
import argparse
import json
from pathlib import Path

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

from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE				:Optional[Any]									=							logging.get_logger(__name__)


def 		UpperCAmelCase     (      a_						,  a_=False						,  a_=False		)						->      str:







				"""simple docstring"""



				__A       =					"backbone." if is_semantic else ""

				__A       =					[]
				for i in range(config.num_hidden_layers		):
								# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
								rename_keys.append((F'''{prefix}blocks.{i}.norm1.weight''', F'''beit.encoder.layer.{i}.layernorm_before.weight''')		)
								rename_keys.append((F'''{prefix}blocks.{i}.norm1.bias''', F'''beit.encoder.layer.{i}.layernorm_before.bias''')		)
								rename_keys.append(
								    (F'''{prefix}blocks.{i}.attn.proj.weight''', F'''beit.encoder.layer.{i}.attention.output.dense.weight''')		)
								rename_keys.append(
								    (F'''{prefix}blocks.{i}.attn.proj.bias''', F'''beit.encoder.layer.{i}.attention.output.dense.bias''')		)
								rename_keys.append((F'''{prefix}blocks.{i}.norm2.weight''', F'''beit.encoder.layer.{i}.layernorm_after.weight''')		)
								rename_keys.append((F'''{prefix}blocks.{i}.norm2.bias''', F'''beit.encoder.layer.{i}.layernorm_after.bias''')		)
								rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc1.weight''', F'''beit.encoder.layer.{i}.intermediate.dense.weight''')		)
								rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc1.bias''', F'''beit.encoder.layer.{i}.intermediate.dense.bias''')		)
								rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.weight''', F'''beit.encoder.layer.{i}.output.dense.weight''')		)
								rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.bias''', F'''beit.encoder.layer.{i}.output.dense.bias''')		)

				# projection layer + position embeddings
				rename_keys.extend(
				    [
				        (F'''{prefix}cls_token''', "beit.embeddings.cls_token"),
				        (F'''{prefix}patch_embed.proj.weight''', "beit.embeddings.patch_embeddings.projection.weight"),
				        (F'''{prefix}patch_embed.proj.bias''', "beit.embeddings.patch_embeddings.projection.bias"),
				        (F'''{prefix}pos_embed''', "beit.embeddings.position_embeddings"),
				    ]		)

				if has_lm_head:
								# mask token + layernorm
								rename_keys.extend(
								    [
								        ("mask_token", "beit.embeddings.mask_token"),
								        ("norm.weight", "layernorm.weight"),
								        ("norm.bias", "layernorm.bias"),
								    ]		)
				else:
								# layernorm + classification head
								rename_keys.extend(
								    [
								        ("fc_norm.weight", "beit.pooler.layernorm.weight"),
								        ("fc_norm.bias", "beit.pooler.layernorm.bias"),
								        ("head.weight", "classifier.weight"),
								        ("head.bias", "classifier.bias"),
								    ]		)

				return rename_keys


def 		UpperCAmelCase     (      a_						,  a_						,  a_=False						,  a_=False		)						->      List[str]:







				"""simple docstring"""



				for i in range(config.num_hidden_layers		):
								__A       =					"backbone." if is_semantic else ""
								# queries, keys and values
								__A       =					state_dict.pop(F'''{prefix}blocks.{i}.attn.qkv.weight'''		)
								__A       =					state_dict.pop(F'''{prefix}blocks.{i}.attn.q_bias'''		)
								__A       =					state_dict.pop(F'''{prefix}blocks.{i}.attn.v_bias'''		)

								__A       =					in_proj_weight[
								    : config.hidden_size, :
								]
								__A       =					q_bias
								__A       =					in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								__A       =					in_proj_weight[
								    -config.hidden_size :, :
								]
								__A       =					v_bias

								# gamma_1 and gamma_2
								# we call them lambda because otherwise they are renamed when using .from_pretrained
								__A       =					state_dict.pop(F'''{prefix}blocks.{i}.gamma_1'''		)
								__A       =					state_dict.pop(F'''{prefix}blocks.{i}.gamma_2'''		)

								__A       =					gamma_a
								__A       =					gamma_a


def 		UpperCAmelCase     (      a_						,  a_						,  a_		)						->      Tuple:







				"""simple docstring"""



				__A       =					dct.pop(a_		)
				__A       =					val


def 		UpperCAmelCase     (      )						->      int:







				"""simple docstring"""



				__A       =					"http://images.cocodataset.org/val2017/000000039769.jpg"
				__A       =					Image.open(requests.get(a_						,  stream=a_		).raw		)
				return im


@torch.no_grad()
def 		UpperCAmelCase     (      a_						,  a_						,  a_=False		)						->      List[Any]:







				"""simple docstring"""



