


import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()


def    __lowerCamelCase   (      A__						: List[str]						,						A__						: List[str]						,						A__						: Optional[Any]						,						A__						: Optional[Any]="attention"					)     ->    Dict:
   lowerCamelCase_						:   Dict		= params[f'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
   lowerCamelCase_						:   Dict		= params[f'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
   lowerCamelCase_						:   Optional[int]		= params[f'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
   lowerCamelCase_						:   Union[str, Any]		= params[f'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
   return k, o, q, v


def    __lowerCamelCase   (      A__						: int						,						A__						: Union[str, Any]						,						A__						: str						,						A__						: Tuple=False					)     ->    str:
   if split_mlp_wi:
      lowerCamelCase_						:   int		= params[f'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
      lowerCamelCase_						:   List[Any]		= params[f'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
      lowerCamelCase_						:   Optional[Any]		= (wi_a, wi_a)
   else:
      lowerCamelCase_						:   Optional[Any]		= params[f'''{prefix}/layers_{i}/mlp/wi/kernel''']

   lowerCamelCase_						:   Union[str, Any]		= params[f'''{prefix}/layers_{i}/mlp/wo/kernel''']
   return wi, wo


def    __lowerCamelCase   (      A__						: Optional[int]						,						A__						: Optional[Any]						,						A__						: Optional[int]						,						A__						: Optional[int]					)     ->    Optional[Any]:
   return params[f'''{prefix}/layers_{i}/{layer_name}/scale''']


def    __lowerCamelCase   (      A__						: dict						,						*, A__						: int						,						A__						: bool					)     ->    str:
   lowerCamelCase_						:   Any		= traverse_util.flatten_dict(variables["""target"""]					)
   lowerCamelCase_						:   Optional[int]		= {"""/""".join(A__					): v for k, v in old.items()}

   # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
   lowerCamelCase_						:   Tuple		= """encoder/layers_0/mlp/wi_0/kernel""" in old
   print("""Split MLP:"""						,						A__					)

   lowerCamelCase_						:   Union[str, Any]		= collections.OrderedDict()

   # Shared embeddings.
   lowerCamelCase_						:   Tuple		= old["""token_embedder/embedding"""]

   # Encoder.
   for i in range(A__					):
      # Block i, layer 0 (Self Attention).
      lowerCamelCase_						:   Dict		= tax_layer_norm_lookup(A__						,						A__						,						"""encoder"""						,						"""pre_attention_layer_norm"""					)
      lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_						:   Union[str, Any]		= tax_attention_lookup(A__						,						A__						,						"""encoder"""						,						"""attention"""					)
      lowerCamelCase_						:   int		= layer_norm
      lowerCamelCase_						:   Any		= k.T
      lowerCamelCase_						:   Dict		= o.T
      lowerCamelCase_						:   int		= q.T
      lowerCamelCase_						:   Optional[int]		= v.T

      # Block i, layer 1 (MLP).
      lowerCamelCase_						:   Any		= tax_layer_norm_lookup(A__						,						A__						,						"""encoder"""						,						"""pre_mlp_layer_norm"""					)
      lowerCamelCase_,	lowerCamelCase_						:   Any		= tax_mlp_lookup(A__						,						A__						,						"""encoder"""						,						A__					)
      lowerCamelCase_						:   Tuple		= layer_norm
      if split_mlp_wi:
         lowerCamelCase_						:   Optional[int]		= wi[0].T
         lowerCamelCase_						:   List[Any]		= wi[1].T
      else:
         lowerCamelCase_						:   int		= wi.T
      lowerCamelCase_						:   List[Any]		= wo.T

   lowerCamelCase_						:   str		= old[
       """encoder/relpos_bias/rel_embedding"""
   ].T
   lowerCamelCase_						:   str		= old["""encoder/encoder_norm/scale"""]

   if not is_encoder_only:
      # Decoder.
      for i in range(A__					):
         # Block i, layer 0 (Self Attention).
         lowerCamelCase_						:   Tuple		= tax_layer_norm_lookup(A__						,						A__						,						"""decoder"""						,						"""pre_self_attention_layer_norm"""					)
         lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_						:   int		= tax_attention_lookup(A__						,						A__						,						"""decoder"""						,						"""self_attention"""					)
         lowerCamelCase_						:   Optional[Any]		= layer_norm
         lowerCamelCase_						:   List[Any]		= k.T
         lowerCamelCase_						:   int		= o.T
         lowerCamelCase_						:   Union[str, Any]		= q.T
         lowerCamelCase_						:   int		= v.T

