


import os
import pickle
import tempfile
import unittest
from typing import Callable, Optional

import numpy as np

from transformers import (
    BatchEncoding,
    BertTokenizer,
    BertTokenizerFast,
    PreTrainedTokenizer,
    PreTrainedTokenizerFast,
    TensorType,
    TokenSpan,
    is_tokenizers_available,
)
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import CaptureStderr, require_flax, require_tf, require_tokenizers, require_torch, slow


if is_tokenizers_available():
	from tokenizers import Tokenizer
	from tokenizers.models import WordPiece



class 							A					( unittest.TestCase							):
					'''simple docstring'''






					def 						lowerCamelCase__  (self					:		int   ,      _UpperCAmelCase					:		Dict	)		->     Optional[Any]:




							"""simple docstring"""


							lowercase__      =	list(tokenizer_class.max_model_input_sizes.keys()	)
							for model_name in sa_models[:1]:
									lowercase__      =	tokenizer_class.from_pretrained(_UpperCAmelCase	)
									self.assertIsNotNone(_UpperCAmelCase	)
									self.assertIsInstance(_UpperCAmelCase   ,      _UpperCAmelCase	)
									self.assertIsInstance(_UpperCAmelCase   ,      _UpperCAmelCase	)

									for special_tok in tokenizer.all_special_tokens:
											self.assertIsInstance(_UpperCAmelCase   ,      _UpperCAmelCase	)
											lowercase__      =	tokenizer.convert_tokens_to_ids(_UpperCAmelCase	)
											self.assertIsInstance(_UpperCAmelCase   ,      _UpperCAmelCase	)

					def 						lowerCamelCase__  (self					:		Optional[Any]   ,      _UpperCAmelCase					:		BatchEncoding   ,      _UpperCAmelCase					:		Optional[Callable] = None	)		->     Union[str, Any]:




							"""simple docstring"""


							lowercase__      =	pickle.dumps(_UpperCAmelCase	)
							self.assertIsNotNone(_UpperCAmelCase	)

							lowercase__      =	pickle.loads(_UpperCAmelCase	)

							# Ensure is_fast is correctly restored
							self.assertEqual(be_restored.is_fast   ,      be_original.is_fast	)

							# Ensure encodings are potentially correctly restored
							if be_original.is_fast:
									self.assertIsNotNone(be_restored.encodings	)
							else:
									self.assertIsNone(be_restored.encodings	)

							# Ensure the keys are the same
							for original_v, restored_v in zip(be_original.values()   ,      be_restored.values()	):
									if equal_op:
											self.assertTrue(equal_op(_UpperCAmelCase   ,      _UpperCAmelCase	)	)
									else:
											self.assertEqual(_UpperCAmelCase   ,      _UpperCAmelCase	)

					@slow
					def 						lowerCamelCase__  (self					:		Tuple	)		->     List[str]:




							"""simple docstring"""


							self.check_tokenizer_from_pretrained(_UpperCAmelCase	)

					def 						lowerCamelCase__  (self					:		List[str]	)		->     Optional[int]:




							"""simple docstring"""


							self.assertEqual(TensorType("""tf"""	)   ,      TensorType.TENSORFLOW	)
							self.assertEqual(TensorType("""pt"""	)   ,      TensorType.PYTORCH	)
							self.assertEqual(TensorType("""np"""	)   ,      TensorType.NUMPY	)

					@require_tokenizers
					def 						lowerCamelCase__  (self					:		int	)		->     str:




							"""simple docstring"""


							import numpy as np

							lowercase__      =	BertTokenizer.from_pretrained("""bert-base-cased"""	)
							lowercase__      =	BertTokenizerFast.from_pretrained("""bert-base-cased"""	)

