





"""simple docstring"""






import os
import tempfile
import unittest

from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    FlaubertForMultipleChoice,
		    FlaubertForQuestionAnswering,
		    FlaubertForQuestionAnsweringSimple,
		    FlaubertForSequenceClassification,
		    FlaubertForTokenClassification,
		    FlaubertModel,
		    FlaubertWithLMHeadModel,
		)
		from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST




class 			__lowercase     (     _UpperCamelCase							):







					'''simple docstring'''




					def __init__(  self		,     _UpperCAmelCase		,     _UpperCAmelCase=13		,     _UpperCAmelCase=7		,     _UpperCAmelCase=True		,     _UpperCAmelCase=True		,     _UpperCAmelCase=True		,     _UpperCAmelCase=True		,     _UpperCAmelCase=True		,     _UpperCAmelCase=False		,     _UpperCAmelCase=False		,     _UpperCAmelCase=False		,     _UpperCAmelCase=2		,     _UpperCAmelCase=99		,     _UpperCAmelCase=0		,     _UpperCAmelCase=32		,     _UpperCAmelCase=5		,     _UpperCAmelCase=4		,     _UpperCAmelCase=0.1		,     _UpperCAmelCase=0.1		,     _UpperCAmelCase=512		,     _UpperCAmelCase=12		,     _UpperCAmelCase=2		,     _UpperCAmelCase=0.0_2		,     _UpperCAmelCase=3		,     _UpperCAmelCase=4		,     _UpperCAmelCase="last"		,     _UpperCAmelCase=None		,     _UpperCAmelCase=None		,     ):
									__a	:  List[Any]         =					parent
									__a	:  List[str]         =					batch_size
									__a	:  List[Any]         =					seq_length
									__a	:  Union[str, Any]         =					is_training
									__a	:  str         =					use_input_lengths
									__a	:  List[Any]         =					use_token_type_ids
									__a	:  Tuple         =					use_labels
									__a	:  Optional[int]         =					gelu_activation
									__a	:  Tuple         =					sinusoidal_embeddings
									__a	:  List[Any]         =					causal
									__a	:  Dict         =					asm
									__a	:  int         =					n_langs
									__a	:  str         =					vocab_size
									__a	:  Any         =					n_special
									__a	:  Optional[Any]         =					hidden_size
									__a	:  Dict         =					num_hidden_layers
									__a	:  str         =					num_attention_heads
									__a	:  str         =					hidden_dropout_prob
									__a	:  Union[str, Any]         =					attention_probs_dropout_prob
									__a	:  Optional[Any]         =					max_position_embeddings
									__a	:  Tuple         =					type_vocab_size
									__a	:  List[str]         =					type_sequence_label_size
									__a	:  List[Any]         =					initializer_range
									__a	:  List[str]         =					num_labels
									__a	:  Optional[Any]         =					num_choices
									__a	:  Optional[int]         =					summary_type
									__a	:  Union[str, Any]         =					use_proj
									__a	:  List[Any]         =					scope







					def 	_lowerCamelCase						(  self       ):
									__a	:  int         =					ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size       )
									__a	:  str         =					random_attention_mask([self.batch_size, self.seq_length]       )

									__a	:  Optional[int]         =					None
									if self.use_input_lengths:
													__a	:  Optional[int]         =					(
													    ids_tensor([self.batch_size]		,     vocab_size=2       ) + self.seq_length - 2
													)  # small variation of seq_length

									__a	:  int         =					None
									if self.use_token_type_ids:
													__a	:  Union[str, Any]         =					ids_tensor([self.batch_size, self.seq_length]		,     self.n_langs       )

									__a	:  int         =					None
									__a	:  Any         =					None
									__a	:  Optional[Any]         =					None
									if self.use_labels:
													__a	:  List[str]         =					ids_tensor([self.batch_size]		,     self.type_sequence_label_size       )
													__a	:  Union[str, Any]         =					ids_tensor([self.batch_size, self.seq_length]		,     self.num_labels       )
													__a	:  Optional[int]         =					ids_tensor([self.batch_size]		,     2       ).float()
													__a	:  Dict         =					ids_tensor([self.batch_size]		,     self.num_choices       )

