
from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
			import tensorflow as tf

			from transformers import TFBlipTextModel
			from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST





class 	_lowerCAmelCase					:




				def __init__(					self				,				_UpperCamelCase				,				_UpperCamelCase=12				,				_UpperCamelCase=7				,				_UpperCamelCase=True				,				_UpperCamelCase=True				,				_UpperCamelCase=True				,				_UpperCamelCase=99				,				_UpperCamelCase=32				,				_UpperCamelCase=32				,				_UpperCamelCase=2				,				_UpperCamelCase=4				,				_UpperCamelCase=37				,				_UpperCamelCase=0.1				,				_UpperCamelCase=0.1				,				_UpperCamelCase=512				,				_UpperCamelCase=0.02				,				_UpperCamelCase=0				,				_UpperCamelCase=None				,				)				->      str:
							lowerCAmelCase_						=	parent
							lowerCAmelCase_						=	batch_size
							lowerCAmelCase_						=	seq_length
							lowerCAmelCase_						=	is_training
							lowerCAmelCase_						=	use_input_mask
							lowerCAmelCase_						=	use_labels
							lowerCAmelCase_						=	vocab_size
							lowerCAmelCase_						=	hidden_size
							lowerCAmelCase_						=	projection_dim
							lowerCAmelCase_						=	num_hidden_layers
							lowerCAmelCase_						=	num_attention_heads
							lowerCAmelCase_						=	intermediate_size
							lowerCAmelCase_						=	dropout
							lowerCAmelCase_						=	attention_dropout
							lowerCAmelCase_						=	max_position_embeddings
							lowerCAmelCase_						=	initializer_range
							lowerCAmelCase_						=	scope
							lowerCAmelCase_						=	bos_token_id




				def      __a						(					self     )				->      Optional[Any]:
							lowerCAmelCase_						=	ids_tensor([self.batch_size, self.seq_length]				,				self.vocab_size     )

							lowerCAmelCase_						=	None
							if self.use_input_mask:
										lowerCAmelCase_						=	random_attention_mask([self.batch_size, self.seq_length]     )

							if input_mask is not None:
										lowerCAmelCase_						=	input_mask.numpy()
										lowerCAmelCase_	,  lowerCAmelCase_						=	input_mask.shape
										lowerCAmelCase_						=	np.random.randint(1				,				seq_length - 1				,				size=(batch_size,)     )
										for batch_idx, start_index in enumerate(_UpperCamelCase     ):
													lowerCAmelCase_						=	1
													lowerCAmelCase_						=	0

							lowerCAmelCase_						=	self.get_config()

							return config, input_ids, tf.convert_to_tensor(_UpperCamelCase     )




				def      __a						(					self     )				->      Union[str, Any]:
							return BlipTextConfig(
							    vocab_size=self.vocab_size				,				hidden_size=self.hidden_size				,				projection_dim=self.projection_dim				,				num_hidden_layers=self.num_hidden_layers				,				num_attention_heads=self.num_attention_heads				,				intermediate_size=self.intermediate_size				,				dropout=self.dropout				,				attention_dropout=self.attention_dropout				,				max_position_embeddings=self.max_position_embeddings				,				initializer_range=self.initializer_range				,				bos_token_id=self.bos_token_id				,				)




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase     )				->      str:
							lowerCAmelCase_						=	TFBlipTextModel(config=_UpperCamelCase     )
							lowerCAmelCase_						=	model(_UpperCamelCase				,				attention_mask=_UpperCamelCase				,				training=_UpperCamelCase     )
							lowerCAmelCase_						=	model(_UpperCamelCase				,				training=_UpperCamelCase     )
							self.parent.assertEqual(result.last_hidden_state.shape				,				(self.batch_size, self.seq_length, self.hidden_size)     )
							self.parent.assertEqual(result.pooler_output.shape				,				(self.batch_size, self.hidden_size)     )




				def      __a						(					self     )				->      Tuple:
							lowerCAmelCase_						=	self.prepare_config_and_inputs()
							lowerCAmelCase_	,  lowerCAmelCase_	,  lowerCAmelCase_						=	config_and_inputs
							lowerCAmelCase_						=	{"input_ids": input_ids, "attention_mask": input_mask}
							return config, inputs_dict





@require_tf
class 	_lowerCAmelCase					(	__a					,      unittest.TestCase       ):
				_lowercase					=(TFBlipTextModel,) if is_tf_available() else ()
				_lowercase					=False
				_lowercase					=False
				_lowercase					=False




				def      __a						(					self     )				->      Tuple:
							lowerCAmelCase_						=	BlipTextModelTester(self     )
							lowerCAmelCase_						=	ConfigTester(self				,				config_class=_UpperCamelCase				,				hidden_size=37     )




				def      __a						(					self     )				->      Optional[Any]:
							self.config_tester.run_common_tests()




				def      __a						(					self     )				->      str:
							lowerCAmelCase_						=	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_UpperCamelCase     )




				def      __a						(					self     )				->      Dict:
							pass




				def      __a						(					self     )				->      Optional[int]:
							pass




				@unittest.skip(reason="Blip does not use inputs_embeds"     )
				def      __a						(					self     )				->      Dict:
							pass




				@unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING"     )
				def      __a						(					self     )				->      Union[str, Any]:
							pass




				@unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING"     )
				def      __a						(					self     )				->      List[str]:
							pass




				@slow
				def      __a						(					self     )				->      Optional[Any]:
							for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowerCAmelCase_						=	TFBlipTextModel.from_pretrained(_UpperCamelCase     )
										self.assertIsNotNone(_UpperCamelCase     )




				def      __a						(					self				,				_UpperCamelCase=True     )				->      Optional[int]:
							super().test_pt_tf_model_equivalence(allow_missing_keys=_UpperCamelCase     )





from pathlib import Path

import fire






def    lowerCamelCase__				(				__lowerCAmelCase  :  str    ,							__lowerCAmelCase  :  str    ,							__lowerCAmelCase  :  int				):




			"""simple docstring"""


			lowerCAmelCase_						=	Path(__lowerCAmelCase				)
			lowerCAmelCase_						=	Path(__lowerCAmelCase				)
			dest_dir.mkdir(exist_ok=__lowerCAmelCase				)
			for path in src_dir.iterdir():
						lowerCAmelCase_						=	[x.rstrip() for x in list(path.open().readlines()				)][:n]
						lowerCAmelCase_						=	dest_dir.joinpath(path.name				)
						print(__lowerCAmelCase				)
						dest_path.open("w"				).write("\n".join(__lowerCAmelCase				)				)


if __name__ == "__main__":
			fire.Fire(minify)



