






from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase=None ,		__lowerCAmelCase=None	)->  int:





						"""simple docstring"""



						if attention_mask is None:
												_UpperCAmelCase									=			tf.cast(tf.math.not_equal(__lowerCAmelCase ,		config.pad_token_id	) ,		tf.inta	)
						return {"input_ids": input_ids, "attention_mask": attention_mask}





@require_tf
class __lowerCamelCase :





	"""simple docstring"""






	UpperCamelCase__								=      OPTConfig
	UpperCamelCase__								=      {}
	UpperCamelCase__								=      "gelu"

	def __init__(							self       ,    UpperCAmelCase       ,    UpperCAmelCase=13       ,    UpperCAmelCase=7       ,    UpperCAmelCase=True       ,    UpperCAmelCase=False       ,    UpperCAmelCase=99       ,    UpperCAmelCase=16       ,    UpperCAmelCase=2       ,    UpperCAmelCase=4       ,    UpperCAmelCase=4       ,    UpperCAmelCase="gelu"       ,    UpperCAmelCase=0.1       ,    UpperCAmelCase=0.1       ,    UpperCAmelCase=20       ,    UpperCAmelCase=2       ,    UpperCAmelCase=1       ,    UpperCAmelCase=0       ,    UpperCAmelCase=16       ,    UpperCAmelCase=16       ,    ):
							"""simple docstring"""







							_UpperCAmelCase									=			parent
							_UpperCAmelCase									=			batch_size
							_UpperCAmelCase									=			seq_length
							_UpperCAmelCase									=			is_training
							_UpperCAmelCase									=			use_labels
							_UpperCAmelCase									=			vocab_size
							_UpperCAmelCase									=			hidden_size
							_UpperCAmelCase									=			num_hidden_layers
							_UpperCAmelCase									=			num_attention_heads
							_UpperCAmelCase									=			intermediate_size
							_UpperCAmelCase									=			hidden_act
							_UpperCAmelCase									=			hidden_dropout_prob
							_UpperCAmelCase									=			attention_probs_dropout_prob
							_UpperCAmelCase									=			max_position_embeddings
							_UpperCAmelCase									=			eos_token_id
							_UpperCAmelCase									=			pad_token_id
							_UpperCAmelCase									=			bos_token_id
							_UpperCAmelCase									=			embed_dim
							_UpperCAmelCase									=			word_embed_proj_dim
							_UpperCAmelCase									=			False

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			ids_tensor([self.batch_size, self.seq_length - 1]       ,    self.vocab_size    )
							_UpperCAmelCase									=			tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size    )       ,    1    )
							_UpperCAmelCase									=			tf.concat([input_ids, eos_tensor]       ,    axis=1    )

							_UpperCAmelCase									=			self.config_cls(
							    vocab_size=self.vocab_size       ,    hidden_size=self.hidden_size       ,    num_hidden_layers=self.num_hidden_layers       ,    num_attention_heads=self.num_attention_heads       ,    ffn_dim=self.intermediate_size       ,    dropout=self.hidden_dropout_prob       ,    attention_dropout=self.attention_probs_dropout_prob       ,    max_position_embeddings=self.max_position_embeddings       ,    eos_token_id=self.eos_token_id       ,    bos_token_id=self.bos_token_id       ,    pad_token_id=self.pad_token_id       ,    embed_dim=self.embed_dim       ,    word_embed_proj_dim=self.word_embed_proj_dim       ,    is_encoder_decoder=UpperCAmelCase       ,    **self.config_updates       ,    )
							_UpperCAmelCase									=			prepare_opt_inputs_dict(UpperCAmelCase       ,    UpperCAmelCase    )
							return config, inputs_dict

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			TFOPTModel(config=UpperCAmelCase    )
							_UpperCAmelCase									=			inputs_dict['input_ids']

							_UpperCAmelCase									=			input_ids[:1, :]
							_UpperCAmelCase									=			inputs_dict['attention_mask'][:1, :]
							_UpperCAmelCase									=			1

							# first forward pass
							_UpperCAmelCase									=			model(UpperCAmelCase       ,    attention_mask=UpperCAmelCase       ,    use_cache=UpperCAmelCase    )

