

import argparse
import gc
import os
import sys
from pathlib import Path
from typing import List  # noqa: F401

import pytorch_lightning as pl
import torch
from finetune import SummarizationModule, TranslationModule
from finetune import main as ft_main
from make_student import create_student_by_copying_alternating_layers, get_layers_to_supervise
from torch import nn

from transformers import AutoModelForSeqaSeqLM, MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import calculate_bleu, check_output_dir, freeze_params, label_smoothed_nll_loss, use_task_specific_params


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import generic_train  # noqa







class  __lowerCamelCase				(	__snake_case	):
      lowerCamelCase_	:      Dict          =					['loss', 'ce_loss', 'mlm_loss', 'hid_loss_enc', 'hid_loss_dec']







      def __init__(     self							,  lowerCamelCase   )						->       Tuple:
        assert Path(hparams.data_dir   ).exists()
        snake_case_       						=      Path(hparams.output_dir   )
        self.output_dir.mkdir(exist_ok=lowerCamelCase   )

        snake_case_       						=      self.output_dir.joinpath("""student"""   )

        snake_case_       						=      str(lowerCamelCase   )  # Tell lightning we are training the student
        snake_case_       						=      AutoModelForSeqaSeqLM.from_pretrained(hparams.teacher   ).eval()
        use_task_specific_params(lowerCamelCase							,  hparams.task   )  # We copy good generation parameters to student by default
        if hparams.student is not None:
          snake_case_       						=      AutoModelForSeqaSeqLM.from_pretrained(hparams.student   )
          use_task_specific_params(lowerCamelCase							,  hparams.task   )
          snake_case_						,				snake_case_       						=      None, None
        else:
          snake_case_						,				snake_case_						,				snake_case_       						=      create_student_by_copying_alternating_layers(
              lowerCamelCase							,  e=hparams.student_encoder_layers							,  d=hparams.student_decoder_layers							,  save_path=lowerCamelCase   )

        if hparams.length_penalty != -1:
          snake_case_       						=      hparams.length_penalty
        snake_case_       						=      hparams.teacher  # Use teacher's tokenizer
        super().__init__(lowerCamelCase							,  model=lowerCamelCase							,  config=student.config   )
        assert student.config.model_type == teacher.config.model_type, (
            f'''teacher, student model types should be the same, got {student.config.model_type} !='''
            f''' {teacher.config.model_type}'''
        )

        if student.config.model_type == "t5":
          snake_case_       						=      len(student.get_encoder().block   )
          snake_case_       						=      len(student.get_decoder().block   )
          snake_case_       						=      len(teacher.get_encoder().block   )
          snake_case_       						=      len(teacher.get_decoder().block   )
        else:
          snake_case_       						=      student.config.encoder_layers
          snake_case_       						=      student.config.decoder_layers
          snake_case_       						=      teacher.config.encoder_layers
          snake_case_       						=      teacher.config.decoder_layers

        snake_case_       						=      not (hparams.student is None or hparams.teacher == hparams.student)
        snake_case_       						=      (not self.different_base_models) and hparams.alpha_hid > 0
        snake_case_       						=      self.different_base_models or (student_encoder_layers != teacher_encoder_layers)
        # self.different_encoder determines whether we need to run the teacher encoder
        snake_case_       						=      teacher
        freeze_params(self.teacher   )

        if not self.different_encoder:  # To save RAM, delete teacher encoder and freeze student encoder.
          try:
            del self.teacher.model.encoder
          except AttributeError:  # T5
            del self.teacher.encoder

        if e_layer_ids is None:
          snake_case_       						=      list(range(lowerCamelCase   )   )
        if d_layer_ids is None:
          snake_case_       						=      list(range(lowerCamelCase   )   )

        snake_case_						,				snake_case_       						=      e_layer_ids, d_layer_ids  # type: List[int], List[int]

        if self.do_calc_hidden_loss:  # Intermediate supervision: Decide which layers to supervise
          if hparams.supervise_forward:
            snake_case_       						=      get_layers_to_supervise(
                n_student=len(self.e_layer_ids   )							,  n_teacher=lowerCamelCase   )
            snake_case_       						=      get_layers_to_supervise(
                n_student=len(self.d_layer_ids   )							,  n_teacher=lowerCamelCase   )
          else:  # student layer should emulate hidden states of the teacher layer it was copied from
            snake_case_       						=      self.e_layer_ids
            snake_case_       						=      self.d_layer_ids
        else:
          snake_case_       						=      None
          snake_case_       						=      None

        snake_case_       						=      nn.KLDivLoss(reduction="""batchmean"""   )
        snake_case_       						=      2.0
        snake_case_       						=      hparams.alpha_mlm
        snake_case_       						=      hparams.alpha_ce
        snake_case_       						=      hparams.alpha_hid
        gc.collect()
        torch.cuda.empty_cache()