         # Block i, layer 1 (Cross Attention).
         lowerCamelCase_						:   int		= tax_layer_norm_lookup(A__						,						A__						,						"""decoder"""						,						"""pre_cross_attention_layer_norm"""					)
         lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_						:   Optional[int]		= tax_attention_lookup(A__						,						A__						,						"""decoder"""						,						"""encoder_decoder_attention"""					)
         lowerCamelCase_						:   int		= layer_norm
         lowerCamelCase_						:   Optional[Any]		= k.T
         lowerCamelCase_						:   List[str]		= o.T
         lowerCamelCase_						:   Union[str, Any]		= q.T
         lowerCamelCase_						:   List[Any]		= v.T

         # Block i, layer 2 (MLP).
         lowerCamelCase_						:   List[str]		= tax_layer_norm_lookup(A__						,						A__						,						"""decoder"""						,						"""pre_mlp_layer_norm"""					)
         lowerCamelCase_,	lowerCamelCase_						:   Dict		= tax_mlp_lookup(A__						,						A__						,						"""decoder"""						,						A__					)
         lowerCamelCase_						:   Union[str, Any]		= layer_norm
         if split_mlp_wi:
            lowerCamelCase_						:   List[str]		= wi[0].T
            lowerCamelCase_						:   Dict		= wi[1].T
         else:
            lowerCamelCase_						:   Any		= wi.T
         lowerCamelCase_						:   Optional[int]		= wo.T

      lowerCamelCase_						:   Optional[Any]		= old["""decoder/decoder_norm/scale"""]
      lowerCamelCase_						:   int		= old[
          """decoder/relpos_bias/rel_embedding"""
      ].T

      # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
      if "decoder/logits_dense/kernel" in old:
         lowerCamelCase_						:   Any		= old["""decoder/logits_dense/kernel"""].T

   return new


def    __lowerCamelCase   (      A__						: Optional[Any]						,						A__						: bool					)     ->    Union[str, Any]:
   lowerCamelCase_						:   str		= collections.OrderedDict([(k, torch.from_numpy(v.copy()					)) for (k, v) in converted_params.items()]					)

   # Add what is missing.
   if "encoder.embed_tokens.weight" not in state_dict:
      lowerCamelCase_						:   Dict		= state_dict["""shared.weight"""]

   if not is_encoder_only:
      if "decoder.embed_tokens.weight" not in state_dict:
         lowerCamelCase_						:   Union[str, Any]		= state_dict["""shared.weight"""]

      if "lm_head.weight" not in state_dict:  # For old 1.0 models.
         print("""Using shared word embeddings as lm_head."""					)
         lowerCamelCase_						:   Any		= state_dict["""shared.weight"""]

   return state_dict


def    __lowerCamelCase   (      A__						: Tuple						,						A__						: List[Any]						,						A__						: List[Any]						,						A__						: Any					)     ->    Dict:
   lowerCamelCase_						:   Tuple		= checkpoints.load_tax_checkpoint(A__					)
   lowerCamelCase_						:   Optional[int]		= convert_tax_to_pytorch(A__						,						num_layers=config.num_layers						,						is_encoder_only=A__					)
   lowerCamelCase_						:   Union[str, Any]		= make_state_dict(A__						,						A__					)
   model.load_state_dict(A__						,						strict=A__					)


def    __lowerCamelCase   (      A__						: Optional[Any]						,						A__						: int						,						A__						: Tuple						,						A__						: bool = False					)     ->    Dict:
   lowerCamelCase_						:   Tuple		= TaConfig.from_json_file(A__					)
   print(f'''Building PyTorch model from configuration: {config}'''					)
   # Non-v1.1 checkpoints could also use T5Model, but this works for all.
   # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
   if is_encoder_only:
      lowerCamelCase_						:   Dict		= TaEncoderModel(A__					)
   else:
      lowerCamelCase_						:   Optional[int]		= TaForConditionalGeneration(A__					)

