





import argparse
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import (
    RobertaTokenizer,
    TrOCRConfig,
    TrOCRForCausalLM,
    TrOCRProcessor,
    VisionEncoderDecoderModel,
    ViTConfig,
    ViTImageProcessor,
    ViTModel,
)
from transformers.utils import logging


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





def      _UpperCamelCase  (a__ :List[str] ,      a__ :str							):






							"""simple docstring"""





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

							# cls token, position embeddings and patch embeddings of encoder
							rename_keys.extend(
							    [
							        ("""encoder.deit.cls_token""", """encoder.embeddings.cls_token"""),
							        ("""encoder.deit.pos_embed""", """encoder.embeddings.position_embeddings"""),
							        ("""encoder.deit.patch_embed.proj.weight""", """encoder.embeddings.patch_embeddings.projection.weight"""),
							        ("""encoder.deit.patch_embed.proj.bias""", """encoder.embeddings.patch_embeddings.projection.bias"""),
							        ("""encoder.deit.norm.weight""", """encoder.layernorm.weight"""),
							        ("""encoder.deit.norm.bias""", """encoder.layernorm.bias"""),
							    ]							)

							return rename_keys





def      _UpperCamelCase  (a__ :List[str] ,      a__ :Optional[int]							):






							"""simple docstring"""





							for i in range(encoder_config.num_hidden_layers							):
														# queries, keys and values (only weights, no biases)
														UpperCamelCase__					  =      state_dict.pop(f"""encoder.deit.blocks.{i}.attn.qkv.weight"""							)

														UpperCamelCase__					  =      in_proj_weight[
														    : encoder_config.hidden_size, :
														]
														UpperCamelCase__					  =      in_proj_weight[
														    encoder_config.hidden_size : encoder_config.hidden_size * 2, :
														]
														UpperCamelCase__					  =      in_proj_weight[
														    -encoder_config.hidden_size :, :
														]





def      _UpperCamelCase  (a__ :Optional[Any] ,      a__ :Optional[int] ,      a__ :Optional[int]							):






							"""simple docstring"""





							UpperCamelCase__					  =      dct.pop(a__							)
							UpperCamelCase__					  =      val





def      _UpperCamelCase  (a__ :int							):






							"""simple docstring"""





							if "handwritten" in checkpoint_url:
														UpperCamelCase__					  =      """https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg"""  # industry
														# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-12.jpg" # have
														# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-10.jpg" # let
														# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg"  #
														# url = "https://fki.tic.heia-fr.ch/static/img/a01-122.jpg"
							elif "printed" in checkpoint_url or "stage1" in checkpoint_url:
														UpperCamelCase__					  =      """https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg"""
							UpperCamelCase__					  =      Image.open(requests.get(a__ ,      stream=a__							).raw							).convert("""RGB"""							)
							return im





@torch.no_grad()
def      _UpperCamelCase  (a__ :Optional[Any] ,      a__ :Optional[Any]							):






							"""simple docstring"""





							UpperCamelCase__					  =      ViTConfig(image_size=384 ,      qkv_bias=a__							)
							UpperCamelCase__					  =      TrOCRConfig()

							# size of the architecture
							if "base" in checkpoint_url:
														UpperCamelCase__					  =      768
							elif "large" in checkpoint_url:
														# use ViT-large encoder
														UpperCamelCase__					  =      1024
														UpperCamelCase__					  =      4096
														UpperCamelCase__					  =      24
														UpperCamelCase__					  =      16
														UpperCamelCase__					  =      1024
							else:
														raise ValueError("""Should either find 'base' or 'large' in checkpoint URL"""							)

							# the large-printed + stage1 checkpoints uses sinusoidal position embeddings, no layernorm afterwards
							if "large-printed" in checkpoint_url or "stage1" in checkpoint_url:
														UpperCamelCase__					  =      False
														UpperCamelCase__					  =      """relu"""
														UpperCamelCase__					  =      1024
														UpperCamelCase__					  =      True
														UpperCamelCase__					  =      False
														UpperCamelCase__					  =      False

							# load HuggingFace model
							UpperCamelCase__					  =      ViTModel(a__ ,      add_pooling_layer=a__							)
							UpperCamelCase__					  =      TrOCRForCausalLM(a__							)
							UpperCamelCase__					  =      VisionEncoderDecoderModel(encoder=a__ ,      decoder=a__							)
							model.eval()

							# load state_dict of original model, rename some keys
							UpperCamelCase__					  =      torch.hub.load_state_dict_from_url(a__ ,      map_location="""cpu""" ,      check_hash=a__							)["""model"""]

