

"""simple docstring"""




import argparse
import json
from pathlib import Path

import requests
import torch
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    BeitConfig,
    BeitForImageClassification,
    BeitForMaskedImageModeling,
    BeitForSemanticSegmentation,
    BeitImageProcessor,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
__A     :    Optional[Any]    =		logging.get_logger(__name__)
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__=False			,							lowercase__=False      ):
					"""simple docstring"""
					A			=					"backbone." if is_semantic else ""

					A			=					[]
					for i in range(config.num_hidden_layers      ):
										# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
										rename_keys.append((F"""{prefix}blocks.{i}.norm1.weight""", F"""beit.encoder.layer.{i}.layernorm_before.weight""")      )
										rename_keys.append((F"""{prefix}blocks.{i}.norm1.bias""", F"""beit.encoder.layer.{i}.layernorm_before.bias""")      )
										rename_keys.append(
										    (F"""{prefix}blocks.{i}.attn.proj.weight""", F"""beit.encoder.layer.{i}.attention.output.dense.weight""")      )
										rename_keys.append(
										    (F"""{prefix}blocks.{i}.attn.proj.bias""", F"""beit.encoder.layer.{i}.attention.output.dense.bias""")      )
										rename_keys.append((F"""{prefix}blocks.{i}.norm2.weight""", F"""beit.encoder.layer.{i}.layernorm_after.weight""")      )
										rename_keys.append((F"""{prefix}blocks.{i}.norm2.bias""", F"""beit.encoder.layer.{i}.layernorm_after.bias""")      )
										rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc1.weight""", F"""beit.encoder.layer.{i}.intermediate.dense.weight""")      )
										rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc1.bias""", F"""beit.encoder.layer.{i}.intermediate.dense.bias""")      )
										rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc2.weight""", F"""beit.encoder.layer.{i}.output.dense.weight""")      )
										rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc2.bias""", F"""beit.encoder.layer.{i}.output.dense.bias""")      )

					# projection layer + position embeddings
					rename_keys.extend(
					    [
					        (F"""{prefix}cls_token""", "beit.embeddings.cls_token"),
					        (F"""{prefix}patch_embed.proj.weight""", "beit.embeddings.patch_embeddings.projection.weight"),
					        (F"""{prefix}patch_embed.proj.bias""", "beit.embeddings.patch_embeddings.projection.bias"),
					    ]      )

					if has_lm_head:
										# mask token + shared relative position bias + layernorm
										rename_keys.extend(
										    [
										        ("mask_token", "beit.embeddings.mask_token"),
										        (
										            "rel_pos_bias.relative_position_bias_table",
										            "beit.encoder.relative_position_bias.relative_position_bias_table",
										        ),
										        (
										            "rel_pos_bias.relative_position_index",
										            "beit.encoder.relative_position_bias.relative_position_index",
										        ),
										        ("norm.weight", "layernorm.weight"),
										        ("norm.bias", "layernorm.bias"),
										    ]      )
					elif is_semantic:
										# semantic segmentation classification heads
										rename_keys.extend(
										    [
										        ("decode_head.conv_seg.weight", "decode_head.classifier.weight"),
										        ("decode_head.conv_seg.bias", "decode_head.classifier.bias"),
										        ("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"),
										        ("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.bias"),
										    ]      )
					else:
										# layernorm + classification head
										rename_keys.extend(
										    [
										        ("fc_norm.weight", "beit.pooler.layernorm.weight"),
										        ("fc_norm.bias", "beit.pooler.layernorm.bias"),
										        ("head.weight", "classifier.weight"),
										        ("head.bias", "classifier.bias"),
										    ]      )

					return rename_keys
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__			,							lowercase__=False			,							lowercase__=False      ):
					"""simple docstring"""
					for i in range(config.num_hidden_layers      ):
										A			=					"backbone." if is_semantic else ""
										# queries, keys and values
										A			=					state_dict.pop(F"""{prefix}blocks.{i}.attn.qkv.weight"""      )
										A			=					state_dict.pop(F"""{prefix}blocks.{i}.attn.q_bias"""      )
										A			=					state_dict.pop(F"""{prefix}blocks.{i}.attn.v_bias"""      )