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )						->       Optional[Any]:
        snake_case_       						=      mask[:, :, None].expand_as(lowerCamelCase   )
        snake_case_       						=      s_logits.size(-1   )
        snake_case_       						=      torch.masked_select(lowerCamelCase							,  lowerCamelCase   )  # (bs * seq_length * voc_size) modulo the 1s in mask
        snake_case_       						=      torch.masked_select(lowerCamelCase							,  lowerCamelCase   )  # (bs * seq_length * voc_size) modulo the 1s in mask
        snake_case_       						=      s_logits_slct.view(-1							,  lowerCamelCase   )  # (bs * seq_length, voc_size) modulo the 1s in mask
        snake_case_       						=      t_logits_slct.view(-1							,  lowerCamelCase   )  # (bs * seq_length, voc_size) modulo the 1s in mask
        assert t_logits_slct.size() == s_logits_slct.size()
        snake_case_       						=      (
            self.ce_loss_fct(
                nn.functional.log_softmax(s_logits_slct / self.temperature							,  dim=-1   )							,  nn.functional.softmax(t_logits_slct / self.temperature							,  dim=-1   )							,  )
            * (self.temperature) ** 2
        )
        return loss_ce







      @staticmethod
      def 	lowerCAmelCase_    (     lowerCamelCase							,  lowerCamelCase   )						->       Optional[Any]:
        SummarizationModule.add_model_specific_args(lowerCamelCase							,  lowerCamelCase   )
        add_distill_args(lowerCamelCase   )
        return parser







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase   )						->       tuple:
        snake_case_       						=      self.tokenizer.pad_token_id
        snake_case_						,				snake_case_						,				snake_case_       						=      batch["""input_ids"""], batch["""attention_mask"""], batch["""labels"""]
        if isinstance(self.model							,  lowerCamelCase   ):
          snake_case_       						=      self.model._shift_right(lowerCamelCase   )
        else:
          snake_case_       						=      shift_tokens_right(lowerCamelCase							,  lowerCamelCase   )

        # noinspection PyCallingNonCallable
        snake_case_       						=      self(
            lowerCamelCase							,  attention_mask=lowerCamelCase							,  decoder_input_ids=lowerCamelCase							,  output_hidden_states=self.do_calc_hidden_loss							,  output_attentions=lowerCamelCase							,  use_cache=lowerCamelCase							,  )
        snake_case_       						=      student_outputs["""logits"""]

        # Same cross entropy vs. label smoothing logic as finetune.py
        assert lm_logits.shape[-1] == self.model.config.vocab_size
        if self.hparams.label_smoothing == 0:
          # Same behavior as modeling_bart.py, besides ignoring pad_token_id
          snake_case_       						=      nn.CrossEntropyLoss(ignore_index=lowerCamelCase   )
          snake_case_       						=      loss_fct(lm_logits.view(-1							,  lm_logits.shape[-1]   )							,  labels.view(-1   )   )
        else:
          snake_case_       						=      nn.functional.log_softmax(lowerCamelCase							,  dim=-1   )
          snake_case_						,				snake_case_       						=      label_smoothed_nll_loss(
              lowerCamelCase							,  lowerCamelCase							,  self.hparams.label_smoothing							,  ignore_index=lowerCamelCase   )

        def zero_tensor():
          return torch.tensor(0.0   ).type_as(lowerCamelCase   )

        snake_case_       						=      student_outputs[
            """encoder_last_hidden_state"""
        ]  # use this unless self.different_base_models
        snake_case_						,				snake_case_       						=      zero_tensor(), zero_tensor()
        if self.different_encoder:  # compute encoder hidden state loss
          snake_case_       						=      self.teacher.get_encoder()(
              lowerCamelCase							,  attention_mask=lowerCamelCase							,  output_hidden_states=self.do_calc_hidden_loss							,  )
          if self.different_base_models:
            snake_case_       						=      all_teacher_encoder_outputs["""last_hidden_state"""]
          elif self.do_calc_hidden_loss:
            snake_case_       						=      self.calc_hidden_loss(
                lowerCamelCase							,  student_outputs["""encoder_hidden_states"""]							,  all_teacher_encoder_outputs["""hidden_states"""]							,  self.e_matches							,  normalize_hidden=self.hparams.normalize_hidden							,  )