							_UpperCAmelCase					, _UpperCAmelCase									=			outputs.to_tuple()

							# create hypothetical next token and extent to next_input_ids
							_UpperCAmelCase									=			ids_tensor((self.batch_size, 3)       ,    config.vocab_size    )
							_UpperCAmelCase									=			tf.cast(ids_tensor((self.batch_size, 3)       ,    2    )       ,    tf.inta    )

							# append to next input_ids and
							_UpperCAmelCase									=			tf.concat([input_ids, next_tokens]       ,    axis=-1    )
							_UpperCAmelCase									=			tf.concat([attention_mask, next_attn_mask]       ,    axis=-1    )

							_UpperCAmelCase									=			model(UpperCAmelCase       ,    attention_mask=UpperCAmelCase    )[0]
							_UpperCAmelCase									=			model(UpperCAmelCase       ,    attention_mask=UpperCAmelCase       ,    past_key_values=UpperCAmelCase    )[0]

							self.parent.assertEqual(next_tokens.shape[1]       ,    output_from_past.shape[1]    )

							# select random slice
							_UpperCAmelCase									=			int(ids_tensor((1,)       ,    output_from_past.shape[-1]    )    )
							_UpperCAmelCase									=			output_from_no_past[:, -3:, random_slice_idx]
							_UpperCAmelCase									=			output_from_past[:, :, random_slice_idx]

							# test that outputs are equal for slice
							tf.debugging.assert_near(UpperCAmelCase       ,    UpperCAmelCase       ,    rtol=1e-3    )





@require_tf
class __lowerCamelCase (      snake_case__     ,       snake_case__     ,       unittest.TestCase):





	"""simple docstring"""






	UpperCamelCase__								=      (TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
	UpperCamelCase__								=      (TFOPTForCausalLM,) if is_tf_available() else ()
	UpperCamelCase__								=      (
	    {"feature-extraction": TFOPTModel, "text-generation": TFOPTForCausalLM} if is_tf_available() else {}
	)
	UpperCamelCase__								=      False
	UpperCamelCase__								=      False
	UpperCamelCase__								=      False
	UpperCamelCase__								=      10

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			TFOPTModelTester(self    )
							_UpperCAmelCase									=			ConfigTester(self       ,    config_class=UpperCAmelCase    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							self.config_tester.run_common_tests()

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.check_decoder_model_past_large_inputs(*UpperCAmelCase    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase					, _UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs_for_common()

							def _get_word_embedding_weight(UpperCAmelCase       ,    UpperCAmelCase    ):
													if hasattr(UpperCAmelCase       ,    'weight'    ):
																			return embedding_layer.weight
													else:
																			# Here we build the word embeddings weights if not exists.
																			# And then we retry to get the attribute once built.
																			model.build()
																			if hasattr(UpperCAmelCase       ,    'weight'    ):
																									return embedding_layer.weight
																			else:
																									return None

							for model_class in self.all_model_classes:
													for size in [config.vocab_size - 10, config.vocab_size + 10]:
																			# build the embeddings
																			_UpperCAmelCase									=			model_class(config=UpperCAmelCase    )
																			_UpperCAmelCase									=			_get_word_embedding_weight(UpperCAmelCase       ,    model.get_input_embeddings()    )
																			_UpperCAmelCase									=			_get_word_embedding_weight(UpperCAmelCase       ,    model.get_output_embeddings()    )

																			# reshape the embeddings
																			model.resize_token_embeddings(UpperCAmelCase    )
																			_UpperCAmelCase									=			_get_word_embedding_weight(UpperCAmelCase       ,    model.get_input_embeddings()    )
																			_UpperCAmelCase									=			_get_word_embedding_weight(UpperCAmelCase       ,    model.get_output_embeddings()    )

																			# check that the resized embeddings size matches the desired size.
																			_UpperCAmelCase									=			size if size is not None else config.vocab_size

																			self.assertEqual(new_input_embeddings.shape[0]       ,    UpperCAmelCase    )