									__a	:  Tuple         =					self.get_config()

									return (
									    config,
									    input_ids,
									    token_type_ids,
									    input_lengths,
									    sequence_labels,
									    token_labels,
									    is_impossible_labels,
									    choice_labels,
									    input_mask,
									)







					def 	_lowerCamelCase						(  self       ):
									return FlaubertConfig(
									    vocab_size=self.vocab_size		,     n_special=self.n_special		,     emb_dim=self.hidden_size		,     n_layers=self.num_hidden_layers		,     n_heads=self.num_attention_heads		,     dropout=self.hidden_dropout_prob		,     attention_dropout=self.attention_probs_dropout_prob		,     gelu_activation=self.gelu_activation		,     sinusoidal_embeddings=self.sinusoidal_embeddings		,     asm=self.asm		,     causal=self.causal		,     n_langs=self.n_langs		,     max_position_embeddings=self.max_position_embeddings		,     initializer_range=self.initializer_range		,     summary_type=self.summary_type		,     use_proj=self.use_proj		,     )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     ):
									__a	:  Tuple         =					FlaubertModel(config=_UpperCAmelCase       )
									model.to(_UpperCAmelCase       )
									model.eval()
									__a	:  Any         =					model(_UpperCAmelCase		,     lengths=_UpperCAmelCase		,     langs=_UpperCAmelCase       )
									__a	:  Optional[Any]         =					model(_UpperCAmelCase		,     langs=_UpperCAmelCase       )
									__a	:  Any         =					model(_UpperCAmelCase       )
									self.parent.assertEqual(result.last_hidden_state.shape		,     (self.batch_size, self.seq_length, self.hidden_size)       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     ):
									__a	:  Optional[Any]         =					FlaubertWithLMHeadModel(_UpperCAmelCase       )
									model.to(_UpperCAmelCase       )
									model.eval()

									__a	:  Union[str, Any]         =					model(_UpperCAmelCase		,     token_type_ids=_UpperCAmelCase		,     labels=_UpperCAmelCase       )
									self.parent.assertEqual(result.loss.shape		,     ()       )
									self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.seq_length, self.vocab_size)       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     ):
									__a	:  int         =					FlaubertForQuestionAnsweringSimple(_UpperCAmelCase       )
									model.to(_UpperCAmelCase       )
									model.eval()

									__a	:  int         =					model(_UpperCAmelCase       )

									__a	:  List[Any]         =					model(_UpperCAmelCase		,     start_positions=_UpperCAmelCase		,     end_positions=_UpperCAmelCase       )
									self.parent.assertEqual(result.start_logits.shape		,     (self.batch_size, self.seq_length)       )
									self.parent.assertEqual(result.end_logits.shape		,     (self.batch_size, self.seq_length)       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     ):
									__a	:  int         =					FlaubertForQuestionAnswering(_UpperCAmelCase       )
									model.to(_UpperCAmelCase       )
									model.eval()

									__a	:  str         =					model(_UpperCAmelCase       )

									__a	:  str         =					model(
									    _UpperCAmelCase		,     start_positions=_UpperCAmelCase		,     end_positions=_UpperCAmelCase		,     cls_index=_UpperCAmelCase		,     is_impossible=_UpperCAmelCase		,     p_mask=_UpperCAmelCase		,     )

									__a	:  List[Any]         =					model(
									    _UpperCAmelCase		,     start_positions=_UpperCAmelCase		,     end_positions=_UpperCAmelCase		,     cls_index=_UpperCAmelCase		,     is_impossible=_UpperCAmelCase		,     )

									((__a)    ,  )	:  List[Any]         =					result_with_labels.to_tuple()

									__a	:  Tuple         =					model(_UpperCAmelCase		,     start_positions=_UpperCAmelCase		,     end_positions=_UpperCAmelCase       )

