






"""simple docstring"""
import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor






def       __UpperCamelCase						(		snake_case__       ):
	if "cls_token" in name:
		A_		:    List[Any]						     = name.replace("""cls_token""" , """vit.embeddings.cls_token"""       )
	if "mask_token" in name:
		A_		:    Union[str, Any]						     = name.replace("""mask_token""" , """decoder.mask_token"""       )
	if "decoder_pos_embed" in name:
		A_		:    Tuple						     = name.replace("""decoder_pos_embed""" , """decoder.decoder_pos_embed"""       )
	if "pos_embed" in name and "decoder" not in name:
		A_		:    Optional[int]						     = name.replace("""pos_embed""" , """vit.embeddings.position_embeddings"""       )
	if "patch_embed.proj" in name:
		A_		:    Dict						     = name.replace("""patch_embed.proj""" , """vit.embeddings.patch_embeddings.projection"""       )
	if "patch_embed.norm" in name:
		A_		:    Optional[Any]						     = name.replace("""patch_embed.norm""" , """vit.embeddings.norm"""       )
	if "decoder_blocks" in name:
		A_		:    Union[str, Any]						     = name.replace("""decoder_blocks""" , """decoder.decoder_layers"""       )
	if "blocks" in name:
		A_		:    Union[str, Any]						     = name.replace("""blocks""" , """vit.encoder.layer"""       )
	if "attn.proj" in name:
		A_		:    Union[str, Any]						     = name.replace("""attn.proj""" , """attention.output.dense"""       )
	if "attn" in name:
		A_		:    str						     = name.replace("""attn""" , """attention.self"""       )
	if "norm1" in name:
		A_		:    Tuple						     = name.replace("""norm1""" , """layernorm_before"""       )
	if "norm2" in name:
		A_		:    Optional[int]						     = name.replace("""norm2""" , """layernorm_after"""       )
	if "mlp.fc1" in name:
		A_		:    Any						     = name.replace("""mlp.fc1""" , """intermediate.dense"""       )
	if "mlp.fc2" in name:
		A_		:    List[Any]						     = name.replace("""mlp.fc2""" , """output.dense"""       )
	if "decoder_embed" in name:
		A_		:    str						     = name.replace("""decoder_embed""" , """decoder.decoder_embed"""       )
	if "decoder_norm" in name:
		A_		:    Optional[int]						     = name.replace("""decoder_norm""" , """decoder.decoder_norm"""       )
	if "decoder_pred" in name:
		A_		:    Union[str, Any]						     = name.replace("""decoder_pred""" , """decoder.decoder_pred"""       )
	if "norm.weight" in name and "decoder" not in name:
		A_		:    Dict						     = name.replace("""norm.weight""" , """vit.layernorm.weight"""       )
	if "norm.bias" in name and "decoder" not in name:
		A_		:    Optional[int]						     = name.replace("""norm.bias""" , """vit.layernorm.bias"""       )

	return name






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	for key in orig_state_dict.copy().keys():
		A_		:    int						     = orig_state_dict.pop(snake_case__       )

		if "qkv" in key:
			A_		:    Optional[int]						     = key.split("""."""       )
			A_		:    List[str]						     = int(key_split[1]       )
			if "decoder_blocks" in key:
				A_		:    List[str]						     = config.decoder_hidden_size
				A_		:    Any						     = """decoder.decoder_layers."""
				if "weight" in key:
					A_		:    int						     = val[:dim, :]
					A_		:    Union[str, Any]						     = val[dim : dim * 2, :]
					A_		:    Union[str, Any]						     = val[-dim:, :]
				elif "bias" in key:
					A_		:    Dict						     = val[:dim]
					A_		:    Dict						     = val[dim : dim * 2]
					A_		:    Dict						     = val[-dim:]
			else:
				A_		:    Optional[Any]						     = config.hidden_size
				A_		:    Tuple						     = """vit.encoder.layer."""
				if "weight" in key:
					A_		:    List[Any]						     = val[:dim, :]
					A_		:    Union[str, Any]						     = val[dim : dim * 2, :]
					A_		:    List[str]						     = val[-dim:, :]
				elif "bias" in key:
					A_		:    Dict						     = val[:dim]
					A_		:    List[Any]						     = val[dim : dim * 2]
					A_		:    List[str]						     = val[-dim:]

		else:
			A_		:    str						     = val

	return orig_state_dict






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	A_		:    int						     = ViTMAEConfig()
	if "large" in checkpoint_url:
		A_		:    str						     = 1_024
		A_		:    int						     = 4_096
		A_		:    str						     = 24
		A_		:    int						     = 16
	elif "huge" in checkpoint_url:
		A_		:    Optional[int]						     = 14
		A_		:    List[str]						     = 1_280
		A_		:    Tuple						     = 5_120
		A_		:    Dict						     = 32
		A_		:    Optional[Any]						     = 16

	A_		:    Any						     = ViTMAEForPreTraining(snake_case__       )

