


"""simple docstring"""


import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
	A     :   Union[str, Any]			    =			"pt"
elif is_tf_available():
	A     :   Optional[Any]			    =			"tf"
else:
	A     :   Any			    =			"jax"
class   _UpperCamelCase  (   lowerCAmelCase__       ,unittest.TestCase		):
							'''simple docstring'''







							__UpperCAmelCase						:    str				      =PerceiverTokenizer
							__UpperCAmelCase						:    List[str]				      =False

							def 			snake_case				(     self	):
										super().setUp()
										__lowerCAmelCase				    =			PerceiverTokenizer()
										tokenizer.save_pretrained(self.tmpdirname	)

							@cached_property
							def 			snake_case				(     self	):
										return PerceiverTokenizer.from_pretrained("deepmind/language-perceiver"	)

							def 			snake_case				(     self    ,   **__a	):
										return self.tokenizer_class.from_pretrained(self.tmpdirname    ,   **__a	)

							def 			snake_case				(     self    ,   __a    ,   __a=False    ,   __a=20    ,   __a=5	):
										# XXX The default common tokenizer tests assume that every ID is decodable on its own.
										# This assumption is invalid for Perceiver because single bytes might not be
										# valid utf-8 (byte 128 for instance).
										# Here we're overriding the smallest possible method to provide
										# a clean sequence without making the same assumption.

										__lowerCAmelCase				    =			[]
										for i in range(len(__a	)	):
													try:
																__lowerCAmelCase				    =			tokenizer.decode([i]    ,   clean_up_tokenization_spaces=__a	)
													except UnicodeDecodeError:
																pass
													toks.append((i, tok)	)

										__lowerCAmelCase				    =			list(filter(lambda __a	: re.match(R"^[ a-zA-Z]+$"    ,   t[1]	)    ,   __a	)	)
										__lowerCAmelCase				    =			list(filter(lambda __a	: [t[0]] == tokenizer.encode(t[1]    ,   add_special_tokens=__a	)    ,   __a	)	)
										if max_length is not None and len(__a	) > max_length:
													__lowerCAmelCase				    =			toks[:max_length]
										if min_length is not None and len(__a	) < min_length and len(__a	) > 0:
													while len(__a	) < min_length:
																__lowerCAmelCase				    =			toks + toks
        # toks_str = [t[1] for t in toks]
										__lowerCAmelCase				    =			[t[0] for t in toks]

										# Ensure consistency
										__lowerCAmelCase				    =			tokenizer.decode(__a    ,   clean_up_tokenization_spaces=__a	)
										if " " not in output_txt and len(__a	) > 1:
													__lowerCAmelCase				    =			(
													    tokenizer.decode([toks_ids[0]]    ,   clean_up_tokenization_spaces=__a	)
													    + " "
													    + tokenizer.decode(toks_ids[1:]    ,   clean_up_tokenization_spaces=__a	)
													)
										if with_prefix_space:
													__lowerCAmelCase				    =			" " + output_txt
										__lowerCAmelCase				    =			tokenizer.encode(__a    ,   add_special_tokens=__a	)
										return output_txt, output_ids

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.perceiver_tokenizer
										__lowerCAmelCase				    =			"Unicode €."
										__lowerCAmelCase				    =			tokenizer(__a	)
										__lowerCAmelCase				    =			[4, 91, 1_16, 1_11, 1_05, 1_17, 1_06, 1_07, 38, 2_32, 1_36, 1_78, 52, 5]
										self.assertEqual(encoded["input_ids"]    ,   __a	)

										# decoding
										__lowerCAmelCase				    =			tokenizer.decode(__a	)
										self.assertEqual(__a    ,   "[CLS]Unicode €.[SEP]"	)