									((__a)    ,  )	:  List[str]         =					result_with_labels.to_tuple()

									self.parent.assertEqual(result_with_labels.loss.shape		,     ()       )
									self.parent.assertEqual(result.start_top_log_probs.shape		,     (self.batch_size, model.config.start_n_top)       )
									self.parent.assertEqual(result.start_top_index.shape		,     (self.batch_size, model.config.start_n_top)       )
									self.parent.assertEqual(
									    result.end_top_log_probs.shape		,     (self.batch_size, model.config.start_n_top * model.config.end_n_top)       )
									self.parent.assertEqual(
									    result.end_top_index.shape		,     (self.batch_size, model.config.start_n_top * model.config.end_n_top)       )
									self.parent.assertEqual(result.cls_logits.shape		,     (self.batch_size,)       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     ):
									__a	:  Union[str, Any]         =					FlaubertForSequenceClassification(_UpperCAmelCase       )
									model.to(_UpperCAmelCase       )
									model.eval()

									__a	:  str         =					model(_UpperCAmelCase       )
									__a	:  int         =					model(_UpperCAmelCase		,     labels=_UpperCAmelCase       )

									self.parent.assertEqual(result.loss.shape		,     ()       )
									self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.type_sequence_label_size)       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     ):
									__a	:  Union[str, Any]         =					self.num_labels
									__a	:  Dict         =					FlaubertForTokenClassification(_UpperCAmelCase       )
									model.to(_UpperCAmelCase       )
									model.eval()

									__a	:  Optional[int]         =					model(_UpperCAmelCase		,     attention_mask=_UpperCAmelCase		,     labels=_UpperCAmelCase       )
									self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.seq_length, self.num_labels)       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     ):
									__a	:  List[Any]         =					self.num_choices
									__a	:  Tuple         =					FlaubertForMultipleChoice(config=_UpperCAmelCase       )
									model.to(_UpperCAmelCase       )
									model.eval()
									__a	:  Union[str, Any]         =					input_ids.unsqueeze(1       ).expand(-1		,     self.num_choices		,     -1       ).contiguous()
									__a	:  Optional[Any]         =					token_type_ids.unsqueeze(1       ).expand(-1		,     self.num_choices		,     -1       ).contiguous()
									__a	:  Dict         =					input_mask.unsqueeze(1       ).expand(-1		,     self.num_choices		,     -1       ).contiguous()
									__a	:  Union[str, Any]         =					model(
									    _UpperCAmelCase		,     attention_mask=_UpperCAmelCase		,     token_type_ids=_UpperCAmelCase		,     labels=_UpperCAmelCase		,     )
									self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.num_choices)       )







					def 	_lowerCamelCase						(  self       ):
									__a	:  Optional[int]         =					self.prepare_config_and_inputs()
									(
									    (
									    __a
									)    ,  (
									    __a
									)    ,  (
									    __a
									)    ,  (
									    __a
									)    ,  (
									    __a
									)    ,  (
									    __a
									)    ,  (
									    __a
									)    ,  (
									    __a
									)    ,  (
									    __a
									)    ,  
									)	:  str         =					config_and_inputs
									__a	:  str         =					{
									    '''input_ids''': input_ids,
									    '''token_type_ids''': token_type_ids,
									    '''lengths''': input_lengths,
									    '''attention_mask''': input_mask,
									}
									return config, inputs_dict






@require_torch
class 			__lowercase     (     _UpperCamelCase				, _UpperCamelCase				, unittest.TestCase							):







					'''simple docstring'''




					__lowerCAmelCase					=					(
					    (
					        FlaubertModel,
					        FlaubertWithLMHeadModel,
					        FlaubertForQuestionAnswering,
					        FlaubertForQuestionAnsweringSimple,
					        FlaubertForSequenceClassification,
					        FlaubertForTokenClassification,
					        FlaubertForMultipleChoice,
					    )
					    if is_torch_available()
					    else ()
					)
					__lowerCAmelCase					=					(
					    {
					        '''feature-extraction''': FlaubertModel,
					        '''fill-mask''': FlaubertWithLMHeadModel,
					        '''question-answering''': FlaubertForQuestionAnsweringSimple,
					        '''text-classification''': FlaubertForSequenceClassification,
					        '''token-classification''': FlaubertForTokenClassification,
					        '''zero-shot''': FlaubertForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
									if (
									    pipeline_test_casse_name == "QAPipelineTests"
									    and tokenizer_name is not None
									    and not tokenizer_name.endswith('''Fast'''       )
									):
													# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
													# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
													# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
													return True