	A_		:    List[Any]						     = torch.hub.load_state_dict_from_url(snake_case__ , map_location="""cpu"""       )["""model"""]

	A_		:    List[str]						     = ViTMAEImageProcessor(size=config.image_size       )

	A_		:    List[str]						     = convert_state_dict(snake_case__ , snake_case__       )

	model.load_state_dict(snake_case__       )
	model.eval()

	A_		:    int						     = """https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg"""

	A_		:    Union[str, Any]						     = Image.open(requests.get(snake_case__ , stream=snake_case__       ).raw       )
	A_		:    Union[str, Any]						     = ViTMAEImageProcessor(size=config.image_size       )
	A_		:    Optional[Any]						     = image_processor(images=snake_case__ , return_tensors="""pt"""       )

	# forward pass
	torch.manual_seed(2       )
	A_		:    Union[str, Any]						     = model(**snake_case__       )
	A_		:    Optional[int]						     = outputs.logits

	if "large" in checkpoint_url:
		A_		:    str						     = torch.tensor(
		    [[-0.7_309, -0.7_128, -1.0_169], [-1.0_161, -0.9_058, -1.1_878], [-1.0_478, -0.9_411, -1.1_911]]       )
	elif "huge" in checkpoint_url:
		A_		:    str						     = torch.tensor(
		    [[-1.1_599, -0.9_199, -1.2_221], [-1.1_952, -0.9_269, -1.2_307], [-1.2_143, -0.9_337, -1.2_262]]       )
	else:
		A_		:    Union[str, Any]						     = torch.tensor(
		    [[-0.9_192, -0.8_481, -1.1_259], [-1.1_349, -1.0_034, -1.2_599], [-1.1_757, -1.0_429, -1.2_726]]       )

	# verify logits
	assert torch.allclose(logits[0, :3, :3] , snake_case__ , atol=1E-4       )

	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__":
							_lowerCAmelCase       =				argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--checkpoint_url",
							    default="https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth",
							    type=str,
							    help="URL of the checkpoint you'd like to convert.",
							)
							parser.add_argument(
							    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
							)

							_lowerCAmelCase       =				parser.parse_args()
							convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)









"""simple docstring"""
import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE							,							unittest.TestCase    ):





	"""simple docstring"""




	_A							:					str 			=					LEDTokenizer
	_A							:					List[Any] 			=					LEDTokenizerFast
	_A							:					Dict 			=					True





	def  lowerCamelCase(self				):
		super().setUp()
		A_		:    Dict						     = [
		    """l""",
		    """o""",
		    """w""",
		    """e""",
		    """r""",
		    """s""",
		    """t""",
		    """i""",
		    """d""",
		    """n""",
		    """\u0120""",
		    """\u0120l""",
		    """\u0120n""",
		    """\u0120lo""",
		    """\u0120low""",
		    """er""",
		    """\u0120lowest""",
		    """\u0120newer""",
		    """\u0120wider""",
		    """<unk>""",
		]
		A_		:    Any						     = dict(zip(lowerCAmelCase_  ,  range(len(lowerCAmelCase_				)				)				)				)
		A_		:    List[Any]						     = ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
		A_		:    List[str]						     = {"""unk_token""": """<unk>"""}

		A_		:    List[Any]						     = os.path.join(self.tmpdirname  ,  VOCAB_FILES_NAMES["""vocab_file"""]				)
		A_		:    Tuple						     = os.path.join(self.tmpdirname  ,  VOCAB_FILES_NAMES["""merges_file"""]				)
		with open(self.vocab_file  ,  """w"""  ,  encoding="""utf-8"""				) as fp:
			fp.write(json.dumps(lowerCAmelCase_				) + """\n"""				)
		with open(self.merges_file  ,  """w"""  ,  encoding="""utf-8"""				) as fp:
			fp.write("""\n""".join(lowerCAmelCase_				)				)





	def  lowerCamelCase(self  ,  **lowerCAmelCase_				):
		kwargs.update(self.special_tokens_map				)
		return self.tokenizer_class.from_pretrained(self.tmpdirname  ,  **lowerCAmelCase_				)





	def  lowerCamelCase(self  ,  **lowerCAmelCase_				):
		kwargs.update(self.special_tokens_map				)
		return self.rust_tokenizer_class.from_pretrained(self.tmpdirname  ,  **lowerCAmelCase_				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_				):
		return "lower newer", "lower newer"





	@cached_property
	def  lowerCamelCase(self				):
		return LEDTokenizer.from_pretrained("""allenai/led-base-16384"""				)





	@cached_property
	def  lowerCamelCase(self				):
		return LEDTokenizerFast.from_pretrained("""allenai/led-base-16384"""				)





