



"""simple docstring"""
import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
						import jax
						import jax.numpy as jnp

						from transformers.modeling_flax_pytorch_utils import (
						    convert_pytorch_state_dict_to_flax,
						    load_flax_weights_in_pytorch_model,
						)
						from transformers.models.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
						import torch
class     lowerCamelCase  :






					'''simple docstring'''





					def __init__(	self:							Tuple						,					snake_case:							Tuple						,					snake_case:							Tuple=14						,					snake_case:							Tuple=7						,					snake_case:							Optional[Any]=True						,					snake_case:							Tuple=True						,					snake_case:							int=False						,					snake_case:							Dict=True						,					snake_case:							Optional[int]=99						,					snake_case:							Union[str, Any]=32						,					snake_case:							Tuple=4						,					snake_case:							Tuple=4						,					snake_case:							Tuple=[[["global", "local"], 2]]						,					snake_case:							Optional[Any]=37						,					snake_case:							List[Any]="gelu"						,					snake_case:							Optional[int]=0.1						,					snake_case:							Optional[Any]=0.1						,					snake_case:							List[str]=512						,					snake_case:							Union[str, Any]=7						,					snake_case:							int=0.0_2						,					)       ->     Dict:
								snake_case_			:Optional[int]						=      parent
								snake_case_			:str						=      batch_size
								snake_case_			:Union[str, Any]						=      seq_length
								snake_case_			:int						=      is_training
								snake_case_			:Optional[Any]						=      use_input_mask
								snake_case_			:Union[str, Any]						=      use_token_type_ids
								snake_case_			:Tuple						=      use_labels
								snake_case_			:int						=      vocab_size
								snake_case_			:List[Any]						=      hidden_size
								snake_case_			:List[Any]						=      num_hidden_layers
								snake_case_			:Any						=      num_attention_heads
								snake_case_			:Optional[int]						=      attention_types
								snake_case_			:List[str]						=      intermediate_size
								snake_case_			:Optional[int]						=      hidden_act
								snake_case_			:Optional[Any]						=      hidden_dropout_prob
								snake_case_			:str						=      attention_probs_dropout_prob
								snake_case_			:Any						=      max_position_embeddings
								snake_case_			:Optional[int]						=      window_size
								snake_case_			:Union[str, Any]						=      initializer_range
								snake_case_			:Tuple						=      None
								snake_case_			:Any						=      vocab_size - 1
								snake_case_			:List[Any]						=      vocab_size - 1
								snake_case_			:str						=      vocab_size - 1






					def        lowerCAmelCase_    (	self:							Any						)       ->     str:
								snake_case_			:Union[str, Any]						=      ids_tensor([self.batch_size, self.seq_length]						,					self.vocab_size						)

								snake_case_			:Union[str, Any]						=      None
								if self.use_input_mask:
											snake_case_			:Tuple						=      random_attention_mask([self.batch_size, self.seq_length]						)

								snake_case_			:List[Any]						=      GPTNeoConfig(
								    vocab_size=self.vocab_size						,					hidden_size=self.hidden_size						,					num_layers=self.num_hidden_layers						,					num_heads=self.num_attention_heads						,					max_position_embeddings=self.max_position_embeddings						,					use_cache=snake_case						,					bos_token_id=self.bos_token_id						,					eos_token_id=self.eos_token_id						,					pad_token_id=self.pad_token_id						,					window_size=self.window_size						,					attention_types=self.attention_types						,					)

								return (config, input_ids, input_mask)






					def        lowerCAmelCase_    (	self:							Any						)       ->     Any:
								snake_case_			:Tuple						=      self.prepare_config_and_inputs()
								snake_case_			:int						=      config_and_inputs
								snake_case_			:Optional[Any]						=      {"""input_ids""": input_ids, """attention_mask""": attention_mask}
								return config, inputs_dict






					def        lowerCAmelCase_    (	self:							Tuple						,					snake_case:							Any						,					snake_case:							Optional[Any]						,					snake_case:							Union[str, Any]						,					snake_case:							Any						)       ->     List[Any]:
								snake_case_			:Dict						=      20
								snake_case_			:Optional[Any]						=      model_class_name(snake_case						)

