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()
__SCREAMING_SNAKE_CASE						:   Dict         =			logging.get_logger(__name__)
def  snake_case						(__lowercase ,      __lowercase						)	->	Union[str, Any]:

						'''simple docstring'''



						_snake_case						:					Tuple    =   []
						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  snake_case						(__lowercase ,      __lowercase						)	->	Dict:

						'''simple docstring'''



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

												_snake_case						:					Any    =   in_proj_weight[
												    : encoder_config.hidden_size, :
												]
												_snake_case						:					Dict    =   in_proj_weight[
												    encoder_config.hidden_size : encoder_config.hidden_size * 2, :
												]
												_snake_case						:					Tuple    =   in_proj_weight[
												    -encoder_config.hidden_size :, :
												]
def  snake_case						(__lowercase ,      __lowercase ,      __lowercase						)	->	Optional[int]:

						'''simple docstring'''



						_snake_case						:					Optional[int]    =   dct.pop(__lowercase						)
						_snake_case						:					Union[str, Any]    =   val
def  snake_case						(__lowercase						)	->	Dict:

						'''simple docstring'''



						if "handwritten" in checkpoint_url:
												_snake_case						:					Optional[int]    =   "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:
												_snake_case						:					str    =   "https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg"
						_snake_case						:					Tuple    =   Image.open(requests.get(__lowercase ,      stream=__lowercase						).raw						).convert("RGB"						)
						return im
@torch.no_grad()
def  snake_case						(__lowercase ,      __lowercase						)	->	Optional[Any]:

						'''simple docstring'''



						_snake_case						:					List[Any]    =   ViTConfig(image_size=384 ,      qkv_bias=__lowercase						)
						_snake_case						:					Tuple    =   TrOCRConfig()

						# size of the architecture
						if "base" in checkpoint_url:
												_snake_case						:					Union[str, Any]    =   768
						elif "large" in checkpoint_url:
												# use ViT-large encoder
												_snake_case						:					List[str]    =   1_024
												_snake_case						:					str    =   4_096
												_snake_case						:					str    =   24
												_snake_case						:					Optional[int]    =   16
												_snake_case						:					int    =   1_024
						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:
												_snake_case						:					Optional[Any]    =   False
												_snake_case						:					Dict    =   "relu"
												_snake_case						:					Any    =   1_024
												_snake_case						:					str    =   True
												_snake_case						:					Tuple    =   False
												_snake_case						:					int    =   False

						# load HuggingFace model
						_snake_case						:					str    =   ViTModel(__lowercase ,      add_pooling_layer=__lowercase						)
						_snake_case						:					str    =   TrOCRForCausalLM(__lowercase						)
						_snake_case						:					Tuple    =   VisionEncoderDecoderModel(encoder=__lowercase ,      decoder=__lowercase						)
						model.eval()

						# load state_dict of original model, rename some keys
						_snake_case						:					List[Any]    =   torch.hub.load_state_dict_from_url(__lowercase ,      map_location="cpu" ,      check_hash=__lowercase						)["model"]

						_snake_case						:					Any    =   create_rename_keys(__lowercase ,      __lowercase						)
						for src, dest in rename_keys:
												rename_key(__lowercase ,      __lowercase ,      __lowercase						)
						read_in_q_k_v(__lowercase ,      __lowercase						)

						# 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():
												_snake_case						:					Optional[int]    =   state_dict.pop(__lowercase						)
												if key.startswith("decoder"						) and "output_projection" not in key:
																		_snake_case						:					List[Any]    =   val
												else:
																		_snake_case						:					Dict    =   val

    # load state dict
						model.load_state_dict(__lowercase						)

						# Check outputs on an image
						_snake_case						:					str    =   ViTImageProcessor(size=encoder_config.image_size						)
						_snake_case						:					List[Any]    =   RobertaTokenizer.from_pretrained("roberta-large"						)
						_snake_case						:					List[str]    =   TrOCRProcessor(__lowercase ,      __lowercase						)

						_snake_case						:					List[Any]    =   processor(images=prepare_img(__lowercase						) ,      return_tensors="pt"						).pixel_values

						# verify logits
						_snake_case						:					Optional[Any]    =   torch.tensor([[model.config.decoder.decoder_start_token_id]]						)
						_snake_case						:					List[Any]    =   model(pixel_values=__lowercase ,      decoder_input_ids=__lowercase						)
						_snake_case						:					int    =   outputs.logits

