
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()
lowerCamelCase__	=							logging.get_logger(__name__)


def 							__A(lowerCAmelCase						,  lowerCAmelCase )		->				List[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 							__A(lowerCAmelCase						,  lowerCAmelCase )		->				str:

		"""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 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )		->				List[str]:

		"""simple docstring"""






		_UpperCamelCase						    =	dct.pop(lowerCAmelCase )
		_UpperCamelCase						    =	val


def 							__A(lowerCAmelCase )		->				List[str]:

		"""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(lowerCAmelCase						,  stream=lowerCAmelCase ).raw ).convert("""RGB""" )
		return im


@torch.no_grad()
def 							__A(lowerCAmelCase						,  lowerCAmelCase )		->				str:

		"""simple docstring"""






		_UpperCamelCase						    =	ViTConfig(image_size=3_8_4						,  qkv_bias=lowerCAmelCase )
		_UpperCamelCase						    =	TrOCRConfig()

		# size of the architecture
		if "base" in checkpoint_url:
				_UpperCamelCase						    =	7_6_8
		elif "large" in checkpoint_url:
				# use ViT-large encoder
				_UpperCamelCase						    =	1_0_2_4
				_UpperCamelCase						    =	4_0_9_6
				_UpperCamelCase						    =	2_4
				_UpperCamelCase						    =	1_6
				_UpperCamelCase						    =	1_0_2_4
		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						    =	1_0_2_4
				_UpperCamelCase						    =	True
				_UpperCamelCase						    =	False
				_UpperCamelCase						    =	False

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

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

		_UpperCamelCase						    =	create_rename_keys(lowerCAmelCase						,  lowerCAmelCase )
		for src, dest in rename_keys:
				rename_key(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )
		read_in_q_k_v(lowerCAmelCase						,  lowerCAmelCase )

		# 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(lowerCAmelCase )
				if key.startswith("""decoder""" ) and "output_projection" not in key:
						_UpperCamelCase						    =	val
				else:
						_UpperCamelCase						    =	val

    # load state dict
		model.load_state_dict(lowerCAmelCase )

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

		_UpperCamelCase						    =	processor(images=prepare_img(lowerCAmelCase )						,  return_tensors="""pt""" ).pixel_values

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

		_UpperCamelCase						    =	torch.Size([1, 1, 5_0_2_6_5] )
		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, :1_0]						,  lowerCAmelCase						,  atol=1e-3 ), "First elements of logits not as expected"

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


if __name__ == "__main__":
		lowerCamelCase__	=							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."
		)
		lowerCamelCase__	=							parser.parse_args()
		convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)





from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand


def 							__A(lowerCAmelCase )		->				List[str]:

		"""simple docstring"""






		return ConvertCommand(
		    args.model_type						,  args.tf_checkpoint						,  args.pytorch_dump_output						,  args.config						,  args.finetuning_task_name )


lowerCamelCase__	=							"\ntransformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires\nTensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.\n"







class        lowerCAmelCase__      (   __lowercase	):

						@staticmethod
						def  A_				(			a				)					->      Optional[int]:


								'''simple docstring'''

								_UpperCamelCase						    =	parser.add_parser(
								    """convert"""       ,							help="""CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints."""       ,							)
								train_parser.add_argument("""--model_type"""       ,							type=a       ,							required=a       ,							help="""Model's type."""				)
								train_parser.add_argument(
								    """--tf_checkpoint"""       ,							type=a       ,							required=a       ,							help="""TensorFlow checkpoint path or folder."""				)
								train_parser.add_argument(
								    """--pytorch_dump_output"""       ,							type=a       ,							required=a       ,							help="""Path to the PyTorch saved model output."""				)
								train_parser.add_argument("""--config"""       ,							type=a       ,							default=""""""       ,							help="""Configuration file path or folder."""				)
								train_parser.add_argument(
								    """--finetuning_task_name"""       ,							type=a       ,							default=a       ,							help="""Optional fine-tuning task name if the TF model was a finetuned model."""       ,							)
								train_parser.set_defaults(func=a				)