								snake_case_			:Optional[int]						=      model.init_cache(input_ids.shape[0]						,					snake_case						)
								snake_case_			:Any						=      jnp.ones((input_ids.shape[0], max_decoder_length)						,					dtype="""i4"""						)

								snake_case_			:List[str]						=      jnp.broadcast_to(
								    jnp.arange(input_ids.shape[-1] - 1						)[None, :]						,					(input_ids.shape[0], input_ids.shape[-1] - 1)						)
								snake_case_			:List[str]						=      model(
								    input_ids[:, :-1]						,					attention_mask=snake_case						,					past_key_values=snake_case						,					position_ids=snake_case						,					)

								snake_case_			:int						=      jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]						,					dtype="""i4"""						)
								snake_case_			:Tuple						=      model(
								    input_ids[:, -1:]						,					attention_mask=snake_case						,					past_key_values=outputs_cache.past_key_values						,					position_ids=snake_case						,					)

								snake_case_			:str						=      model(snake_case						)

								snake_case_			:List[str]						=      np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
								self.parent.assertTrue(diff < 1E-3						,					msg=f"""Max diff is {diff}"""						)






					def        lowerCAmelCase_    (	self:							Any						,					snake_case:							Tuple						,					snake_case:							List[Any]						,					snake_case:							int						,					snake_case:							Optional[int]						)       ->     Tuple:
								snake_case_			:Any						=      20
								snake_case_			:Union[str, Any]						=      model_class_name(snake_case						)

								snake_case_			:Optional[int]						=      jnp.concatenate(
								    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])						)]						,					axis=-1						,					)

								snake_case_			:int						=      model.init_cache(input_ids.shape[0]						,					snake_case						)
								snake_case_			:Union[str, Any]						=      jnp.broadcast_to(
								    jnp.arange(input_ids.shape[-1] - 1						)[None, :]						,					(input_ids.shape[0], input_ids.shape[-1] - 1)						)

								snake_case_			:Any						=      model(
								    input_ids[:, :-1]						,					attention_mask=snake_case						,					past_key_values=snake_case						,					position_ids=snake_case						,					)
								snake_case_			:Any						=      jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]						,					dtype="""i4"""						)
								snake_case_			:Dict						=      model(
								    input_ids[:, -1:]						,					past_key_values=outputs_cache.past_key_values						,					attention_mask=snake_case						,					position_ids=snake_case						,					)

								snake_case_			:Dict						=      model(snake_case						,					attention_mask=snake_case						)

								snake_case_			:Optional[int]						=      np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
								self.parent.assertTrue(diff < 1E-3						,					msg=f"""Max diff is {diff}"""						)

@require_flax
class     lowerCamelCase  (      _lowerCAmelCase   ,						_lowerCAmelCase   ,						unittest.TestCase    ):






					'''simple docstring'''





					_A		:      Optional[Any]						=					(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
					_A		:      Optional[Any]						=					(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()






					def        lowerCAmelCase_    (	self:							int						)       ->     Union[str, Any]:
								snake_case_			:Any						=      FlaxGPTNeoModelTester(self						)






					def        lowerCAmelCase_    (	self:							List[Any]						)       ->     Tuple:
								for model_class_name in self.all_model_classes:
											snake_case_			:Dict						=      self.model_tester.prepare_config_and_inputs()
											self.model_tester.check_use_cache_forward(snake_case						,					snake_case						,					snake_case						,					snake_case						)






					def        lowerCAmelCase_    (	self:							Union[str, Any]						)       ->     Any:
								for model_class_name in self.all_model_classes:
											snake_case_			:Union[str, Any]						=      self.model_tester.prepare_config_and_inputs()
											self.model_tester.check_use_cache_forward_with_attn_mask(
											    snake_case						,					snake_case						,					snake_case						,					snake_case						)






