





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 :Optional[int]     =    logging.get_logger(__name__)







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

										_snake_case       :					Any				       =   in_proj_weight[
										    : encoder_config.hidden_size, :
										]
										_snake_case       :					List[str]				       =   in_proj_weight[
										    encoder_config.hidden_size : encoder_config.hidden_size * 2, :
										]
										_snake_case       :					Optional[int]				       =   in_proj_weight[
										    -encoder_config.hidden_size :, :
										]







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					Optional[int]				       =   dct.pop(UpperCAmelCase				)
					_snake_case       :					Union[str, Any]				       =   val







def    A				(							UpperCAmelCase				):
					if "handwritten" in checkpoint_url:
										_snake_case       :					Any				       =   "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       :					List[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       :					List[str]				       =   Image.open(requests.get(UpperCAmelCase  ,  stream=UpperCAmelCase				).raw				).convert("RGB"				)
					return im







@torch.no_grad()
def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					List[str]				       =   ViTConfig(image_size=384  ,  qkv_bias=UpperCAmelCase				)
					_snake_case       :					Any				       =   TrOCRConfig()

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

					# load HuggingFace model
					_snake_case       :					Optional[Any]				       =   ViTModel(UpperCAmelCase  ,  add_pooling_layer=UpperCAmelCase				)
					_snake_case       :					Union[str, Any]				       =   TrOCRForCausalLM(UpperCAmelCase				)
					_snake_case       :					Dict				       =   VisionEncoderDecoderModel(encoder=UpperCAmelCase  ,  decoder=UpperCAmelCase				)
					model.eval()

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

					_snake_case       :					str				       =   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():
										_snake_case       :					List[str]				       =   state_dict.pop(UpperCAmelCase				)
										if key.startswith("decoder"				) and "output_projection" not in key:
															_snake_case       :					Tuple				       =   val
										else:
															_snake_case       :					Any				       =   val

    # load state dict
					model.load_state_dict(UpperCAmelCase				)

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

					_snake_case       :					Any				       =   processor(images=prepare_img(UpperCAmelCase				)  ,  return_tensors="pt"				).pixel_values

					# verify logits
					_snake_case       :					Dict				       =   torch.tensor([[model.config.decoder.decoder_start_token_id]]				)
					_snake_case       :					Union[str, Any]				       =   model(pixel_values=UpperCAmelCase  ,  decoder_input_ids=UpperCAmelCase				)
					_snake_case       :					Dict				       =   outputs.logits

					_snake_case       :					Optional[Any]				       =   torch.Size([1, 1, 50_265]				)
					if "trocr-base-handwritten" in checkpoint_url:
										_snake_case       :					Dict				       =   torch.tensor(
										    [-1.4_5_0_2, -4.6_6_8_3, -0.5_3_4_7, -2.9_2_9_1, 9.1_4_3_5, -3.0_5_7_1, 8.9_7_6_4, 1.7_5_6_0, 8.7_3_5_8, -1.5_3_1_1]				)
					elif "trocr-large-handwritten" in checkpoint_url:
										_snake_case       :					str				       =   torch.tensor(
										    [-2.6_4_3_7, -1.3_1_2_9, -2.2_5_9_6, -5.3_4_5_5, 6.3_5_3_9, 1.7_6_0_4, 5.4_9_9_1, 1.4_7_0_2, 5.6_1_1_3, 2.0_1_7_0]				)
					elif "trocr-base-printed" in checkpoint_url:
										_snake_case       :					Optional[int]				       =   torch.tensor(
										    [-5.6_8_1_6, -5.8_3_8_8, 1.1_3_9_8, -6.9_0_3_4, 6.8_5_0_5, -2.4_3_9_3, 1.2_2_8_4, -1.0_2_3_2, -1.9_6_6_1, -3.9_2_1_0]				)
					elif "trocr-large-printed" in checkpoint_url:
										_snake_case       :					List[Any]				       =   torch.tensor(
										    [-6.0_1_6_2, -7.0_9_5_9, 4.4_1_5_5, -5.1_0_6_3, 7.0_4_6_8, -3.1_6_3_1, 2.6_4_6_6, -0.3_0_8_1, -0.8_1_0_6, -1.7_5_3_5]				)

					if "stage1" not in checkpoint_url:
										assert logits.shape == expected_shape, "Shape of logits not as expected"
										assert torch.allclose(logits[0, 0, :10]  ,  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 :Tuple     =    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 :List[Any]     =    parser.parse_args()
							convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)






import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase="attention"				):
					_snake_case       :					Any				       =   params[F"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
					_snake_case       :					Tuple				       =   params[F"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
					_snake_case       :					str				       =   params[F"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
					_snake_case       :					List[Any]				       =   params[F"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
					return k, o, q, v







