


import argparse
import glob
import importlib.util
import os
import re

import black
from doc_builder.style_doc import style_docstrings_in_code


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
snake_case							:	Optional[int]					      =				"src/diffusers"
snake_case							:	int					      =				"."


# This is to make sure the diffusers module imported is the one in the repo.
snake_case							:	List[Any]					      =				importlib.util.spec_from_file_location(
    '''diffusers''',
    os.path.join(DIFFUSERS_PATH, '''__init__.py'''),
    submodule_search_locations=[DIFFUSERS_PATH],
)
snake_case							:	Tuple					      =				spec.loader.load_module()






def 							__lowercase       (			__lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    Any       ):
							return line.startswith(lowerCAmelCase__       ) or len(lowerCAmelCase__       ) <= 1 or re.search(R'^\s*\)(\s*->.*:|:)\s*$' ,   lowerCAmelCase__       ) is not None






def 							__lowercase       (			__lowerCAmelCase							:    List[str]       ):
							a__					=					object_name.split('.'       )
							a__					=					0

							# First let's find the module where our object lives.
							a__					=					parts[i]
							while i < len(lowerCAmelCase__       ) and not os.path.isfile(os.path.join(lowerCAmelCase__ ,   F'{module}.py'       )       ):
														i += 1
														if i < len(lowerCAmelCase__       ):
																					a__					=					os.path.join(lowerCAmelCase__ ,   parts[i]       )
							if i >= len(lowerCAmelCase__       ):
														raise ValueError(F'`object_name` should begin with the name of a module of diffusers but got {object_name}.'       )

							with open(os.path.join(lowerCAmelCase__ ,   F'{module}.py'       ) ,   'r' ,   encoding='utf-8' ,   newline='\n'       ) as f:
														a__					=					f.readlines()

							# Now let's find the class / func in the code!
							a__					=					''
							a__					=					0
							for name in parts[i + 1 :]:
														while (
														    line_index < len(lowerCAmelCase__       ) and re.search(RF'^{indent}(class|def)\s+{name}(\(|\:)' ,   lines[line_index]       ) is None
														):
																					line_index += 1
														indent += "    "
														line_index += 1

							if line_index >= len(lowerCAmelCase__       ):
														raise ValueError(F' {object_name} does not match any function or class in {module}.'       )

							# We found the beginning of the class / func, now let's find the end (when the indent diminishes).
							a__					=					line_index
							while line_index < len(lowerCAmelCase__       ) and _should_continue(lines[line_index] ,   lowerCAmelCase__       ):
														line_index += 1
							# Clean up empty lines at the end (if any).
							while len(lines[line_index - 1]       ) <= 1:
														line_index -= 1

							a__					=					lines[start_index:line_index]
							return "".join(lowerCAmelCase__       )


snake_case							:	Union[str, Any]					      =				re.compile(r'''^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)''')
snake_case							:	Tuple					      =				re.compile(r'''^\s*(\S+)->(\S+)(\s+.*|$)''')
snake_case							:	Dict					      =				re.compile(r'''<FILL\s+[^>]*>''')






def 							__lowercase       (			__lowerCAmelCase							:    List[Any]       ):
							a__					=					code.split('\n'       )
							a__					=					0
							while idx < len(lowerCAmelCase__       ) and len(lines[idx]       ) == 0:
														idx += 1
							if idx < len(lowerCAmelCase__       ):
														return re.search(R'^(\s*)\S' ,   lines[idx]       ).groups()[0]
							return ""






def 							__lowercase       (			__lowerCAmelCase							:    List[Any]       ):
							a__					=					len(get_indent(lowerCAmelCase__       )       ) > 0
							if has_indent:
														a__					=					F'class Bla:\n{code}'
							a__					=					black.Mode(target_versions={black.TargetVersion.PYaa} ,   line_length=1_1_9 ,   preview=lowerCAmelCase__       )
							a__					=					black.format_str(lowerCAmelCase__ ,   mode=lowerCAmelCase__       )
							a__				, a__					=					style_docstrings_in_code(lowerCAmelCase__       )
							return result[len('class Bla:\n'       ) :] if has_indent else result






def 							__lowercase       (			__lowerCAmelCase							:    Any ,   __lowerCAmelCase							:    Dict=False       ):



							with open(lowerCAmelCase__ ,   'r' ,   encoding='utf-8' ,   newline='\n'       ) as f:
														a__					=					f.readlines()
							a__					=					[]
							a__					=					0
							# Not a for loop cause `lines` is going to change (if `overwrite=True`).
							while line_index < len(lowerCAmelCase__       ):
														a__					=					_re_copy_warning.search(lines[line_index]       )
														if search is None:
																					line_index += 1
																					continue

														# There is some copied code here, let's retrieve the original.
														a__				, a__				, a__					=					search.groups()
														a__					=					find_code_in_diffusers(lowerCAmelCase__       )
														a__					=					get_indent(lowerCAmelCase__       )

														a__					=					line_index + 1 if indent == theoretical_indent else line_index + 2
														a__					=					theoretical_indent
														a__					=					start_index

														# Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
														a__					=					True
														while line_index < len(lowerCAmelCase__       ) and should_continue:
																					line_index += 1
																					if line_index >= len(lowerCAmelCase__       ):
																												break
																					a__					=					lines[line_index]
																					a__					=					_should_continue(lowerCAmelCase__ ,   lowerCAmelCase__       ) and re.search(F'^{indent}# End copy' ,   lowerCAmelCase__       ) is None
														# Clean up empty lines at the end (if any).
														while len(lines[line_index - 1]       ) <= 1:
																					line_index -= 1

														a__					=					lines[start_index:line_index]
														a__					=					''.join(lowerCAmelCase__       )

														# Remove any nested `Copied from` comments to avoid circular copies
														a__					=					[line for line in theoretical_code.split('\n'       ) if _re_copy_warning.search(lowerCAmelCase__       ) is None]
														a__					=					'\n'.join(lowerCAmelCase__       )

														# Before comparing, use the `replace_pattern` on the original code.
														if len(lowerCAmelCase__       ) > 0:
																					a__					=					replace_pattern.replace('with' ,   ''       ).split(','       )
																					a__					=					[_re_replace_pattern.search(lowerCAmelCase__       ) for p in patterns]
																					for pattern in patterns:
																												if pattern is None:
																																			continue
																												a__				, a__				, a__					=					pattern.groups()
																												a__					=					re.sub(lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__       )
																												if option.strip() == "all-casing":
																																			a__					=					re.sub(obja.lower() ,   obja.lower() ,   lowerCAmelCase__       )
																																			a__					=					re.sub(obja.upper() ,   obja.upper() ,   lowerCAmelCase__       )

            # Blackify after replacement. To be able to do that, we need the header (class or function definition)
            # from the previous line
																					a__					=					blackify(lines[start_index - 1] + theoretical_code       )
																					a__					=					theoretical_code[len(lines[start_index - 1]       ) :]

														# Test for a diff and act accordingly.
														if observed_code != theoretical_code:
																					diffs.append([object_name, start_index]       )
																					if overwrite:
																												a__					=					lines[:start_index] + [theoretical_code] + lines[line_index:]
																												a__					=					start_index + 1

							if overwrite and len(lowerCAmelCase__       ) > 0:
														# Warn the user a file has been modified.
														print(F'Detected changes, rewriting {filename}.'       )
														with open(lowerCAmelCase__ ,   'w' ,   encoding='utf-8' ,   newline='\n'       ) as f:
																					f.writelines(lowerCAmelCase__       )
							return diffs






def 							__lowercase       (			__lowerCAmelCase							:    bool = False       ):
							a__					=					glob.glob(os.path.join(lowerCAmelCase__ ,   '**/*.py'       ) ,   recursive=lowerCAmelCase__       )
							a__					=					[]
							for filename in all_files:
														a__					=					is_copy_consistent(lowerCAmelCase__ ,   lowerCAmelCase__       )
														diffs += [F'- {filename}: copy does not match {d[0]} at line {d[1]}' for d in new_diffs]
							if not overwrite and len(lowerCAmelCase__       ) > 0:
														a__					=					'\n'.join(lowerCAmelCase__       )
														raise Exception(
														    'Found the following copy inconsistencies:\n'
														    + diff
														    + '\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them.'       )


if __name__ == "__main__":
						snake_case							:	List[str]					      =				argparse.ArgumentParser()
						parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
						snake_case							:	Optional[Any]					      =				parser.parse_args()

						check_copies(args.fix_and_overwrite)








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()
snake_case							:	int					      =				logging.get_logger(__name__)






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

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






def 							__lowercase       (			__lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    Optional[Any]       ):
							a__					=					dct.pop(__lowerCAmelCase       )
							a__					=					val






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






@torch.no_grad()
def 							__lowercase       (			__lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    int       ):
							a__					=					ViTConfig(image_size=3_8_4 ,   qkv_bias=__lowerCAmelCase       )
							a__					=					TrOCRConfig()

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

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

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

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

    # load state dict
							model.load_state_dict(__lowerCAmelCase       )

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

							a__					=					processor(images=prepare_img(__lowerCAmelCase       ) ,   return_tensors='pt'       ).pixel_values

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

							a__					=					torch.Size([1, 1, 5_0_2_6_5]       )
							if "trocr-base-handwritten" in checkpoint_url:
														a__					=					torch.tensor(
														    [-1.4_502, -4.6_683, -0.5_347, -2.9_291, 9.1_435, -3.0_571, 8.9_764, 1.7_560, 8.7_358, -1.5_311]       )
							elif "trocr-large-handwritten" in checkpoint_url:
														a__					=					torch.tensor(
														    [-2.6_437, -1.3_129, -2.2_596, -5.3_455, 6.3_539, 1.7_604, 5.4_991, 1.4_702, 5.6_113, 2.0_170]       )
							elif "trocr-base-printed" in checkpoint_url:
														a__					=					torch.tensor(
														    [-5.6_816, -5.8_388, 1.1_398, -6.9_034, 6.8_505, -2.4_393, 1.2_284, -1.0_232, -1.9_661, -3.9_210]       )
							elif "trocr-large-printed" in checkpoint_url:
														a__					=					torch.tensor(
														    [-6.0_162, -7.0_959, 4.4_155, -5.1_063, 7.0_468, -3.1_631, 2.6_466, -0.3_081, -0.8_106, -1.7_535]       )

							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__":
						snake_case							:	Optional[int]					      =				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.'''
						)
						snake_case							:	int					      =				parser.parse_args()
						convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