							# Python no tensor
							with self.subTest("""BatchEncoding (Python, return_tensors=None)"""	):
									self.assert_dump_and_restore(tokenizer_p("""Small example to encode"""	)	)

							with self.subTest("""BatchEncoding (Python, return_tensors=NUMPY)"""	):
									self.assert_dump_and_restore(
									    tokenizer_p("""Small example to encode"""   ,      return_tensors=TensorType.NUMPY	)   ,      np.array_equal	)

							with self.subTest("""BatchEncoding (Rust, return_tensors=None)"""	):
									self.assert_dump_and_restore(tokenizer_r("""Small example to encode"""	)	)

							with self.subTest("""BatchEncoding (Rust, return_tensors=NUMPY)"""	):
									self.assert_dump_and_restore(
									    tokenizer_r("""Small example to encode"""   ,      return_tensors=TensorType.NUMPY	)   ,      np.array_equal	)

					@require_tf
					@require_tokenizers
					def 						lowerCamelCase__  (self					:		List[Any]	)		->     Any:




							"""simple docstring"""


							import tensorflow as tf

							def tf_array_equals(_UpperCAmelCase					:		Optional[Any]   ,      _UpperCAmelCase					:		int	):
									return tf.reduce_all(tf.equal(_UpperCAmelCase   ,      _UpperCAmelCase	)	)

							lowercase__      =	BertTokenizer.from_pretrained("""bert-base-cased"""	)
							lowercase__      =	BertTokenizerFast.from_pretrained("""bert-base-cased"""	)

							with self.subTest("""BatchEncoding (Python, return_tensors=TENSORFLOW)"""	):
									self.assert_dump_and_restore(
									    tokenizer_p("""Small example to encode"""   ,      return_tensors=TensorType.TENSORFLOW	)   ,      _UpperCAmelCase	)

							with self.subTest("""BatchEncoding (Rust, return_tensors=TENSORFLOW)"""	):
									self.assert_dump_and_restore(
									    tokenizer_r("""Small example to encode"""   ,      return_tensors=TensorType.TENSORFLOW	)   ,      _UpperCAmelCase	)

					@require_torch
					@require_tokenizers
					def 						lowerCamelCase__  (self					:		str	)		->     int:




							"""simple docstring"""


							import torch

							lowercase__      =	BertTokenizer.from_pretrained("""bert-base-cased"""	)
							lowercase__      =	BertTokenizerFast.from_pretrained("""bert-base-cased"""	)

							with self.subTest("""BatchEncoding (Python, return_tensors=PYTORCH)"""	):
									self.assert_dump_and_restore(
									    tokenizer_p("""Small example to encode"""   ,      return_tensors=TensorType.PYTORCH	)   ,      torch.equal	)

							with self.subTest("""BatchEncoding (Rust, return_tensors=PYTORCH)"""	):
									self.assert_dump_and_restore(
									    tokenizer_r("""Small example to encode"""   ,      return_tensors=TensorType.PYTORCH	)   ,      torch.equal	)

					@require_tokenizers
					def 						lowerCamelCase__  (self					:		str	)		->     str:




							"""simple docstring"""


							lowercase__      =	BertTokenizer.from_pretrained("""bert-base-cased"""	)
							lowercase__      =	BertTokenizerFast.from_pretrained("""bert-base-cased"""	)

							with self.subTest("""Python Tokenizer"""	):
									self.assertFalse(tokenizer_p("""Small example to_encode"""	).is_fast	)

							with self.subTest("""Rust Tokenizer"""	):
									self.assertTrue(tokenizer_r("""Small example to_encode"""	).is_fast	)

					@require_tokenizers
					def 						lowerCamelCase__  (self					:		int	)		->     Any:




							"""simple docstring"""


							lowercase__      =	BertTokenizerFast.from_pretrained("""bert-base-cased"""	)
							lowercase__      =	tokenizer_r(["""Test""", """\xad""", """test"""]   ,      is_split_into_words=_UpperCAmelCase	)

							self.assertEqual(encoded.word_to_tokens(0	)   ,      TokenSpan(start=1   ,      end=2	)	)
							self.assertEqual(encoded.word_to_tokens(1	)   ,      _UpperCAmelCase	)
							self.assertEqual(encoded.word_to_tokens(2	)   ,      TokenSpan(start=2   ,      end=3	)	)

					def 						lowerCamelCase__  (self					:		List[str]	)		->     List[str]:




							"""simple docstring"""


							lowercase__      =	BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]}	)
							lowercase__      =	batch.convert_to_tensors(tensor_type="""np"""	)
							self.assertEqual(tensor_batch["""inputs"""].shape   ,      (2, 3)	)
							self.assertEqual(tensor_batch["""labels"""].shape   ,      (2,)	)
							# test converting the converted
							with CaptureStderr() as cs:
									lowercase__      =	batch.convert_to_tensors(tensor_type="""np"""	)
							self.assertFalse(len(cs.err	)   ,      msg=f'''should have no warning, but got {cs.err}'''	)

							lowercase__      =	BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}	)
							lowercase__      =	batch.convert_to_tensors(tensor_type="""np"""   ,      prepend_batch_axis=_UpperCAmelCase	)
							self.assertEqual(tensor_batch["""inputs"""].shape   ,      (1, 3)	)
							self.assertEqual(tensor_batch["""labels"""].shape   ,      (1,)	)

					@require_torch
					def 						lowerCamelCase__  (self					:		Tuple	)		->     Optional[int]:




							"""simple docstring"""


							lowercase__      =	BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]}	)
							lowercase__      =	batch.convert_to_tensors(tensor_type="""pt"""	)
							self.assertEqual(tensor_batch["""inputs"""].shape   ,      (2, 3)	)
							self.assertEqual(tensor_batch["""labels"""].shape   ,      (2,)	)
							# test converting the converted
							with CaptureStderr() as cs:
									lowercase__      =	batch.convert_to_tensors(tensor_type="""pt"""	)
							self.assertFalse(len(cs.err	)   ,      msg=f'''should have no warning, but got {cs.err}'''	)

							lowercase__      =	BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}	)
							lowercase__      =	batch.convert_to_tensors(tensor_type="""pt"""   ,      prepend_batch_axis=_UpperCAmelCase	)
							self.assertEqual(tensor_batch["""inputs"""].shape   ,      (1, 3)	)
							self.assertEqual(tensor_batch["""labels"""].shape   ,      (1,)	)

					@require_tf
					def 						lowerCamelCase__  (self					:		Optional[Any]	)		->     int:




							"""simple docstring"""


							lowercase__      =	BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]}	)
							lowercase__      =	batch.convert_to_tensors(tensor_type="""tf"""	)
							self.assertEqual(tensor_batch["""inputs"""].shape   ,      (2, 3)	)
							self.assertEqual(tensor_batch["""labels"""].shape   ,      (2,)	)
							# test converting the converted
							with CaptureStderr() as cs:
									lowercase__      =	batch.convert_to_tensors(tensor_type="""tf"""	)
							self.assertFalse(len(cs.err	)   ,      msg=f'''should have no warning, but got {cs.err}'''	)

							lowercase__      =	BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}	)
							lowercase__      =	batch.convert_to_tensors(tensor_type="""tf"""   ,      prepend_batch_axis=_UpperCAmelCase	)
							self.assertEqual(tensor_batch["""inputs"""].shape   ,      (1, 3)	)
							self.assertEqual(tensor_batch["""labels"""].shape   ,      (1,)	)

					@require_flax
					def 						lowerCamelCase__  (self					:		Union[str, Any]	)		->     List[str]:




							"""simple docstring"""


							lowercase__      =	BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]}	)
							lowercase__      =	batch.convert_to_tensors(tensor_type="""jax"""	)
							self.assertEqual(tensor_batch["""inputs"""].shape   ,      (2, 3)	)
							self.assertEqual(tensor_batch["""labels"""].shape   ,      (2,)	)
							# test converting the converted
							with CaptureStderr() as cs:
									lowercase__      =	batch.convert_to_tensors(tensor_type="""jax"""	)
							self.assertFalse(len(cs.err	)   ,      msg=f'''should have no warning, but got {cs.err}'''	)

							lowercase__      =	BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}	)
							lowercase__      =	batch.convert_to_tensors(tensor_type="""jax"""   ,      prepend_batch_axis=_UpperCAmelCase	)
							self.assertEqual(tensor_batch["""inputs"""].shape   ,      (1, 3)	)
							self.assertEqual(tensor_batch["""labels"""].shape   ,      (1,)	)

					def 						lowerCamelCase__  (self					:		List[Any]	)		->     Optional[Any]:




							"""simple docstring"""


							lowercase__      =	[{"""input_ids""": np.array([0, 1, 2]	)}, {"""input_ids""": np.array([0, 1, 2, 3]	)}]
							lowercase__      =	BertTokenizer.from_pretrained("""bert-base-cased"""	)

							lowercase__      =	tokenizer.pad(_UpperCAmelCase   ,      padding=_UpperCAmelCase	)
							self.assertTrue(isinstance(batch["""input_ids"""]   ,      np.ndarray	)	)
							self.assertEqual(batch["""input_ids"""].tolist()   ,      [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]	)
							lowercase__      =	tokenizer.pad(_UpperCAmelCase   ,      padding=_UpperCAmelCase   ,      return_tensors="""np"""	)
							self.assertTrue(isinstance(batch["""input_ids"""]   ,      np.ndarray	)	)
							self.assertEqual(batch["""input_ids"""].tolist()   ,      [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]	)

					@require_torch
					def 						lowerCamelCase__  (self					:		str	)		->     Dict:




							"""simple docstring"""


							import torch

							lowercase__      =	[{"""input_ids""": torch.tensor([0, 1, 2]	)}, {"""input_ids""": torch.tensor([0, 1, 2, 3]	)}]
							lowercase__      =	BertTokenizer.from_pretrained("""bert-base-cased"""	)

							lowercase__      =	tokenizer.pad(_UpperCAmelCase   ,      padding=_UpperCAmelCase	)
							self.assertTrue(isinstance(batch["""input_ids"""]   ,      torch.Tensor	)	)
							self.assertEqual(batch["""input_ids"""].tolist()   ,      [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]	)
							lowercase__      =	tokenizer.pad(_UpperCAmelCase   ,      padding=_UpperCAmelCase   ,      return_tensors="""pt"""	)
							self.assertTrue(isinstance(batch["""input_ids"""]   ,      torch.Tensor	)	)
							self.assertEqual(batch["""input_ids"""].tolist()   ,      [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]	)

					@require_tf
					def 						lowerCamelCase__  (self					:		Any	)		->     Optional[Any]:




							"""simple docstring"""


							import tensorflow as tf

							lowercase__      =	[{"""input_ids""": tf.constant([0, 1, 2]	)}, {"""input_ids""": tf.constant([0, 1, 2, 3]	)}]
							lowercase__      =	BertTokenizer.from_pretrained("""bert-base-cased"""	)

							lowercase__      =	tokenizer.pad(_UpperCAmelCase   ,      padding=_UpperCAmelCase	)
							self.assertTrue(isinstance(batch["""input_ids"""]   ,      tf.Tensor	)	)
							self.assertEqual(batch["""input_ids"""].numpy().tolist()   ,      [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]	)
							lowercase__      =	tokenizer.pad(_UpperCAmelCase   ,      padding=_UpperCAmelCase   ,      return_tensors="""tf"""	)
							self.assertTrue(isinstance(batch["""input_ids"""]   ,      tf.Tensor	)	)
							self.assertEqual(batch["""input_ids"""].numpy().tolist()   ,      [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]	)

					@require_tokenizers
					def 						lowerCamelCase__  (self					:		Tuple	)		->     str:




							"""simple docstring"""


							lowercase__      =	Tokenizer(WordPiece(unk_token="""[UNK]"""	)	)
							PreTrainedTokenizerFast(tokenizer_object=_UpperCAmelCase	)


					@require_tokenizers
					def 						lowerCamelCase__  (self					:		Optional[int]	)		->     Dict:




							"""simple docstring"""


							lowercase__      =	Tokenizer(WordPiece(unk_token="""[UNK]"""	)	)
							with tempfile.TemporaryDirectory() as tmpdirname:
									bert_tokenizer.save(os.path.join(_UpperCAmelCase   ,      """tokenizer.json"""	)	)
									PreTrainedTokenizerFast(tokenizer_file=os.path.join(_UpperCAmelCase   ,      """tokenizer.json"""	)	)





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A :	Optional[Any]												=     logging.get_logger(__name__)

A :	Union[str, Any]												=     {
    'facebook/sam-vit-huge': 'https://huggingface.co/facebook/sam-vit-huge/resolve/main/config.json',
    'facebook/sam-vit-large': 'https://huggingface.co/facebook/sam-vit-large/resolve/main/config.json',
    'facebook/sam-vit-base': 'https://huggingface.co/facebook/sam-vit-base/resolve/main/config.json',
}



class 							A					( UpperCAmelCase__							):
					'''simple docstring'''






					def __init__(self					:		Dict   ,      _UpperCAmelCase					:		List[str]=256   ,      _UpperCAmelCase					:		Tuple=1024   ,      _UpperCAmelCase					:		List[str]=16   ,      _UpperCAmelCase					:		Optional[Any]=16   ,      _UpperCAmelCase					:		Union[str, Any]=4   ,      _UpperCAmelCase					:		str="gelu"   ,      _UpperCAmelCase					:		List[str]=1E-6   ,      **_UpperCAmelCase					:		List[Any]   ,      )		->     Tuple:




							"""simple docstring"""


							super().__init__(**_UpperCAmelCase	)
							lowercase__      =	hidden_size
							lowercase__      =	image_size
							lowercase__      =	patch_size
							lowercase__      =	image_size // patch_size
							lowercase__      =	mask_input_channels
							lowercase__      =	num_point_embeddings
							lowercase__      =	hidden_act
							lowercase__      =	layer_norm_eps






class 							A					( UpperCAmelCase__							):
					'''simple docstring'''






					def __init__(self					:		List[str]   ,      _UpperCAmelCase					:		Optional[int]=256   ,      _UpperCAmelCase					:		int="relu"   ,      _UpperCAmelCase					:		Union[str, Any]=2048   ,      _UpperCAmelCase					:		int=2   ,      _UpperCAmelCase					:		Optional[Any]=8   ,      _UpperCAmelCase					:		Any=2   ,      _UpperCAmelCase					:		Tuple=3   ,      _UpperCAmelCase					:		List[str]=3   ,      _UpperCAmelCase					:		List[str]=256   ,      _UpperCAmelCase					:		Tuple=1E-6   ,      **_UpperCAmelCase					:		int   ,      )		->     Tuple:




							"""simple docstring"""


							super().__init__(**_UpperCAmelCase	)
							lowercase__      =	hidden_size
							lowercase__      =	hidden_act
							lowercase__      =	mlp_dim
							lowercase__      =	num_hidden_layers
							lowercase__      =	num_attention_heads
							lowercase__      =	attention_downsample_rate
							lowercase__      =	num_multimask_outputs
							lowercase__      =	iou_head_depth
							lowercase__      =	iou_head_hidden_dim
							lowercase__      =	layer_norm_eps






class 							A					( UpperCAmelCase__							):
					'''simple docstring'''