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase=False				):
					if split_mlp_wi:
										_snake_case       :					List[Any]				       =   params[F"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
										_snake_case       :					Tuple				       =   params[F"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
										_snake_case       :					Union[str, Any]				       =   (wi_a, wi_a)
					else:
										_snake_case       :					Optional[int]				       =   params[F"""{prefix}/layers_{i}/mlp/wi/kernel"""]

					_snake_case       :					List[str]				       =   params[F"""{prefix}/layers_{i}/mlp/wo/kernel"""]
					return wi, wo







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				):
					return params[F"""{prefix}/layers_{i}/{layer_name}/scale"""]







def    A				(							UpperCAmelCase  ,  *, UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					Any				       =   traverse_util.flatten_dict(variables["target"]				)
					_snake_case       :					str				       =   {"/".join(UpperCAmelCase				): v for k, v in old.items()}

					# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
					_snake_case       :					Tuple				       =   "encoder/layers_0/mlp/wi_0/kernel" in old
					print("Split MLP:"  ,  UpperCAmelCase				)

					_snake_case       :					List[Any]				       =   collections.OrderedDict()

					# Shared embeddings.
					_snake_case       :					Tuple				       =   old["token_embedder/embedding"]

					# Encoder.
					for i in range(UpperCAmelCase				):
										# Block i, layer 0 (Self Attention).
										_snake_case       :					Optional[Any]				       =   tax_layer_norm_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "encoder"  ,  "pre_attention_layer_norm"				)
										_snake_case       , _snake_case       , _snake_case       , _snake_case       :					Union[str, Any]				       =   tax_attention_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "encoder"  ,  "attention"				)
										_snake_case       :					Optional[Any]				       =   layer_norm
										_snake_case       :					int				       =   k.T
										_snake_case       :					Optional[int]				       =   o.T
										_snake_case       :					Dict				       =   q.T
										_snake_case       :					Any				       =   v.T

										# Block i, layer 1 (MLP).
										_snake_case       :					Any				       =   tax_layer_norm_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "encoder"  ,  "pre_mlp_layer_norm"				)
										_snake_case       , _snake_case       :					Tuple				       =   tax_mlp_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "encoder"  ,  UpperCAmelCase				)
										_snake_case       :					Any				       =   layer_norm
										if split_mlp_wi:
															_snake_case       :					Union[str, Any]				       =   wi[0].T
															_snake_case       :					List[str]				       =   wi[1].T
										else:
															_snake_case       :					List[Any]				       =   wi.T
										_snake_case       :					Dict				       =   wo.T

					_snake_case       :					Dict				       =   old[
					    "encoder/relpos_bias/rel_embedding"
					].T
					_snake_case       :					Dict				       =   old["encoder/encoder_norm/scale"]

					if not is_encoder_only:
										# Decoder.
										for i in range(UpperCAmelCase				):
															# Block i, layer 0 (Self Attention).
															_snake_case       :					str				       =   tax_layer_norm_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "decoder"  ,  "pre_self_attention_layer_norm"				)
															_snake_case       , _snake_case       , _snake_case       , _snake_case       :					Optional[Any]				       =   tax_attention_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "decoder"  ,  "self_attention"				)
															_snake_case       :					Optional[Any]				       =   layer_norm
															_snake_case       :					Dict				       =   k.T
															_snake_case       :					List[str]				       =   o.T
															_snake_case       :					List[Any]				       =   q.T
															_snake_case       :					List[Any]				       =   v.T