									return False







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase=False       ):
									__a	:  str         =					super()._prepare_for_class(_UpperCAmelCase		,     _UpperCAmelCase		,     return_labels=_UpperCAmelCase       )

									if return_labels:
													if model_class.__name__ == "FlaubertForQuestionAnswering":
																	__a	:  Optional[int]         =					torch.zeros(
																	    self.model_tester.batch_size		,     dtype=torch.long		,     device=_UpperCAmelCase       )
																	__a	:  List[Any]         =					torch.zeros(
																	    self.model_tester.batch_size		,     dtype=torch.long		,     device=_UpperCAmelCase       )

									return inputs_dict







					def 	_lowerCamelCase						(  self       ):
									__a	:  List[Any]         =					FlaubertModelTester(self       )
									__a	:  List[Any]         =					ConfigTester(self		,     config_class=_UpperCAmelCase		,     emb_dim=37       )







					def 	_lowerCamelCase						(  self       ):
									self.config_tester.run_common_tests()







					def 	_lowerCamelCase						(  self       ):
									__a	:  int         =					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_model(*_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self       ):
									__a	:  List[Any]         =					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_lm_head(*_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self       ):
									__a	:  Union[str, Any]         =					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_simple_qa(*_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self       ):
									__a	:  Optional[Any]         =					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_qa(*_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self       ):
									__a	:  Tuple         =					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_sequence_classif(*_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self       ):
									__a	:  List[Any]         =					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_token_classif(*_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self       ):
									__a	:  Dict         =					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_multiple_choice(*_UpperCAmelCase       )







					@slow
					def 	_lowerCamelCase						(  self       ):
									for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													__a	:  str         =					FlaubertModel.from_pretrained(_UpperCAmelCase       )
													self.assertIsNotNone(_UpperCAmelCase       )







					@slow
					@require_torch_gpu
					def 	_lowerCamelCase						(  self       ):
									__a    ,  __a	:  List[str]         =					self.model_tester.prepare_config_and_inputs_for_common()
									for model_class in self.all_model_classes:
													# FlauBertForMultipleChoice behaves incorrectly in JIT environments.
													if model_class == FlaubertForMultipleChoice:
																	return

													__a	:  Dict         =					True
													__a	:  List[Any]         =					model_class(config=_UpperCAmelCase       )

													__a	:  List[Any]         =					self._prepare_for_class(_UpperCAmelCase		,     _UpperCAmelCase       )
													__a	:  int         =					torch.jit.trace(
													    _UpperCAmelCase		,     (inputs_dict['''input_ids'''].to('''cpu'''       ), inputs_dict['''attention_mask'''].to('''cpu'''       ))       )

													with tempfile.TemporaryDirectory() as tmp:
																	torch.jit.save(_UpperCAmelCase		,     os.path.join(_UpperCAmelCase		,     '''traced_model.pt'''       )       )
																	__a	:  Tuple         =					torch.jit.load(os.path.join(_UpperCAmelCase		,     '''traced_model.pt'''       )		,     map_location=_UpperCAmelCase       )
																	loaded(inputs_dict['''input_ids'''].to(_UpperCAmelCase       )		,     inputs_dict['''attention_mask'''].to(_UpperCAmelCase       )       )






@require_torch
class 			__lowercase     (     unittest.TestCase							):







					'''simple docstring'''