        snake_case_       						=      self.teacher(
            lowerCamelCase							,  attention_mask=lowerCamelCase							,  encoder_outputs=(teacher_enc_outputs,)							,  decoder_input_ids=lowerCamelCase							,  output_hidden_states=self.do_calc_hidden_loss							,  use_cache=lowerCamelCase							,  )
        snake_case_       						=      decoder_input_ids.ne(lowerCamelCase   )
        snake_case_       						=      self.calc_ce_loss(lowerCamelCase							,  lowerCamelCase							,  teacher_outputs["""logits"""]   )
        if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
          snake_case_       						=      self.calc_hidden_loss(
              lowerCamelCase							,  student_outputs["""decoder_hidden_states"""]							,  teacher_outputs["""decoder_hidden_states"""]							,  self.d_matches							,  normalize_hidden=self.hparams.normalize_hidden							,  )

        snake_case_       						=      (
            self.alpha_ce * loss_ce
            + self.alpha_mlm * student_lm_loss
            + self.hparams.alpha_hid * (hid_loss_enc + hid_loss_dec)
        )
        return blended_loss, loss_ce, student_lm_loss, hid_loss_enc, hid_loss_dec







      @staticmethod
      def 	lowerCAmelCase_    (     lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )						->       int:
        snake_case_       						=      """expected list or tuple for hidden_states, got tensor of shape: """
        assert not isinstance(lowerCamelCase							,  torch.Tensor   ), f'''{msg}{hidden_states.shape}'''
        assert not isinstance(lowerCamelCase							,  torch.Tensor   ), f'''{msg}{hidden_states_T.shape}'''
        snake_case_       						=      attention_mask.to(hidden_states[0]   )
        snake_case_       						=      mask.sum() * hidden_states[0].size(-1   )
        snake_case_       						=      torch.stack([hidden_states[i] for i in range(len(lowerCamelCase   )   )]   )
        snake_case_       						=      torch.stack([hidden_states_T[j] for j in matches]   )
        assert student_states.shape == teacher_states.shape, f'''{student_states.shape} != {teacher_states.shape}'''
        if normalize_hidden:
          snake_case_       						=      nn.functional.layer_norm(lowerCamelCase							,  student_states.shape[1:]   )
          snake_case_       						=      nn.functional.layer_norm(lowerCamelCase							,  teacher_states.shape[1:]   )
        snake_case_       						=      nn.functional.mse_loss(lowerCamelCase							,  lowerCamelCase							,  reduction="""none"""   )
        snake_case_       						=      (mse * mask.unsqueeze(0   ).unsqueeze(-1   )).sum() / valid_count
        return masked_mse



def    UpperCamelCase(					lowercase_							)	->				Tuple:





  '''simple docstring'''





  parser.add_argument("""--teacher"""			,					type=lowercase_							)
  parser.add_argument("""--alpha_ce"""			,					default=0.8			,					type=lowercase_							)
  parser.add_argument("""--alpha_mlm"""			,					default=0.2			,					type=lowercase_							)
  parser.add_argument("""--alpha_hid"""			,					default=0.0			,					type=lowercase_			,					required=lowercase_							)
  parser.add_argument("""--student"""			,					type=lowercase_			,					required=lowercase_							)
  parser.add_argument("""--student_decoder_layers"""			,					default=12			,					type=lowercase_			,					required=lowercase_							)
  parser.add_argument("""--student_encoder_layers"""			,					default=12			,					type=lowercase_			,					required=lowercase_							)
  parser.add_argument("""--no_teacher"""			,					action="""store_true"""			,					default=lowercase_							)
  parser.add_argument("""--length_penalty"""			,					type=lowercase_			,					default=-1							)
  parser.add_argument("""--supervise_forward"""			,					action="""store_true"""			,					default=lowercase_							)
  parser.add_argument("""--normalize_hidden"""			,					action="""store_true"""			,					default=lowercase_							)







class  __lowerCamelCase				(	__snake_case	):
      lowerCamelCase_	:      Optional[Any]          =					'translation'
      lowerCamelCase_	:      Any          =					['bleu']
      lowerCamelCase_	:      Any          =					'bleu'







      def __init__(     self							,  lowerCamelCase							,  **lowerCamelCase   )						->       List[Any]:
        super().__init__(lowerCamelCase							,  **lowerCamelCase   )
        assert hparams.src_lang is not None
        assert hparams.tgt_lang is not None
        snake_case_       						=      hparams.src_lang
        snake_case_       						=      hparams.tgt_lang
        if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer							,  lowerCamelCase   ):
          snake_case_       						=      self.tokenizer.lang_code_to_id[hparams.tgt_lang]







