






import unittest

import numpy as np

from transformers import (
    GPTSanJapaneseConfig,
    GPTSanJapaneseForConditionalGeneration,
    GPTSanJapaneseModel,
    GPTSanJapaneseTokenizer,
    is_torch_available,
)
from transformers.generation import GenerationConfig
from transformers.testing_utils import require_torch, slow, tooslow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
class 				_UpperCAmelCase :

				"""simple docstring"""




				def __init__(					self						:   List[str],  lowerCamelCase						:   Union[str, Any],  lowerCamelCase						:   Optional[Any]=36_000,  lowerCamelCase						:   Optional[int]=13,  lowerCamelCase						:   str=7,  lowerCamelCase						:   Tuple=True,  lowerCamelCase						:   Dict=32,  lowerCamelCase						:   Optional[int]=42,  lowerCamelCase						:   Optional[int]=5,  lowerCamelCase						:   Union[str, Any]=2,  lowerCamelCase						:   Optional[Any]=4,  lowerCamelCase						:   Any=2,  lowerCamelCase						:   int=32,  lowerCamelCase						:   Dict=80,  lowerCamelCase						:   List[Any]=33,  lowerCamelCase						:   Any=0.0,  lowerCamelCase						:   Tuple=1E-6,  lowerCamelCase						:   str=100,  lowerCamelCase						:   Tuple=0.0,  ):

								'''simple docstring'''
								lowercase__       			=      vocab_size
								lowercase__       			=      parent
								lowercase__       			=      batch_size
								lowercase__       			=      num_contexts
								# For common tests
								lowercase__       			=      self.num_contexts
								lowercase__       			=      is_training
								lowercase__       			=      hidden_size
								lowercase__       			=      num_ext_layers
								lowercase__       			=      ext_size
								lowercase__       			=      num_hidden_layers
								lowercase__       			=      num_attention_heads
								lowercase__       			=      num_experts
								lowercase__       			=      d_ff
								lowercase__       			=      d_ext
								lowercase__       			=      d_spout
								lowercase__       			=      dropout_rate
								lowercase__       			=      layer_norm_epsilon
								lowercase__       			=      expert_capacity
								lowercase__       			=      router_jitter_noise




				def     lowercase__       (					self						:   Optional[int]  ):

								'''simple docstring'''
								return GPTSanJapaneseConfig.from_pretrained('''Tanrei/GPTSAN-japanese'''  )




				def     lowercase__       (					self						:   List[str]  ):

								'''simple docstring'''
								lowercase__       			=      ids_tensor([self.batch_size, self.seq_length],  self.vocab_size  )

								lowercase__       			=      self.get_config()

								return (config, input_ids)




				def     lowercase__       (					self						:   Union[str, Any]  ):

								'''simple docstring'''
								lowercase__       			=      ids_tensor([self.batch_size, self.seq_length],  self.vocab_size  )

								lowercase__       			=      self.get_config()

								return (config, {"input_ids": input_ids})




				def     lowercase__       (					self						:   List[Any]  ):

								'''simple docstring'''
								return GPTSanJapaneseConfig(
								    vocab_size=self.vocab_size,  num_contexts=self.seq_length,  d_model=self.hidden_size,  d_ff=self.d_ff,  d_ext=self.d_ext,  d_spout=self.d_spout,  num_switch_layers=self.num_hidden_layers - self.num_ext_layers,  num_ext_layers=self.num_ext_layers,  num_heads=self.num_attention_heads,  num_experts=self.num_experts,  expert_capacity=self.expert_capacity,  dropout_rate=self.dropout_rate,  layer_norm_epsilon=self.layer_norm_epsilon,  router_jitter_noise=self.router_jitter_noise,  )




				def     lowercase__       (					self						:   Tuple,  lowerCamelCase						:   Tuple,  lowerCamelCase						:   Tuple,  ):

