




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()
_A     =			logging.get_logger(__name__)





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

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





def __UpperCamelCase			(       _A ,  _A ,  _A  ):
						lowerCAmelCase_      			=  dct.pop(_A  )
						lowerCAmelCase_      			=  val





def __UpperCamelCase			(       _A  ):
						if "handwritten" in checkpoint_url:
												lowerCAmelCase_      			=  '''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:
												lowerCAmelCase_      			=  '''https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg'''
						lowerCAmelCase_      			=  Image.open(requests.get(_A ,  stream=_A  ).raw  ).convert('''RGB'''  )
						return im





@torch.no_grad()
def __UpperCamelCase			(       _A ,  _A  ):
						lowerCAmelCase_      			=  ViTConfig(image_size=384 ,  qkv_bias=_A  )
						lowerCAmelCase_      			=  TrOCRConfig()

						# size of the architecture
						if "base" in checkpoint_url:
												lowerCAmelCase_      			=  768
						elif "large" in checkpoint_url:
												# use ViT-large encoder
												lowerCAmelCase_      			=  1024
												lowerCAmelCase_      			=  4096
												lowerCAmelCase_      			=  24
												lowerCAmelCase_      			=  16
												lowerCAmelCase_      			=  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:
												lowerCAmelCase_      			=  False
												lowerCAmelCase_      			=  '''relu'''
												lowerCAmelCase_      			=  1024
												lowerCAmelCase_      			=  True
												lowerCAmelCase_      			=  False
												lowerCAmelCase_      			=  False

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

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

						lowerCAmelCase_      			=  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():
												lowerCAmelCase_      			=  state_dict.pop(_A  )
												if key.startswith('''decoder'''  ) and "output_projection" not in key:
																		lowerCAmelCase_      			=  val
												else:
																		lowerCAmelCase_      			=  val

    # load state dict
						model.load_state_dict(_A  )

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

						lowerCAmelCase_      			=  processor(images=prepare_img(_A  ) ,  return_tensors='''pt'''  ).pixel_values

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

						lowerCAmelCase_      			=  torch.Size([1, 1, 50265]  )
						if "trocr-base-handwritten" in checkpoint_url:
												lowerCAmelCase_      			=  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:
												lowerCAmelCase_      			=  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:
												lowerCAmelCase_      			=  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:
												lowerCAmelCase_      			=  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] ,  _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__":
						_A     =			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.'''
						)
						_A     =			parser.parse_args()
						convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)










# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

_A     =			'''tiny-wmt19-en-ru'''

# Build

# borrowed from a test
_A     =			[
    '''l''',
    '''o''',
    '''w''',
    '''e''',
    '''r''',
    '''s''',
    '''t''',
    '''i''',
    '''d''',
    '''n''',
    '''w</w>''',
    '''r</w>''',
    '''t</w>''',
    '''lo''',
    '''low''',
    '''er</w>''',
    '''low</w>''',
    '''lowest</w>''',
    '''newer</w>''',
    '''wider</w>''',
    '''<unk>''',
]
_A     =			dict(zip(vocab, range(len(vocab))))
_A     =			['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

with tempfile.TemporaryDirectory() as tmpdirname:
						_A     =			Path(tmpdirname)
						_A     =			build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
						_A     =			build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
						_A     =			build_dir / VOCAB_FILES_NAMES['''merges_file''']
						with open(src_vocab_file, '''w''') as fp:
												fp.write(json.dumps(vocab_tokens))
						with open(tgt_vocab_file, '''w''') as fp:
												fp.write(json.dumps(vocab_tokens))
						with open(merges_file, '''w''') as fp:
												fp.write('''\n'''.join(merges))

						_A     =			FSMTTokenizer(
						    langs=['''en''', '''ru'''],
						    src_vocab_size=len(vocab),
						    tgt_vocab_size=len(vocab),
						    src_vocab_file=src_vocab_file,
						    tgt_vocab_file=tgt_vocab_file,
						    merges_file=merges_file,
						)

_A     =			FSMTConfig(
    langs=['''ru''', '''en'''],
    src_vocab_size=1_000,
    tgt_vocab_size=1_000,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

_A     =			FSMTForConditionalGeneration(config)
print(f"num of params {tiny_model.num_parameters()}")

# Test
_A     =			tokenizer(['''Making tiny model'''], return_tensors='''pt''')
_A     =			tiny_model(**batch)

print('''test output:''', len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f"Generated {mname_tiny}")

# Upload
# transformers-cli upload tiny-wmt19-en-ru