					@slow
					def 	_lowerCamelCase						(  self       ):
									__a	:  str         =					FlaubertModel.from_pretrained('''flaubert/flaubert_base_cased'''       )
									__a	:  str         =					torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]       )
									with torch.no_grad():
													__a	:  Union[str, Any]         =					model(_UpperCAmelCase       )[0]
									__a	:  Any         =					torch.Size((1, 11, 768)       )
									self.assertEqual(output.shape		,     _UpperCAmelCase       )
									__a	:  Tuple         =					torch.tensor(
									    [[[-2.6_2_5_1, -1.4_2_9_8, -0.0_2_2_7], [-2.8_5_1_0, -1.6_3_8_7, 0.2_2_5_8], [-2.8_1_1_4, -1.1_8_3_2, -0.3_0_6_6]]]       )

									self.assertTrue(torch.allclose(output[:, :3, :3]		,     _UpperCAmelCase		,     atol=1e-4       )       )






"""simple docstring"""






import argparse
import re

import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SamConfig,
    SamImageProcessor,
    SamModel,
    SamProcessor,
    SamVisionConfig,
)


A	     =					{
    '''iou_prediction_head.layers.0''': '''iou_prediction_head.proj_in''',
    '''iou_prediction_head.layers.1''': '''iou_prediction_head.layers.0''',
    '''iou_prediction_head.layers.2''': '''iou_prediction_head.proj_out''',
    '''mask_decoder.output_upscaling.0''': '''mask_decoder.upscale_conv1''',
    '''mask_decoder.output_upscaling.1''': '''mask_decoder.upscale_layer_norm''',
    '''mask_decoder.output_upscaling.3''': '''mask_decoder.upscale_conv2''',
    '''mask_downscaling.0''': '''mask_embed.conv1''',
    '''mask_downscaling.1''': '''mask_embed.layer_norm1''',
    '''mask_downscaling.3''': '''mask_embed.conv2''',
    '''mask_downscaling.4''': '''mask_embed.layer_norm2''',
    '''mask_downscaling.6''': '''mask_embed.conv3''',
    '''point_embeddings''': '''point_embed''',
    '''pe_layer.positional_encoding_gaussian_matrix''': '''shared_embedding.positional_embedding''',
    '''image_encoder''': '''vision_encoder''',
    '''neck.0''': '''neck.conv1''',
    '''neck.1''': '''neck.layer_norm1''',
    '''neck.2''': '''neck.conv2''',
    '''neck.3''': '''neck.layer_norm2''',
    '''patch_embed.proj''': '''patch_embed.projection''',
    '''.norm''': '''.layer_norm''',
    '''blocks''': '''layers''',
}

def       __A (     a_  :List[Any])						->						List[Any]:
				__a	:  List[Any]         =					{}
				state_dict.pop('''pixel_mean'''				,    a_)
				state_dict.pop('''pixel_std'''				,    a_)

				__a	:  List[Any]         =					R'''.*.output_hypernetworks_mlps.(\d+).layers.(\d+).*'''

				for key, value in state_dict.items():
								for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
												if key_to_modify in key:
																__a	:  int         =					key.replace(a_				,    a_)

								if re.match(a_				,    a_):
												__a	:  Optional[Any]         =					int(re.match(a_				,    a_).group(2))
												if layer_nb == 0:
																__a	:  Any         =					key.replace('''layers.0'''				,    '''proj_in''')
												elif layer_nb == 1:
																__a	:  Dict         =					key.replace('''layers.1'''				,    '''layers.0''')
												elif layer_nb == 2:
																__a	:  Optional[int]         =					key.replace('''layers.2'''				,    '''proj_out''')

								__a	:  int         =					value

				__a	:  Union[str, Any]         =					model_state_dict[
				    '''prompt_encoder.shared_embedding.positional_embedding'''
				]

				return model_state_dict

def       __A (     a_  :Optional[int]				,    a_  :Optional[Any]				,    a_  :Dict				,    a_  :Optional[int]="ybelkada/segment-anything")						->						Dict:
				__a	:  Dict         =					hf_hub_download(a_				,    F"""checkpoints/{model_name}.pth""")

				if "sam_vit_b" in model_name:
								__a	:  List[str]         =					SamConfig()
				elif "sam_vit_l" in model_name:
								__a	:  List[Any]         =					SamVisionConfig(
								    hidden_size=10_24				,    num_hidden_layers=24				,    num_attention_heads=16				,    global_attn_indexes=[5, 11, 17, 23]				,    )