								'''simple docstring'''
								lowercase__       			=      GPTSanJapaneseForConditionalGeneration(config=lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__       			=      model(
								    input_ids=lowerCamelCase,  )
								self.parent.assertIsNotNone(lowerCamelCase  )



@require_torch
class 				_UpperCAmelCase (	A__		,A__		,unittest.TestCase     ):

				"""simple docstring"""




				lowercase__    							=	(GPTSanJapaneseModel,) if is_torch_available() else ()
				lowercase__    							=	(
				    {
				        """conversational""": GPTSanJapaneseForConditionalGeneration,
				        """feature-extraction""": GPTSanJapaneseForConditionalGeneration,
				        """summarization""": GPTSanJapaneseForConditionalGeneration,
				        """text2text-generation""": GPTSanJapaneseForConditionalGeneration,
				        """translation""": GPTSanJapaneseForConditionalGeneration,
				    }
				    if is_torch_available()
				    else {}
				)
				lowercase__    							=	False
				lowercase__    							=	False
				lowercase__    							=	False
				lowercase__    							=	False
				lowercase__    							=	False
				lowercase__    							=	False
				lowercase__    							=	False
				lowercase__    							=	False
				# The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
				lowercase__    							=	[0.8, 0.9]




				def     lowercase__       (					self						:   List[Any],  lowerCamelCase						:   Tuple,  lowerCamelCase						:   Tuple,  lowerCamelCase						:   Union[str, Any],  lowerCamelCase						:   List[Any],  lowerCamelCase						:   Any  ):

								'''simple docstring'''
								if pipeline_test_casse_name == "SummarizationPipelineTests":
												# TODO: fix `_reorder_cache` is not implemented for this model
												return True
								elif pipeline_test_casse_name == "Text2TextGenerationPipelineTests":
												# TODO: check this.
												return True

								return False




				def     lowercase__       (					self						:   Optional[Any]  ):

								'''simple docstring'''
								lowercase__       			=      GPTSanJapaneseTester(self  )
								lowercase__       			=      ConfigTester(self,  config_class=lowerCamelCase,  d_model=37  )




				def     lowercase__       (					self						:   str  ):

								'''simple docstring'''
								GPTSanJapaneseConfig()




				def     lowercase__       (					self						:   Union[str, Any]  ):

								'''simple docstring'''
								lowercase__       			=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCamelCase  )




				@unittest.skip(
				    reason='''skip for now as the computed `max_memory` by `model_split_percents` in the test method will be changed inside `from_pretrained`'''  )
				def     lowercase__       (					self						:   int  ):

								'''simple docstring'''
								super().test_model_parallelism()




				@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''  )
				def     lowercase__       (					self						:   Tuple  ):

								'''simple docstring'''
								pass



@require_torch
class 				_UpperCAmelCase (	A__		,A__		,unittest.TestCase     ):

				"""simple docstring"""




				lowercase__    							=	(GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
				lowercase__    							=	False
				lowercase__    							=	False
				lowercase__    							=	False
				lowercase__    							=	False
				lowercase__    							=	False
				lowercase__    							=	False
				# The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
				lowercase__    							=	[0.8, 0.9]




				def     lowercase__       (					self						:   str  ):

								'''simple docstring'''
								lowercase__       			=      GPTSanJapaneseTester(self  )
								lowercase__       			=      ConfigTester(self,  config_class=lowerCamelCase,  d_model=37  )




				def     lowercase__       (					self						:   str  ):

								'''simple docstring'''
								GPTSanJapaneseConfig()




				def     lowercase__       (					self						:   Optional[Any]  ):

								'''simple docstring'''
								lowercase__       			=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCamelCase  )




				@unittest.skip(
				    reason='''skip for now as the computed `max_memory` by `model_split_percents` in the test method will be changed inside `from_pretrained`'''  )
				def     lowercase__       (					self						:   Tuple  ):

								'''simple docstring'''
								super().test_model_parallelism()




				@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''  )
				def     lowercase__       (					self						:   Tuple  ):