										__lowerCAmelCase				    =			tokenizer("e è é ê ë"	)
										__lowerCAmelCase				    =			[4, 1_07, 38, 2_01, 1_74, 38, 2_01, 1_75, 38, 2_01, 1_76, 38, 2_01, 1_77, 5]
										self.assertEqual(encoded["input_ids"]    ,   __a	)
										# decoding
										__lowerCAmelCase				    =			tokenizer.decode(__a	)
										self.assertEqual(__a    ,   "[CLS]e è é ê ë[SEP]"	)

										# encode/decode, but with `encode` instead of `__call__`
										self.assertEqual(tokenizer.decode(tokenizer.encode("e è é ê ë"	)	)    ,   "[CLS]e è é ê ë[SEP]"	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.perceiver_tokenizer
										__lowerCAmelCase				    =			["A long paragraph for summarization.", "Another paragraph for summarization."]
										# fmt: off
										__lowerCAmelCase				    =			[4, 71, 38, 1_14, 1_17, 1_16, 1_09, 38, 1_18, 1_03, 1_20, 1_03, 1_09, 1_20, 1_03, 1_18, 1_10, 38, 1_08, 1_17, 1_20, 38, 1_21, 1_23, 1_15, 1_15, 1_03, 1_20, 1_11, 1_28, 1_03, 1_22, 1_11, 1_17, 1_16, 52, 5, 0]
										# fmt: on
										__lowerCAmelCase				    =			tokenizer(__a    ,   padding=__a    ,   return_tensors=__a	)
										self.assertIsInstance(__a    ,   __a	)

										if FRAMEWORK != "jax":
													__lowerCAmelCase				    =			list(batch.input_ids.numpy()[0]	)
										else:
													__lowerCAmelCase				    =			list(batch.input_ids.tolist()[0]	)

										self.assertListEqual(__a    ,   __a	)

										self.assertEqual((2, 38)    ,   batch.input_ids.shape	)
										self.assertEqual((2, 38)    ,   batch.attention_mask.shape	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.perceiver_tokenizer
										__lowerCAmelCase				    =			["A long paragraph for summarization.", "Another paragraph for summarization."]
										__lowerCAmelCase				    =			tokenizer(__a    ,   padding=__a    ,   return_tensors=__a	)
										# check if input_ids are returned and no decoder_input_ids
										self.assertIn("input_ids"    ,   __a	)
										self.assertIn("attention_mask"    ,   __a	)
										self.assertNotIn("decoder_input_ids"    ,   __a	)
										self.assertNotIn("decoder_attention_mask"    ,   __a	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.perceiver_tokenizer
										__lowerCAmelCase				    =			[
										    "Summary of the text.",
										    "Another summary.",
										]
										__lowerCAmelCase				    =			tokenizer(
										    text_target=__a    ,   max_length=32    ,   padding="max_length"    ,   truncation=__a    ,   return_tensors=__a	)
										self.assertEqual(32    ,   targets["input_ids"].shape[1]	)

							def 			snake_case				(     self	):
										# safety check on max_len default value so we are sure the test works
										__lowerCAmelCase				    =			self.get_tokenizers()
										for tokenizer in tokenizers:
													with self.subTest(f"{tokenizer.__class__.__name__}"	):
																self.assertNotEqual(tokenizer.model_max_length    ,   42	)

        # Now let's start the test
										__lowerCAmelCase				    =			self.get_tokenizers()
										for tokenizer in tokenizers:
													with self.subTest(f"{tokenizer.__class__.__name__}"	):
																# Isolate this from the other tests because we save additional tokens/etc
																__lowerCAmelCase				    =			tempfile.mkdtemp()

																__lowerCAmelCase				    =			" He is very happy, UNwant\u00E9d,running"
																__lowerCAmelCase				    =			tokenizer.encode(__a    ,   add_special_tokens=__a	)
																tokenizer.save_pretrained(__a	)

