



"""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 timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_=False						):
						UpperCAmelCase					=			[]

						# fmt: off
						# stem:
						rename_keys.append(('cls_token', 'vit.embeddings.cls_token')						)
						rename_keys.append(('pos_embed', 'vit.embeddings.position_embeddings')						)

						rename_keys.append(('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight')						)
						rename_keys.append(('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias')						)

						# backbone
						rename_keys.append(('patch_embed.backbone.stem.conv.weight', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight')						)
						rename_keys.append(('patch_embed.backbone.stem.norm.weight', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight')						)
						rename_keys.append(('patch_embed.backbone.stem.norm.bias', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias')						)

						for stage_idx in range(len(config.backbone_config.depths						)						):
												for layer_idx in range(config.backbone_config.depths[stage_idx]						):
																		rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight""")						)
																		rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight""")						)
																		rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias""")						)
																		rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight""")						)
																		rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight""")						)
																		rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias""")						)
																		rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight""")						)
																		rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight""")						)
																		rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias""")						)

												rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight""")						)
												rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight""")						)
												rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias""")						)

						# transformer encoder
						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""")						)

						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"
												UpperCAmelCase					=			[(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'),
												    ]						)
						# fmt: on

						return rename_keys

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

def 			_lowerCAmelCase				(       lowercase_						):
						UpperCAmelCase					=			['head.weight', 'head.bias']
						for k in ignore_keys:
												state_dict.pop(snake_case__		,     snake_case__						)

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_		,     lowercase_						):
						UpperCAmelCase					=			dct.pop(snake_case__						)
						UpperCAmelCase					=			val

def 			_lowerCAmelCase				(       ):
						UpperCAmelCase					=			'http://images.cocodataset.org/val2017/000000039769.jpg'
						UpperCAmelCase					=			Image.open(requests.get(snake_case__		,     stream=snake_case__						).raw						)
						return im

@torch.no_grad()
def 			_lowerCAmelCase				(       lowercase_		,     lowercase_		,     lowercase_=False						):
						UpperCAmelCase					=			BitConfig(
						    global_padding='same'		,     layer_type='bottleneck'		,     depths=(3, 4, 9)		,     out_features=['stage3']		,     embedding_dynamic_padding=snake_case__		,     )
						UpperCAmelCase					=			ViTHybridConfig(backbone_config=snake_case__		,     image_size=384		,     num_labels=1000						)
						UpperCAmelCase					=			False

						# load original model from timm
						UpperCAmelCase					=			timm.create_model(snake_case__		,     pretrained=snake_case__						)
						timm_model.eval()

						# load state_dict of original model, remove and rename some keys
						UpperCAmelCase					=			timm_model.state_dict()
						if base_model:
												remove_classification_head_(snake_case__						)
						UpperCAmelCase					=			create_rename_keys(snake_case__		,     snake_case__						)
						for src, dest in rename_keys:
												rename_key(snake_case__		,     snake_case__		,     snake_case__						)
						read_in_q_k_v(snake_case__		,     snake_case__		,     snake_case__						)

						UpperCAmelCase					=			'huggingface/label-files'
						UpperCAmelCase					=			'imagenet-1k-id2label.json'
						UpperCAmelCase					=			json.load(open(hf_hub_download(snake_case__		,     snake_case__		,     repo_type='dataset'						)		,     'r'						)						)
						UpperCAmelCase					=			{int(snake_case__						): v for k, v in idalabel.items()}
						UpperCAmelCase					=			idalabel
						UpperCAmelCase					=			{v: k for k, v in idalabel.items()}

						# load HuggingFace model
						if vit_name[-5:] == "in21k":
												UpperCAmelCase					=			ViTHybridModel(snake_case__						).eval()
						else:
												UpperCAmelCase					=			ViTHybridForImageClassification(snake_case__						).eval()
						model.load_state_dict(snake_case__						)

						# create image processor
						UpperCAmelCase					=			create_transform(**resolve_data_config({}		,     model=snake_case__						)						)
						UpperCAmelCase					=			transform.transforms

