



'''simple docstring'''






import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase__:   List[str]								=			logging.get_logger(__name__)


def  snake_case_     (					_lowerCAmelCase  :						List[Any]   ,      _lowerCAmelCase  :						Optional[int]=False  )      ->   str:
							UpperCAmelCase :       List[Any] 							=		[]
							for i in range(config.num_hidden_layers  ):
														# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
														rename_keys.append((f"""blocks.{i}.norm1.weight""", f"""deit.encoder.layer.{i}.layernorm_before.weight""")  )
														rename_keys.append((f"""blocks.{i}.norm1.bias""", f"""deit.encoder.layer.{i}.layernorm_before.bias""")  )
														rename_keys.append((f"""blocks.{i}.attn.proj.weight""", f"""deit.encoder.layer.{i}.attention.output.dense.weight""")  )
														rename_keys.append((f"""blocks.{i}.attn.proj.bias""", f"""deit.encoder.layer.{i}.attention.output.dense.bias""")  )
														rename_keys.append((f"""blocks.{i}.norm2.weight""", f"""deit.encoder.layer.{i}.layernorm_after.weight""")  )
														rename_keys.append((f"""blocks.{i}.norm2.bias""", f"""deit.encoder.layer.{i}.layernorm_after.bias""")  )
														rename_keys.append((f"""blocks.{i}.mlp.fc1.weight""", f"""deit.encoder.layer.{i}.intermediate.dense.weight""")  )
														rename_keys.append((f"""blocks.{i}.mlp.fc1.bias""", f"""deit.encoder.layer.{i}.intermediate.dense.bias""")  )
														rename_keys.append((f"""blocks.{i}.mlp.fc2.weight""", f"""deit.encoder.layer.{i}.output.dense.weight""")  )
														rename_keys.append((f"""blocks.{i}.mlp.fc2.bias""", f"""deit.encoder.layer.{i}.output.dense.bias""")  )

							# projection layer + position embeddings
							rename_keys.extend(
							    [
							        ('''cls_token''', '''deit.embeddings.cls_token'''),
							        ('''dist_token''', '''deit.embeddings.distillation_token'''),
							        ('''patch_embed.proj.weight''', '''deit.embeddings.patch_embeddings.projection.weight'''),
							        ('''patch_embed.proj.bias''', '''deit.embeddings.patch_embeddings.projection.bias'''),
							        ('''pos_embed''', '''deit.embeddings.position_embeddings'''),
							    ]  )

							if base_model:
														# layernorm + pooler
														rename_keys.extend(
														    [
														        ('''norm.weight''', '''layernorm.weight'''),
														        ('''norm.bias''', '''layernorm.bias'''),
														        ('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
														        ('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
														    ]  )

														# if just the base model, we should remove "deit" from all keys that start with "deit"
														UpperCAmelCase :       Union[str, Any] 							=		[(pair[0], pair[1][4:]) if pair[1].startswith('''deit'''  ) else pair for pair in rename_keys]
							else:
														# layernorm + classification heads
														rename_keys.extend(
														    [
														        ('''norm.weight''', '''deit.layernorm.weight'''),
														        ('''norm.bias''', '''deit.layernorm.bias'''),
														        ('''head.weight''', '''cls_classifier.weight'''),
														        ('''head.bias''', '''cls_classifier.bias'''),
														        ('''head_dist.weight''', '''distillation_classifier.weight'''),
														        ('''head_dist.bias''', '''distillation_classifier.bias'''),
														    ]  )

							return rename_keys


def  snake_case_     (					_lowerCAmelCase  :						str   ,      _lowerCAmelCase  :						List[Any]   ,      _lowerCAmelCase  :						List[Any]=False  )      ->   Dict:
							for i in range(config.num_hidden_layers  ):
														if base_model:
																					UpperCAmelCase :       Optional[Any] 							=		''''''
														else:
																					UpperCAmelCase :       Dict 							=		'''deit.'''
														# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
														UpperCAmelCase :       List[Any] 							=		state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""  )
														UpperCAmelCase :       str 							=		state_dict.pop(f"""blocks.{i}.attn.qkv.bias"""  )
														# next, add query, keys and values (in that order) to the state dict
														UpperCAmelCase :       List[str] 							=		in_proj_weight[
														    : config.hidden_size, :
														]
														UpperCAmelCase :       Union[str, Any] 							=		in_proj_bias[: config.hidden_size]
														UpperCAmelCase :       Union[str, Any] 							=		in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														UpperCAmelCase :       List[Any] 							=		in_proj_bias[
														    config.hidden_size : config.hidden_size * 2
														]
														UpperCAmelCase :       Optional[int] 							=		in_proj_weight[
														    -config.hidden_size :, :
														]
														UpperCAmelCase :       List[Any] 							=		in_proj_bias[-config.hidden_size :]


def  snake_case_     (					_lowerCAmelCase  :						Optional[int]   ,      _lowerCAmelCase  :						str   ,      _lowerCAmelCase  :						Tuple  )      ->   Optional[int]:
							UpperCAmelCase :       Optional[int] 							=		dct.pop(_lowerCAmelCase  )
							UpperCAmelCase :       List[str] 							=		val


def  snake_case_     (					)      ->   str:
							UpperCAmelCase :       Union[str, Any] 							=		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
							UpperCAmelCase :       Union[str, Any] 							=		Image.open(requests.get(_lowerCAmelCase   ,      stream=_lowerCAmelCase  ).raw  )
							return im


