

import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel

def 							__SCREAMING_SNAKE_CASE		(				lowerCAmelCase:  List[str] )   ->       Tuple:






				if "img_encoder.pos_embed" in name:
								_UpperCAmelCase       :		str 		=    name.replace("img_encoder.pos_embed"  ,      "vision_model.embeddings.position_embeddings" )
				if "img_encoder.patch_embed.proj" in name:
								_UpperCAmelCase       :		Tuple 		=    name.replace("img_encoder.patch_embed.proj"  ,      "vision_model.embeddings.patch_embeddings.projection" )
				if "img_encoder.patch_embed.norm" in name:
								_UpperCAmelCase       :		Tuple 		=    name.replace("img_encoder.patch_embed.norm"  ,      "vision_model.embeddings.layernorm" )
				if "img_encoder.layers" in name:
								_UpperCAmelCase       :		Tuple 		=    name.replace("img_encoder.layers"  ,      "vision_model.encoder.stages" )
				if "blocks" in name and "res" not in name:
								_UpperCAmelCase       :		Dict 		=    name.replace("blocks"  ,      "layers" )
				if "attn" in name and "pre_assign" not in name:
								_UpperCAmelCase       :		Any 		=    name.replace("attn"  ,      "self_attn" )
				if "proj" in name and "self_attn" in name and "text" not in name:
								_UpperCAmelCase       :		Any 		=    name.replace("proj"  ,      "out_proj" )
				if "pre_assign_attn.attn.proj" in name:
								_UpperCAmelCase       :		int 		=    name.replace("pre_assign_attn.attn.proj"  ,      "pre_assign_attn.attn.out_proj" )
				if "norm1" in name:
								_UpperCAmelCase       :		Union[str, Any] 		=    name.replace("norm1"  ,      "layer_norm1" )
				if "norm2" in name and "pre_assign" not in name:
								_UpperCAmelCase       :		Dict 		=    name.replace("norm2"  ,      "layer_norm2" )
				if "img_encoder.norm" in name:
								_UpperCAmelCase       :		Optional[int] 		=    name.replace("img_encoder.norm"  ,      "vision_model.layernorm" )
				# text encoder
				if "text_encoder.token_embedding" in name:
								_UpperCAmelCase       :		Optional[int] 		=    name.replace("text_encoder.token_embedding"  ,      "text_model.embeddings.token_embedding" )
				if "text_encoder.positional_embedding" in name:
								_UpperCAmelCase       :		Optional[int] 		=    name.replace("text_encoder.positional_embedding"  ,      "text_model.embeddings.position_embedding.weight" )
				if "text_encoder.transformer.resblocks." in name:
								_UpperCAmelCase       :		Optional[Any] 		=    name.replace("text_encoder.transformer.resblocks."  ,      "text_model.encoder.layers." )
				if "ln_1" in name:
								_UpperCAmelCase       :		List[Any] 		=    name.replace("ln_1"  ,      "layer_norm1" )
				if "ln_2" in name:
								_UpperCAmelCase       :		str 		=    name.replace("ln_2"  ,      "layer_norm2" )
				if "c_fc" in name:
								_UpperCAmelCase       :		Optional[int] 		=    name.replace("c_fc"  ,      "fc1" )
				if "c_proj" in name:
								_UpperCAmelCase       :		Tuple 		=    name.replace("c_proj"  ,      "fc2" )
				if "text_encoder" in name:
								_UpperCAmelCase       :		int 		=    name.replace("text_encoder"  ,      "text_model" )
				if "ln_final" in name:
								_UpperCAmelCase       :		List[str] 		=    name.replace("ln_final"  ,      "final_layer_norm" )
				# projection layers
				if "img_projector.linear_hidden." in name:
								_UpperCAmelCase       :		List[str] 		=    name.replace("img_projector.linear_hidden."  ,      "visual_projection." )
				if "img_projector.linear_out." in name:
								_UpperCAmelCase       :		List[Any] 		=    name.replace("img_projector.linear_out."  ,      "visual_projection.3." )
				if "text_projector.linear_hidden" in name:
								_UpperCAmelCase       :		Any 		=    name.replace("text_projector.linear_hidden"  ,      "text_projection" )
				if "text_projector.linear_out" in name:
								_UpperCAmelCase       :		Dict 		=    name.replace("text_projector.linear_out"  ,      "text_projection.3" )

				return name

def 							__SCREAMING_SNAKE_CASE		(				lowerCAmelCase:  Any  ,      lowerCAmelCase:  List[str] )   ->       Any:






				for key in orig_state_dict.copy().keys():
								_UpperCAmelCase       :		Union[str, Any] 		=    orig_state_dict.pop(a_ )