					@slow
					def        lowerCAmelCase_    (	self:							Dict						)       ->     Union[str, Any]:
								snake_case_			:Tuple						=      GPTaTokenizer.from_pretrained("""gpt2"""						,					pad_token="""<|endoftext|>"""						,					padding_side="""left"""						)
								snake_case_			:Optional[Any]						=      tokenizer(["""Hello this is a long string""", """Hey"""]						,					return_tensors="""np"""						,					padding=snake_case						,					truncation=snake_case						)

								snake_case_			:int						=      FlaxGPTNeoForCausalLM.from_pretrained("""EleutherAI/gpt-neo-125M"""						)
								snake_case_			:List[str]						=      False
								snake_case_			:Union[str, Any]						=      model.config.eos_token_id

								snake_case_			:List[str]						=      jax.jit(model.generate						)

								snake_case_			:Tuple						=      jit_generate(
								    inputs["""input_ids"""]						,					attention_mask=inputs["""attention_mask"""]						,					pad_token_id=tokenizer.pad_token_id						).sequences

								snake_case_			:Union[str, Any]						=      tokenizer.batch_decode(snake_case						,					skip_special_tokens=snake_case						)

								snake_case_			:Dict						=      [
								    """Hello this is a long string of text.\n\nI'm trying to get the text of the""",
								    """Hey, I'm a little late to the party. I'm going to""",
								]

								self.assertListEqual(snake_case						,					snake_case						)






					@is_pt_flax_cross_test
					def        lowerCAmelCase_    (	self:							Optional[int]						)       ->     Optional[int]:
								snake_case_			:Optional[int]						=      self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__						):
														# prepare inputs
														snake_case_			:Optional[Any]						=      self._prepare_for_class(snake_case						,					snake_case						)
														snake_case_			:List[str]						=      {k: torch.tensor(v.tolist()						) for k, v in prepared_inputs_dict.items()}

														# load corresponding PyTorch class
														snake_case_			:str						=      model_class.__name__[4:]  # Skip the "Flax" at the beginning
														snake_case_			:int						=      getattr(snake_case						,					snake_case						)

														snake_case_			:List[str]						=      pt_inputs["""input_ids"""].shape
														snake_case_			:str						=      np.random.randint(0						,					seq_length - 1						,					size=(batch_size,)						)
														for batch_idx, start_index in enumerate(snake_case						):
																	snake_case_			:int						=      0
																	snake_case_			:List[str]						=      1
																	snake_case_			:int						=      0
																	snake_case_			:List[str]						=      1
														snake_case_			:Tuple						=      pt_model_class(snake_case						).eval()
														snake_case_			:Union[str, Any]						=      model_class(snake_case						,					dtype=jnp.floataa						)

														snake_case_			:Union[str, Any]						=      convert_pytorch_state_dict_to_flax(pt_model.state_dict()						,					snake_case						)
														snake_case_			:Dict						=      fx_state

														with torch.no_grad():
																	snake_case_			:List[str]						=      pt_model(**snake_case						).to_tuple()

														snake_case_			:int						=      fx_model(**snake_case						).to_tuple()
														self.assertEqual(len(snake_case						)						,					len(snake_case						)						,					"""Output lengths differ between Flax and PyTorch"""						)
														for fx_output, pt_output in zip(snake_case						,					snake_case						):
																	self.assert_almost_equals(fx_output[:, -1]						,					pt_output[:, -1].numpy()						,					4E-2						)

														with tempfile.TemporaryDirectory() as tmpdirname:
																	pt_model.save_pretrained(snake_case						)
																	snake_case_			:Dict						=      model_class.from_pretrained(snake_case						,					from_pt=snake_case						)

														snake_case_			:Any						=      fx_model_loaded(**snake_case						).to_tuple()
														self.assertEqual(
														    len(snake_case						)						,					len(snake_case						)						,					"""Output lengths differ between Flax and PyTorch"""						)
														for fx_output_loaded, pt_output in zip(snake_case						,					snake_case						):
																	self.assert_almost_equals(fx_output_loaded[:, -1]						,					pt_output[:, -1].numpy()						,					4E-2						)






					@is_pt_flax_cross_test
					def        lowerCAmelCase_    (	self:							str						)       ->     Any:
								snake_case_			:Tuple						=      self.model_tester.prepare_config_and_inputs_for_common()
								for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__						):
														# prepare inputs
														snake_case_			:int						=      self._prepare_for_class(snake_case						,					snake_case						)
														snake_case_			:Optional[int]						=      {k: torch.tensor(v.tolist()						) for k, v in prepared_inputs_dict.items()}