																			# check that weights remain the same after resizing
																			_UpperCAmelCase									=			True
																			for pa, pa in zip(old_input_embeddings.value()       ,    new_input_embeddings.value()    ):
																									if tf.math.reduce_sum(tf.math.abs(pa - pa    )    ) > 0:
																															_UpperCAmelCase									=			False
																			self.assertTrue(UpperCAmelCase    )

																			if old_output_embeddings is not None and new_output_embeddings is not None:
																									self.assertEqual(new_output_embeddings.shape[0]       ,    UpperCAmelCase    )

																									_UpperCAmelCase									=			True
																									for pa, pa in zip(old_output_embeddings.value()       ,    new_output_embeddings.value()    ):
																															if tf.math.reduce_sum(tf.math.abs(pa - pa    )    ) > 0:
																																					_UpperCAmelCase									=			False
																									self.assertTrue(UpperCAmelCase    )



def __A    (      __lowerCAmelCase	)->  Optional[Any]:





						"""simple docstring"""



						return tf.constant(__lowerCAmelCase ,		dtype=tf.intaa	)





@require_tf
class __lowerCamelCase (      unittest.TestCase):





	"""simple docstring"""






	UpperCamelCase__								=      99

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			tf.ones((4, 1)       ,    dtype=tf.intaa    ) * 2
							_UpperCAmelCase									=			tf.concat([ids_tensor((4, 6)       ,    self.vocab_size - 3    ) + 3, eos_column_vector]       ,    axis=1    )
							_UpperCAmelCase									=			input_ids.shape[0]
							_UpperCAmelCase									=			OPTConfig(
							    vocab_size=self.vocab_size       ,    hidden_size=24       ,    num_hidden_layers=2       ,    num_attention_heads=2       ,    ffn_dim=32       ,    max_position_embeddings=48       ,    eos_token_id=2       ,    pad_token_id=1       ,    bos_token_id=0       ,    )
							return config, input_ids, batch_size





@require_sentencepiece
@require_tf
class __lowerCamelCase (      unittest.TestCase):





	"""simple docstring"""






	@slow
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			TFOPTModel.from_pretrained('facebook/opt-350m'    )
							_UpperCAmelCase									=			_long_tensor([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]    )
							_UpperCAmelCase									=			tf.not_equal(UpperCAmelCase       ,    model.config.pad_token_id    )
							with tf.GradientTape():
													_UpperCAmelCase									=			model(input_ids=UpperCAmelCase       ,    attention_mask=UpperCAmelCase    ).last_hidden_state
							_UpperCAmelCase									=			(1, 11, 512)
							self.assertEqual(output.shape       ,    UpperCAmelCase    )
							_UpperCAmelCase									=			tf.constant(
							    [[-0.28_73, -1.92_18, -0.30_33], [-1.27_10, -0.13_38, -0.19_02], [0.40_95, 0.12_14, -1.31_21]]    )
							self.assertTrue(np.allclose(output[:, :3, :3]       ,    UpperCAmelCase       ,    atol=4e-3    )    )

							_UpperCAmelCase									=			tf.function(UpperCAmelCase       ,    jit_compile=UpperCAmelCase    )
							_UpperCAmelCase									=			xla_generate(UpperCAmelCase       ,    UpperCAmelCase    )[0]
							self.assertTrue(np.allclose(output[:, :3, :3]       ,    UpperCAmelCase       ,    atol=4e-2    )    )





@require_tf
@slow
class __lowerCamelCase (      unittest.TestCase):





	"""simple docstring"""






	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							super().setUp()
							_UpperCAmelCase									=			'facebook/opt-350m'

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			TFOPTForCausalLM.from_pretrained(self.path_model    )
							_UpperCAmelCase									=			GPTaTokenizer.from_pretrained(self.path_model    )