										A			=					in_proj_weight[
										    : config.hidden_size, :
										]
										A			=					q_bias
										A			=					in_proj_weight[
										    config.hidden_size : config.hidden_size * 2, :
										]
										A			=					in_proj_weight[
										    -config.hidden_size :, :
										]
										A			=					v_bias

										# gamma_1 and gamma_2
										# we call them lambda because otherwise they are renamed when using .from_pretrained
										A			=					state_dict.pop(F"""{prefix}blocks.{i}.gamma_1"""      )
										A			=					state_dict.pop(F"""{prefix}blocks.{i}.gamma_2"""      )

										A			=					gamma_a
										A			=					gamma_a

										# relative_position bias table + index
										if not has_lm_head:
															# each layer has its own relative position bias
															A			=					state_dict.pop(F"""{prefix}blocks.{i}.attn.relative_position_bias_table"""      )
															A			=					state_dict.pop(F"""{prefix}blocks.{i}.attn.relative_position_index"""      )

															A			=					table
															A			=					index
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__			,							lowercase__      ):
					"""simple docstring"""
					A			=					dct.pop(lowercase__      )
					A			=					val
def    __SCREAMING_SNAKE_CASE						(						):
					"""simple docstring"""
					A			=					"http://images.cocodataset.org/val2017/000000039769.jpg"
					A			=					Image.open(requests.get(lowercase__			,							stream=lowercase__      ).raw      )
					return im
@torch.no_grad()
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__      ):
					"""simple docstring"""
					A			=					BeitConfig()
					A			=					False
					A			=					False
					A			=					"huggingface/label-files"
					# set config parameters based on URL
					if checkpoint_url[-9:-4] == "pt22k":
										# masked image modeling
										A			=					True
										A			=					True
										A			=					True
					elif checkpoint_url[-9:-4] == "ft22k":
										# intermediate fine-tuning on ImageNet-22k
										A			=					True
										A			=					21_841
										A			=					"imagenet-22k-id2label.json"
										A			=					json.load(open(hf_hub_download(lowercase__			,							lowercase__			,							repo_type="dataset"      )			,							"r"      )      )
										A			=					{int(lowercase__      ): v for k, v in idalabel.items()}
										# this dataset contains 21843 labels but the model only has 21841
										# we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
										del idalabel[9_205]
										del idalabel[15_027]
										A			=					idalabel
										A			=					{v: k for k, v in idalabel.items()}
					elif checkpoint_url[-8:-4] == "to1k":
										# fine-tuning on ImageNet-1k
										A			=					True
										A			=					1_000
										A			=					"imagenet-1k-id2label.json"
										A			=					json.load(open(hf_hub_download(lowercase__			,							lowercase__			,							repo_type="dataset"      )			,							"r"      )      )
										A			=					{int(lowercase__      ): v for k, v in idalabel.items()}
										A			=					idalabel
										A			=					{v: k for k, v in idalabel.items()}
										if "384" in checkpoint_url:
															A			=					384
										if "512" in checkpoint_url:
															A			=					512
					elif "ade20k" in checkpoint_url:
										# fine-tuning
										A			=					True
										A			=					150
										A			=					"ade20k-id2label.json"
										A			=					json.load(open(hf_hub_download(lowercase__			,							lowercase__			,							repo_type="dataset"      )			,							"r"      )      )
										A			=					{int(lowercase__      ): v for k, v in idalabel.items()}
										A			=					idalabel
										A			=					{v: k for k, v in idalabel.items()}
										A			=					640
										A			=					True
					else:
										raise ValueError("Checkpoint not supported, URL should either end with 'pt22k', 'ft22k', 'to1k' or 'ade20k'"      )

					# size of the architecture
					if "base" in checkpoint_url:
										pass
					elif "large" in checkpoint_url:
										A			=					1_024
										A			=					4_096
										A			=					24
										A			=					16
										if "ade20k" in checkpoint_url:
															A			=					640
															A			=					[7, 11, 15, 23]
					else:
										raise ValueError("Should either find 'base' or 'large' in checkpoint URL"      )