														# load corresponding PyTorch class
														snake_case_			:Tuple						=      model_class.__name__[4:]  # Skip the "Flax" at the beginning
														snake_case_			:str						=      getattr(snake_case						,					snake_case						)

														snake_case_			:Dict						=      pt_model_class(snake_case						).eval()
														snake_case_			:Tuple						=      model_class(snake_case						,					dtype=jnp.floataa						)

														snake_case_			:Union[str, Any]						=      load_flax_weights_in_pytorch_model(snake_case						,					fx_model.params						)
														snake_case_			:List[Any]						=      pt_inputs["""input_ids"""].shape
														snake_case_			:Union[str, Any]						=      np.random.randint(0						,					seq_length - 1						,					size=(batch_size,)						)
														for batch_idx, start_index in enumerate(snake_case						):
																	snake_case_			:Union[str, Any]						=      0
																	snake_case_			:Any						=      1
																	snake_case_			:str						=      0
																	snake_case_			:Union[str, Any]						=      1

														# make sure weights are tied in PyTorch
														pt_model.tie_weights()

														with torch.no_grad():
																	snake_case_			:Tuple						=      pt_model(**snake_case						).to_tuple()

														snake_case_			:Any						=      fx_model(**snake_case						).to_tuple()
														self.assertEqual(len(snake_case						)						,					len(snake_case						)						,					"""Output lengths differ between Flax and PyTorch"""						)
														for fx_output, pt_output in zip(snake_case						,					snake_case						):
																	self.assert_almost_equals(fx_output[:, -1]						,					pt_output[:, -1].numpy()						,					4E-2						)

														with tempfile.TemporaryDirectory() as tmpdirname:
																	fx_model.save_pretrained(snake_case						)
																	snake_case_			:Optional[int]						=      pt_model_class.from_pretrained(snake_case						,					from_flax=snake_case						)

														with torch.no_grad():
																	snake_case_			:Any						=      pt_model_loaded(**snake_case						).to_tuple()

														self.assertEqual(
														    len(snake_case						)						,					len(snake_case						)						,					"""Output lengths differ between Flax and PyTorch"""						)
														for fx_output, pt_output in zip(snake_case						,					snake_case						):
																	self.assert_almost_equals(fx_output[:, -1]						,					pt_output[:, -1].numpy()						,					4E-2						)






					@slow
					def        lowerCAmelCase_    (	self:							int						)       ->     Optional[Any]:
								for model_class_name in self.all_model_classes:
											snake_case_			:List[Any]						=      model_class_name.from_pretrained("""EleutherAI/gpt-neo-125M"""						)
											snake_case_			:Optional[int]						=      model(np.ones((1, 1)						)						)
											self.assertIsNotNone(snake_case						)





"""simple docstring"""
import argparse
import json

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)
def        A_  (      _lowercase, _lowercase=False, _lowercase=False, _lowercase="data2vec."     ):







						'''simple docstring'''


						snake_case_			:List[str]						=      """backbone.""" if is_semantic else """"""

						snake_case_			:Tuple						=      []
						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"""{hf_prefix}encoder.layer.{i}.layernorm_before.weight""")     )
												rename_keys.append((f"""{prefix}blocks.{i}.norm1.bias""", f"""{hf_prefix}encoder.layer.{i}.layernorm_before.bias""")     )
												rename_keys.append(
												    (f"""{prefix}blocks.{i}.attn.proj.weight""", f"""{hf_prefix}encoder.layer.{i}.attention.output.dense.weight""")     )
												rename_keys.append(
												    (f"""{prefix}blocks.{i}.attn.proj.bias""", f"""{hf_prefix}encoder.layer.{i}.attention.output.dense.bias""")     )
												rename_keys.append(
												    (f"""{prefix}blocks.{i}.norm2.weight""", f"""{hf_prefix}encoder.layer.{i}.layernorm_after.weight""")     )
												rename_keys.append((f"""{prefix}blocks.{i}.norm2.bias""", f"""{hf_prefix}encoder.layer.{i}.layernorm_after.bias""")     )
												rename_keys.append(
												    (f"""{prefix}blocks.{i}.mlp.fc1.weight""", f"""{hf_prefix}encoder.layer.{i}.intermediate.dense.weight""")     )
												rename_keys.append(
												    (f"""{prefix}blocks.{i}.mlp.fc1.bias""", f"""{hf_prefix}encoder.layer.{i}.intermediate.dense.bias""")     )
												rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.weight""", f"""{hf_prefix}encoder.layer.{i}.output.dense.weight""")     )
												rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.bias""", f"""{hf_prefix}encoder.layer.{i}.output.dense.bias""")     )