															# Block i, layer 1 (Cross Attention).
															_snake_case       :					Tuple				       =   tax_layer_norm_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "decoder"  ,  "pre_cross_attention_layer_norm"				)
															_snake_case       , _snake_case       , _snake_case       , _snake_case       :					Union[str, Any]				       =   tax_attention_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "decoder"  ,  "encoder_decoder_attention"				)
															_snake_case       :					Tuple				       =   layer_norm
															_snake_case       :					Union[str, Any]				       =   k.T
															_snake_case       :					str				       =   o.T
															_snake_case       :					List[str]				       =   q.T
															_snake_case       :					List[Any]				       =   v.T

															# Block i, layer 2 (MLP).
															_snake_case       :					Dict				       =   tax_layer_norm_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "decoder"  ,  "pre_mlp_layer_norm"				)
															_snake_case       , _snake_case       :					Union[str, Any]				       =   tax_mlp_lookup(UpperCAmelCase  ,  UpperCAmelCase  ,  "decoder"  ,  UpperCAmelCase				)
															_snake_case       :					Optional[Any]				       =   layer_norm
															if split_mlp_wi:
																				_snake_case       :					str				       =   wi[0].T
																				_snake_case       :					Union[str, Any]				       =   wi[1].T
															else:
																				_snake_case       :					Optional[Any]				       =   wi.T
															_snake_case       :					int				       =   wo.T

										_snake_case       :					Optional[int]				       =   old["decoder/decoder_norm/scale"]
										_snake_case       :					int				       =   old[
										    "decoder/relpos_bias/rel_embedding"
										].T

										# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
										if "decoder/logits_dense/kernel" in old:
															_snake_case       :					Tuple				       =   old["decoder/logits_dense/kernel"].T

					return new







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					Tuple				       =   collections.OrderedDict([(k, torch.from_numpy(v.copy()				)) for (k, v) in converted_params.items()]				)

					# Add what is missing.
					if "encoder.embed_tokens.weight" not in state_dict:
										_snake_case       :					Tuple				       =   state_dict["shared.weight"]

					if not is_encoder_only:
										if "decoder.embed_tokens.weight" not in state_dict:
															_snake_case       :					List[Any]				       =   state_dict["shared.weight"]

										if "lm_head.weight" not in state_dict:  # For old 1.0 models.
															print("Using shared word embeddings as lm_head."				)
															_snake_case       :					Optional[int]				       =   state_dict["shared.weight"]

					return state_dict







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					List[str]				       =   checkpoints.load_tax_checkpoint(UpperCAmelCase				)
					_snake_case       :					Dict				       =   convert_tax_to_pytorch(UpperCAmelCase  ,  num_layers=config.num_layers  ,  is_encoder_only=UpperCAmelCase				)
					_snake_case       :					Dict				       =   make_state_dict(UpperCAmelCase  ,  UpperCAmelCase				)
					model.load_state_dict(UpperCAmelCase  ,  strict=UpperCAmelCase				)







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase = False				):
					_snake_case       :					str				       =   TaConfig.from_json_file(UpperCAmelCase				)
					print(F"""Building PyTorch model from configuration: {config}"""				)
					# Non-v1.1 checkpoints could also use T5Model, but this works for all.
					# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
					if is_encoder_only:
										_snake_case       :					List[Any]				       =   TaEncoderModel(UpperCAmelCase				)
					else:
										_snake_case       :					List[str]				       =   TaForConditionalGeneration(UpperCAmelCase				)

					# Load weights from tf checkpoint
					load_tax_weights_in_ta(UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				)

					# Save pytorch-model
					print(F"""Save PyTorch model to {pytorch_dump_path}"""				)
					model.save_pretrained(UpperCAmelCase				)

					# Verify that we can load the checkpoint.
					model.from_pretrained(UpperCAmelCase				)
					print("Done"				)


if __name__ == "__main__":
							__lowerCAmelCase :Any     =    argparse.ArgumentParser(description='Converts a native T5X checkpoint into a PyTorch checkpoint.')
							# Required parameters
							parser.add_argument(
							    '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path to the T5X checkpoint.'
							)
							parser.add_argument(
							    '--config_file',
							    default=None,
							    type=str,
							    required=True,
							    help='The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.',
							)
							parser.add_argument(
							    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
							)
							parser.add_argument(
							    '--is_encoder_only', action='store_true', help='Check if the model is encoder-decoder model', default=False
							)
							__lowerCAmelCase :Optional[int]     =    parser.parse_args()
							convert_tax_checkpoint_to_pytorch(
							    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
							)