import warnings
from pathlib import Path
from typing import List, Tuple, Union

import fire
from torch import nn

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging


UpperCamelCase__     :	Optional[int]				=			logging.get_logger(__name__)







def __UpperCamelCase(   _A					:				nn.ModuleList  ,			_A					:				nn.ModuleList  ,			_A					:				List[int]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Optional[int]   				=  nn.ModuleList([src_layers[i] for i in layers_to_copy]    )
   assert len(_A    ) == len(_A    ), F'''{len(_A    )} != {len(_A    )}'''
   dest_layers.load_state_dict(layers_to_copy.state_dict()    )


UpperCamelCase__     :	Optional[int]				=			{
    # maps  num layers in teacher -> num_layers in student -> which teacher layers to copy.
    # 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
    12: {
        1: [0],  # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
        2: [0, 6],
        3: [0, 6, 11],
        4: [0, 4, 8, 11],
        6: [0, 2, 4, 7, 9, 11],
        9: [0, 1, 2, 4, 5, 7, 9, 10, 11],
        12: list(range(12)),
    },
    16: {  # maps  num layers in student -> which teacher layers to copy
        1: [0],
        2: [0, 15],
        3: [0, 8, 15],
        4: [0, 5, 10, 15],
        6: [0, 3, 6, 9, 12, 15],
        8: [0, 2, 4, 6, 8, 10, 12, 15],
        9: [0, 1, 3, 5, 7, 9, 11, 13, 15],
        12: [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 15],
        16: list(range(16)),
    },
    6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
UpperCamelCase__     :	str				=			{
    # maps  num layers in student -> which teacher layers to copy.
    6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
    12: {1: [11], 2: [5, 11], 3: [3, 7, 11], 6: [1, 3, 5, 8, 10, 11]},
    16: {1: [15], 4: [4, 9, 12, 15], 8: [1, 3, 5, 7, 9, 11, 13, 15]},
}







def __UpperCamelCase(   _A					:				str  ,			_A					:				Union[str, Any]    ):
   '''simple docstring'''
   try:
      UpperCAmelCase__							:				Union[str, Any]   				=  LAYERS_TO_COPY[n_teacher][n_student]
      return val
   except KeyError:
      if n_student != n_teacher:
         warnings.warn(
             F'''no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first'''
             F''' {n_student}'''    )
      return list(range(_A    )    )







def __UpperCamelCase(   _A					:				Dict  ,			_A					:				Optional[int]    ):
   '''simple docstring'''
   if n_student > n_teacher:
      raise ValueError(F'''Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}'''    )
   elif n_teacher == n_student:
      return list(range(_A    )    )
   elif n_student == 1:
      return [n_teacher - 1]
   else:
      return LAYERS_TO_SUPERVISE[n_teacher][n_student]







def __UpperCamelCase(   _A					:				Union[str, PreTrainedModel]  ,			_A					:				Union[str, Path] = "student"  ,			_A					:				Union[int, None] = None  ,			_A					:				Union[int, None] = None  ,			_A					:				Any=False  ,			_A					:				Union[str, Any]=None  ,			_A					:				Any=None  ,			**_A					:				Any  ,			):
   '''simple docstring'''
   UpperCAmelCase__							:				int   				=  '''encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher.'''
   assert (e is not None) or (d is not None), _msg
   if isinstance(_A  ,			_A    ):
      AutoTokenizer.from_pretrained(_A    ).save_pretrained(_A    )  # purely for convenience
      UpperCAmelCase__							:				List[Any]   				=  AutoModelForSeqaSeqLM.from_pretrained(_A    ).eval()
   else:
      assert isinstance(_A  ,			_A    ), F'''teacher must be a model or string got type {type(_A    )}'''
   UpperCAmelCase__							:				Optional[Any]   				=  teacher.config.to_diff_dict()

   try:
      UpperCAmelCase__							:				Optional[int]   				=  teacher.config.encoder_layers, teacher.config.decoder_layers
      if e is None:
         UpperCAmelCase__							:				Union[str, Any]   				=  teacher_e
      if d is None:
         UpperCAmelCase__							:				Optional[int]   				=  teacher_d
      init_kwargs.update({'''encoder_layers''': e, '''decoder_layers''': d}    )
   except AttributeError:  # T5
      if hasattr(teacher.config  ,			'''num_encoder_layers'''    ):
         UpperCAmelCase__							:				Optional[Any]   				=  teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
      else:
         UpperCAmelCase__							:				Any   				=  teacher.config.num_layers, teacher.config.num_decoder_layers
      if e is None:
         UpperCAmelCase__							:				str   				=  teacher_e
      if d is None:
         UpperCAmelCase__							:				Tuple   				=  teacher_d
      if hasattr(teacher.config  ,			'''num_encoder_layers'''    ):
         init_kwargs.update({'''num_encoder_layers''': e, '''num_decoder_layers''': d}    )
      else:
         init_kwargs.update({'''num_layers''': e, '''num_decoder_layers''': d}    )

    # Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
   init_kwargs.update(_A    )

   # Copy weights
   UpperCAmelCase__							:				str   				=  teacher.config_class(**_A    )
   UpperCAmelCase__							:				Optional[Any]   				=  AutoModelForSeqaSeqLM.from_config(_A    )
   # Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
   UpperCAmelCase__							:				int   				=  student.load_state_dict(teacher.state_dict()  ,			strict=_A    )
   assert info.missing_keys == [], info.missing_keys  # every student key should have a teacher keys.

   if copy_first_teacher_layers:  # Our copying is done. We just log and save
      UpperCAmelCase__							:				Union[str, Any]   				=  list(range(_A    )    ), list(range(_A    )    )
      logger.info(
          F'''Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to'''
          F''' {save_path}'''    )
      student.save_pretrained(_A    )
      return student, e_layers_to_copy, d_layers_to_copy

   # Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
   if e_layers_to_copy is None:
      UpperCAmelCase__							:				List[int]   				=  pick_layers_to_copy(_A  ,			_A    )
   if d_layers_to_copy is None:
      UpperCAmelCase__							:				List[int]   				=  pick_layers_to_copy(_A  ,			_A    )

   try:
      if hasattr(
          _A  ,			'''prophetnet'''    ):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
         copy_layers(teacher.prophetnet.encoder.layers  ,			student.prophetnet.encoder.layers  ,			_A    )
         copy_layers(teacher.prophetnet.decoder.layers  ,			student.prophetnet.decoder.layers  ,			_A    )
      else:
         copy_layers(teacher.model.encoder.layers  ,			student.model.encoder.layers  ,			_A    )
         copy_layers(teacher.model.decoder.layers  ,			student.model.decoder.layers  ,			_A    )
   except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
      copy_layers(teacher.encoder.block  ,			student.encoder.block  ,			_A    )
      copy_layers(teacher.decoder.block  ,			student.decoder.block  ,			_A    )
   logger.info(
       F'''Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}'''    )
   UpperCAmelCase__							:				List[str]   				=  {
       '''teacher_type''': teacher.config.model_type,
       '''copied_encoder_layers''': e_layers_to_copy,
       '''copied_decoder_layers''': d_layers_to_copy,
   }
   student.save_pretrained(_A    )
   # Save information about copying for easier reproducibility

   return student, e_layers_to_copy, d_layers_to_copy


if __name__ == "__main__":
  fire.Fire(create_student_by_copying_alternating_layers)





'''simple docstring'''

import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification







def __UpperCamelCase(   _A					:				Optional[Any]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				List[str]   				=  []
   embed.append(
       (
           F'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight''',
           F'''stage{idx}.patch_embed.proj.weight''',
       )    )
   embed.append(
       (
           F'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias''',
           F'''stage{idx}.patch_embed.proj.bias''',
       )    )
   embed.append(
       (
           F'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight''',
           F'''stage{idx}.patch_embed.norm.weight''',
       )    )
   embed.append(
       (
           F'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias''',
           F'''stage{idx}.patch_embed.norm.bias''',
       )    )
   return embed







def __UpperCamelCase(   _A					:				Union[str, Any]  ,			_A					:				List[str]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				int   				=  []
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight''',
           F'''stage{idx}.blocks.{cnt}.attn.proj_q.weight''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias''',
           F'''stage{idx}.blocks.{cnt}.attn.proj_q.bias''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight''',
           F'''stage{idx}.blocks.{cnt}.attn.proj_k.weight''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias''',
           F'''stage{idx}.blocks.{cnt}.attn.proj_k.bias''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight''',
           F'''stage{idx}.blocks.{cnt}.attn.proj_v.weight''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias''',
           F'''stage{idx}.blocks.{cnt}.attn.proj_v.bias''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight''',
           F'''stage{idx}.blocks.{cnt}.attn.proj.weight''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias''',
           F'''stage{idx}.blocks.{cnt}.attn.proj.bias''',
       )    )
   attention_weights.append(
       (F'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight''', F'''stage{idx}.blocks.{cnt}.mlp.fc1.weight''')    )
   attention_weights.append(
       (F'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias''', F'''stage{idx}.blocks.{cnt}.mlp.fc1.bias''')    )
   attention_weights.append(
       (F'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight''', F'''stage{idx}.blocks.{cnt}.mlp.fc2.weight''')    )
   attention_weights.append(
       (F'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias''', F'''stage{idx}.blocks.{cnt}.mlp.fc2.bias''')    )
   attention_weights.append(
       (F'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight''', F'''stage{idx}.blocks.{cnt}.norm1.weight''')    )
   attention_weights.append(
       (F'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias''', F'''stage{idx}.blocks.{cnt}.norm1.bias''')    )
   attention_weights.append(
       (F'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight''', F'''stage{idx}.blocks.{cnt}.norm2.weight''')    )
   attention_weights.append(
       (F'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias''', F'''stage{idx}.blocks.{cnt}.norm2.bias''')    )
   return attention_weights