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

						if has_lm_head:
												# mask token + shared relative position bias + layernorm
												rename_keys.extend(
												    [
												        ("""mask_token""", f"""{hf_prefix}embeddings.mask_token"""),
												        (
												            """rel_pos_bias.relative_position_bias_table""",
												            f"""{hf_prefix}encoder.relative_position_bias.relative_position_bias_table""",
												        ),
												        (
												            """rel_pos_bias.relative_position_index""",
												            f"""{hf_prefix}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""", f"""{hf_prefix}pooler.layernorm.weight"""),
												        ("""fc_norm.bias""", f"""{hf_prefix}pooler.layernorm.bias"""),
												        ("""head.weight""", """classifier.weight"""),
												        ("""head.bias""", """classifier.bias"""),
												    ]     )

						return rename_keys
def        A_  (      _lowercase, _lowercase, _lowercase=False, _lowercase=False, _lowercase="data2vec_vision."     ):







						'''simple docstring'''


						for i in range(config.num_hidden_layers     ):
												snake_case_			:int						=      """backbone.""" if is_semantic else """"""
												# queries, keys and values
												snake_case_			:Union[str, Any]						=      state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight"""     )
												snake_case_			:Dict						=      state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias"""     )
												snake_case_			:Optional[int]						=      state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias"""     )

												snake_case_			:Any						=      in_proj_weight[
												    : config.hidden_size, :
												]
												snake_case_			:Union[str, Any]						=      q_bias
												snake_case_			:Optional[Any]						=      in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												snake_case_			:int						=      in_proj_weight[
												    -config.hidden_size :, :
												]
												snake_case_			:Any						=      v_bias

												# gamma_1 and gamma_2
												# we call them lambda because otherwise they are renamed when using .from_pretrained
												snake_case_			:int						=      state_dict.pop(f"""{prefix}blocks.{i}.gamma_1"""     )
												snake_case_			:List[Any]						=      state_dict.pop(f"""{prefix}blocks.{i}.gamma_2"""     )

												snake_case_			:str						=      gamma_a
												snake_case_			:Tuple						=      gamma_a

												# relative_position bias table + index
												if not has_lm_head:
																		# each layer has its own relative position bias
																		snake_case_			:List[Any]						=      state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_bias_table"""     )
																		snake_case_			:Union[str, Any]						=      state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_index"""     )

																		snake_case_			:Tuple						=      table
																		snake_case_			:List[Any]						=      index
def        A_  (      ):







						'''simple docstring'''


						snake_case_			:Tuple						=      argparse.ArgumentParser(
						    """Convert Data2VecVision to HF for image classification and pretraining""", add_help=_lowercase     )
						parser.add_argument("""--hf_checkpoint_name""", type=_lowercase     )
						parser.add_argument("""--input_size""", default=224, type=_lowercase, help="""images input size"""     )
						parser.add_argument("""--beit_checkpoint""", default="""""", help="""beit checkpoint"""     )

						return parser.parse_args()
def        A_  (      _lowercase, _lowercase, _lowercase     ):







						'''simple docstring'''


						def load_state_dict(_lowercase, _lowercase, _lowercase="", _lowercase="relative_position_index"     ):
												snake_case_			:Any						=      []
												snake_case_			:Union[str, Any]						=      []
												snake_case_			:Dict						=      []
												# copy state_dict so _load_from_state_dict can modify it
												snake_case_			:Dict						=      getattr(_lowercase, """_metadata""", _lowercase     )
												snake_case_			:List[Any]						=      state_dict.copy()
												if metadata is not None:
																		snake_case_			:Any						=      metadata

												def load(_lowercase, _lowercase=""     ):
																		snake_case_			:List[Any]						=      {} if metadata is None else metadata.get(prefix[:-1], {}     )
																		module._load_from_state_dict(
																		    _lowercase, _lowercase, _lowercase, _lowercase, _lowercase, _lowercase, _lowercase     )
																		for name, child in module._modules.items():
																								if child is not None:
																														load(_lowercase, prefix + name + """."""     )

												load(_lowercase, prefix=_lowercase     )

