




"""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 DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
a_        =       logging.get_logger(__name__)







def      __UpperCAmelCase     ( __UpperCamelCase	,				__UpperCamelCase=False	):
			__lowercase			:  str								=       []
			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"""vit.encoder.layer.{i}.layernorm_before.weight""")	)
						rename_keys.append((f"""blocks.{i}.norm1.bias""", f"""vit.encoder.layer.{i}.layernorm_before.bias""")	)
						rename_keys.append((f"""blocks.{i}.attn.proj.weight""", f"""vit.encoder.layer.{i}.attention.output.dense.weight""")	)
						rename_keys.append((f"""blocks.{i}.attn.proj.bias""", f"""vit.encoder.layer.{i}.attention.output.dense.bias""")	)
						rename_keys.append((f"""blocks.{i}.norm2.weight""", f"""vit.encoder.layer.{i}.layernorm_after.weight""")	)
						rename_keys.append((f"""blocks.{i}.norm2.bias""", f"""vit.encoder.layer.{i}.layernorm_after.bias""")	)
						rename_keys.append((f"""blocks.{i}.mlp.fc1.weight""", f"""vit.encoder.layer.{i}.intermediate.dense.weight""")	)
						rename_keys.append((f"""blocks.{i}.mlp.fc1.bias""", f"""vit.encoder.layer.{i}.intermediate.dense.bias""")	)
						rename_keys.append((f"""blocks.{i}.mlp.fc2.weight""", f"""vit.encoder.layer.{i}.output.dense.weight""")	)
						rename_keys.append((f"""blocks.{i}.mlp.fc2.bias""", f"""vit.encoder.layer.{i}.output.dense.bias""")	)

			# projection layer + position embeddings
			rename_keys.extend(
			    [
			        ('''cls_token''', '''vit.embeddings.cls_token'''),
			        ('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''),
			        ('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''),
			        ('''pos_embed''', '''vit.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 "vit" from all keys that start with "vit"
						__lowercase			:  Tuple								=       [(pair[0], pair[1][4:]) if pair[1].startswith('''vit'''	) else pair for pair in rename_keys]
			else:
						# layernorm + classification head
						rename_keys.extend(
						    [
						        ('''norm.weight''', '''vit.layernorm.weight'''),
						        ('''norm.bias''', '''vit.layernorm.bias'''),
						        ('''head.weight''', '''classifier.weight'''),
						        ('''head.bias''', '''classifier.bias'''),
						    ]	)

			return rename_keys







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







def      __UpperCAmelCase     ( __UpperCamelCase	):
			__lowercase			:  Optional[int]								=       ['''head.weight''', '''head.bias''']
			for k in ignore_keys:
						state_dict.pop(__UpperCamelCase	,				__UpperCamelCase	)







def      __UpperCAmelCase     ( __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	):
			__lowercase			:  Any								=       dct.pop(__UpperCamelCase	)
			__lowercase			:  Optional[Any]								=       val







def      __UpperCAmelCase     ( ):
			__lowercase			:  Any								=       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
			__lowercase			:  Any								=       Image.open(requests.get(__UpperCamelCase	,				stream=__UpperCamelCase	).raw	)
			return im







@torch.no_grad()
def      __UpperCAmelCase     ( __UpperCamelCase	,				__UpperCamelCase	):
			__lowercase			:  Union[str, Any]								=       ViTConfig()
			__lowercase			:  Optional[Any]								=       False
			# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
			if vit_name[-5:] == "in21k":
						__lowercase			:  Tuple								=       True
						__lowercase			:  int								=       int(vit_name[-12:-10]	)
						__lowercase			:  str								=       int(vit_name[-9:-6]	)
			else:
						__lowercase			:  Union[str, Any]								=       10_00
						__lowercase			:  Any								=       '''huggingface/label-files'''
						__lowercase			:  str								=       '''imagenet-1k-id2label.json'''
						__lowercase			:  int								=       json.load(open(hf_hub_download(__UpperCamelCase	,				__UpperCamelCase	,				repo_type='''dataset'''	)	,				'''r'''	)	)
						__lowercase			:  Any								=       {int(__UpperCamelCase	): v for k, v in idalabel.items()}
						__lowercase			:  str								=       idalabel
						__lowercase			:  Optional[Any]								=       {v: k for k, v in idalabel.items()}
						__lowercase			:  Dict								=       int(vit_name[-6:-4]	)
						__lowercase			:  List[Any]								=       int(vit_name[-3:]	)
			# size of the architecture
			if "deit" in vit_name:
						if vit_name[9:].startswith('''tiny'''	):
									__lowercase			:  Union[str, Any]								=       1_92
									__lowercase			:  int								=       7_68
									__lowercase			:  Optional[Any]								=       12
									__lowercase			:  Optional[Any]								=       3
						elif vit_name[9:].startswith('''small'''	):
									__lowercase			:  Optional[int]								=       3_84
									__lowercase			:  List[Any]								=       15_36
									__lowercase			:  Optional[Any]								=       12
									__lowercase			:  str								=       6
						else:
									pass
			else:
						if vit_name[4:].startswith('''small'''	):
									__lowercase			:  Dict								=       7_68
									__lowercase			:  List[str]								=       23_04
									__lowercase			:  Union[str, Any]								=       8
									__lowercase			:  Dict								=       8
						elif vit_name[4:].startswith('''base'''	):
									pass
						elif vit_name[4:].startswith('''large'''	):
									__lowercase			:  Optional[int]								=       10_24
									__lowercase			:  Union[str, Any]								=       40_96
									__lowercase			:  Union[str, Any]								=       24
									__lowercase			:  Tuple								=       16
						elif vit_name[4:].startswith('''huge'''	):
									__lowercase			:  str								=       12_80
									__lowercase			:  Tuple								=       51_20
									__lowercase			:  Union[str, Any]								=       32
									__lowercase			:  Any								=       16