								'''simple docstring'''
								pass




				@slow
				def     lowercase__       (					self						:   List[Any]  ):

								'''simple docstring'''
								lowercase__       			=      GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''  )
								lowercase__       			=      GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''  )
								lowercase__       			=      tokenizer.encode('''武田信玄は''',  return_tensors='''pt'''  )
								lowercase__       			=      model(lowerCamelCase  )
								lowercase__       			=      outputs.logits.detach().cpu().numpy()
								# Output of original model created with mesh-tensoflow
								lowercase__       			=      [
								    # fmt: off
								    [-12.037839889526367, -12.433061599731445, -14.333840370178223, -12.450345993041992, -11.1661376953125,
								    -11.930137634277344, -10.659740447998047, -12.909574508666992, -13.241043090820312, -13.398579597473145,
								    -11.107524871826172, -12.3685941696167, -22.97943115234375, -10.481067657470703, -12.484030723571777,
								    -12.807360649108887, -14.769700050354004, -12.233579635620117, -13.428145408630371, -22.624177932739258],
								    [-7.511149883270264, -8.281851768493652, -7.943127155303955, -7.55021333694458, -6.49869966506958,
								    -7.586796283721924, -6.978085994720459, -7.839145183563232, -8.21964168548584, -8.695091247558594,
								    -6.706910610198975, -6.6585798263549805, -19.565698623657227, -5.353842735290527, -8.350686073303223,
								    -8.039388656616211, -10.856569290161133, -7.75154447555542, -8.819022178649902, -19.51532745361328],
								    [-9.73066234588623, -10.223922729492188, -9.932981491088867, -11.857836723327637, -7.662626266479492,
								    -11.13529109954834, -7.765097618103027, -11.472923278808594, -9.543149948120117, -11.905633926391602,
								    -9.366164207458496, -11.5734281539917, -23.699003219604492, -9.429590225219727, -10.42839241027832,
								    -10.585240364074707, -10.94771957397461, -11.095416069030762, -10.390240669250488, -23.769372940063477],
								    [-9.728265762329102, -9.859712600708008, -10.09729290008545, -9.678522109985352, -6.879519939422607,
								    -9.68487548828125, -4.2803425788879395, -10.018914222717285, -9.308445930480957, -10.63394546508789,
								    -8.083646774291992, -9.06301498413086, -21.904266357421875, -8.90160846710205, -8.841876029968262,
								    -11.856719970703125, -12.079398155212402, -11.233753204345703, -10.177338600158691, -21.87256622314453],
								    [-9.669764518737793, -9.614198684692383, -9.814510345458984, -9.996501922607422, -11.375690460205078,
								    -10.113405227661133, -10.546867370605469, -10.04369068145752, -10.907809257507324, -10.504216194152832,
								    -11.129199028015137, -10.151124000549316, -21.96586799621582, -9.086349487304688, -11.730339050292969,
								    -10.460667610168457, -10.298049926757812, -10.784148216247559, -10.840693473815918, -22.03152847290039],
								    # fmt: on
								]
								lowercase__       			=      np.array(lowerCamelCase  ).flatten()
								lowercase__       			=      output_logits[0, :, :20].flatten()

								def check(lowerCamelCase						:   int,  lowerCamelCase						:   str,  lowerCamelCase						:   List[str]=5E-4  ):
												return abs(a - b  ) < epsilon * max(abs(lowerCamelCase  ),  abs(lowerCamelCase  )  )

								self.assertTrue(np.all([check(target[i],  predict[i]  ) for i in range(len(lowerCamelCase  )  )]  )  )




				@slow
				def     lowercase__       (					self						:   Any  ):

								'''simple docstring'''
								lowercase__       			=      GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''  )
								lowercase__       			=      GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''  )
								model.to(lowerCamelCase  )

								# set deterministically
								lowercase__       			=      GenerationConfig.from_pretrained('''Tanrei/GPTSAN-japanese'''  )
								lowercase__       			=      1