												snake_case_			:Dict						=      []
												snake_case_			:Optional[int]						=      []
												for key in missing_keys:
																		snake_case_			:Dict						=      True
																		for ignore_key in ignore_missing.split("""|"""     ):
																								if ignore_key in key:
																														snake_case_			:int						=      False
																														break
																		if keep_flag:
																								warn_missing_keys.append(_lowercase     )
																		else:
																								ignore_missing_keys.append(_lowercase     )

												snake_case_			:int						=      warn_missing_keys

												if len(_lowercase     ) > 0:
																		print(
																		    """Weights of {} not initialized from pretrained model: {}""".format(
																		        model.__class__.__name__, _lowercase     )     )
												if len(_lowercase     ) > 0:
																		print("""Weights from pretrained model not used in {}: {}""".format(model.__class__.__name__, _lowercase     )     )
												if len(_lowercase     ) > 0:
																		print(
																		    """Ignored weights of {} not initialized from pretrained model: {}""".format(
																		        model.__class__.__name__, _lowercase     )     )
												if len(_lowercase     ) > 0:
																		print("""\n""".join(_lowercase     )     )

						snake_case_			:Optional[int]						=      {
						    """pretrained""": False,
						    """use_shared_rel_pos_bias""": True,
						    """use_abs_pos_emb""": False,
						    """init_values""": 0.1,
						}

						if is_finetuned:
												model_kwargs.update(
												    {
												        """num_classes""": 1000,
												        """use_mean_pooling""": True,
												        """init_scale""": 0.001,
												        """use_rel_pos_bias""": True,
												    }     )

						snake_case_			:int						=      create_model(
						    """beit_large_patch16_224""" if is_large else """beit_base_patch16_224""", **_lowercase, )
						snake_case_			:Tuple						=      model.patch_embed.patch_size
						snake_case_			:Optional[int]						=      (args.input_size // patch_size[0], args.input_size // patch_size[1])
						snake_case_			:Tuple						=      torch.load(args.beit_checkpoint, map_location="""cpu"""     )

						print(f"""Load ckpt from {args.beit_checkpoint}"""     )
						snake_case_			:Optional[int]						=      None
						for model_key in ("model", "module"):
												if model_key in checkpoint:
																		snake_case_			:Union[str, Any]						=      checkpoint[model_key]
																		print(f"""Load state_dict by model_key = {model_key}"""     )
																		break

						snake_case_			:Dict						=      list(checkpoint_model.keys()     )
						for key in all_keys:
												if "relative_position_index" in key:
																		checkpoint_model.pop(_lowercase     )

												if "relative_position_bias_table" in key:
																		snake_case_			:Optional[Any]						=      checkpoint_model[key]
																		snake_case_,	snake_case_			:Union[str, Any]						=      rel_pos_bias.size()
																		snake_case_,	snake_case_			:Optional[int]						=      model.state_dict()[key].size()
																		snake_case_			:Union[str, Any]						=      model.patch_embed.patch_shape
																		if dst_patch_shape[0] != dst_patch_shape[1]:
																								raise NotImplementedError()

						load_state_dict(_lowercase, _lowercase, prefix=""""""     )

						return model
def        A_  (      ):







						'''simple docstring'''


						snake_case_			:int						=      get_args()

						snake_case_			:str						=      """ft1k""" in args.hf_checkpoint_name
						snake_case_			:Optional[int]						=      """large""" in args.hf_checkpoint_name

						if is_finetuned:
												# To convert Beit's data2vec_vision to HF you need to copy
												# https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
												# into this folder.
												import modeling_finetune  # noqa: F401
						else:
												# To convert Beit's data2vec_vision to HF you need to copy
												# https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
												# into this folder
												# IMPORTANT: Note that for now we've only converted the down-stream
												# model and not the full pretrained model. This means for the integration
												# test you need to add a `return x` after the following line:
												# https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
												# to make the integration test pass.
												import modeling_cyclical  # noqa: F401

