




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()
lowercase_			:       Tuple						       =				logging.get_logger(__name__)


def 							__SCREAMING_SNAKE_CASE						(    snake_case_			,					snake_case_=False			,					snake_case_=False		):

						'''simple docstring'''


						_UpperCAmelCase									=  "backbone." if is_semantic else ""

						_UpperCAmelCase									=  []
						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						(    snake_case_			,					snake_case_			,					snake_case_=False			,					snake_case_=False		):

						'''simple docstring'''


						for i in range(config.num_hidden_layers		):
												_UpperCAmelCase									=  "backbone." if is_semantic else ""
												# queries, keys and values
												_UpperCAmelCase									=  state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight"""		)
												_UpperCAmelCase									=  state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias"""		)
												_UpperCAmelCase									=  state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias"""		)

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

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

												_UpperCAmelCase									=  gamma_a
												_UpperCAmelCase									=  gamma_a

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

																		_UpperCAmelCase									=  table
																		_UpperCAmelCase									=  index


def 							__SCREAMING_SNAKE_CASE						(    snake_case_			,					snake_case_			,					snake_case_		):

						'''simple docstring'''


						_UpperCAmelCase									=  dct.pop(snake_case_		)
						_UpperCAmelCase									=  val


def 							__SCREAMING_SNAKE_CASE						(    ):

						'''simple docstring'''


						_UpperCAmelCase									=  "http://images.cocodataset.org/val2017/000000039769.jpg"
						_UpperCAmelCase									=  Image.open(requests.get(snake_case_			,					stream=snake_case_		).raw		)
						return im


@torch.no_grad()
def 							__SCREAMING_SNAKE_CASE						(    snake_case_			,					snake_case_		):

						'''simple docstring'''


						_UpperCAmelCase									=  BeitConfig()
						_UpperCAmelCase									=  False
						_UpperCAmelCase									=  False
						_UpperCAmelCase									=  "huggingface/label-files"
						# set config parameters based on URL
						if checkpoint_url[-9:-4] == "pt22k":
												# masked image modeling
												_UpperCAmelCase									=  True
												_UpperCAmelCase									=  True
												_UpperCAmelCase									=  True
						elif checkpoint_url[-9:-4] == "ft22k":
												# intermediate fine-tuning on ImageNet-22k
												_UpperCAmelCase									=  True
												_UpperCAmelCase									=  2_1841
												_UpperCAmelCase									=  "imagenet-22k-id2label.json"
												_UpperCAmelCase									=  json.load(open(hf_hub_download(snake_case_			,					snake_case_			,					repo_type="dataset"		)			,					"r"		)		)
												_UpperCAmelCase									=  {int(snake_case_		): 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[9205]
												del idalabel[1_5027]
												_UpperCAmelCase									=  idalabel
												_UpperCAmelCase									=  {v: k for k, v in idalabel.items()}
						elif checkpoint_url[-8:-4] == "to1k":
												# fine-tuning on ImageNet-1k
												_UpperCAmelCase									=  True
												_UpperCAmelCase									=  1000
												_UpperCAmelCase									=  "imagenet-1k-id2label.json"
												_UpperCAmelCase									=  json.load(open(hf_hub_download(snake_case_			,					snake_case_			,					repo_type="dataset"		)			,					"r"		)		)
												_UpperCAmelCase									=  {int(snake_case_		): v for k, v in idalabel.items()}
												_UpperCAmelCase									=  idalabel
												_UpperCAmelCase									=  {v: k for k, v in idalabel.items()}
												if "384" in checkpoint_url:
																		_UpperCAmelCase									=  384
												if "512" in checkpoint_url:
																		_UpperCAmelCase									=  512
						elif "ade20k" in checkpoint_url:
												# fine-tuning
												_UpperCAmelCase									=  True
												_UpperCAmelCase									=  150
												_UpperCAmelCase									=  "ade20k-id2label.json"
												_UpperCAmelCase									=  json.load(open(hf_hub_download(snake_case_			,					snake_case_			,					repo_type="dataset"		)			,					"r"		)		)
												_UpperCAmelCase									=  {int(snake_case_		): v for k, v in idalabel.items()}
												_UpperCAmelCase									=  idalabel
												_UpperCAmelCase									=  {v: k for k, v in idalabel.items()}
												_UpperCAmelCase									=  640
												_UpperCAmelCase									=  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:
												_UpperCAmelCase									=  1024
												_UpperCAmelCase									=  4096
												_UpperCAmelCase									=  24
												_UpperCAmelCase									=  16
												if "ade20k" in checkpoint_url:
																		_UpperCAmelCase									=  640
																		_UpperCAmelCase									=  [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
						_UpperCAmelCase									=  torch.hub.load_state_dict_from_url(snake_case_			,					map_location="cpu"			,					check_hash=snake_case_		)
						_UpperCAmelCase									=  state_dict["model"] if "ade20k" not in checkpoint_url else state_dict["state_dict"]