								# use different length sentences to test batching
								lowercase__       			=      [
								    '''甲斐なら武田と言うほど''',
								    '''織田信長は、''',
								]

								lowercase__       			=      '''left'''
								lowercase__       			=      tokenizer(lowerCamelCase,  return_tensors='''pt''',  padding=lowerCamelCase  )
								lowercase__       			=      inputs['''input_ids'''].to(lowerCamelCase  )

								self.assertNotEqual(inputs['''attention_mask'''][0].numpy().tolist(),  inputs['''attention_mask'''][1].numpy().tolist()  )

								lowercase__       			=      model.generate(
								    input_ids=lowerCamelCase,  attention_mask=inputs['''attention_mask'''].to(lowerCamelCase  ),  max_new_tokens=3,  generation_config=lowerCamelCase,  )

								lowercase__       			=      tokenizer(sentences[0],  return_tensors='''pt'''  ).input_ids.to(lowerCamelCase  )
								lowercase__       			=      model.generate(
								    input_ids=lowerCamelCase,  max_new_tokens=3,  generation_config=lowerCamelCase  )

								lowercase__       			=      tokenizer(sentences[1],  return_tensors='''pt'''  ).input_ids.to(lowerCamelCase  )
								lowercase__       			=      model.generate(input_ids=lowerCamelCase,  max_new_tokens=3,  generation_config=lowerCamelCase  )

								self.assertNotEqual(inputs_non_padded.shape,  inputs_padded.shape  )

								lowercase__       			=      tokenizer.batch_decode(lowerCamelCase,  skip_special_tokens=lowerCamelCase  )
								lowercase__       			=      tokenizer.decode(output_non_padded[0],  skip_special_tokens=lowerCamelCase  )
								lowercase__       			=      tokenizer.decode(output_padded[0],  skip_special_tokens=lowerCamelCase  )

								lowercase__       			=      [
								    '''甲斐なら武田と言うほど甲斐の武田''',
								    '''織田信長は、このような''',
								]
								self.assertListEqual(lowerCamelCase,  lowerCamelCase  )
								self.assertListEqual(lowerCamelCase,  [non_padded_sentence, padded_sentence]  )




				@tooslow
				def     lowercase__       (					self						:   Dict  ):

								'''simple docstring'''
								lowercase__       			=      GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''  )
								lowercase__       			=      GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''  )
								# Output of original model created with mesh-tensoflow
								lowercase__       			=      [
								    ('''武田信玄は''', 35_675),
								    ('''武田信玄は、''', 45),
								    ('''武田信玄は、この''', 29),
								    ('''武田信玄は、このよう''', 30_642),
								    ('''武田信玄は、このような''', 35_680),
								    ('''武田信玄は、このような「''', 8_640),
								    ('''武田信玄は、このような「武田''', 31_617),
								    ('''武田信玄は、このような「武田家''', 30_646),
								    ('''武田信玄は、このような「武田家の''', 31_617),
								    ('''武田信玄は、このような「武田家の家''', 31_381),
								]
								for input, output in target:
												lowercase__       			=      tokenizer.encode(lowerCamelCase,  return_tensors='''pt'''  )
												lowercase__       			=      model(lowerCamelCase  )
												lowercase__       			=      outputs.logits.detach().cpu().numpy()[0]
												lowercase__       			=      np.argmax(output_logits[-1]  )
												self.assertEqual(lowerCamelCase,  lowerCamelCase  )




				@slow
				def     lowercase__       (					self						:   List[Any]  ):

								'''simple docstring'''
								lowercase__       			=      GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''  )
								lowercase__       			=      GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''  )
								model.to(lowerCamelCase  )