								if "qkv" in key:
												# weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
												# we need to split them up into separate matrices/vectors
												_UpperCAmelCase       :		List[str] 		=    key.split("." )
												_UpperCAmelCase       :		Dict 		=    int(key_split[2] ), int(key_split[4] )
												_UpperCAmelCase       :		Any 		=    config.vision_config.hidden_size
												if "weight" in key:
																_UpperCAmelCase       :		str 		=    val[:dim, :]
																_UpperCAmelCase       :		List[str] 		=    val[dim : dim * 2, :]
																_UpperCAmelCase       :		Union[str, Any] 		=    val[-dim:, :]
												else:
																_UpperCAmelCase       :		str 		=    val[:dim]
																_UpperCAmelCase       :		Dict 		=    val[dim : dim * 2]
																_UpperCAmelCase       :		Union[str, Any] 		=    val[-dim:]
								elif "in_proj" in key:
												# weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
												# we need to split them up into separate matrices/vectors
												_UpperCAmelCase       :		Any 		=    key.split("." )
												_UpperCAmelCase       :		Any 		=    int(key_split[3] )
												_UpperCAmelCase       :		List[str] 		=    config.text_config.hidden_size
												if "weight" in key:
																_UpperCAmelCase       :		Tuple 		=    val[:dim, :]
																_UpperCAmelCase       :		str 		=    val[
																    dim : dim * 2, :
																]
																_UpperCAmelCase       :		List[str] 		=    val[-dim:, :]
												else:
																_UpperCAmelCase       :		Tuple 		=    val[:dim]
																_UpperCAmelCase       :		Any 		=    val[dim : dim * 2]
																_UpperCAmelCase       :		List[Any] 		=    val[-dim:]
								else:
												_UpperCAmelCase       :		List[Any] 		=    rename_key(a_ )
												# squeeze if necessary
												if (
												    "text_projection.0" in new_name
												    or "text_projection.3" in new_name
												    or "visual_projection.0" in new_name
												    or "visual_projection.3" in new_name
												):
																_UpperCAmelCase       :		Union[str, Any] 		=    val.squeeze_()
												else:
																_UpperCAmelCase       :		Dict 		=    val

				return orig_state_dict

def 							__SCREAMING_SNAKE_CASE		(				)   ->       List[Any]:
				_UpperCAmelCase       :		Dict 		=    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				_UpperCAmelCase       :		Any 		=    Image.open(requests.get(a_  ,      stream=a_ ).raw )
				return im

@torch.no_grad()
def 							__SCREAMING_SNAKE_CASE		(				lowerCAmelCase:  List[Any]  ,      lowerCAmelCase:  Any  ,      lowerCAmelCase:  Any="groupvit-gcc-yfcc"  ,      lowerCAmelCase:  Optional[int]=False )   ->       Any:
				_UpperCAmelCase       :		Tuple 		=    GroupViTConfig()
				_UpperCAmelCase       :		Optional[int] 		=    GroupViTModel(a_ ).eval()

				_UpperCAmelCase       :		str 		=    torch.load(a_  ,      map_location="cpu" )['''model''']
				_UpperCAmelCase       :		str 		=    convert_state_dict(a_  ,      a_ )
				_UpperCAmelCase       :		List[Any] 		=    model.load_state_dict(a_  ,      strict=a_ )
				assert missing_keys == ["text_model.embeddings.position_ids"]
				assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(a_ ) == 0)

				# verify result
				_UpperCAmelCase       :		Tuple 		=    CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32" )
				_UpperCAmelCase       :		List[str] 		=    prepare_img()
				_UpperCAmelCase       :		List[str] 		=    processor(text=["a photo of a cat", "a photo of a dog"]  ,      images=a_  ,      padding=a_  ,      return_tensors="pt" )

				with torch.no_grad():
								_UpperCAmelCase       :		str 		=    model(**a_ )

				if model_name == "groupvit-gcc-yfcc":
								_UpperCAmelCase       :		Union[str, Any] 		=    torch.tensor([[13.3_523, 6.3_629]] )
				elif model_name == "groupvit-gcc-redcaps":
								_UpperCAmelCase       :		List[Any] 		=    torch.tensor([[16.1_873, 8.6_230]] )
				else:
								raise ValueError(F'Model name {model_name} not supported.' )
				assert torch.allclose(outputs.logits_per_image  ,      a_  ,      atol=1E-3 )

				processor.save_pretrained(a_ )
				model.save_pretrained(a_ )
				print("Successfully saved processor and model to"  ,      a_ )

				if push_to_hub:
								print("Pushing to the hub..." )
								processor.push_to_hub(a_  ,      organization="nielsr" )
								model.push_to_hub(a_  ,      organization="nielsr" )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_        =      argparse.ArgumentParser()
					parser.add_argument(
					    '--pytorch_dump_folder_path', default=None, type=str, help='Path to dump the processor and PyTorch model.'
					)
					parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to GroupViT checkpoint')
					parser.add_argument(
					    '--model_name',
					    default='groupvit-gccy-fcc',
					    type=str,
					    help='Name of the model. Expecting either \'groupvit-gcc-yfcc\' or \'groupvit-gcc-redcaps\'',
					)
					parser.add_argument(
					    '--push_to_hub',
					    action='store_true',
					    help='Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.',
					)
					SCREAMING_SNAKE_CASE_        =      parser.parse_args()

					convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)