def __UpperCamelCase(   _A					:				Optional[Any]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Union[str, Any]   				=  []
   token.append((F'''cvt.encoder.stages.{idx}.cls_token''', '''stage2.cls_token''')    )
   return token







def __UpperCamelCase(   ):
   '''simple docstring'''
   UpperCAmelCase__							:				List[Any]   				=  []
   head.append(('''layernorm.weight''', '''norm.weight''')    )
   head.append(('''layernorm.bias''', '''norm.bias''')    )
   head.append(('''classifier.weight''', '''head.weight''')    )
   head.append(('''classifier.bias''', '''head.bias''')    )
   return head







def __UpperCamelCase(   _A					:				List[Any]  ,			_A					:				Dict  ,			_A					:				str  ,			_A					:				Tuple    ):
   '''simple docstring'''
   UpperCAmelCase__							:				List[str]   				=  '''imagenet-1k-id2label.json'''
   UpperCAmelCase__							:				str   				=  10_00

   UpperCAmelCase__							:				str   				=  '''huggingface/label-files'''
   UpperCAmelCase__							:				List[Any]   				=  num_labels
   UpperCAmelCase__							:				Tuple   				=  json.load(open(cached_download(hf_hub_url(_A  ,			_A  ,			repo_type='''dataset'''    )    )  ,			'''r'''    )    )
   UpperCAmelCase__							:				Any   				=  {int(_A    ): v for k, v in idalabel.items()}

   UpperCAmelCase__							:				List[str]   				=  idalabel
   UpperCAmelCase__							:				Any   				=  {v: k for k, v in idalabel.items()}

   UpperCAmelCase__							:				int   				=  CvtConfig(num_labels=_A  ,			idalabel=_A  ,			labelaid=_A    )

   # For depth size 13 (13 = 1+2+10)
   if cvt_model.rsplit('''/'''  ,			1    )[-1][4:6] == "13":
      UpperCAmelCase__							:				Tuple   				=  [1, 2, 10]

   # For depth size 21 (21 = 1+4+16)
   elif cvt_model.rsplit('''/'''  ,			1    )[-1][4:6] == "21":
      UpperCAmelCase__							:				List[str]   				=  [1, 4, 16]

   # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
   else:
      UpperCAmelCase__							:				Optional[int]   				=  [2, 2, 20]
      UpperCAmelCase__							:				str   				=  [3, 12, 16]
      UpperCAmelCase__							:				Union[str, Any]   				=  [1_92, 7_68, 10_24]

   UpperCAmelCase__							:				Optional[int]   				=  CvtForImageClassification(_A    )
   UpperCAmelCase__							:				Any   				=  AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''    )
   UpperCAmelCase__							:				Dict   				=  image_size
   UpperCAmelCase__							:				Union[str, Any]   				=  torch.load(_A  ,			map_location=torch.device('''cpu'''    )    )

   UpperCAmelCase__							:				Union[str, Any]   				=  OrderedDict()
   UpperCAmelCase__							:				str   				=  []

   for idx in range(len(config.depth    )    ):
      if config.cls_token[idx]:
         UpperCAmelCase__							:				Optional[int]   				=  list_of_state_dict + cls_token(_A    )
      UpperCAmelCase__							:				Union[str, Any]   				=  list_of_state_dict + embeddings(_A    )
      for cnt in range(config.depth[idx]    ):
         UpperCAmelCase__							:				str   				=  list_of_state_dict + attention(_A  ,			_A    )

   UpperCAmelCase__							:				int   				=  list_of_state_dict + final()
   for gg in list_of_state_dict:
      print(_A    )
   for i in range(len(_A    )    ):
      UpperCAmelCase__							:				List[Any]   				=  original_weights[list_of_state_dict[i][1]]

   model.load_state_dict(_A    )
   model.save_pretrained(_A    )
   image_processor.save_pretrained(_A    )


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
  UpperCamelCase__     :	Tuple				=			argparse.ArgumentParser()
  parser.add_argument(
      '--cvt_model',
      default='cvt-w24',
      type=str,
      help='Name of the cvt model you\'d like to convert.',
  )
  parser.add_argument(
      '--image_size',
      default=384,
      type=int,
      help='Input Image Size',
  )
  parser.add_argument(
      '--cvt_file_name',
      default=r'cvtmodels\CvT-w24-384x384-IN-22k.pth',
      type=str,
      help='Input Image Size',
  )
  parser.add_argument(
      '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
  )

  UpperCamelCase__     :	Dict				=			parser.parse_args()
  convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)