					# load state_dict of original model, remove and rename some keys
					A			=					torch.hub.load_state_dict_from_url(lowercase__			,							map_location="cpu"			,							check_hash=lowercase__      )
					A			=					state_dict["model"] if "ade20k" not in checkpoint_url else state_dict["state_dict"]

					A			=					create_rename_keys(lowercase__			,							has_lm_head=lowercase__			,							is_semantic=lowercase__      )
					for src, dest in rename_keys:
										rename_key(lowercase__			,							lowercase__			,							lowercase__      )
					read_in_q_k_v(lowercase__			,							lowercase__			,							has_lm_head=lowercase__			,							is_semantic=lowercase__      )
					if is_semantic:
										# add prefix to decoder keys
										for key, val in state_dict.copy().items():
															A			=					state_dict.pop(lowercase__      )
															if key.startswith("backbone.fpn"      ):
																				A			=					key.replace("backbone.fpn"			,							"fpn"      )
															A			=					val

    # load HuggingFace model
					if checkpoint_url[-9:-4] == "pt22k":
										A			=					BeitForMaskedImageModeling(lowercase__      )
					elif "ade20k" in checkpoint_url:
										A			=					BeitForSemanticSegmentation(lowercase__      )
					else:
										A			=					BeitForImageClassification(lowercase__      )
					model.eval()
					model.load_state_dict(lowercase__      )

					# Check outputs on an image
					if is_semantic:
										A			=					BeitImageProcessor(size=config.image_size			,							do_center_crop=lowercase__      )
										A			=					load_dataset("hf-internal-testing/fixtures_ade20k"			,							split="test"      )
										A			=					Image.open(ds[0]["file"]      )
					else:
										A			=					BeitImageProcessor(
										    size=config.image_size			,							resample=PILImageResampling.BILINEAR			,							do_center_crop=lowercase__      )
										A			=					prepare_img()

					A			=					image_processor(images=lowercase__			,							return_tensors="pt"      )
					A			=					encoding["pixel_values"]

					A			=					model(lowercase__      )
					A			=					outputs.logits

					# verify logits
					A			=					torch.Size([1, 1_000]      )
					if checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k"      ):
										A			=					torch.Size([1, 196, 8_192]      )
					elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k"      ):
										A			=					torch.Size([1, 196, 8_192]      )
					elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22k"      ):
										A			=					torch.Size([1, 21_841]      )
										A			=					torch.tensor([2.22_88, 2.46_71, 0.73_95]      )
										A			=					2_397
					elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft22k"      ):
										A			=					torch.Size([1, 21_841]      )
										A			=					torch.tensor([1.68_81, -0.27_87, 0.59_01]      )
										A			=					2_396
					elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft1k"      ):
										A			=					torch.tensor([0.12_41, 0.07_98, -0.65_69]      )
										A			=					285
					elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22kto1k"      ):
										A			=					torch.tensor([-1.23_85, -1.09_87, -1.01_08]      )
										A			=					281
					elif checkpoint_url[:-4].endswith("beit_base_patch16_384_pt22k_ft22kto1k"      ):
										A			=					torch.tensor([-1.53_03, -0.94_84, -0.31_47]      )
										A			=					761
					elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft1k"      ):
										A			=					torch.tensor([0.46_10, -0.09_28, 0.20_86]      )
										A			=					761
					elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft22kto1k"      ):
										A			=					torch.tensor([-0.48_04, 0.62_57, -0.18_37]      )
										A			=					761
					elif checkpoint_url[:-4].endswith("beit_large_patch16_384_pt22k_ft22kto1k"      ):
										A			=					torch.tensor([[-0.51_22, 0.51_17, -0.21_13]]      )
										A			=					761
					elif checkpoint_url[:-4].endswith("beit_large_patch16_512_pt22k_ft22kto1k"      ):
										A			=					torch.tensor([-0.30_62, 0.72_61, 0.48_52]      )
										A			=					761
					elif checkpoint_url[:-4].endswith("beit_base_patch16_640_pt22k_ft22ktoade20k"      ):
										A			=					(1, 150, 160, 160)
										A			=					torch.tensor(
										    [
										        [[-4.92_25, -2.39_54, -3.05_22], [-2.88_22, -1.00_46, -1.75_61], [-2.95_49, -1.32_28, -2.13_47]],
										        [[-5.81_68, -3.41_29, -4.07_78], [-3.86_51, -2.22_14, -3.02_77], [-3.83_56, -2.46_43, -3.35_35]],
										        [[-0.00_78, 3.99_52, 4.07_54], [2.98_56, 4.69_44, 5.00_35], [3.24_13, 4.78_13, 4.99_69]],
										    ]      )
					elif checkpoint_url[:-4].endswith("beit_large_patch16_640_pt22k_ft22ktoade20k"      ):
										A			=					(1, 150, 160, 160)
										A			=					torch.tensor(
										    [
										        [[-4.33_05, -2.30_49, -3.01_61], [-2.95_91, -1.53_05, -2.22_51], [-3.41_98, -1.80_04, -2.90_62]],
										        [[-5.89_22, -3.74_35, -4.39_78], [-4.20_63, -2.78_72, -3.47_55], [-4.27_91, -3.18_74, -4.16_81]],
										        [[0.98_95, 4.34_67, 4.76_63], [4.24_76, 5.68_30, 6.15_18], [4.55_50, 6.24_95, 6.51_54]],
										    ]      )
					else:
										raise ValueError("Can't verify logits as model is not supported"      )