							_UpperCAmelCase									=			[
							    'Today is a beautiful day and I want to',
							    'In the city of',
							    'Paris is the capital of France and',
							    'Computers and mobile phones have taken',
							]
							# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
							_UpperCAmelCase									=			tokenizer(UpperCAmelCase       ,    return_tensors='tf'       ,    padding=UpperCAmelCase       ,    add_special_tokens=UpperCAmelCase    )
							_UpperCAmelCase									=			tf.math.reduce_mean(model(inputs.input_ids       ,    attention_mask=inputs.attention_mask    )[0]       ,    axis=-1    )
							_UpperCAmelCase									=			tf.constant(
							    [
							        [1.38_51, -13.89_23, -10.52_29, -10.75_33, -0.23_09, -10.23_84, -0.53_65, -9.09_47, -5.16_70],
							        [-4.70_73, -10.62_76, -3.94_15, -21.52_42, -0.28_22, -0.28_22, -0.28_22, -0.28_22, -0.28_22],
							        [0.62_47, -3.42_29, -8.91_79, -1.42_97, -14.16_50, 1.41_46, -9.02_18, -0.27_03, -0.27_03],
							        [6.47_83, -1.99_13, -10.79_26, -2.33_36, 1.50_92, -0.99_74, -6.82_13, 1.34_77, 1.34_77],
							    ]    )
							self.assertTrue(np.allclose(UpperCAmelCase       ,    UpperCAmelCase       ,    atol=1e-4    )    )

							_UpperCAmelCase									=			tf.function(UpperCAmelCase       ,    jit_compile=UpperCAmelCase    )
							_UpperCAmelCase									=			tf.math.reduce_mean(xla_generate(inputs.input_ids       ,    attention_mask=inputs.attention_mask    )[0]       ,    axis=-1    )
							self.assertTrue(np.allclose(UpperCAmelCase       ,    UpperCAmelCase       ,    atol=1e-4    )    )





@require_tf
@slow
class __lowerCamelCase (      unittest.TestCase):





	"""simple docstring"""






	@property
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							return [
							    "Today is a beautiful day and I want",
							    "In the city of",
							    "Paris is the capital of France and",
							    "Computers and mobile phones have taken",
							]

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			'facebook/opt-125m'

							_UpperCAmelCase									=			[
							    'Today is a beautiful day and I want to',
							    'In the city of New York, the city',
							    'Paris is the capital of France and the capital',
							    'Computers and mobile phones have taken over the',
							]

							_UpperCAmelCase									=			[]
							_UpperCAmelCase									=			GPTaTokenizer.from_pretrained(UpperCAmelCase    )
							_UpperCAmelCase									=			TFOPTForCausalLM.from_pretrained(UpperCAmelCase    )

							for prompt in self.prompts:
													_UpperCAmelCase									=			tokenizer(UpperCAmelCase       ,    return_tensors='tf'    ).input_ids

													_UpperCAmelCase									=			model.generate(UpperCAmelCase       ,    max_length=10    )

													_UpperCAmelCase									=			tokenizer.batch_decode(UpperCAmelCase       ,    skip_special_tokens=UpperCAmelCase    )
													predicted_outputs += generated_string

							self.assertListEqual(UpperCAmelCase       ,    UpperCAmelCase    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			'facebook/opt-350m'

							_UpperCAmelCase									=			GPTaTokenizer.from_pretrained(UpperCAmelCase    )
							_UpperCAmelCase									=			TFOPTForCausalLM.from_pretrained(UpperCAmelCase    )

							_UpperCAmelCase									=			'left'

							# use different length sentences to test batching
							_UpperCAmelCase									=			[
							    'Hello, my dog is a little',
							    'Today, I',
							]

							_UpperCAmelCase									=			tokenizer(UpperCAmelCase       ,    return_tensors='tf'       ,    padding=UpperCAmelCase    )
							_UpperCAmelCase									=			inputs['input_ids']

							_UpperCAmelCase									=			model.generate(input_ids=UpperCAmelCase       ,    attention_mask=inputs['attention_mask']    )

							_UpperCAmelCase									=			tokenizer(sentences[0]       ,    return_tensors='tf'    ).input_ids
							_UpperCAmelCase									=			model.generate(input_ids=UpperCAmelCase    )

							_UpperCAmelCase									=			inputs_non_padded.shape[-1] - tf.math.reduce_sum(
							    tf.cast(inputs['attention_mask'][-1]       ,    tf.intaa    )    )
							_UpperCAmelCase									=			tokenizer(sentences[1]       ,    return_tensors='tf'    ).input_ids
							_UpperCAmelCase									=			model.generate(input_ids=UpperCAmelCase       ,    max_length=model.config.max_length - num_paddings    )