	@require_torch
	def  lowerCamelCase(self				):
		A_		:    Optional[int]						     = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
		A_		:    Any						     = [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

		for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
			A_		:    Optional[int]						     = tokenizer(lowerCAmelCase_  ,  max_length=len(lowerCAmelCase_				)  ,  padding=lowerCAmelCase_  ,  return_tensors="""pt"""				)
			self.assertIsInstance(lowerCAmelCase_  ,  lowerCAmelCase_				)

			self.assertEqual((2, 9)  ,  batch.input_ids.shape				)
			self.assertEqual((2, 9)  ,  batch.attention_mask.shape				)
			A_		:    int						     = batch.input_ids.tolist()[0]
			self.assertListEqual(lowerCAmelCase_  ,  lowerCAmelCase_				)





	@require_torch
	def  lowerCamelCase(self				):
		A_		:    Union[str, Any]						     = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
		for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
			A_		:    Tuple						     = tokenizer(lowerCAmelCase_  ,  padding=lowerCAmelCase_  ,  return_tensors="""pt"""				)
			self.assertIn("""input_ids"""  ,  lowerCAmelCase_				)
			self.assertIn("""attention_mask"""  ,  lowerCAmelCase_				)
			self.assertNotIn("""labels"""  ,  lowerCAmelCase_				)
			self.assertNotIn("""decoder_attention_mask"""  ,  lowerCAmelCase_				)





	@require_torch
	def  lowerCamelCase(self				):
		A_		:    Tuple						     = [
		    """Summary of the text.""",
		    """Another summary.""",
		]
		for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
			A_		:    Optional[int]						     = tokenizer(text_target=lowerCAmelCase_  ,  max_length=32  ,  padding="""max_length"""  ,  return_tensors="""pt"""				)
			self.assertEqual(32  ,  targets["""input_ids"""].shape[1]				)





	@require_torch
	def  lowerCamelCase(self				):
		for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
			A_		:    Optional[Any]						     = tokenizer(
			    ["""I am a small frog""" * 1024, """I am a small frog"""]  ,  padding=lowerCAmelCase_  ,  truncation=lowerCAmelCase_  ,  return_tensors="""pt"""				)
			self.assertIsInstance(lowerCAmelCase_  ,  lowerCAmelCase_				)
			self.assertEqual(batch.input_ids.shape  ,  (2, 5122)				)





	@require_torch
	def  lowerCamelCase(self				):
		A_		:    Dict						     = ["""A long paragraph for summarization."""]
		A_		:    Any						     = [
		    """Summary of the text.""",
		]
		for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
			A_		:    int						     = tokenizer(lowerCAmelCase_  ,  return_tensors="""pt"""				)
			A_		:    Optional[int]						     = tokenizer(text_target=lowerCAmelCase_  ,  return_tensors="""pt"""				)
			A_		:    str						     = inputs["""input_ids"""]
			A_		:    Tuple						     = targets["""input_ids"""]
			self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()				)
			self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()				)
			self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()				)
			self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()				)





	@require_torch
	def  lowerCamelCase(self				):
		for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
			A_		:    str						     = ["""Summary of the text.""", """Another summary."""]
			A_		:    Dict						     = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

			A_		:    Optional[Any]						     = tokenizer(lowerCAmelCase_  ,  padding=lowerCAmelCase_				)
			A_		:    Any						     = [[0] * len(lowerCAmelCase_				) for x in encoded_output["""input_ids"""]]
			A_		:    Tuple						     = tokenizer.pad(lowerCAmelCase_				)
			self.assertSequenceEqual(outputs["""global_attention_mask"""]  ,  lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		pass





	def  lowerCamelCase(self				):
		for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
			with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
				A_		:    Any						     = self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_  ,  **lowerCAmelCase_				)
				A_		:    Optional[Any]						     = self.tokenizer_class.from_pretrained(lowerCAmelCase_  ,  **lowerCAmelCase_				)
				A_		:    Optional[Any]						     = """A, <mask> AllenNLP sentence."""
				A_		:    List[Any]						     = tokenizer_r.encode_plus(lowerCAmelCase_  ,  add_special_tokens=lowerCAmelCase_  ,  return_token_type_ids=lowerCAmelCase_				)
				A_		:    Any						     = tokenizer_p.encode_plus(lowerCAmelCase_  ,  add_special_tokens=lowerCAmelCase_  ,  return_token_type_ids=lowerCAmelCase_				)
				self.assertEqual(sum(tokens_r["""token_type_ids"""]				)  ,  sum(tokens_p["""token_type_ids"""]				)				)
				self.assertEqual(
				    sum(tokens_r["""attention_mask"""]				) / len(tokens_r["""attention_mask"""]				)  ,  sum(tokens_p["""attention_mask"""]				) / len(tokens_p["""attention_mask"""]				)  ,  )

				A_		:    List[Any]						     = tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]				)
				A_		:    Union[str, Any]						     = tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]				)
				self.assertSequenceEqual(tokens_p["""input_ids"""]  ,  [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]				)
				self.assertSequenceEqual(tokens_r["""input_ids"""]  ,  [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]				)

				self.assertSequenceEqual(
				    lowerCAmelCase_  ,  ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]				)
				self.assertSequenceEqual(
				    lowerCAmelCase_  ,  ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]				)

