'''simple docstring'''


import argparse
import os
import re

import tensorflow as tf
import torch

from transformers import BertConfig, BertModel
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCAmelCase	:       List[str]						       =	logging.get_logger(__name__)





def       lowerCAmelCase					(		UpperCamelCase__				:   Dict    ,					UpperCamelCase__				:   List[str]    ,					UpperCamelCase__				:   str       ):


	"""simple docstring"""
	__UpperCAmelCase							=			os.path.abspath(UpperCamelCase__       )
	logger.info(f"""Converting TensorFlow checkpoint from {tf_path}"""       )
	# Load weights from TF model
	__UpperCAmelCase							=			tf.train.list_variables(UpperCamelCase__       )
	__UpperCAmelCase							=			[]
	__UpperCAmelCase							=			[]
	__UpperCAmelCase							=			[]
	for full_name, shape in init_vars:
		# logger.info(f"Loading TF weight {name} with shape {shape}")
		__UpperCAmelCase							=			full_name.split('''/'''       )
		if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
			logger.info(f"""Skipping non-model layer {full_name}"""       )
			continue
		if "optimizer" in full_name:
			logger.info(f"""Skipping optimization layer {full_name}"""       )
			continue
		if name[0] == "model":
			# ignore initial 'model'
			__UpperCAmelCase							=			name[1:]
		# figure out how many levels deep the name is
		__UpperCAmelCase							=			0
		for _name in name:
			if _name.startswith('''layer_with_weights'''       ):
				depth += 1
			else:
				break
		layer_depth.append(UpperCamelCase__       )
		# read data
		__UpperCAmelCase							=			tf.train.load_variable(UpperCamelCase__    ,					UpperCamelCase__       )
		names.append('''/'''.join(UpperCamelCase__       )       )
		arrays.append(UpperCamelCase__       )
	logger.info(f"""Read a total of {len(UpperCamelCase__       ):,} layers"""       )

	# Sanity check
	if len(set(UpperCamelCase__       )       ) != 1:
		raise ValueError(f"""Found layer names with different depths (layer depth {list(set(UpperCamelCase__       )       )})"""       )
	__UpperCAmelCase							=			list(set(UpperCamelCase__       )       )[0]
	if layer_depth != 1:
		raise ValueError(
		    '''The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP'''
		    ''' heads.'''       )