   # Load weights from tf checkpoint
   load_tax_weights_in_ta(A__						,						A__						,						A__						,						A__					)

   # Save pytorch-model
   print(f'''Save PyTorch model to {pytorch_dump_path}'''					)
   model.save_pretrained(A__					)

   # Verify that we can load the checkpoint.
   model.from_pretrained(A__					)
   print("""Done"""					)


if __name__ == "__main__":
      snake_case__				:	Tuple					       =					argparse.ArgumentParser(description='Converts a native T5X checkpoint into a PyTorch checkpoint.')
      # Required parameters
      parser.add_argument(
          '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path to the T5X checkpoint.'
      )
      parser.add_argument(
          '--config_file',
          default=None,
          type=str,
          required=True,
          help='The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.',
      )
      parser.add_argument(
          '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
      )
      parser.add_argument(
          '--is_encoder_only', action='store_true', help='Check if the model is encoder-decoder model', default=False
      )
      snake_case__				:	Dict					       =					parser.parse_args()
      convert_tax_checkpoint_to_pytorch(
          args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
      )









import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel

from ...utils import logging


snake_case__				:	int					       =					logging.get_logger(__name__)





class   SCREAMING_SNAKE_CASE_			(a__						):




       '''simple docstring'''



       _a   							=							CLIPConfig

       _a   							=							["CLIPEncoderLayer"]



       def __init__(						self				:				Tuple		,				__a				:				CLIPConfig							)					->Union[str, Any]:
          super().__init__(__a							)

          lowerCamelCase_						:   List[Any]		= CLIPVisionModelWithProjection(config.vision_config							)

          lowerCamelCase_						:   int		= nn.Linear(config.vision_config.projection_dim		,				1							)
          lowerCamelCase_						:   int		= nn.Linear(config.vision_config.projection_dim		,				1							)




       @torch.no_grad()
       def 				_lowerCAmelCase							(						self				:				Dict		,				__a				:				List[Any]		,				__a				:				Tuple		,				__a				:				int=0.5		,				__a				:				Optional[Any]=0.5							)					->Union[str, Any]:
          lowerCamelCase_						:   Dict		= self.vision_model(__a							)[0]

          lowerCamelCase_						:   str		= self.p_head(__a							)
          lowerCamelCase_						:   Union[str, Any]		= nsfw_detected.flatten()
          lowerCamelCase_						:   Tuple		= nsfw_detected > p_threshold
          lowerCamelCase_						:   Dict		= nsfw_detected.tolist()

          if any(__a							):
             logger.warning(
                 """Potential NSFW content was detected in one or more images. A black image will be returned instead."""
                 """ Try again with a different prompt and/or seed."""							)

          for idx, nsfw_detected_ in enumerate(__a							):
             if nsfw_detected_:
                lowerCamelCase_						:   int		= np.zeros(images[idx].shape							)

          lowerCamelCase_						:   Union[str, Any]		= self.w_head(__a							)
          lowerCamelCase_						:   List[str]		= watermark_detected.flatten()
          lowerCamelCase_						:   Dict		= watermark_detected > w_threshold
          lowerCamelCase_						:   Optional[int]		= watermark_detected.tolist()

          if any(__a							):
             logger.warning(
                 """Potential watermarked content was detected in one or more images. A black image will be returned instead."""
                 """ Try again with a different prompt and/or seed."""							)

          for idx, watermark_detected_ in enumerate(__a							):
             if watermark_detected_:
                lowerCamelCase_						:   Tuple		= np.zeros(images[idx].shape							)

          return images, nsfw_detected, watermark_detected