																__lowerCAmelCase				    =			tokenizer.__class__.from_pretrained(__a	)
																__lowerCAmelCase				    =			after_tokenizer.encode(__a    ,   add_special_tokens=__a	)
																self.assertListEqual(__a    ,   __a	)

																shutil.rmtree(__a	)

										__lowerCAmelCase				    =			self.get_tokenizers(model_max_length=42	)
										for tokenizer in tokenizers:
													with self.subTest(f"{tokenizer.__class__.__name__}"	):
																# Isolate this from the other tests because we save additional tokens/etc
																__lowerCAmelCase				    =			tempfile.mkdtemp()

																__lowerCAmelCase				    =			" He is very happy, UNwant\u00E9d,running"
																tokenizer.add_tokens(["bim", "bambam"]	)
																__lowerCAmelCase				    =			tokenizer.additional_special_tokens
																additional_special_tokens.append("new_additional_special_token"	)
																tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens}	)
																__lowerCAmelCase				    =			tokenizer.encode(__a    ,   add_special_tokens=__a	)
																tokenizer.save_pretrained(__a	)

																__lowerCAmelCase				    =			tokenizer.__class__.from_pretrained(__a	)
																__lowerCAmelCase				    =			after_tokenizer.encode(__a    ,   add_special_tokens=__a	)
																self.assertListEqual(__a    ,   __a	)
																self.assertIn("new_additional_special_token"    ,   after_tokenizer.additional_special_tokens	)
																self.assertEqual(after_tokenizer.model_max_length    ,   42	)

																__lowerCAmelCase				    =			tokenizer.__class__.from_pretrained(__a    ,   model_max_length=43	)
																self.assertEqual(tokenizer.model_max_length    ,   43	)

																shutil.rmtree(__a	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			[]
										if self.test_slow_tokenizer:
													tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())	)

										if self.test_rust_tokenizer:
													tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())	)

										for tokenizer_class, tokenizer_utils in tokenizer_list:
													with tempfile.TemporaryDirectory() as tmp_dir:
																tokenizer_utils.save_pretrained(__a	)

																with open(os.path.join(__a    ,   "special_tokens_map.json"	)    ,   encoding="utf-8"	) as json_file:
																			__lowerCAmelCase				    =			json.load(__a	)

																with open(os.path.join(__a    ,   "tokenizer_config.json"	)    ,   encoding="utf-8"	) as json_file:
																			__lowerCAmelCase				    =			json.load(__a	)

																__lowerCAmelCase				    =			[f"<extra_id_{i}>" for i in range(1_25	)]

																__lowerCAmelCase				    =			added_tokens_extra_ids + [
																    "an_additional_special_token"
																]
																__lowerCAmelCase				    =			added_tokens_extra_ids + [
																    "an_additional_special_token"
																]

																with open(os.path.join(__a    ,   "special_tokens_map.json"	)    ,   "w"    ,   encoding="utf-8"	) as outfile:
																			json.dump(__a    ,   __a	)
																with open(os.path.join(__a    ,   "tokenizer_config.json"	)    ,   "w"    ,   encoding="utf-8"	) as outfile:
																			json.dump(__a    ,   __a	)

																# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
																# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
																# "special_tokens_map.json" files
																__lowerCAmelCase				    =			tokenizer_class.from_pretrained(
																    __a    ,   )
																self.assertIn(
																    "an_additional_special_token"    ,   tokenizer_without_change_in_init.additional_special_tokens	)
																self.assertEqual(
																    ["an_additional_special_token"]    ,   tokenizer_without_change_in_init.convert_ids_to_tokens(
																        tokenizer_without_change_in_init.convert_tokens_to_ids(["an_additional_special_token"]	)	)    ,   )