	# convert layers
	logger.info('''Converting weights...'''       )
	for full_name, array in zip(UpperCamelCase__    ,					UpperCamelCase__       ):
		__UpperCAmelCase							=			full_name.split('''/'''       )
		__UpperCAmelCase							=			model
		__UpperCAmelCase							=			[]
		for i, m_name in enumerate(UpperCamelCase__       ):
			if m_name == ".ATTRIBUTES":
				# variable names end with .ATTRIBUTES/VARIABLE_VALUE
				break
			if m_name.startswith('''layer_with_weights'''       ):
				__UpperCAmelCase							=			int(m_name.split('''-'''       )[-1]       )
				if layer_num <= 2:
					# embedding layers
					# layer_num 0: word_embeddings
					# layer_num 1: position_embeddings
					# layer_num 2: token_type_embeddings
					continue
				elif layer_num == 3:
					# embedding LayerNorm
					trace.extend(['''embeddings''', '''LayerNorm''']       )
					__UpperCAmelCase							=			getattr(UpperCamelCase__    ,					'''embeddings'''       )
					__UpperCAmelCase							=			getattr(UpperCamelCase__    ,					'''LayerNorm'''       )
				elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
					# encoder layers
					trace.extend(['''encoder''', '''layer''', str(layer_num - 4       )]       )
					__UpperCAmelCase							=			getattr(UpperCamelCase__    ,					'''encoder'''       )
					__UpperCAmelCase							=			getattr(UpperCamelCase__    ,					'''layer'''       )
					__UpperCAmelCase							=			pointer[layer_num - 4]
				elif layer_num == config.num_hidden_layers + 4:
					# pooler layer
					trace.extend(['''pooler''', '''dense''']       )
					__UpperCAmelCase							=			getattr(UpperCamelCase__    ,					'''pooler'''       )
					__UpperCAmelCase							=			getattr(UpperCamelCase__    ,					'''dense'''       )
			elif m_name == "embeddings":
				trace.append('''embeddings'''       )
				__UpperCAmelCase							=			getattr(UpperCamelCase__    ,					'''embeddings'''       )
				if layer_num == 0:
					trace.append('''word_embeddings'''       )
					__UpperCAmelCase							=			getattr(UpperCamelCase__    ,					'''word_embeddings'''       )
				elif layer_num == 1:
					trace.append('''position_embeddings'''       )
					__UpperCAmelCase							=			getattr(UpperCamelCase__    ,					'''position_embeddings'''       )
				elif layer_num == 2:
					trace.append('''token_type_embeddings'''       )
					__UpperCAmelCase							=			getattr(UpperCamelCase__    ,					'''token_type_embeddings'''       )
				else:
					raise ValueError(f"""Unknown embedding layer with name {full_name}"""       )
				trace.append('''weight'''       )
				__UpperCAmelCase							=			getattr(UpperCamelCase__    ,					'''weight'''       )
			elif m_name == "_attention_layer":
				# self-attention layer
				trace.extend(['''attention''', '''self''']       )
				__UpperCAmelCase							=			getattr(UpperCamelCase__    ,					'''attention'''       )
				__UpperCAmelCase							=			getattr(UpperCamelCase__    ,					'''self'''       )
			elif m_name == "_attention_layer_norm":
				# output attention norm
				trace.extend(['''attention''', '''output''', '''LayerNorm''']       )
				__UpperCAmelCase							=			getattr(UpperCamelCase__    ,					'''attention'''       )
				__UpperCAmelCase							=			getattr(UpperCamelCase__    ,					'''output'''       )
				__UpperCAmelCase							=			getattr(UpperCamelCase__    ,					'''LayerNorm'''       )
			elif m_name == "_attention_output_dense":
				# output attention dense
				trace.extend(['''attention''', '''output''', '''dense''']       )
				__UpperCAmelCase							=			getattr(UpperCamelCase__    ,					'''attention'''       )
				__UpperCAmelCase							=			getattr(UpperCamelCase__    ,					'''output'''       )
				__UpperCAmelCase							=			getattr(UpperCamelCase__    ,					'''dense'''       )
			elif m_name == "_output_dense":
				# output dense
				trace.extend(['''output''', '''dense''']       )
				__UpperCAmelCase							=			getattr(UpperCamelCase__    ,					'''output'''       )
				__UpperCAmelCase							=			getattr(UpperCamelCase__    ,					'''dense'''       )
			elif m_name == "_output_layer_norm":
				# output dense
				trace.extend(['''output''', '''LayerNorm''']       )
				__UpperCAmelCase							=			getattr(UpperCamelCase__    ,					'''output'''       )
				__UpperCAmelCase							=			getattr(UpperCamelCase__    ,					'''LayerNorm'''       )
			elif m_name == "_key_dense":
				# attention key
				trace.append('''key'''       )
				__UpperCAmelCase							=			getattr(UpperCamelCase__    ,					'''key'''       )
			elif m_name == "_query_dense":
				# attention query
				trace.append('''query'''       )
				__UpperCAmelCase							=			getattr(UpperCamelCase__    ,					'''query'''       )
			elif m_name == "_value_dense":
				# attention value
				trace.append('''value'''       )
				__UpperCAmelCase							=			getattr(UpperCamelCase__    ,					'''value'''       )
			elif m_name == "_intermediate_dense":
				# attention intermediate dense
				trace.extend(['''intermediate''', '''dense''']       )
				__UpperCAmelCase							=			getattr(UpperCamelCase__    ,					'''intermediate'''       )
				__UpperCAmelCase							=			getattr(UpperCamelCase__    ,					'''dense'''       )
			elif m_name == "_output_layer_norm":
				# output layer norm
				trace.append('''output'''       )
				__UpperCAmelCase							=			getattr(UpperCamelCase__    ,					'''output'''       )
			# weights & biases
			elif m_name in ["bias", "beta"]:
				trace.append('''bias'''       )
				__UpperCAmelCase							=			getattr(UpperCamelCase__    ,					'''bias'''       )
			elif m_name in ["kernel", "gamma"]:
				trace.append('''weight'''       )
				__UpperCAmelCase							=			getattr(UpperCamelCase__    ,					'''weight'''       )
			else:
				logger.warning(f"""Ignored {m_name}"""       )
        # for certain layers reshape is necessary
		__UpperCAmelCase							=			'''.'''.join(UpperCamelCase__       )
		if re.match(R'''(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)'''    ,					UpperCamelCase__       ) or re.match(
		    R'''(\S+)\.attention\.output\.dense\.weight'''    ,					UpperCamelCase__       ):
			__UpperCAmelCase							=			array.reshape(pointer.data.shape       )
		if "kernel" in full_name:
			__UpperCAmelCase							=			array.transpose()
		if pointer.shape == array.shape:
			__UpperCAmelCase							=			torch.from_numpy(UpperCamelCase__       )
		else:
			raise ValueError(
			    f"""Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:"""
			    f""" {array.shape}"""       )
		logger.info(f"""Successfully set variable {full_name} to PyTorch layer {trace}"""       )
	return model





def       lowerCAmelCase					(		UpperCamelCase__				:   Union[str, Any]    ,					UpperCamelCase__				:   Dict    ,					UpperCamelCase__				:   Any       ):


