Union[str, Any]         =  len(
             {".".join(shave_segments(lowercase       ,							3				).split("."				)[:3]				) for layer in down_blocks[i] if "block" in layer}				)
         lowerCAmelCase_:       Optional[Any]         =  {
             layer_id: [key for key in down_blocks[i] if F'''block.{layer_id}''' in key]
             for layer_id in range(lowercase				)
         }

         if num_blocks > 0:
            for j in range(config["layers_per_block"]				):
               lowerCAmelCase_:       Dict         =  renew_resnet_paths(blocks[j]				)
               assign_to_checkpoint(lowercase       ,							lowercase       ,							lowercase				)

      if any("attn" in layer for layer in down_blocks[i]				):
         lowerCAmelCase_:       Dict         =  len(
             {".".join(shave_segments(lowercase       ,							3				).split("."				)[:3]				) for layer in down_blocks[i] if "attn" in layer}				)
         lowerCAmelCase_:       List[str]         =  {
             layer_id: [key for key in down_blocks[i] if F'''attn.{layer_id}''' in key]
             for layer_id in range(lowercase				)
         }

         if num_attn > 0:
            for j in range(config["layers_per_block"]				):
               lowerCAmelCase_:       List[str]         =  renew_attention_paths(attns[j]				)
               assign_to_checkpoint(lowercase       ,							lowercase       ,							lowercase       ,							config=lowercase				)

   lowerCAmelCase_:       Any         =  [key for key in checkpoint if "mid.block_1" in key]
   lowerCAmelCase_:       List[Any]         =  [key for key in checkpoint if "mid.block_2" in key]
   lowerCAmelCase_:       Any         =  [key for key in checkpoint if "mid.attn_1" in key]

   # Mid new 2
   lowerCAmelCase_:       Dict         =  renew_resnet_paths(lowercase				)
   assign_to_checkpoint(
       lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}]       ,							)

   lowerCAmelCase_:       List[str]         =  renew_resnet_paths(lowercase				)
   assign_to_checkpoint(
       lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}]       ,							)

   lowerCAmelCase_:       List[Any]         =  renew_attention_paths(lowercase       ,							in_mid=lowercase				)
   assign_to_checkpoint(
       lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}]       ,							)

   for i in range(lowercase				):
      lowerCAmelCase_:       str         =  num_up_blocks - 1 - i

      if any("upsample" in layer for layer in up_blocks[i]				):
         lowerCAmelCase_:       Optional[Any]         =  checkpoint[
             F'''decoder.up.{i}.upsample.conv.weight'''
         ]
         lowerCAmelCase_:       str         =  checkpoint[
             F'''decoder.up.{i}.upsample.conv.bias'''
         ]

      if any("block" in layer for layer in up_blocks[i]				):
         lowerCAmelCase_:       str         =  len(
             {".".join(shave_segments(lowercase       ,							3				).split("."				)[:3]				) for layer in up_blocks[i] if "block" in layer}				)
         lowerCAmelCase_:       Optional[Any]         =  {
             layer_id: [key for key in up_blocks[i] if F'''block.{layer_id}''' in key] for layer_id in range(lowercase				)
         }

         if num_blocks > 0:
            for j in range(config["layers_per_block"] + 1				):
               lowerCAmelCase_:       Dict         =  {"old": F'''up_blocks.{i}''', "new": F'''up_blocks.{block_id}'''}
               lowerCAmelCase_:       str         =  renew_resnet_paths(blocks[j]				)
               assign_to_checkpoint(lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[replace_indices]				)

      if any("attn" in layer for layer in up_blocks[i]				):
         lowerCAmelCase_:       Dict         =  len(
             {".".join(shave_segments(lowercase       ,							3				).split("."				)[:3]				) for layer in up_blocks[i] if "attn" in layer}				)
         lowerCAmelCase_:       Optional[int]         =  {
             layer_id: [key for key in up_blocks[i] if F'''attn.{layer_id}''' in key] for layer_id in range(lowercase				)
         }

         if num_attn > 0:
            for j in range(config["layers_per_block"] + 1				):
               lowerCAmelCase_:       Any         =  {"old": F'''up_blocks.{i}''', "new": F'''up_blocks.{block_id}'''}
               lowerCAmelCase_:       List[Any]         =  renew_attention_paths(attns[j]				)
               assign_to_checkpoint(lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[replace_indices]				)

   lowerCAmelCase_:       Optional[Any]         =  {k.replace("mid_new_2"       ,							"mid_block"				): v for k, v in new_checkpoint.items()}
   lowerCAmelCase_:       Optional[int]         =  checkpoint["quant_conv.weight"]
   lowerCAmelCase_:       Optional[Any]         =  checkpoint["quant_conv.bias"]
   if "quantize.embedding.weight" in checkpoint:
      lowerCAmelCase_:       List[Any]         =  checkpoint["quantize.embedding.weight"]
   lowerCAmelCase_:       List[Any]         =  checkpoint["post_quant_conv.weight"]
   lowerCAmelCase_:       Any         =  checkpoint["post_quant_conv.bias"]

   return new_checkpoint


if __name__ == "__main__":
 a     :			Any					    =						argparse.ArgumentParser()

 parser.add_argument(
     """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
 )

 parser.add_argument(
     """--config_file""",
     default=None,
     type=str,
     required=True,
     help="""The config json file corresponding to the architecture.""",
 )

 parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")

 a     :			Union[str, Any]					    =						parser.parse_args()
 a     :			List[Any]					    =						torch.load(args.checkpoint_path)

 with open(args.config_file) as f:
  a     :			Any					    =						json.loads(f.read())

 # unet case
 a     :			List[str]					    =						{key.split(""".""")[0] for key in checkpoint.keys()}
 if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
  a     :			Optional[Any]					    =						convert_vq_autoenc_checkpoint(checkpoint, config)
 else:
  a     :			Optional[Any]					    =						convert_ddpm_checkpoint(checkpoint, config)

 if "ddpm" in config:
  del config["ddpm"]

 if config["_class_name"] == "VQModel":
  a     :			Any					    =						VQModel(**config)
  model.load_state_dict(converted_checkpoint)
  model.save_pretrained(args.dump_path)
 elif config["_class_name"] == "AutoencoderKL":
  a     :			Tuple					    =						AutoencoderKL(**config)
  model.load_state_dict(converted_checkpoint)
  model.save_pretrained(args.dump_path)
 else:
  a     :			Optional[Any]					    =						UNetaDModel(**config)
  model.load_state_dict(converted_checkpoint)

  a     :			Dict					    =						DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

  a     :			int					    =						DDPMPipeline(unet=model, scheduler=scheduler)
  pipe.save_pretrained(args.dump_path)
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__)