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase   )						->       dict:
        return calculate_bleu(lowerCamelCase							,  lowerCamelCase   )







      @staticmethod
      def 	lowerCAmelCase_    (     lowerCamelCase							,  lowerCamelCase   )						->       Tuple:
        TranslationModule.add_model_specific_args(lowerCamelCase							,  lowerCamelCase   )
        add_distill_args(lowerCamelCase   )
        return parser



def    UpperCamelCase(					lowercase_							)	->				List[Any]:





  '''simple docstring'''





  if args.no_teacher:
    snake_case_       						=      TranslationModule if """translation""" in args.task else SummarizationModule
  else:  # DISTILL WITH TEACHER
    snake_case_       						=      TranslationDistiller if """translation""" in args.task else SummarizationDistiller
  snake_case_       						=      module_cls.__name__
  print(f'''using module {args.setup_cls}'''							)
  snake_case_       						=      module_cls(lowercase_							)
  return model



def    UpperCamelCase(					lowercase_							)	->				Union[str, Any]:





  '''simple docstring'''





  Path(args.output_dir							).mkdir(exist_ok=lowercase_							)
  check_output_dir(lowercase_			,					expected_items=3							)

  snake_case_       						=      create_module(lowercase_							)
  return ft_main(lowercase_			,					model=lowercase_							)


if __name__ == "__main__":
    lowerCamelCase_    =					argparse.ArgumentParser()
    lowerCamelCase_    =					pl.Trainer.add_argparse_args(parser)
    lowerCamelCase_    =					SummarizationDistiller.add_model_specific_args(parser, os.getcwd())
    lowerCamelCase_    =					parser.parse_args()

    distill_main(args)
'''simple docstring'''




import argparse

import requests
import torch
from PIL import Image

from transformers import (
    CLIPSegConfig,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPSegTextConfig,
    CLIPSegVisionConfig,
    CLIPTokenizer,
    ViTImageProcessor,
)





def      _UpperCAmelCase    (							_UpperCamelCase    :	Dict   )	->      Union[str, Any]:
    A_     =   CLIPSegTextConfig()
    A_     =   CLIPSegVisionConfig(patch_size=16   )

    A_     =   True if '''refined''' in model_name else False
    A_     =   16 if '''rd16''' in model_name else 64

    A_     =   CLIPSegConfig.from_text_vision_configs(
        _UpperCamelCase,   _UpperCamelCase,   use_complex_transposed_convolution=_UpperCamelCase,   reduce_dim=_UpperCamelCase,   )
    return config