					assert logits.shape == expected_shape, "Shape of logits not as expected"
					if not has_lm_head:
										if is_semantic:
															assert torch.allclose(
															    logits[0, :3, :3, :3]			,							lowercase__			,							atol=1e-3      ), "First elements of logits not as expected"
										else:
															print("Predicted class idx:"			,							logits.argmax(-1      ).item()      )
															assert torch.allclose(
															    logits[0, :3]			,							lowercase__			,							atol=1e-3      ), "First elements of logits not as expected"
															assert logits.argmax(-1      ).item() == expected_class_idx, "Predicted class index 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 image processor to {pytorch_dump_folder_path}"""      )
					image_processor.save_pretrained(lowercase__      )


if __name__ == "__main__":
	__A     :    Any    =		argparse.ArgumentParser()

	parser.add_argument(
	    '--checkpoint_url',
	    default='https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth',
	    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     :    int    =		parser.parse_args()
	convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)








"""simple docstring"""




import argparse
import collections
import importlib.util
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
__A     :    List[str]    =		'src/diffusers'
__A     :    Optional[int]    =		'docs/source/en'
__A     :    Union[str, Any]    =		'.'
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__			,							lowercase__      ):
					"""simple docstring"""
					with open(lowercase__			,							"r"			,							encoding="utf-8"			,							newline="\n"      ) as f:
										A			=					f.readlines()
					# Find the start prompt.
					A			=					0
					while not lines[start_index].startswith(lowercase__      ):
										start_index += 1
					start_index += 1

					A			=					start_index
					while not lines[end_index].startswith(lowercase__      ):
										end_index += 1
					end_index -= 1

					while len(lines[start_index]      ) <= 1:
										start_index += 1
					while len(lines[end_index]      ) <= 1:
										end_index -= 1
					end_index += 1
					return "".join(lines[start_index:end_index]      ), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
__A     :    Any    =		'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
__A     :    Union[str, Any]    =		re.compile(R'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
__A     :    int    =		re.compile(R'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
__A     :    Any    =		re.compile(R'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# This is to make sure the diffusers module imported is the one in the repo.
__A     :    Optional[int]    =		importlib.util.spec_from_file_location(
    'diffusers',
    os.path.join(TRANSFORMERS_PATH, '__init__.py'),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
__A     :    Tuple    =		spec.loader.load_module()
def    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					A			=					re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"			,							lowercase__      )
					return [m.group(0      ) for m in matches]
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__      ):
					"""simple docstring"""
					A			=					2 if text == "✅" or text == "❌" else len(lowercase__      )
					A			=					(width - text_length) // 2
					A			=					width - text_length - left_indent
					return " " * left_indent + text + " " * right_indent
def    __SCREAMING_SNAKE_CASE						(						):
					"""simple docstring"""
					A			=					diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
					A			=					{
					    name: config_mapping_names[code]
					    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
					    if code in config_mapping_names
					}
					A			=					{name: config.replace("ConfigMixin"			,							""      ) for name, config in model_name_to_config.items()}

					# Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
					A			=					collections.defaultdict(lowercase__      )
					A			=					collections.defaultdict(lowercase__      )
					A			=					collections.defaultdict(lowercase__      )
					A			=					collections.defaultdict(lowercase__      )
					A			=					collections.defaultdict(lowercase__      )

					# Let's lookup through all diffusers object (once).
					for attr_name in dir(lowercase__      ):
										A			=					None
										if attr_name.endswith("Tokenizer"      ):
															A			=					slow_tokenizers
															A			=					attr_name[:-9]
										elif attr_name.endswith("TokenizerFast"      ):
															A			=					fast_tokenizers
															A			=					attr_name[:-13]
										elif _re_tf_models.match(lowercase__      ) is not None:
															A			=					tf_models
															A			=					_re_tf_models.match(lowercase__      ).groups()[0]
										elif _re_flax_models.match(lowercase__      ) is not None:
															A			=					flax_models
															A			=					_re_flax_models.match(lowercase__      ).groups()[0]
										elif _re_pt_models.match(lowercase__      ) is not None:
															A			=					pt_models
															A			=					_re_pt_models.match(lowercase__      ).groups()[0]

										if lookup_dict is not None:
															while len(lowercase__      ) > 0:
																				if attr_name in model_name_to_prefix.values():
																									A			=					True
																									break
																				# Try again after removing the last word in the name
																				A			=					"".join(camel_case_split(lowercase__      )[:-1]      )

    # Let's build that table!
					A			=					list(model_name_to_config.keys()      )
					model_names.sort(key=str.lower      )
					A			=					["Model", "Tokenizer slow", "Tokenizer fast", "PyTorch support", "TensorFlow support", "Flax Support"]
					# We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
					A			=					[len(lowercase__      ) + 2 for c in columns]
					A			=					max([len(lowercase__      ) for name in model_names]      ) + 2

					# Build the table per se
					A			=					"|" + "|".join([_center_text(lowercase__			,							lowercase__      ) for c, w in zip(lowercase__			,							lowercase__      )]      ) + "|\n"
					# Use ":-----:" format to center-aligned table cell texts
					table += "|" + "|".join([":" + "-" * (w - 2) + ":" for w in widths]      ) + "|\n"

					A			=					{True: "✅", False: "❌"}
					for name in model_names:
										A			=					model_name_to_prefix[name]
										A			=					[
										    name,
										    check[slow_tokenizers[prefix]],
										    check[fast_tokenizers[prefix]],
										    check[pt_models[prefix]],
										    check[tf_models[prefix]],
										    check[flax_models[prefix]],
										]
										table += "|" + "|".join([_center_text(lowercase__			,							lowercase__      ) for l, w in zip(lowercase__			,							lowercase__      )]      ) + "|\n"
					return table
def    __SCREAMING_SNAKE_CASE						(						lowercase__=False      ):
					"""simple docstring"""
					A			,    A			,    A			,    A			=					_find_text_in_file(
					    filename=os.path.join(lowercase__			,							"index.mdx"      )			,							start_prompt="<!--This table is updated automatically from the auto modules"			,							end_prompt="<!-- End table-->"			,							)
					A			=					get_model_table_from_auto_modules()

					if current_table != new_table:
										if overwrite:
															with open(os.path.join(lowercase__			,							"index.mdx"      )			,							"w"			,							encoding="utf-8"			,							newline="\n"      ) as f:
																				f.writelines(lines[:start_index] + [new_table] + lines[end_index:]      )
										else:
															raise ValueError(
															    "The model table in the `index.mdx` has not been updated. Run `make fix-copies` to fix this."      )


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

	check_model_table(args.fix_and_overwrite)