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


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  nn.ModuleList  ,      lowerCAmelCase:  nn.ModuleList  ,      lowerCAmelCase:  List[int] )   ->       None:
				_UpperCAmelCase       :		str 		=    nn.ModuleList([src_layers[i] for i in layers_to_copy] )
				assert len(lowerCAmelCase ) == len(lowerCAmelCase ), F'{len(lowerCAmelCase )} != {len(lowerCAmelCase )}'
				dest_layers.load_state_dict(layers_to_copy.state_dict() )


SCREAMING_SNAKE_CASE_        =      {
    # 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))},
}
SCREAMING_SNAKE_CASE_        =      {
    # 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 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Any  ,      lowerCAmelCase:  List[str] )   ->       Dict:
				try:
								_UpperCAmelCase       :		int 		=    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(lowerCAmelCase ) )

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  List[Any]  ,      lowerCAmelCase:  Optional[Any] )   ->       List[int]:
				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(lowerCAmelCase ) )
				elif n_student == 1:
								return [n_teacher - 1]
				else:
								return LAYERS_TO_SUPERVISE[n_teacher][n_student]

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Union[str, PreTrainedModel]  ,      lowerCAmelCase:  Union[str, Path] = "student"  ,      lowerCAmelCase:  Union[int, None] = None  ,      lowerCAmelCase:  Union[int, None] = None  ,      lowerCAmelCase:  str=False  ,      lowerCAmelCase:  Union[str, Any]=None  ,      lowerCAmelCase:  Any=None  ,      **lowerCAmelCase:  str  ,      )   ->       Tuple[PreTrainedModel, List[int], List[int]]:
				_UpperCAmelCase       :		Dict 		=    "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(lowerCAmelCase  ,      lowerCAmelCase ):
								AutoTokenizer.from_pretrained(lowerCAmelCase ).save_pretrained(lowerCAmelCase )  # purely for convenience
								_UpperCAmelCase       :		Dict 		=    AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase ).eval()
				else:
								assert isinstance(lowerCAmelCase  ,      lowerCAmelCase ), F'teacher must be a model or string got type {type(lowerCAmelCase )}'
				_UpperCAmelCase       :		List[str] 		=    teacher.config.to_diff_dict()

				try:
								_UpperCAmelCase		,							_UpperCAmelCase       :		Any 		=    teacher.config.encoder_layers, teacher.config.decoder_layers
								if e is None:
												_UpperCAmelCase       :		int 		=    teacher_e
								if d is None:
												_UpperCAmelCase       :		Optional[Any] 		=    teacher_d
								init_kwargs.update({"encoder_layers": e, "decoder_layers": d} )
				except AttributeError:  # T5
								if hasattr(teacher.config  ,      "num_encoder_layers" ):
												_UpperCAmelCase		,							_UpperCAmelCase       :		int 		=    teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
								else:
												_UpperCAmelCase		,							_UpperCAmelCase       :		Dict 		=    teacher.config.num_layers, teacher.config.num_decoder_layers
								if e is None:
												_UpperCAmelCase       :		List[Any] 		=    teacher_e
								if d is None:
												_UpperCAmelCase       :		Optional[int] 		=    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(lowerCAmelCase )

				# Copy weights
				_UpperCAmelCase       :		Any 		=    teacher.config_class(**lowerCAmelCase )
				_UpperCAmelCase       :		Tuple 		=    AutoModelForSeqaSeqLM.from_config(lowerCAmelCase )
				# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
				_UpperCAmelCase       :		List[Any] 		=    student.load_state_dict(teacher.state_dict()  ,      strict=lowerCAmelCase )
				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		,							_UpperCAmelCase       :		Any 		=    list(range(lowerCAmelCase ) ), list(range(lowerCAmelCase ) )
								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(lowerCAmelCase )
								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(lowerCAmelCase  ,      lowerCAmelCase )
				if d_layers_to_copy is None:
								_UpperCAmelCase       :		List[int] 		=    pick_layers_to_copy(lowerCAmelCase  ,      lowerCAmelCase )

				try:
								if hasattr(
								    lowerCAmelCase  ,      "prophetnet" ):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
												copy_layers(teacher.prophetnet.encoder.layers  ,      student.prophetnet.encoder.layers  ,      lowerCAmelCase )
												copy_layers(teacher.prophetnet.decoder.layers  ,      student.prophetnet.decoder.layers  ,      lowerCAmelCase )
								else:
												copy_layers(teacher.model.encoder.layers  ,      student.model.encoder.layers  ,      lowerCAmelCase )
												copy_layers(teacher.model.decoder.layers  ,      student.model.decoder.layers  ,      lowerCAmelCase )
				except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
								copy_layers(teacher.encoder.block  ,      student.encoder.block  ,      lowerCAmelCase )
								copy_layers(teacher.decoder.block  ,      student.decoder.block  ,      lowerCAmelCase )
				logger.info(
				    F'Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}' )
				_UpperCAmelCase       :		str 		=    {
				    "teacher_type": teacher.config.model_type,
				    "copied_encoder_layers": e_layers_to_copy,
				    "copied_decoder_layers": d_layers_to_copy,
				}
				student.save_pretrained(lowerCAmelCase )
				# 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)