def      _UpperCAmelCase    (							_UpperCamelCase    :	Union[str, Any]   )	->      Dict:
    # update prefixes
    if "clip_model" in name:
        A_     =   name.replace('''clip_model''',   '''clip'''   )
    if "transformer" in name:
        if "visual" in name:
            A_     =   name.replace('''visual.transformer''',   '''vision_model'''   )
        else:
            A_     =   name.replace('''transformer''',   '''text_model'''   )
    if "resblocks" in name:
        A_     =   name.replace('''resblocks''',   '''encoder.layers'''   )
    if "ln_1" in name:
        A_     =   name.replace('''ln_1''',   '''layer_norm1'''   )
    if "ln_2" in name:
        A_     =   name.replace('''ln_2''',   '''layer_norm2'''   )
    if "c_fc" in name:
        A_     =   name.replace('''c_fc''',   '''fc1'''   )
    if "c_proj" in name:
        A_     =   name.replace('''c_proj''',   '''fc2'''   )
    if "attn" in name and "self" not in name:
        A_     =   name.replace('''attn''',   '''self_attn'''   )
    # text encoder
    if "token_embedding" in name:
        A_     =   name.replace('''token_embedding''',   '''text_model.embeddings.token_embedding'''   )
    if "positional_embedding" in name and "visual" not in name:
        A_     =   name.replace('''positional_embedding''',   '''text_model.embeddings.position_embedding.weight'''   )
    if "ln_final" in name:
        A_     =   name.replace('''ln_final''',   '''text_model.final_layer_norm'''   )
    # vision encoder
    if "visual.class_embedding" in name:
        A_     =   name.replace('''visual.class_embedding''',   '''vision_model.embeddings.class_embedding'''   )
    if "visual.conv1" in name:
        A_     =   name.replace('''visual.conv1''',   '''vision_model.embeddings.patch_embedding'''   )
    if "visual.positional_embedding" in name:
        A_     =   name.replace('''visual.positional_embedding''',   '''vision_model.embeddings.position_embedding.weight'''   )
    if "visual.ln_pre" in name:
        A_     =   name.replace('''visual.ln_pre''',   '''vision_model.pre_layrnorm'''   )
    if "visual.ln_post" in name:
        A_     =   name.replace('''visual.ln_post''',   '''vision_model.post_layernorm'''   )
    # projection layers
    if "visual.proj" in name:
        A_     =   name.replace('''visual.proj''',   '''visual_projection.weight'''   )
    if "text_projection" in name:
        A_     =   name.replace('''text_projection''',   '''text_projection.weight'''   )
    # decoder
    if "trans_conv" in name:
        A_     =   name.replace('''trans_conv''',   '''transposed_convolution'''   )
    if "film_mul" in name or "film_add" in name or "reduce" in name or "transposed_convolution" in name:
        A_     =   '''decoder.''' + name
    if "blocks" in name:
        A_     =   name.replace('''blocks''',   '''decoder.layers'''   )
    if "linear1" in name:
        A_     =   name.replace('''linear1''',   '''mlp.fc1'''   )
    if "linear2" in name:
        A_     =   name.replace('''linear2''',   '''mlp.fc2'''   )
    if "norm1" in name and "layer_" not in name:
        A_     =   name.replace('''norm1''',   '''layer_norm1'''   )
    if "norm2" in name and "layer_" not in name:
        A_     =   name.replace('''norm2''',   '''layer_norm2'''   )

    return name





def      _UpperCAmelCase    (							_UpperCamelCase    :	int,   _UpperCamelCase    :	Dict   )	->      Union[str, Any]:
    for key in orig_state_dict.copy().keys():
        A_     =   orig_state_dict.pop(_UpperCamelCase   )

        if key.startswith('''clip_model'''   ) and "attn.in_proj" in key:
            A_     =   key.split('''.'''   )
            if "visual" in key:
                A_     =   int(key_split[4]   )
                A_     =   config.vision_config.hidden_size
                A_     =   '''vision_model'''
            else:
                A_     =   int(key_split[3]   )
                A_     =   config.text_config.hidden_size
                A_     =   '''text_model'''

            if "weight" in key:
                A_     =   val[:dim, :]
                A_     =   val[
                    dim : dim * 2, :
                ]
                A_     =   val[-dim:, :]
            else:
                A_     =   val[:dim]
                A_     =   val[dim : dim * 2]
                A_     =   val[-dim:]
        elif "self_attn" in key and "out_proj" not in key:
            A_     =   key.split('''.'''   )
            A_     =   int(key_split[1]   )
            A_     =   config.reduce_dim
            if "weight" in key:
                A_     =   val[:dim, :]
                A_     =   val[dim : dim * 2, :]
                A_     =   val[-dim:, :]
            else:
                A_     =   val[:dim]
                A_     =   val[dim : dim * 2]
                A_     =   val[-dim:]
        else:
            A_     =   rename_key(_UpperCamelCase   )
            if "visual_projection" in new_name or "text_projection" in new_name:
                A_     =   val.T
            A_     =   val

    return orig_state_dict





def      _UpperCAmelCase    (							)	->      Tuple:
    A_     =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    A_     =   Image.open(requests.get(_UpperCamelCase,   stream=_UpperCamelCase   ).raw   )
    return image





def      _UpperCAmelCase    (							_UpperCamelCase    :	Tuple,   _UpperCamelCase    :	int,   _UpperCamelCase    :	List[Any],   _UpperCamelCase    :	str   )	->      List[str]:
    A_     =   get_clipseg_config(_UpperCamelCase   )
    A_     =   CLIPSegForImageSegmentation(_UpperCamelCase   )
    model.eval()

    A_     =   torch.load(_UpperCamelCase,   map_location='''cpu'''   )

    # remove some keys
    for key in state_dict.copy().keys():
        if key.startswith('''model'''   ):
            state_dict.pop(_UpperCamelCase,   _UpperCamelCase   )