				__A       =					False if "rvlcdip" in checkpoint_url else True
				__A       =					BeitConfig(use_absolute_position_embeddings=a_						,  use_mask_token=a_		)

				# size of the architecture
				if "large" in checkpoint_url or "dit-l" in checkpoint_url:
								__A       =					1_0_2_4
								__A       =					4_0_9_6
								__A       =					2_4
								__A       =					1_6

				# labels
				if "rvlcdip" in checkpoint_url:
								__A       =					1_6
								__A       =					"huggingface/label-files"
								__A       =					"rvlcdip-id2label.json"
								__A       =					json.load(open(hf_hub_download(a_						,  a_						,  repo_type="dataset"		)						,  "r"		)		)
								__A       =					{int(a_		): v for k, v in idalabel.items()}
								__A       =					idalabel
								__A       =					{v: k for k, v in idalabel.items()}

				# load state_dict of original model, remove and rename some keys
				__A       =					torch.hub.load_state_dict_from_url(a_						,  map_location="cpu"		)["model"]

				__A       =					create_rename_keys(a_						,  has_lm_head=a_		)
				for src, dest in rename_keys:
								rename_key(a_						,  a_						,  a_		)
				read_in_q_k_v(a_						,  a_						,  has_lm_head=a_		)

				# load HuggingFace model
				__A       =					BeitForMaskedImageModeling(a_		) if has_lm_head else BeitForImageClassification(a_		)
				model.eval()
				model.load_state_dict(a_		)

				# Check outputs on an image
				__A       =					BeitImageProcessor(
				    size=config.image_size						,  resample=PILImageResampling.BILINEAR						,  do_center_crop=a_		)
				__A       =					prepare_img()

				__A       =					image_processor(images=a_						,  return_tensors="pt"		)
				__A       =					encoding["pixel_values"]

				__A       =					model(a_		)
				__A       =					outputs.logits

				# verify logits
				__A       =					[1, 1_6] if "rvlcdip" in checkpoint_url else [1, 1_9_6, 8_1_9_2]
				assert logits.shape == torch.Size(a_		), "Shape of logits not as expected"

				Path(a_		).mkdir(exist_ok=a_		)
				print(F'''Saving model to {pytorch_dump_folder_path}'''		)
				model.save_pretrained(a_		)
				print(F'''Saving image processor to {pytorch_dump_folder_path}'''		)
				image_processor.save_pretrained(a_		)

				if push_to_hub:
								if has_lm_head:
												__A       =					"dit-base" if "base" in checkpoint_url else "dit-large"
								else:
												__A       =					"dit-base-finetuned-rvlcdip" if "dit-b" in checkpoint_url else "dit-large-finetuned-rvlcdip"
								image_processor.push_to_hub(
								    repo_path_or_name=Path(a_						,  a_		)						,  organization="nielsr"						,  commit_message="Add image processor"						,  use_temp_dir=a_						,  )
								model.push_to_hub(
								    repo_path_or_name=Path(a_						,  a_		)						,  organization="nielsr"						,  commit_message="Add model"						,  use_temp_dir=a_						,  )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE				:int									=							argparse.ArgumentParser()

					parser.add_argument(
					    '--checkpoint_url',
					    default='https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth',
					    type=str,
					    help='URL to the original PyTorch checkpoint (.pth file).',
					)
					parser.add_argument(
					    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
					)
					parser.add_argument(
					    '--push_to_hub',
					    action='store_true',
					)
					SCREAMING_SNAKE_CASE				:Optional[Any]									=							parser.parse_args()
					convert_dit_checkpoint(args.checkpoint_url, 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.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool





class 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_							):
			snake_case__	=					"openai/whisper-base"
			snake_case__	=					(
			    "This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the "
			    "transcribed text."
			)
			snake_case__	=					"transcriber"
			snake_case__	=					WhisperProcessor
			snake_case__	=					WhisperForConditionalGeneration

			snake_case__	=					["audio"]
			snake_case__	=					["text"]






			def       SCREAMING_SNAKE_CASE   (			self   :      str	,    __SCREAMING_SNAKE_CASE   :      Tuple       )      ->      str:
							return self.pre_processor(__SCREAMING_SNAKE_CASE	,    return_tensors='''pt'''       ).input_features






			def       SCREAMING_SNAKE_CASE   (			self   :      List[Any]	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]       )      ->      List[str]:
							return self.model.generate(inputs=__SCREAMING_SNAKE_CASE       )







			def       SCREAMING_SNAKE_CASE   (			self   :      Dict	,    __SCREAMING_SNAKE_CASE   :      Optional[int]       )      ->      Optional[Any]:
							return self.pre_processor.batch_decode(__SCREAMING_SNAKE_CASE	,    skip_special_tokens=__SCREAMING_SNAKE_CASE       )[0]