							UpperCamelCase__					  =      create_rename_keys(a__ ,      a__							)
							for src, dest in rename_keys:
														rename_key(a__ ,      a__ ,      a__							)
							read_in_q_k_v(a__ ,      a__							)

							# remove parameters we don't need
							del state_dict["encoder.deit.head.weight"]
							del state_dict["encoder.deit.head.bias"]
							del state_dict["decoder.version"]

							# add prefix to decoder keys
							for key, val in state_dict.copy().items():
														UpperCamelCase__					  =      state_dict.pop(a__							)
														if key.startswith("""decoder"""							) and "output_projection" not in key:
																					UpperCamelCase__					  =      val
														else:
																					UpperCamelCase__					  =      val

    # load state dict
							model.load_state_dict(a__							)

							# Check outputs on an image
							UpperCamelCase__					  =      ViTImageProcessor(size=encoder_config.image_size							)
							UpperCamelCase__					  =      RobertaTokenizer.from_pretrained("""roberta-large"""							)
							UpperCamelCase__					  =      TrOCRProcessor(a__ ,      a__							)

							UpperCamelCase__					  =      processor(images=prepare_img(a__							) ,      return_tensors="""pt"""							).pixel_values

							# verify logits
							UpperCamelCase__					  =      torch.tensor([[model.config.decoder.decoder_start_token_id]]							)
							UpperCamelCase__					  =      model(pixel_values=a__ ,      decoder_input_ids=a__							)
							UpperCamelCase__					  =      outputs.logits

							UpperCamelCase__					  =      torch.Size([1, 1, 5_0265]							)
							if "trocr-base-handwritten" in checkpoint_url:
														UpperCamelCase__					  =      torch.tensor(
														    [-1.4502, -4.6683, -0.5347, -2.9291, 9.1435, -3.0571, 8.9764, 1.7560, 8.7358, -1.5311]							)
							elif "trocr-large-handwritten" in checkpoint_url:
														UpperCamelCase__					  =      torch.tensor(
														    [-2.6437, -1.3129, -2.2596, -5.3455, 6.3539, 1.7604, 5.4991, 1.4702, 5.6113, 2.0170]							)
							elif "trocr-base-printed" in checkpoint_url:
														UpperCamelCase__					  =      torch.tensor(
														    [-5.6816, -5.8388, 1.1398, -6.9034, 6.8505, -2.4393, 1.2284, -1.0232, -1.9661, -3.9210]							)
							elif "trocr-large-printed" in checkpoint_url:
														UpperCamelCase__					  =      torch.tensor(
														    [-6.0162, -7.0959, 4.4155, -5.1063, 7.0468, -3.1631, 2.6466, -0.3081, -0.8106, -1.7535]							)

							if "stage1" not in checkpoint_url:
														assert logits.shape == expected_shape, "Shape of logits not as expected"
														assert torch.allclose(logits[0, 0, :10] ,      a__ ,      atol=1e-3							), "First elements 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 processor to {pytorch_dump_folder_path}"""							)
							processor.save_pretrained(a__							)


if __name__ == "__main__":
					UpperCamelCase__           =			argparse.ArgumentParser()

					parser.add_argument(
					    "--checkpoint_url",
					    default="https://layoutlm.blob.core.windows.net/trocr/model_zoo/fairseq/trocr-base-handwritten.pt",
					    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."
					)
					UpperCamelCase__           =			parser.parse_args()
					convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)











# 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.
import re

from ..utils import cached_file


# docstyle-ignore
UpperCamelCase__           =			"\nHuman: <<task>>\n\nAssistant: "


UpperCamelCase__           =			"huggingface-tools/default-prompts"
UpperCamelCase__           =			{"chat": "chat_prompt_template.txt", "run": "run_prompt_template.txt"}





def      _UpperCamelCase  (a__ :List[Any] ,      a__ :Optional[Any] ,      a__ :List[str]="run"							):






							"""simple docstring"""





							if prompt_or_repo_id is None:
														UpperCamelCase__					  =      DEFAULT_PROMPTS_REPO

							# prompt is considered a repo ID when it does not contain any kind of space
							if re.search("""\\s""" ,      a__							) is not None:
														return prompt_or_repo_id

							UpperCamelCase__					  =      cached_file(
							    a__ ,      PROMPT_FILES[mode] ,      repo_type="""dataset""" ,      user_agent={"""agent""": agent_name}							)
							with open(a__ ,      """r""" ,      encoding="""utf-8"""							) as f:
														return f.read()