@torch.no_grad()
def  snake_case_     (					_lowerCAmelCase  :						Any   ,      _lowerCAmelCase  :						int  )      ->   List[Any]:
							UpperCAmelCase :       Union[str, Any] 							=		DeiTConfig()
							# all deit models have fine-tuned heads
							UpperCAmelCase :       str 							=		False
							# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
							UpperCAmelCase :       int 							=		1000
							UpperCAmelCase :       List[str] 							=		'''huggingface/label-files'''
							UpperCAmelCase :       Union[str, Any] 							=		'''imagenet-1k-id2label.json'''
							UpperCAmelCase :       Dict 							=		json.load(open(hf_hub_download(_lowerCAmelCase   ,      _lowerCAmelCase   ,      repo_type='''dataset'''  )   ,      '''r'''  )  )
							UpperCAmelCase :       Dict 							=		{int(_lowerCAmelCase  ): v for k, v in idalabel.items()}
							UpperCAmelCase :       Union[str, Any] 							=		idalabel
							UpperCAmelCase :       List[Any] 							=		{v: k for k, v in idalabel.items()}
							UpperCAmelCase :       Union[str, Any] 							=		int(deit_name[-6:-4]  )
							UpperCAmelCase :       Optional[int] 							=		int(deit_name[-3:]  )
							# size of the architecture
							if deit_name[9:].startswith('''tiny'''  ):
														UpperCAmelCase :       Tuple 							=		192
														UpperCAmelCase :       List[Any] 							=		768
														UpperCAmelCase :       Optional[Any] 							=		12
														UpperCAmelCase :       Dict 							=		3
							elif deit_name[9:].startswith('''small'''  ):
														UpperCAmelCase :       int 							=		384
														UpperCAmelCase :       str 							=		1536
														UpperCAmelCase :       str 							=		12
														UpperCAmelCase :       List[Any] 							=		6
							if deit_name[9:].startswith('''base'''  ):
														pass
							elif deit_name[4:].startswith('''large'''  ):
														UpperCAmelCase :       List[Any] 							=		1024
														UpperCAmelCase :       Any 							=		4096
														UpperCAmelCase :       Optional[int] 							=		24
														UpperCAmelCase :       int 							=		16

							# load original model from timm
							UpperCAmelCase :       Optional[int] 							=		timm.create_model(_lowerCAmelCase   ,      pretrained=_lowerCAmelCase  )
							timm_model.eval()

							# load state_dict of original model, remove and rename some keys
							UpperCAmelCase :       Optional[Any] 							=		timm_model.state_dict()
							UpperCAmelCase :       Any 							=		create_rename_keys(_lowerCAmelCase   ,      _lowerCAmelCase  )
							for src, dest in rename_keys:
														rename_key(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
							read_in_q_k_v(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )

							# load HuggingFace model
							UpperCAmelCase :       List[Any] 							=		DeiTForImageClassificationWithTeacher(_lowerCAmelCase  ).eval()
							model.load_state_dict(_lowerCAmelCase  )

							# Check outputs on an image, prepared by DeiTImageProcessor
							UpperCAmelCase :       List[Any] 							=		int(
							    (256 / 224) * config.image_size  )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
							UpperCAmelCase :       int 							=		DeiTImageProcessor(size=_lowerCAmelCase   ,      crop_size=config.image_size  )
							UpperCAmelCase :       List[Any] 							=		image_processor(images=prepare_img()   ,      return_tensors='''pt'''  )
							UpperCAmelCase :       List[Any] 							=		encoding['''pixel_values''']
							UpperCAmelCase :       int 							=		model(_lowerCAmelCase  )

							UpperCAmelCase :       List[str] 							=		timm_model(_lowerCAmelCase  )
							assert timm_logits.shape == outputs.logits.shape
							assert torch.allclose(_lowerCAmelCase   ,      outputs.logits   ,      atol=1e-3  )

							Path(_lowerCAmelCase  ).mkdir(exist_ok=_lowerCAmelCase  )
							print(f"""Saving model {deit_name} to {pytorch_dump_folder_path}"""  )
							model.save_pretrained(_lowerCAmelCase  )
							print(f"""Saving image processor to {pytorch_dump_folder_path}"""  )
							image_processor.save_pretrained(_lowerCAmelCase  )


if __name__ == "__main__":
						UpperCamelCase__:   List[Any]								=			argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--deit_name",
						    default="vit_deit_base_distilled_patch16_224",
						    type=str,
						    help="Name of the DeiT timm model you'd like to convert.",
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
						)