						UpperCAmelCase					=			{
						    'bilinear': PILImageResampling.BILINEAR,
						    'bicubic': PILImageResampling.BICUBIC,
						    'nearest': PILImageResampling.NEAREST,
						}

						UpperCAmelCase					=			ViTHybridImageProcessor(
						    do_resize=snake_case__		,     size={'shortest_edge': timm_transforms[0].size}		,     resample=pillow_resamplings[timm_transforms[0].interpolation.value]		,     do_center_crop=snake_case__		,     crop_size={'height': timm_transforms[1].size[0], 'width': timm_transforms[1].size[1]}		,     do_normalize=snake_case__		,     image_mean=timm_transforms[-1].mean.tolist()		,     image_std=timm_transforms[-1].std.tolist()		,     )

						UpperCAmelCase					=			prepare_img()
						UpperCAmelCase					=			transform(snake_case__						).unsqueeze(0						)
						UpperCAmelCase					=			processor(snake_case__		,     return_tensors='pt'						).pixel_values

						# verify pixel values
						assert torch.allclose(snake_case__		,     snake_case__						)

						# verify logits
						with torch.no_grad():
												UpperCAmelCase					=			model(snake_case__						)
												UpperCAmelCase					=			outputs.logits

						print('Predicted class:'		,     logits.argmax(-1						).item()						)
						if base_model:
												UpperCAmelCase					=			timm_model.forward_features(snake_case__						)
												assert timm_pooled_output.shape == outputs.pooler_output.shape
												assert torch.allclose(snake_case__		,     outputs.pooler_output		,     atol=1e-3						)
						else:
												UpperCAmelCase					=			timm_model(snake_case__						)
												assert timm_logits.shape == outputs.logits.shape
												assert torch.allclose(snake_case__		,     outputs.logits		,     atol=1e-3						)
						print('Looks ok!'						)

						if pytorch_dump_folder_path is not None:
												Path(snake_case__						).mkdir(exist_ok=snake_case__						)
												print(F"""Saving model {vit_name} to {pytorch_dump_folder_path}"""						)
												model.save_pretrained(snake_case__						)
												print(F"""Saving processor to {pytorch_dump_folder_path}"""						)
												processor.save_pretrained(snake_case__						)

						if push_to_hub:
												print(F"""Pushing model and processor to the hub {vit_name}"""						)
												model.push_to_hub(F"""ybelkada/{vit_name}"""						)
												processor.push_to_hub(F"""ybelkada/{vit_name}"""						)


if __name__ == "__main__":
				snake_case_    					=	argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    """--vit_name""",
				    default="""vit_base_r50_s16_384""",
				    type=str,
				    help="""Name of the hybrid 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."""
				)
				parser.add_argument(
				    """--push_to_hub""", action="""store_true""", help="""Whether to upload the model to the HuggingFace hub."""
				)

				snake_case_    					=	parser.parse_args()
				convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)










"""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_      (					SCREAMING_SNAKE_CASE_    ):



						"""simple docstring"""
						__UpperCamelCase		    =							"""philschmid/bart-large-cnn-samsum"""
						__UpperCamelCase		    =							(
						    """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."""
						)
						__UpperCamelCase		    =							"""summarizer"""
						__UpperCamelCase		    =							AutoTokenizer
						__UpperCamelCase		    =							AutoModelForSeqaSeqLM

						__UpperCamelCase		    =							["""text"""]
						__UpperCamelCase		    =							["""text"""]






						def      UpperCAmelCase__					( self			:int       ,  lowercase_			:Tuple )    ->      int:
												return self.pre_processor(lowercase_       ,  return_tensors='pt'       ,  truncation=lowercase_ )






						def      UpperCAmelCase__					( self			:Dict       ,  lowercase_			:Dict )    ->      Optional[int]:
												return self.model.generate(**lowercase_ )[0]






						def      UpperCAmelCase__					( self			:Dict       ,  lowercase_			:Tuple )    ->      List[str]:
												return self.pre_processor.decode(lowercase_       ,  skip_special_tokens=lowercase_       ,  clean_up_tokenization_spaces=lowercase_ )