    # rename some keys
    A_     =   convert_state_dict(_UpperCamelCase,   _UpperCamelCase   )
    A_   ,A_     =   model.load_state_dict(_UpperCamelCase,   strict=_UpperCamelCase   )

    if missing_keys != ["clip.text_model.embeddings.position_ids", "clip.vision_model.embeddings.position_ids"]:
        raise ValueError('''Missing keys that are not expected: {}'''.format(_UpperCamelCase   )   )
    if unexpected_keys != ["decoder.reduce.weight", "decoder.reduce.bias"]:
        raise ValueError(F'''Unexpected keys: {unexpected_keys}'''   )

    A_     =   ViTImageProcessor(size=3_52   )
    A_     =   CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''   )
    A_     =   CLIPSegProcessor(image_processor=_UpperCamelCase,   tokenizer=_UpperCamelCase   )

    A_     =   prepare_img()
    A_     =   ['''a glass''', '''something to fill''', '''wood''', '''a jar''']

    A_     =   processor(text=_UpperCamelCase,   images=[image] * len(_UpperCamelCase   ),   padding='''max_length''',   return_tensors='''pt'''   )

    with torch.no_grad():
        A_     =   model(**_UpperCamelCase   )

    # verify values
    A_     =   torch.tensor([0.1_1_1_0, -0.1_8_8_2, 0.1_6_4_5]   )
    A_     =   torch.tensor([0.2_6_9_2, -0.7_1_9_7, -0.1_3_2_8]   )
    if model_name == "clipseg-rd64-refined":
        A_     =   torch.tensor(
            [[-1_0.0_4_0_7, -9.9_4_3_1, -1_0.2_6_4_6], [-9.9_7_5_1, -9.7_0_6_4, -9.9_5_8_6], [-9.6_8_9_1, -9.5_6_4_5, -9.9_6_1_8]]   )
    elif model_name == "clipseg-rd64":
        A_     =   torch.tensor(
            [[-7.2_8_7_7, -7.2_7_1_1, -7.2_4_6_3], [-7.2_6_5_2, -7.2_7_8_0, -7.2_5_2_0], [-7.2_2_3_9, -7.2_2_0_4, -7.2_0_0_1]]   )
    elif model_name == "clipseg-rd16":
        A_     =   torch.tensor(
            [[-6.3_9_5_5, -6.4_0_5_5, -6.4_1_5_1], [-6.3_9_1_1, -6.4_0_3_3, -6.4_1_0_0], [-6.3_4_7_4, -6.3_7_0_2, -6.3_7_6_2]]   )
    else:
        raise ValueError(F'''Model name {model_name} not supported.'''   )

    assert torch.allclose(outputs.logits[0, :3, :3],   _UpperCamelCase,   atol=1E-3   )
    assert torch.allclose(outputs.conditional_embeddings[0, :3],   _UpperCamelCase,   atol=1E-3   )
    assert torch.allclose(outputs.pooled_output[0, :3],   _UpperCamelCase,   atol=1E-3   )
    print('''Looks ok!'''   )

    if pytorch_dump_folder_path is not None:
        print(F'''Saving model and processor to {pytorch_dump_folder_path}'''   )
        model.save_pretrained(_UpperCamelCase   )
        processor.save_pretrained(_UpperCamelCase   )

    if push_to_hub:
        print(F'''Pushing model and processor for {model_name} to the hub'''   )
        model.push_to_hub(F'''CIDAS/{model_name}'''   )
        processor.push_to_hub(F'''CIDAS/{model_name}'''   )


if __name__ == "__main__":
  __snake_case   :      Dict						    =  argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--model_name',
      default='clipseg-rd64',
      type=str,
      choices=['clipseg-rd16', 'clipseg-rd64', 'clipseg-rd64-refined'],
      help=(
          'Name of the model. Supported models are: clipseg-rd64, clipseg-rd16 and clipseg-rd64-refined (rd meaning'
          ' reduce dimension)'
      ),
  )
  parser.add_argument(
      '--checkpoint_path',
      default='/Users/nielsrogge/Documents/CLIPSeg/clip_plus_rd64-uni.pth',
      type=str,
      help=(
          'Path to the original checkpoint. Note that the script assumes that the checkpoint includes both CLIP and'
          ' the decoder weights.'
      ),
  )
  parser.add_argument(
      '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
  )
  parser.add_argument(
      '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
  )

  __snake_case   :      str						    =  parser.parse_args()
  convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)