						def __init__(			self       ,							a       ,							a       ,							a       ,							a       ,							a       ,							*a       ,							)					->      Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	logging.get_logger("""transformers-cli/converting"""				)

								self._logger.info(F'Loading model {model_type}'				)
								_UpperCamelCase						    =	model_type
								_UpperCamelCase						    =	tf_checkpoint
								_UpperCamelCase						    =	pytorch_dump_output
								_UpperCamelCase						    =	config
								_UpperCamelCase						    =	finetuning_task_name







						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

								if self._model_type == "albert":
										try:
												from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
												    convert_tf_checkpoint_to_pytorch,
												)
										except ImportError:
												raise ImportError(a				)

										convert_tf_checkpoint_to_pytorch(self._tf_checkpoint       ,							self._config       ,							self._pytorch_dump_output				)
								elif self._model_type == "bert":
										try:
												from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
												    convert_tf_checkpoint_to_pytorch,
												)
										except ImportError:
												raise ImportError(a				)

										convert_tf_checkpoint_to_pytorch(self._tf_checkpoint       ,							self._config       ,							self._pytorch_dump_output				)
								elif self._model_type == "funnel":
										try:
												from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
												    convert_tf_checkpoint_to_pytorch,
												)
										except ImportError:
												raise ImportError(a				)

										convert_tf_checkpoint_to_pytorch(self._tf_checkpoint       ,							self._config       ,							self._pytorch_dump_output				)
								elif self._model_type == "t5":
										try:
												from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
										except ImportError:
												raise ImportError(a				)

										convert_tf_checkpoint_to_pytorch(self._tf_checkpoint       ,							self._config       ,							self._pytorch_dump_output				)
								elif self._model_type == "gpt":
										from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
										    convert_openai_checkpoint_to_pytorch,
										)

										convert_openai_checkpoint_to_pytorch(self._tf_checkpoint       ,							self._config       ,							self._pytorch_dump_output				)
								elif self._model_type == "transfo_xl":
										try:
												from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
												    convert_transfo_xl_checkpoint_to_pytorch,
												)
										except ImportError:
												raise ImportError(a				)

										if "ckpt" in self._tf_checkpoint.lower():
												_UpperCamelCase						    =	self._tf_checkpoint
												_UpperCamelCase						    =	""""""
										else:
												_UpperCamelCase						    =	self._tf_checkpoint
												_UpperCamelCase						    =	""""""
										convert_transfo_xl_checkpoint_to_pytorch(
										    a       ,							self._config       ,							self._pytorch_dump_output       ,							a				)
								elif self._model_type == "gpt2":
										try:
												from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
												    convert_gpta_checkpoint_to_pytorch,
												)
										except ImportError:
												raise ImportError(a				)

										convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint       ,							self._config       ,							self._pytorch_dump_output				)
								elif self._model_type == "xlnet":
										try:
												from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
												    convert_xlnet_checkpoint_to_pytorch,
												)
										except ImportError:
												raise ImportError(a				)

										convert_xlnet_checkpoint_to_pytorch(
										    self._tf_checkpoint       ,							self._config       ,							self._pytorch_dump_output       ,							self._finetuning_task_name				)
								elif self._model_type == "xlm":
										from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
										    convert_xlm_checkpoint_to_pytorch,
										)

										convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint       ,							self._pytorch_dump_output				)
								elif self._model_type == "lxmert":
										from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
										    convert_lxmert_checkpoint_to_pytorch,
										)

										convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint       ,							self._pytorch_dump_output				)
								elif self._model_type == "rembert":
										from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
										    convert_rembert_tf_checkpoint_to_pytorch,
										)

										convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint       ,							self._config       ,							self._pytorch_dump_output				)
								else:
										raise ValueError(
										    """--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]"""				)