							_UpperCAmelCase									=			tokenizer.batch_decode(UpperCAmelCase       ,    skip_special_tokens=UpperCAmelCase    )
							_UpperCAmelCase									=			tokenizer.decode(output_non_padded[0]       ,    skip_special_tokens=UpperCAmelCase    )
							_UpperCAmelCase									=			tokenizer.decode(output_padded[0]       ,    skip_special_tokens=UpperCAmelCase    )

							_UpperCAmelCase									=			[
							    'Hello, my dog is a little bit of a dork.\nI\'m a little bit',
							    'Today, I was in the middle of a conversation with a friend about the',
							]
							self.assertListEqual(UpperCAmelCase       ,    UpperCAmelCase    )
							self.assertListEqual(UpperCAmelCase       ,    [non_padded_sentence, padded_sentence]    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			'facebook/opt-350m'

							_UpperCAmelCase									=			[
							    'Today is a beautiful day and I want to',
							    'In the city of San Francisco, the city',
							    'Paris is the capital of France and the capital',
							    'Computers and mobile phones have taken over the',
							]

							_UpperCAmelCase									=			[]
							_UpperCAmelCase									=			GPTaTokenizer.from_pretrained(UpperCAmelCase    )
							_UpperCAmelCase									=			TFOPTForCausalLM.from_pretrained(UpperCAmelCase    )

							for prompt in self.prompts:
													_UpperCAmelCase									=			tokenizer(UpperCAmelCase       ,    return_tensors='tf'    ).input_ids

													_UpperCAmelCase									=			model.generate(UpperCAmelCase       ,    max_length=10    )

													_UpperCAmelCase									=			tokenizer.batch_decode(UpperCAmelCase       ,    skip_special_tokens=UpperCAmelCase    )
													predicted_outputs += generated_string

							self.assertListEqual(UpperCAmelCase       ,    UpperCAmelCase    )








import argparse
import json
from pathlib import Path

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

from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
_a      	=  logging.get_logger(__name__)


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase=False	)->  Union[str, Any]:





						"""simple docstring"""



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

						# projection layer + position embeddings
						rename_keys.extend(
						    [
						        ('cls_token', 'vit.embeddings.cls_token'),
						        ('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight'),
						        ('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias'),
						        ('pos_embed', 'vit.embeddings.position_embeddings'),
						    ]	)

						if base_model:
												# layernorm + pooler
												rename_keys.extend(
												    [
												        ('norm.weight', 'layernorm.weight'),
												        ('norm.bias', 'layernorm.bias'),
												    ]	)

												# if just the base model, we should remove "vit" from all keys that start with "vit"
												_UpperCAmelCase									=			[(pair[0], pair[1][4:]) if pair[1].startswith('vit'	) else pair for pair in rename_keys]
						else:
												# layernorm + classification head
												rename_keys.extend(
												    [
												        ('norm.weight', 'vit.layernorm.weight'),
												        ('norm.bias', 'vit.layernorm.bias'),
												        ('head.weight', 'classifier.weight'),
												        ('head.bias', 'classifier.bias'),
												    ]	)

						return rename_keys


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase=False	)->  List[str]:





						"""simple docstring"""



						for i in range(config.num_hidden_layers	):
												if base_model:
																		_UpperCAmelCase									=			''
												else:
																		_UpperCAmelCase									=			'vit.'
												# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
												_UpperCAmelCase									=			state_dict.pop(F"""blocks.{i}.attn.qkv.weight"""	)
												_UpperCAmelCase									=			state_dict.pop(F"""blocks.{i}.attn.qkv.bias"""	)
												# next, add query, keys and values (in that order) to the state dict
												_UpperCAmelCase									=			in_proj_weight[
												    : config.hidden_size, :
												]
												_UpperCAmelCase									=			in_proj_bias[: config.hidden_size]
												_UpperCAmelCase									=			in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												_UpperCAmelCase									=			in_proj_bias[
												    config.hidden_size : config.hidden_size * 2
												]
												_UpperCAmelCase									=			in_proj_weight[
												    -config.hidden_size :, :
												]
												_UpperCAmelCase									=			in_proj_bias[-config.hidden_size :]


def __A    (      __lowerCAmelCase	)->  Optional[Any]:





						"""simple docstring"""



						_UpperCAmelCase									=			['head.weight', 'head.bias']
						for k in ignore_keys:
												state_dict.pop(__lowerCAmelCase ,		__lowerCAmelCase	)


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)->  int:





						"""simple docstring"""



						_UpperCAmelCase									=			dct.pop(__lowerCAmelCase	)
						_UpperCAmelCase									=			val


def __A    (      )->  str:





						"""simple docstring"""



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


@torch.no_grad()
def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase=True	)->  List[str]:





						"""simple docstring"""



						_UpperCAmelCase									=			ViTConfig()
						# patch_size
						if model_name[-1] == "8":
												_UpperCAmelCase									=			8
						# set labels if required
						if not base_model:
												_UpperCAmelCase									=			1_000
												_UpperCAmelCase									=			'huggingface/label-files'
												_UpperCAmelCase									=			'imagenet-1k-id2label.json'
												_UpperCAmelCase									=			json.load(open(hf_hub_download(__lowerCAmelCase ,		__lowerCAmelCase ,		repo_type='dataset'	) ,		'r'	)	)
												_UpperCAmelCase									=			{int(__lowerCAmelCase	): v for k, v in idalabel.items()}
												_UpperCAmelCase									=			idalabel
												_UpperCAmelCase									=			{v: k for k, v in idalabel.items()}
						# size of the architecture
						if model_name in ["dino_vits8", "dino_vits16"]:
												_UpperCAmelCase									=			384
												_UpperCAmelCase									=			1_536
												_UpperCAmelCase									=			12
												_UpperCAmelCase									=			6

						# load original model from torch hub
						_UpperCAmelCase									=			torch.hub.load('facebookresearch/dino:main' ,		__lowerCAmelCase	)
						original_model.eval()

						# load state_dict of original model, remove and rename some keys
						_UpperCAmelCase									=			original_model.state_dict()
						if base_model:
												remove_classification_head_(__lowerCAmelCase	)
						_UpperCAmelCase									=			create_rename_keys(__lowerCAmelCase ,		base_model=__lowerCAmelCase	)
						for src, dest in rename_keys:
												rename_key(__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)
						read_in_q_k_v(__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)

						# load HuggingFace model
						if base_model:
												_UpperCAmelCase									=			ViTModel(__lowerCAmelCase ,		add_pooling_layer=__lowerCAmelCase	).eval()
						else:
												_UpperCAmelCase									=			ViTForImageClassification(__lowerCAmelCase	).eval()
						model.load_state_dict(__lowerCAmelCase	)

						# Check outputs on an image, prepared by ViTImageProcessor
						_UpperCAmelCase									=			ViTImageProcessor()
						_UpperCAmelCase									=			image_processor(images=prepare_img() ,		return_tensors='pt'	)
						_UpperCAmelCase									=			encoding['pixel_values']
						_UpperCAmelCase									=			model(__lowerCAmelCase	)

						if base_model:
												_UpperCAmelCase									=			original_model(__lowerCAmelCase	)
												assert torch.allclose(__lowerCAmelCase ,		outputs.last_hidden_state[:, 0, :] ,		atol=1E-1	)
						else:
												_UpperCAmelCase									=			original_model(__lowerCAmelCase	)
												assert logits.shape == outputs.logits.shape
												assert torch.allclose(__lowerCAmelCase ,		outputs.logits ,		atol=1E-3	)

						Path(__lowerCAmelCase	).mkdir(exist_ok=__lowerCAmelCase	)
						print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""	)
						model.save_pretrained(__lowerCAmelCase	)
						print(F"""Saving image processor to {pytorch_dump_folder_path}"""	)
						image_processor.save_pretrained(__lowerCAmelCase	)


if __name__ == "__main__":
		_a      	=  argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--model_name''',
		    default='''dino_vitb16''',
		    type=str,
		    help='''Name of the model trained with DINO you\'d like to convert.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
		)
		parser.add_argument(
		    '''--base_model''',
		    action='''store_true''',
		    help='''Whether to only convert the base model (no projection head weights).''',
		)

		parser.set_defaults(base_model=True)
		_a      	=  parser.parse_args()
		convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)