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

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

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

						_UpperCAmelCase									=  image_processor(images=snake_case_			,					return_tensors="pt"		)
						_UpperCAmelCase									=  encoding["pixel_values"]

						_UpperCAmelCase									=  model(snake_case_		)
						_UpperCAmelCase									=  outputs.logits

						# verify logits
						_UpperCAmelCase									=  torch.Size([1, 1000]		)
						if checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k"		):
												_UpperCAmelCase									=  torch.Size([1, 196, 8192]		)
						elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k"		):
												_UpperCAmelCase									=  torch.Size([1, 196, 8192]		)
						elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22k"		):
												_UpperCAmelCase									=  torch.Size([1, 2_1841]		)
												_UpperCAmelCase									=  torch.tensor([2.2288, 2.4671, 0.7395]		)
												_UpperCAmelCase									=  2397
						elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft22k"		):
												_UpperCAmelCase									=  torch.Size([1, 2_1841]		)
												_UpperCAmelCase									=  torch.tensor([1.6881, -0.2787, 0.5901]		)
												_UpperCAmelCase									=  2396
						elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft1k"		):
												_UpperCAmelCase									=  torch.tensor([0.1241, 0.0798, -0.6569]		)
												_UpperCAmelCase									=  285
						elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22kto1k"		):
												_UpperCAmelCase									=  torch.tensor([-1.2385, -1.0987, -1.0108]		)
												_UpperCAmelCase									=  281
						elif checkpoint_url[:-4].endswith("beit_base_patch16_384_pt22k_ft22kto1k"		):
												_UpperCAmelCase									=  torch.tensor([-1.5303, -0.9484, -0.3147]		)
												_UpperCAmelCase									=  761
						elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft1k"		):
												_UpperCAmelCase									=  torch.tensor([0.4610, -0.0928, 0.2086]		)
												_UpperCAmelCase									=  761
						elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft22kto1k"		):
												_UpperCAmelCase									=  torch.tensor([-0.4804, 0.6257, -0.1837]		)
												_UpperCAmelCase									=  761
						elif checkpoint_url[:-4].endswith("beit_large_patch16_384_pt22k_ft22kto1k"		):
												_UpperCAmelCase									=  torch.tensor([[-0.5122, 0.5117, -0.2113]]		)
												_UpperCAmelCase									=  761
						elif checkpoint_url[:-4].endswith("beit_large_patch16_512_pt22k_ft22kto1k"		):
												_UpperCAmelCase									=  torch.tensor([-0.3062, 0.7261, 0.4852]		)
												_UpperCAmelCase									=  761
						elif checkpoint_url[:-4].endswith("beit_base_patch16_640_pt22k_ft22ktoade20k"		):
												_UpperCAmelCase									=  (1, 150, 160, 160)
												_UpperCAmelCase									=  torch.tensor(
												    [
												        [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
												        [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
												        [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
												    ]		)
						elif checkpoint_url[:-4].endswith("beit_large_patch16_640_pt22k_ft22ktoade20k"		):
												_UpperCAmelCase									=  (1, 150, 160, 160)
												_UpperCAmelCase									=  torch.tensor(
												    [
												        [[-4.3305, -2.3049, -3.0161], [-2.9591, -1.5305, -2.2251], [-3.4198, -1.8004, -2.9062]],
												        [[-5.8922, -3.7435, -4.3978], [-4.2063, -2.7872, -3.4755], [-4.2791, -3.1874, -4.1681]],
												        [[0.9895, 4.3467, 4.7663], [4.2476, 5.6830, 6.1518], [4.5550, 6.2495, 6.5154]],
												    ]		)
						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]			,					snake_case_			,					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]			,					snake_case_			,					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(snake_case_		).mkdir(exist_ok=snake_case_		)
						print(f"""Saving model to {pytorch_dump_folder_path}"""		)
						model.save_pretrained(snake_case_		)
						print(f"""Saving image processor to {pytorch_dump_folder_path}"""		)
						image_processor.save_pretrained(snake_case_		)