	"""simple docstring"""
	# Instantiate model
	logger.info(f"""Loading model based on config from {config_path}..."""       )
	__UpperCAmelCase							=			BertConfig.from_json_file(UpperCamelCase__       )
	__UpperCAmelCase							=			BertModel(UpperCamelCase__       )

	# Load weights from checkpoint
	logger.info(f"""Loading weights from checkpoint {tf_checkpoint_path}..."""       )
	load_tfa_weights_in_bert(UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__       )

	# Save pytorch-model
	logger.info(f"""Saving PyTorch model to {pytorch_dump_path}..."""       )
	torch.save(model.state_dict()    ,					UpperCamelCase__       )


if __name__ == "__main__":
					__lowerCAmelCase	:       Any						       =	argparse.ArgumentParser()
					parser.add_argument(
					    "--tf_checkpoint_path", type=str, required=True, help="Path to the TensorFlow 2.x checkpoint path."
					)
					parser.add_argument(
					    "--bert_config_file",
					    type=str,
					    required=True,
					    help="The config json file corresponding to the BERT model. This specifies the model architecture.",
					)
					parser.add_argument(
					    "--pytorch_dump_path",
					    type=str,
					    required=True,
					    help="Path to the output PyTorch model (must include filename).",
					)
					__lowerCAmelCase	:       List[str]						       =	parser.parse_args()
					convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)

'''simple docstring'''


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	:       Dict						       =	logging.get_logger(__name__)





def       lowerCAmelCase					(		UpperCamelCase__				:   Optional[Any]    ,					UpperCamelCase__				:   Union[str, Any]       ):


	"""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       lowerCAmelCase					(		UpperCamelCase__				:   Optional[Any]    ,					UpperCamelCase__				:   List[Any]       ):


	"""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       lowerCAmelCase					(		UpperCamelCase__				:   Any    ,					UpperCamelCase__				:   Optional[Any]    ,					UpperCamelCase__				:   List[str]       ):


	"""simple docstring"""
	__UpperCAmelCase							=			dct.pop(UpperCamelCase__       )
	__UpperCAmelCase							=			val





def       lowerCAmelCase					(		UpperCamelCase__				:   Dict       ):


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





@torch.no_grad()
def       lowerCAmelCase					(		UpperCamelCase__				:   Dict    ,					UpperCamelCase__				:   Union[str, Any]       ):


	"""simple docstring"""
	__UpperCAmelCase							=			ViTConfig(image_size=3_8_4    ,					qkv_bias=UpperCamelCase__       )
	__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(UpperCamelCase__    ,					add_pooling_layer=UpperCamelCase__       )
	__UpperCAmelCase							=			TrOCRForCausalLM(UpperCamelCase__       )
	__UpperCAmelCase							=			VisionEncoderDecoderModel(encoder=UpperCamelCase__    ,					decoder=UpperCamelCase__       )
	model.eval()

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

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

	# 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(UpperCamelCase__       )
		if key.startswith('''decoder'''       ) and "output_projection" not in key:
			__UpperCAmelCase							=			val
		else:
			__UpperCAmelCase							=			val

    # load state dict
	model.load_state_dict(UpperCamelCase__       )

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

	__UpperCAmelCase							=			processor(images=prepare_img(UpperCamelCase__       )    ,					return_tensors='''pt'''       ).pixel_values

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

	__UpperCAmelCase							=			torch.Size([1, 1, 5_0_2_6_5]       )
	if "trocr-base-handwritten" in checkpoint_url:
		__UpperCAmelCase							=			torch.tensor(
		    [-1.45_02, -4.66_83, -0.53_47, -2.92_91, 9.14_35, -3.05_71, 8.97_64, 1.75_60, 8.73_58, -1.53_11]       )
	elif "trocr-large-handwritten" in checkpoint_url:
		__UpperCAmelCase							=			torch.tensor(
		    [-2.64_37, -1.31_29, -2.25_96, -5.34_55, 6.35_39, 1.76_04, 5.49_91, 1.47_02, 5.61_13, 2.01_70]       )
	elif "trocr-base-printed" in checkpoint_url:
		__UpperCAmelCase							=			torch.tensor(
		    [-5.68_16, -5.83_88, 1.13_98, -6.90_34, 6.85_05, -2.43_93, 1.22_84, -1.02_32, -1.96_61, -3.92_10]       )
	elif "trocr-large-printed" in checkpoint_url:
		__UpperCAmelCase							=			torch.tensor(
		    [-6.01_62, -7.09_59, 4.41_55, -5.10_63, 7.04_68, -3.16_31, 2.64_66, -0.30_81, -0.81_06, -1.75_35]       )

	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]    ,					UpperCamelCase__    ,					atol=1E-3       ), "First elements of logits not as expected"

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


if __name__ == "__main__":
					__lowerCAmelCase	:       Any						       =	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	:       Optional[int]						       =	parser.parse_args()
					convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