								# set deterministically
								lowercase__       			=      GenerationConfig.from_pretrained('''Tanrei/GPTSAN-japanese'''  )
								lowercase__       			=      1

								lowercase__       			=      '''武田信玄は、'''
								lowercase__       			=      tokenizer(lowerCamelCase,  return_tensors='''pt'''  ).input_ids.to(lowerCamelCase  )
								lowercase__       			=      tokenizer([input_text, input_text],  return_tensors='''pt'''  ).input_ids.to(lowerCamelCase  )

								# spout from uniform and one-hot
								lowercase__       			=      [
								    # fmt: off
								    [0.87882208, 0.38426396, 0.33220248, 0.43890406, 0.16562252,
								    0.04803985, 0.211572  , 0.23188473, 0.37153068, 0.7836377 ,
								    0.02160172, 0.38761719, 0.75290772, 0.90198857, 0.34365777,
								    0.64168169, 0.44318471, 0.14575746, 0.92562881, 0.40812148,
								    0.29019122, 0.88861599, 0.65524846, 0.43563456, 0.38177187,
								    0.70832965, 0.81527892, 0.68832812, 0.38833192, 0.4561522 ,
								    0.14828817, 0.47248213, 0.54357335, 0.82009566, 0.1338884 ,
								    0.02755417, 0.19764677, 0.2422084 , 0.04757674, 0.65409606,
								    0.0824589 , 0.03304383, 0.94387689, 0.98764509, 0.82433901,
								    0.27646741, 0.64907493, 0.76009406, 0.30087915, 0.17904689,
								    0.41601714, 0.67046398, 0.10422822, 0.08447374, 0.07354344,
								    0.61423565, 0.70284866, 0.7532333 , 0.1972038 , 0.29575659,
								    0.90583886, 0.29265307, 0.50000175, 0.70407655, 0.889363  ,
								    0.81904418, 0.66829128, 0.64468815, 0.56563723, 0.85601875,
								    0.94924672, 0.00166762, 0.25220643, 0.74540219, 0.67993247,
								    0.1549675 , 0.39385352, 0.92153607, 0.63745931, 0.27759043,
								    0.84702295, 0.65904271, 0.58676614, 0.8666936 , 0.39607438,
								    0.79954983, 0.42220697, 0.39650381, 0.7849864 , 0.56150201,
								    0.15678925, 0.14746032, 0.34542114, 0.47026783, 0.11956489,
								    0.25421435, 0.33788901, 0.68934842, 0.36424685, 0.71737898,
								    0.38983449, 0.94393779, 0.39575588, 0.36616553, 0.87104665,
								    0.64630203, 0.22516905, 0.88270804, 0.15031338, 0.75144345,
								    0.46459025, 0.85396454, 0.86355643, 0.65139851, 0.70266061,
								    0.30241389, 0.81056497, 0.88865969, 0.38773807, 0.70635849,
								    0.90718459, 0.43245789, 0.28000654, 0.45935562, 0.08773519,
								    0.9552151 , 0.93901511, 0.22489288], # uniform
								    [1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
								     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
								     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
								     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
								     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
								     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
								     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
								     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
								     0., 0., 0., 0., 0., 0., 0., 0.],
								    # fmt: on
								]

								lowercase__       			=      model.generate(
								    input_ids=lowerCamelCase,  spout=spouts[0],  max_new_tokens=20,  generation_config=lowerCamelCase,  )

								lowercase__       			=      model.generate(
								    input_ids=lowerCamelCase,  spout=spouts[1],  max_new_tokens=20,  generation_config=lowerCamelCase,  )

								lowercase__       			=      model.generate(
								    input_ids=lowerCamelCase,  spout=lowerCamelCase,  max_new_tokens=20,  generation_config=lowerCamelCase,  )

								lowercase__       			=      tokenizer.decode(outputa[0]  )
								lowercase__       			=      tokenizer.decode(outputa[0]  )
								lowercase__       			=      tokenizer.batch_decode(lowerCamelCase  )

								lowercase__       			=      [
								    '''武田信玄は、武田氏の滅亡後、武田氏の居城であった甲斐武田氏の居城である''',
								    '''武田信玄は、武田家の滅亡を防ぐため、武田家の家臣である武田信虎を討''',
								]
								self.assertListEqual(lowerCamelCase,  lowerCamelCase  )
								self.assertListEqual(lowerCamelCase,  [outa_sentence, outa_sentence]  )