if __name__ == "__main__":
				lowercase_			:       List[str]						       =				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.'
				)
				lowercase_			:       List[str]						       =				parser.parse_args()
				convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)










import os
import shutil
import unittest
from unittest.mock import patch

from transformers.testing_utils import CaptureStd, is_pt_tf_cross_test, require_torch






class 					__lowerCAmelCase				( unittest.TestCase				):







			@patch("sys.argv"					,					["fakeprogrampath", "env"]   )
			def UpperCamelCase    ( self					:    List[str]   ):




									"""simple docstring"""


									import transformers.commands.transformers_cli

									with CaptureStd() as cs:
															transformers.commands.transformers_cli.main()
									self.assertIn("Python version"					,					cs.out   )
									self.assertIn("Platform"					,					cs.out   )
									self.assertIn("Using distributed or parallel set-up in script?"					,					cs.out   )







			@is_pt_tf_cross_test
			@patch(
			    "sys.argv"					,					["fakeprogrampath", "pt-to-tf", "--model-name", "hf-internal-testing/tiny-random-gptj", "--no-pr"]   )
			def UpperCamelCase    ( self					:    Optional[Any]   ):




									"""simple docstring"""


									import transformers.commands.transformers_cli

									shutil.rmtree("/tmp/hf-internal-testing/tiny-random-gptj"					,					ignore_errors=snake_case__   )  # cleans potential past runs
									transformers.commands.transformers_cli.main()

									# The original repo has no TF weights -- if they exist, they were created by the CLI
									self.assertTrue(os.path.exists("/tmp/hf-internal-testing/tiny-random-gptj/tf_model.h5"   )   )







			@require_torch
			@patch("sys.argv"					,					["fakeprogrampath", "download", "hf-internal-testing/tiny-random-gptj", "--cache-dir", "/tmp"]   )
			def UpperCamelCase    ( self					:    Any   ):




									"""simple docstring"""


									import transformers.commands.transformers_cli

									# # remove any previously downloaded model to start clean
									shutil.rmtree("/tmp/models--hf-internal-testing--tiny-random-gptj"					,					ignore_errors=snake_case__   )

									# run the command
									transformers.commands.transformers_cli.main()

									# check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--tiny-random-gptj
									self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--tiny-random-gptj/blobs"   )   )
									self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--tiny-random-gptj/refs"   )   )
									self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--tiny-random-gptj/snapshots"   )   )







			@require_torch
			@patch(
			    "sys.argv"					,					[
			        "fakeprogrampath",
			        "download",
			        "hf-internal-testing/test_dynamic_model_with_tokenizer",
			        "--trust-remote-code",
			        "--cache-dir",
			        "/tmp",
			    ]					,					)
			def UpperCamelCase    ( self					:    int   ):




									"""simple docstring"""


									import transformers.commands.transformers_cli

									# # remove any previously downloaded model to start clean
									shutil.rmtree("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer"					,					ignore_errors=snake_case__   )

									# run the command
									transformers.commands.transformers_cli.main()

									# check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer
									self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/blobs"   )   )
									self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/refs"   )   )
									self.assertTrue(
									    os.path.exists("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/snapshots"   )   )