					def __init__(self					:		Optional[int]   ,      _UpperCAmelCase					:		List[Any]=768   ,      _UpperCAmelCase					:		Union[str, Any]=256   ,      _UpperCAmelCase					:		Union[str, Any]=12   ,      _UpperCAmelCase					:		Optional[Any]=12   ,      _UpperCAmelCase					:		Tuple=3   ,      _UpperCAmelCase					:		Optional[int]=1024   ,      _UpperCAmelCase					:		str=16   ,      _UpperCAmelCase					:		Tuple="gelu"   ,      _UpperCAmelCase					:		Any=1E-0_6   ,      _UpperCAmelCase					:		Any=0.0   ,      _UpperCAmelCase					:		str=1E-1_0   ,      _UpperCAmelCase					:		Tuple=True   ,      _UpperCAmelCase					:		Tuple=4.0   ,      _UpperCAmelCase					:		Optional[Any]=True   ,      _UpperCAmelCase					:		Optional[int]=True   ,      _UpperCAmelCase					:		Optional[Any]=14   ,      _UpperCAmelCase					:		Optional[int]=[2, 5, 8, 11]   ,      _UpperCAmelCase					:		List[Any]=128   ,      _UpperCAmelCase					:		int=None   ,      **_UpperCAmelCase					:		int   ,      )		->     Dict:




							"""simple docstring"""


							super().__init__(**_UpperCAmelCase	)

							lowercase__      =	hidden_size
							lowercase__      =	output_channels
							lowercase__      =	num_hidden_layers
							lowercase__      =	num_attention_heads
							lowercase__      =	num_channels
							lowercase__      =	image_size
							lowercase__      =	patch_size
							lowercase__      =	hidden_act
							lowercase__      =	layer_norm_eps
							lowercase__      =	attention_dropout
							lowercase__      =	initializer_range
							lowercase__      =	qkv_bias
							lowercase__      =	mlp_ratio
							lowercase__      =	use_abs_pos
							lowercase__      =	use_rel_pos
							lowercase__      =	window_size
							lowercase__      =	global_attn_indexes
							lowercase__      =	num_pos_feats
							lowercase__      =	int(hidden_size * mlp_ratio	) if mlp_dim is None else mlp_dim






class 							A					( UpperCAmelCase__							):
					'''simple docstring'''






					A__    =				'''sam'''
					A__    =				True

					def __init__(self					:		List[str]   ,      _UpperCAmelCase					:		Any=None   ,      _UpperCAmelCase					:		List[Any]=None   ,      _UpperCAmelCase					:		List[Any]=None   ,      _UpperCAmelCase					:		List[Any]=0.02   ,      **_UpperCAmelCase					:		Optional[Any]   ,      )		->     Dict:




							"""simple docstring"""


							super().__init__(**_UpperCAmelCase	)
							lowercase__      =	vision_config if vision_config is not None else {}
							lowercase__      =	prompt_encoder_config if prompt_encoder_config is not None else {}
							lowercase__      =	mask_decoder_config if mask_decoder_config is not None else {}

							if isinstance(_UpperCAmelCase   ,      _UpperCAmelCase	):
									lowercase__      =	vision_config.to_dict()
							if isinstance(_UpperCAmelCase   ,      _UpperCAmelCase	):
									lowercase__      =	prompt_encoder_config.to_dict()
							if isinstance(_UpperCAmelCase   ,      _UpperCAmelCase	):
									lowercase__      =	mask_decoder_config.to_dict()

							lowercase__      =	SamVisionConfig(**_UpperCAmelCase	)
							lowercase__      =	SamPromptEncoderConfig(**_UpperCAmelCase	)
							lowercase__      =	SamMaskDecoderConfig(**_UpperCAmelCase	)
							lowercase__      =	initializer_range


					def 						lowerCamelCase__  (self					:		Union[str, Any]	)		->     Tuple:




							"""simple docstring"""


							lowercase__      =	copy.deepcopy(self.__dict__	)
							lowercase__      =	self.vision_config.to_dict()
							lowercase__      =	self.prompt_encoder_config.to_dict()
							lowercase__      =	self.mask_decoder_config.to_dict()
							lowercase__      =	self.__class__.model_type
							return output