				@slow
				def     lowercase__       (					self						:   str  ):

								'''simple docstring'''
								lowercase__       			=      GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''  )
								lowercase__       			=      GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''  )
								model.to(lowerCamelCase  )

								# set deterministically
								lowercase__       			=      GenerationConfig.from_pretrained('''Tanrei/GPTSAN-japanese'''  )
								lowercase__       			=      1

								lowercase__       			=      '''武田信玄'''
								lowercase__       			=      '''織田信長'''
								lowercase__       			=      '''は、'''
								lowercase__       			=      '''が、'''
								lowercase__       			=      tokenizer(lowerCamelCase,  prefix_text=lowerCamelCase,  return_tensors='''pt'''  )
								lowercase__       			=      tokenizer(lowerCamelCase,  prefix_text=lowerCamelCase,  return_tensors='''pt'''  )
								lowercase__       			=      tokenizer([[prefix_text_a, input_text_a], [prefix_text_a, input_text_a]],  return_tensors='''pt'''  )

								lowercase__       			=      model.generate(
								    input_ids=input_tok_a.input_ids.to(lowerCamelCase  ),  token_type_ids=input_tok_a.token_type_ids.to(lowerCamelCase  ),  max_new_tokens=20,  generation_config=lowerCamelCase,  )

								lowercase__       			=      model.generate(
								    input_ids=input_tok_a.input_ids.to(lowerCamelCase  ),  token_type_ids=input_tok_a.token_type_ids.to(lowerCamelCase  ),  max_new_tokens=20,  generation_config=lowerCamelCase,  )

								lowercase__       			=      model.generate(
								    input_ids=input_tok_a.input_ids.to(lowerCamelCase  ),  token_type_ids=input_tok_a.token_type_ids.to(lowerCamelCase  ),  attention_mask=input_tok_a.attention_mask.to(lowerCamelCase  ),  max_new_tokens=20,  generation_config=lowerCamelCase,  )

								lowercase__       			=      tokenizer.decode(outputa[0]  )
								lowercase__       			=      tokenizer.decode(outputa[0]  )
								lowercase__       			=      tokenizer.batch_decode(lowerCamelCase  )

								lowercase__       			=      [
								    '''武田信玄は、武田氏の祖である武田信虎を、その子・武田信友を擁して''',
								    '''織田信長が、織田信長の妻・お市の方を妻として迎えたという逸話が残''',
								]
								self.assertListEqual(lowerCamelCase,  lowerCamelCase  )
								self.assertListEqual(lowerCamelCase,  [outa_sentence, outa_sentence]  )












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


A__     :      int             =			{
    'configuration_sam': [
        'SAM_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SamConfig',
        'SamMaskDecoderConfig',
        'SamPromptEncoderConfig',
        'SamVisionConfig',
    ],
    'processing_sam': ['SamProcessor'],
}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__     :      List[Any]             =			[
							    'SAM_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'SamModel',
							    'SamPreTrainedModel',
							]
try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__     :      int             =			[
							    'TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TFSamModel',
							    'TFSamPreTrainedModel',
							]
try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__     :      Tuple             =			['SamImageProcessor']


if TYPE_CHECKING:
							from .configuration_sam import (
							    SAM_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    SamConfig,
							    SamMaskDecoderConfig,
							    SamPromptEncoderConfig,
							    SamVisionConfig,
							)
							from .processing_sam import SamProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_sam import SAM_PRETRAINED_MODEL_ARCHIVE_LIST, SamModel, SamPreTrainedModel

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_sam import TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST, TFSamModel, TFSamPreTrainedModel

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_sam import SamImageProcessor

else:
							import sys

							A__     :      Dict             =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