						UpperCamelCase__:   List[str]								=			parser.parse_args()
						convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)






"""simple docstring"""




from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
     from PIL import Image

     from ..image_utils import load_image

if is_tf_available():
     from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
     import torch

     from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

_a      :		Dict           =   logging.get_logger(__name__)
@add_end_docstrings(SCREAMING_SNAKE_CASE_		)
class 						__A							( SCREAMING_SNAKE_CASE_		):






     def __init__(		self				,     *a__				,     **a__  ):
       super().__init__(*a__				,     **a__  )
       requires_backends(self				,     """vision"""  )
       self.check_model_type(
           TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == """tf""" else MODEL_FOR_VISION_2_SEQ_MAPPING  )






     def     __A  (		self				,     a__=None				,     a__=None				,     a__=None  ):
       _lowerCAmelCase						:       List[str]  			=     {}
       _lowerCAmelCase						:       Union[str, Any]  			=     {}

       if prompt is not None:
         _lowerCAmelCase						:       List[Any]  			=     prompt

       if generate_kwargs is not None:
         _lowerCAmelCase						:       List[str]  			=     generate_kwargs
       if max_new_tokens is not None:
         if "generate_kwargs" not in forward_kwargs:
           _lowerCAmelCase						:       str  			=     {}
         if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
           raise ValueError(
               """'max_new_tokens' is defined twice, once in 'generate_kwargs' and once as a direct parameter,"""
               """ please use only one"""  )
         _lowerCAmelCase						:       Optional[Any]  			=     max_new_tokens
       return preprocess_params, forward_kwargs, {}






     def __call__(		self				,     a__				,     **a__  ):
       return super().__call__(a__				,     **a__  )






     def     __A  (		self				,     a__				,     a__=None  ):
       _lowerCAmelCase						:       Tuple  			=     load_image(a__  )

       if prompt is not None:
         if not isinstance(a__				,     a__  ):
           raise ValueError(
               F"Received an invalid text input, got - {type(a__  )} - but expected a single string. "
               """Note also that one single text can be provided for conditional image to text generation."""  )

         _lowerCAmelCase						:       Optional[int]  			=     self.model.config.model_type

         if model_type == "git":
           _lowerCAmelCase						:       Optional[Any]  			=     self.image_processor(images=a__				,     return_tensors=self.framework  )
           _lowerCAmelCase						:       List[str]  			=     self.tokenizer(text=a__				,     add_special_tokens=a__  ).input_ids
           _lowerCAmelCase						:       Union[str, Any]  			=     [self.tokenizer.cls_token_id] + input_ids
           _lowerCAmelCase						:       Dict  			=     torch.tensor(a__  ).unsqueeze(0  )
           model_inputs.update({"""input_ids""": input_ids}  )

         elif model_type == "pix2struct":
           _lowerCAmelCase						:       Tuple  			=     self.image_processor(images=a__				,     header_text=a__				,     return_tensors=self.framework  )

         elif model_type != "vision-encoder-decoder":
           # vision-encoder-decoder does not support conditional generation
           _lowerCAmelCase						:       Optional[int]  			=     self.image_processor(images=a__				,     return_tensors=self.framework  )
           _lowerCAmelCase						:       Optional[int]  			=     self.tokenizer(a__				,     return_tensors=self.framework  )
           model_inputs.update(a__  )

         else:
           raise ValueError(F"Model type {model_type} does not support conditional text generation"  )

       else:
         _lowerCAmelCase						:       Any  			=     self.image_processor(images=a__				,     return_tensors=self.framework  )

       if self.model.config.model_type == "git" and prompt is None:
         _lowerCAmelCase						:       Union[str, Any]  			=     None

       return model_inputs






     def     __A  (		self				,     a__				,     a__=None  ):
       # Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
       # pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
       if (
           "input_ids" in model_inputs
           and isinstance(model_inputs["""input_ids"""]				,     a__  )
           and all(x is None for x in model_inputs["""input_ids"""]  )
       ):
         _lowerCAmelCase						:       Optional[int]  			=     None

       if generate_kwargs is None:
         _lowerCAmelCase						:       List[str]  			=     {}
       # FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
       #  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
       #  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
       #  in the `_prepare_model_inputs` method.
       _lowerCAmelCase						:       Tuple  			=     model_inputs.pop(self.model.main_input_name  )
       _lowerCAmelCase						:       Union[str, Any]  			=     self.model.generate(a__				,     **a__				,     **a__  )
       return model_outputs






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       Optional[int]  			=     []
       for output_ids in model_outputs:
         _lowerCAmelCase						:       Any  			=     {
             """generated_text""": self.tokenizer.decode(
                 a__				,     skip_special_tokens=a__				,     )
         }
         records.append(a__  )
       return records