    # load original model from timm
			__lowercase			:  Union[str, Any]								=       timm.create_model(__UpperCamelCase	,				pretrained=__UpperCamelCase	)
			timm_model.eval()

			# load state_dict of original model, remove and rename some keys
			__lowercase			:  Optional[int]								=       timm_model.state_dict()
			if base_model:
						remove_classification_head_(__UpperCamelCase	)
			__lowercase			:  int								=       create_rename_keys(__UpperCamelCase	,				__UpperCamelCase	)
			for src, dest in rename_keys:
						rename_key(__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	)
			read_in_q_k_v(__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	)

			# load HuggingFace model
			if vit_name[-5:] == "in21k":
						__lowercase			:  Dict								=       ViTModel(__UpperCamelCase	).eval()
			else:
						__lowercase			:  Any								=       ViTForImageClassification(__UpperCamelCase	).eval()
			model.load_state_dict(__UpperCamelCase	)

			# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
			if "deit" in vit_name:
						__lowercase			:  Optional[int]								=       DeiTImageProcessor(size=config.image_size	)
			else:
						__lowercase			:  int								=       ViTImageProcessor(size=config.image_size	)
			__lowercase			:  str								=       image_processor(images=prepare_img()	,				return_tensors='''pt'''	)
			__lowercase			:  str								=       encoding['''pixel_values''']
			__lowercase			:  Dict								=       model(__UpperCamelCase	)

			if base_model:
						__lowercase			:  Tuple								=       timm_model.forward_features(__UpperCamelCase	)
						assert timm_pooled_output.shape == outputs.pooler_output.shape
						assert torch.allclose(__UpperCamelCase	,				outputs.pooler_output	,				atol=1e-3	)
			else:
						__lowercase			:  Any								=       timm_model(__UpperCamelCase	)
						assert timm_logits.shape == outputs.logits.shape
						assert torch.allclose(__UpperCamelCase	,				outputs.logits	,				atol=1e-3	)

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


if __name__ == "__main__":
			a_        =       argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--vit_name',
			    default='vit_base_patch16_224',
			    type=str,
			    help='Name of the ViT 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.'
			)

			a_        =       parser.parse_args()
			convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)




"""simple docstring"""






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool





class 					_A		(			lowerCAmelCase      ):
      snake_case__     :  List[Any]				    =      'philschmid/bart-large-cnn-samsum'
      snake_case__     :  int				    =      (
          'This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, '
          'and returns a summary of the text.'
      )
      snake_case__     :  Tuple				    =      'summarizer'
      snake_case__     :  int				    =      AutoTokenizer
      snake_case__     :  Any				    =      AutoModelForSeqaSeqLM

      snake_case__     :  Optional[int]				    =      ['text']
      snake_case__     :  Optional[int]				    =      ['text']

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            return self.pre_processor(__lowerCAmelCase						,			return_tensors="""pt"""						,			truncation=__lowerCAmelCase		)

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            return self.model.generate(**__lowerCAmelCase		)[0]







      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            return self.pre_processor.decode(__lowerCAmelCase						,			skip_special_tokens=__lowerCAmelCase						,			clean_up_tokenization_spaces=__lowerCAmelCase		)