								__a	:  List[Any]         =					SamConfig(
								    vision_config=a_				,    )
				elif "sam_vit_h" in model_name:
								__a	:  List[str]         =					SamVisionConfig(
								    hidden_size=12_80				,    num_hidden_layers=32				,    num_attention_heads=16				,    global_attn_indexes=[7, 15, 23, 31]				,    )

								__a	:  Optional[int]         =					SamConfig(
								    vision_config=a_				,    )

				__a	:  int         =					torch.load(a_				,    map_location='''cpu''')
				__a	:  Tuple         =					replace_keys(a_)

				__a	:  Optional[int]         =					SamImageProcessor()

				__a	:  Any         =					SamProcessor(image_processor=a_)
				__a	:  Any         =					SamModel(a_)

				hf_model.load_state_dict(a_)
				__a	:  Dict         =					hf_model.to('''cuda''')

				__a	:  Tuple         =					'''https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png'''
				__a	:  str         =					Image.open(requests.get(a_				,    stream=a_).raw).convert('''RGB''')

				__a	:  Tuple         =					[[[4_00, 6_50]]]
				__a	:  Tuple         =					[[1]]

				__a	:  Tuple         =					processor(images=np.array(a_)				,    return_tensors='''pt''').to('''cuda''')

				with torch.no_grad():
								__a	:  str         =					hf_model(**a_)
				__a	:  Optional[int]         =					output.iou_scores.squeeze()

				if model_name == "sam_vit_h_4b8939":
								assert scores[-1].item() == 0.5_7_9_8_9_0_2_5_1_1_5_9_6_6_8

								__a	:  Any         =					processor(
								    images=np.array(a_)				,    input_points=a_				,    input_labels=a_				,    return_tensors='''pt''').to('''cuda''')

								with torch.no_grad():
												__a	:  Optional[int]         =					hf_model(**a_)
								__a	:  Optional[int]         =					output.iou_scores.squeeze()

								assert scores[-1].item() == 0.9_7_1_2_6_0_3_0_9_2_1_9_3_6_0_4

								__a	:  str         =					((75, 2_75, 17_25, 8_50),)

								__a	:  List[str]         =					processor(images=np.array(a_)				,    input_boxes=a_				,    return_tensors='''pt''').to('''cuda''')

								with torch.no_grad():
												__a	:  Any         =					hf_model(**a_)
								__a	:  Any         =					output.iou_scores.squeeze()

								assert scores[-1].item() == 0.8_6_8_6_0_1_5_6_0_5_9_2_6_5_1_4

								# Test with 2 points and 1 image.
								__a	:  int         =					[[[4_00, 6_50], [8_00, 6_50]]]
								__a	:  Dict         =					[[1, 1]]

								__a	:  Optional[Any]         =					processor(
								    images=np.array(a_)				,    input_points=a_				,    input_labels=a_				,    return_tensors='''pt''').to('''cuda''')

								with torch.no_grad():
												__a	:  int         =					hf_model(**a_)
								__a	:  Any         =					output.iou_scores.squeeze()

								assert scores[-1].item() == 0.9_9_3_6_0_4_7_7_9_2_4_3_4_6_9_2


if __name__ == "__main__":
		A	     =					argparse.ArgumentParser()
		A	     =					['''sam_vit_b_01ec64''', '''sam_vit_h_4b8939''', '''sam_vit_l_0b3195''']
		parser.add_argument(
		    '''--model_name''',
		    default='''sam_vit_h_4b8939''',
		    choices=choices,
		    type=str,
		    help='''Path to hf config.json of model to convert''',
		)
		parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
		parser.add_argument(
		    '''--push_to_hub''',
		    action='''store_true''',
		    help='''Whether to push the model and processor to the hub after converting''',
		)
		parser.add_argument(
		    '''--model_hub_id''',
		    default='''ybelkada/segment-anything''',
		    choices=choices,
		    type=str,
		    help='''Path to hf config.json of model to convert''',
		)

		A	     =					parser.parse_args()

		convert_sam_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub, args.model_hub_id)