																# Now we test that we can change the value of additional_special_tokens in the from_pretrained
																__lowerCAmelCase				    =			added_tokens_extra_ids + [AddedToken("a_new_additional_special_token"    ,   lstrip=__a	)]
																__lowerCAmelCase				    =			tokenizer_class.from_pretrained(
																    __a    ,   additional_special_tokens=__a    ,   )

																self.assertIn("a_new_additional_special_token"    ,   tokenizer.additional_special_tokens	)
																self.assertEqual(
																    ["a_new_additional_special_token"]    ,   tokenizer.convert_ids_to_tokens(
																        tokenizer.convert_tokens_to_ids(["a_new_additional_special_token"]	)	)    ,   )

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.perceiver_tokenizer
										self.assertEqual(tokenizer.decode([1_78]	)    ,   "�"	)

							def 			snake_case				(     self	):
										pass

							def 			snake_case				(     self	):
										pass

							def 			snake_case				(     self	):
										pass

							def 			snake_case				(     self	):
										pass



							def 			snake_case				(     self	):
										# The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
										# strings and special added tokens as tokens
										__lowerCAmelCase				    =			self.get_tokenizers(fast=__a    ,   do_lower_case=__a	)
										for tokenizer in tokenizers:
													with self.subTest(f"{tokenizer.__class__.__name__}"	):
																__lowerCAmelCase				    =			["[CLS]", "t", "h", "i", "s", " ", "i", "s", " ", "a", " ", "t", "e", "s", "t", "[SEP]"]
																__lowerCAmelCase				    =			tokenizer.convert_tokens_to_string(__a	)

																self.assertIsInstance(__a    ,   __a	)












"""simple docstring"""
import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor

def 				snake_case      (   A__						):
			UpperCAmelCase_     : Dict			     =					SwinConfig(image_size=1_92						)

			if "base" in model_name:
						UpperCAmelCase_     : Any			     =					6
						UpperCAmelCase_     : Optional[Any]			     =					1_28
						UpperCAmelCase_     : Optional[int]			     =					(2, 2, 18, 2)
						UpperCAmelCase_     : List[str]			     =					(4, 8, 16, 32)
			elif "large" in model_name:
						UpperCAmelCase_     : Dict			     =					12
						UpperCAmelCase_     : int			     =					1_92
						UpperCAmelCase_     : List[Any]			     =					(2, 2, 18, 2)
						UpperCAmelCase_     : int			     =					(6, 12, 24, 48)
			else:
						raise ValueError("Model not supported, only supports base and large variants"						)

			UpperCAmelCase_     : str			     =					window_size
			UpperCAmelCase_     : Any			     =					embed_dim
			UpperCAmelCase_     : int			     =					depths
			UpperCAmelCase_     : Any			     =					num_heads

			return config

def 				snake_case      (   A__						):
			if "encoder.mask_token" in name:
						UpperCAmelCase_     : str			     =					name.replace("encoder.mask_token"	,"embeddings.mask_token"						)
			if "encoder.patch_embed.proj" in name:
						UpperCAmelCase_     : Optional[int]			     =					name.replace("encoder.patch_embed.proj"	,"embeddings.patch_embeddings.projection"						)
			if "encoder.patch_embed.norm" in name:
						UpperCAmelCase_     : List[str]			     =					name.replace("encoder.patch_embed.norm"	,"embeddings.norm"						)
			if "attn.proj" in name:
						UpperCAmelCase_     : Optional[Any]			     =					name.replace("attn.proj"	,"attention.output.dense"						)
			if "attn" in name:
						UpperCAmelCase_     : Any			     =					name.replace("attn"	,"attention.self"						)
			if "norm1" in name:
						UpperCAmelCase_     : str			     =					name.replace("norm1"	,"layernorm_before"						)
			if "norm2" in name:
						UpperCAmelCase_     : Tuple			     =					name.replace("norm2"	,"layernorm_after"						)
			if "mlp.fc1" in name:
						UpperCAmelCase_     : List[str]			     =					name.replace("mlp.fc1"	,"intermediate.dense"						)
			if "mlp.fc2" in name:
						UpperCAmelCase_     : str			     =					name.replace("mlp.fc2"	,"output.dense"						)