						_snake_case						:					int    =   torch.Size([1, 1, 50_265]						)
						if "trocr-base-handwritten" in checkpoint_url:
												_snake_case						:					Optional[Any]    =   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:
												_snake_case						:					Optional[Any]    =   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:
												_snake_case						:					Any    =   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:
												_snake_case						:					str    =   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] ,      __lowercase ,      atol=1e-3						), "First elements of logits not as expected"

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


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE						:   List[str]         =			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.'
				)
				__SCREAMING_SNAKE_CASE						:   Dict         =			parser.parse_args()
				convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
				import tensorflow as tf


__SCREAMING_SNAKE_CASE						:   str         =			logging.get_logger(__name__)



@dataclass
class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		[
							    'no_inference',
							    'no_cuda',
							    'no_tpu',
							    'no_speed',
							    'no_memory',
							    'no_env_print',
							    'no_multi_process',
							]



							def __init__(						self				,    **lowercase_    ):
													for deprecated_arg in self.deprecated_args:
																			if deprecated_arg in kwargs:
																									_snake_case						:					List[str]    =   deprecated_arg[3:]
																									_snake_case						:					Optional[int]    =   not kwargs.pop(lowercase_    )
																									logger.warning(
																									    f"""{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"""
																									    f""" {positive_arg}={kwargs[positive_arg]}"""    )
													_snake_case						:					Tuple    =   kwargs.pop("tpu_name"				,    self.tpu_name    )
													_snake_case						:					Any    =   kwargs.pop("device_idx"				,    self.device_idx    )
													_snake_case						:					List[str]    =   kwargs.pop("eager_mode"				,    self.eager_mode    )
													_snake_case						:					List[str]    =   kwargs.pop("use_xla"				,    self.use_xla    )
													super().__init__(**lowercase_    )

							_lowerCamelCase						=		field(
							    default=__snake_case						,			metadata={'help': 'Name of TPU'}						,			)
							_lowerCamelCase						=		field(
							    default=0						,			metadata={'help': 'CPU / GPU device index. Defaults to 0.'}						,			)
							_lowerCamelCase						=		field(default=__snake_case						,			metadata={'help': 'Benchmark models in eager model.'}		)
							_lowerCamelCase						=		field(
							    default=__snake_case						,			metadata={
							        'help': 'Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'
							    }						,			)



							@cached_property
							def 	UpperCamelCase	(						self    ):
													requires_backends(self				,    ["tf"]    )
													_snake_case						:					str    =   None
													if self.tpu:
																			try:
																									if self.tpu_name:
																															_snake_case						:					Optional[Any]    =   tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name    )
																									else:
																															_snake_case						:					List[str]    =   tf.distribute.cluster_resolver.TPUClusterResolver()
																			except ValueError:
																									_snake_case						:					Union[str, Any]    =   None
													return tpu



							@cached_property
							def 	UpperCamelCase	(						self    ):
													requires_backends(self				,    ["tf"]    )
													if self.is_tpu:
																			tf.config.experimental_connect_to_cluster(self._setup_tpu    )
																			tf.tpu.experimental.initialize_tpu_system(self._setup_tpu    )

																			_snake_case						:					List[str]    =   tf.distribute.TPUStrategy(self._setup_tpu    )
													else:
																			# currently no multi gpu is allowed
																			if self.is_gpu:
																									# TODO: Currently only single GPU is supported
																									tf.config.set_visible_devices(self.gpu_list[self.device_idx]				,    "GPU"    )
																									_snake_case						:					Any    =   tf.distribute.OneDeviceStrategy(device=f"""/gpu:{self.device_idx}"""    )
																			else:
																									tf.config.set_visible_devices([]				,    "GPU"    )  # disable GPU
																									_snake_case						:					Any    =   tf.distribute.OneDeviceStrategy(device=f"""/cpu:{self.device_idx}"""    )

													return strategy



							@property
							def 	UpperCamelCase	(						self    ):
													requires_backends(self				,    ["tf"]    )
													return self._setup_tpu is not None



							@property
							def 	UpperCamelCase	(						self    ):
													requires_backends(self				,    ["tf"]    )
													return self._setup_strategy



							@property
							def 	UpperCamelCase	(						self    ):
													requires_backends(self				,    ["tf"]    )
													return tf.config.list_physical_devices("GPU"    )



							@property
							def 	UpperCamelCase	(						self    ):
													requires_backends(self				,    ["tf"]    )
													if self.cuda:
																			return len(self.gpu_list    )
													return 0



							@property
							def 	UpperCamelCase	(						self    ):
													return self.n_gpu > 0