						# 1. Create model config
						snake_case_			:Dict						=      DataaVecVisionConfig()
						if is_finetuned:
												snake_case_			:Optional[int]						=      True
												snake_case_			:List[str]						=      False
												snake_case_			:Dict						=      True
												snake_case_			:Union[str, Any]						=      1000

												snake_case_			:Optional[int]						=      """huggingface/label-files"""
												snake_case_			:List[str]						=      """imagenet-1k-id2label.json"""
												snake_case_			:List[str]						=      json.load(open(hf_hub_download(_lowercase, _lowercase, repo_type="""dataset"""     ), """r"""     )     )
												snake_case_			:int						=      {int(_lowercase     ): v for k, v in idalabel.items()}
												snake_case_			:Optional[int]						=      idalabel
												snake_case_			:Any						=      {v: k for k, v in idalabel.items()}
						else:
												snake_case_			:Any						=      False
												snake_case_			:List[Any]						=      True
												snake_case_			:str						=      False

						if is_large:
												snake_case_			:Dict						=      1024
												snake_case_			:Union[str, Any]						=      4096
												snake_case_			:Optional[int]						=      24
												snake_case_			:Dict						=      16

						# 2. Load Beit model
						snake_case_			:Optional[Any]						=      load_beit_model(_lowercase, _lowercase, _lowercase     )
						orig_model.eval()

						# 3. Forward Beit model
						snake_case_			:Tuple						=      BeitImageProcessor(size=config.image_size, do_center_crop=_lowercase     )
						snake_case_			:List[Any]						=      Image.open("""../../../../tests/fixtures/tests_samples/COCO/000000039769.png"""     )
						snake_case_			:List[Any]						=      image_processor(images=_lowercase, return_tensors="""pt"""     )
						snake_case_			:str						=      encoding["""pixel_values"""]

						snake_case_			:List[Any]						=      (pixel_values,) if is_finetuned else (pixel_values, None)
						with torch.no_grad():
												snake_case_			:str						=      orig_model(*_lowercase     )

						# 4. Load HF Data2VecVision model
						if is_finetuned:
												snake_case_			:Union[str, Any]						=      DataaVecVisionForImageClassification(_lowercase     )
												hf_model.eval()
												snake_case_			:List[Any]						=      False
												snake_case_			:Dict						=      """data2vec_vision."""
						else:
												snake_case_			:Tuple						=      DataaVecVisionModel(_lowercase     )
												hf_model.eval()
												snake_case_			:Dict						=      True
												snake_case_			:int						=      """"""

						snake_case_			:int						=      create_rename_keys(_lowercase, hf_prefix=_lowercase, has_lm_head=_lowercase     )
						snake_case_			:int						=      orig_model.state_dict()
						for src, dest in rename_keys:
												snake_case_			:Optional[int]						=      state_dict.pop(_lowercase     )
												snake_case_			:Union[str, Any]						=      val

						read_in_q_k_v(_lowercase, _lowercase, hf_prefix=_lowercase, has_lm_head=_lowercase     )
						snake_case_,	snake_case_			:List[Any]						=      hf_model.load_state_dict(_lowercase, strict=_lowercase     )
						print("""HF missing""", _lowercase     )
						print("""HF unexpected_keys""", _lowercase     )

						# 5. Forward HF Data2VecVision model
						with torch.no_grad():
												snake_case_			:Any						=      hf_model(_lowercase     )

						snake_case_			:Any						=      hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

						# 6. Compare
						snake_case_			:str						=      torch.max(torch.abs(hf_output - orig_model_output     )     ).item()

						print(f"""max_absolute_diff = {max_absolute_diff}"""     )
						snake_case_			:str						=      torch.allclose(_lowercase, _lowercase, atol=1e-3     )
						print("""Do both models output the same tensors?""", """🔥""" if success else """💩"""     )
						if not success:
												raise Exception("""Something went wRoNg"""     )

						# 7. Save
						print(f"""Saving to {args.hf_checkpoint_name}"""     )
						hf_model.save_pretrained(args.hf_checkpoint_name     )
						image_processor.save_pretrained(args.hf_checkpoint_name     )


if __name__ == "__main__":
						main()
						# Run the following to convert checkpoints
						#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
						#          --beit_checkpoint ./pretrained_base.pt \
						#          --hf_checkpoint_name "./data2vec-vision-base"
						#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
						#          --beit_checkpoint ./finetuned_base.pt \
						#          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
						#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
						#          --beit_checkpoint ./pretrained_large.pt \
						#          --hf_checkpoint_name "./data2vec-vision-large"
						#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
						#          --beit_checkpoint ./finetuned_large.pt \
						#          --hf_checkpoint_name "./data2vec-vision-large-ft1k"