			if name == "encoder.norm.weight":
						UpperCAmelCase_     : List[str]			     =					"layernorm.weight"
			if name == "encoder.norm.bias":
						UpperCAmelCase_     : int			     =					"layernorm.bias"

			if "decoder" in name:
						pass
			else:
						UpperCAmelCase_     : Any			     =					"swin." + name

			return name

def 				snake_case      (   A__	,A__						):
			for key in orig_state_dict.copy().keys():
						UpperCAmelCase_     : Tuple			     =					orig_state_dict.pop(A__						)

						if "attn_mask" in key:
									pass
						elif "qkv" in key:
									UpperCAmelCase_     : Optional[int]			     =					key.split("."						)
									UpperCAmelCase_     : str			     =					int(key_split[2]						)
									UpperCAmelCase_     : Union[str, Any]			     =					int(key_split[4]						)
									UpperCAmelCase_     : Optional[Any]			     =					model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

									if "weight" in key:
												UpperCAmelCase_     : List[Any]			     =					val[:dim, :]
												UpperCAmelCase_     : str			     =					val[
												    dim : dim * 2, :
												]
												UpperCAmelCase_     : str			     =					val[-dim:, :]
									else:
												UpperCAmelCase_     : List[str]			     =					val[
												    :dim
												]
												UpperCAmelCase_     : str			     =					val[
												    dim : dim * 2
												]
												UpperCAmelCase_     : Optional[Any]			     =					val[
												    -dim:
												]
						else:
									UpperCAmelCase_     : Tuple			     =					val

			return orig_state_dict

def 				snake_case      (   A__	,A__	,A__	,A__						):
			UpperCAmelCase_     : List[Any]			     =					torch.load(A__	,map_location="cpu"						)["model"]

			UpperCAmelCase_     : Optional[Any]			     =					get_swin_config(A__						)
			UpperCAmelCase_     : List[Any]			     =					SwinForMaskedImageModeling(A__						)
			model.eval()

			UpperCAmelCase_     : str			     =					convert_state_dict(A__	,A__						)
			model.load_state_dict(A__						)

			UpperCAmelCase_     : int			     =					"http://images.cocodataset.org/val2017/000000039769.jpg"

			UpperCAmelCase_     : int			     =					ViTImageProcessor(size={"height": 1_92, "width": 1_92}						)
			UpperCAmelCase_     : Any			     =					Image.open(requests.get(A__	,stream=A__						).raw						)
			UpperCAmelCase_     : Any			     =					image_processor(images=A__	,return_tensors="pt"						)

			with torch.no_grad():
						UpperCAmelCase_     : List[Any]			     =					model(**A__						).logits

			print(outputs.keys()						)
			print("Looks ok!"						)

			if pytorch_dump_folder_path is not None:
						print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""						)
						model.save_pretrained(A__						)

						print(F"""Saving image processor to {pytorch_dump_folder_path}"""						)
						image_processor.save_pretrained(A__						)

			if push_to_hub:
						print(F"""Pushing model and image processor for {model_name} to hub"""						)
						model.push_to_hub(F"""microsoft/{model_name}"""						)
						image_processor.push_to_hub(F"""microsoft/{model_name}"""						)


if __name__ == "__main__":
			lowerCamelCase_					=      argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--model_name''',
			    default='''swin-base-simmim-window6-192''',
			    type=str,
			    choices=['''swin-base-simmim-window6-192''', '''swin-large-simmim-window12-192'''],
			    help='''Name of the Swin SimMIM model you\'d like to convert.''',
			)
			parser.add_argument(
			    '''--checkpoint_path''',
			    default='''/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth''',
			    type=str,
			    help='''Path to the original PyTorch checkpoint (.pth file).''',
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
			)
			parser.add_argument(
			    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
			)

			lowerCamelCase_					=      parser.parse_args()
			convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)



