
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class    _UpperCamelCase(   SCREAMING_SNAKE_CASE		,						SCREAMING_SNAKE_CASE		,						SCREAMING_SNAKE_CASE				):

						@register_to_config
						def __init__(					self		:			Tuple	,						_lowerCamelCase		:			int	,						_lowerCamelCase		:			int	,						_lowerCamelCase		:			int	,						_lowerCamelCase		:			float	,						_lowerCamelCase		:			int	,						_lowerCamelCase		:			int	,						_lowerCamelCase		:			int	,						_lowerCamelCase		:			int	,						_lowerCamelCase		:			str	,						_lowerCamelCase		:			bool = False	,						):
													super().__init__()

													_UpperCAmelCase  :			Dict	       =		nn.Linear(_lowerCamelCase	,						_lowerCamelCase	,						bias=_lowerCamelCase			)

													_UpperCAmelCase  :			str	       =		nn.Embedding(_lowerCamelCase	,						_lowerCamelCase			)
													_UpperCAmelCase  :			List[Any]	       =		False

													_UpperCAmelCase  :			Union[str, Any]	       =		nn.Dropout(p=_lowerCamelCase			)

													_UpperCAmelCase  :			List[str]	       =		TaConfig(
													    d_model=_lowerCamelCase	,						num_heads=_lowerCamelCase	,						d_kv=_lowerCamelCase	,						d_ff=_lowerCamelCase	,						feed_forward_proj=_lowerCamelCase	,						dropout_rate=_lowerCamelCase	,						is_decoder=_lowerCamelCase	,						is_encoder_decoder=_lowerCamelCase	,						)
													_UpperCAmelCase  :			Optional[int]	       =		nn.ModuleList()
													for lyr_num in range(_lowerCamelCase			):
																				_UpperCAmelCase  :			int	       =		TaBlock(_lowerCamelCase			)
																				self.encoders.append(_lowerCamelCase			)

													_UpperCAmelCase  :			List[Any]	       =		TaLayerNorm(_lowerCamelCase			)
													_UpperCAmelCase  :			Union[str, Any]	       =		nn.Dropout(p=_lowerCamelCase			)






						def 						a__ (					self		:			List[str]	,						_lowerCamelCase		:			Tuple	,						_lowerCamelCase		:			Dict			):
													_UpperCAmelCase  :			Optional[Any]	       =		self.input_proj(_lowerCamelCase			)

													# terminal relative positional encodings
													_UpperCAmelCase  :			Any	       =		encoder_inputs.shape[1]
													_UpperCAmelCase  :			Any	       =		torch.arange(_lowerCamelCase	,						device=encoder_inputs.device			)

													_UpperCAmelCase  :			Optional[int]	       =		encoder_inputs_mask.sum(-1			)
													_UpperCAmelCase  :			Optional[int]	       =		torch.roll(input_positions.unsqueeze(0			)	,						tuple(seq_lens.tolist()			)	,						dims=0			)
													x += self.position_encoding(_lowerCamelCase			)

													_UpperCAmelCase  :			int	       =		self.dropout_pre(_lowerCamelCase			)

													# inverted the attention mask
													_UpperCAmelCase  :			Any	       =		encoder_inputs.size()
													_UpperCAmelCase  :			Union[str, Any]	       =		self.get_extended_attention_mask(_lowerCamelCase	,						_lowerCamelCase			)

													for lyr in self.encoders:
																				_UpperCAmelCase  :			List[Any]	       =		lyr(_lowerCamelCase	,						_lowerCamelCase			)[0]
													_UpperCAmelCase  :			Union[str, Any]	       =		self.layer_norm(_lowerCamelCase			)

													return self.dropout_post(_lowerCamelCase			), encoder_inputs_mask






import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
				from PIL import Image
class    _UpperCamelCase(   unittest.TestCase		,						SCREAMING_SNAKE_CASE				):

						def 						a__ (					self		:			Optional[int]			):
													_UpperCAmelCase  :			Any	       =		load_tool("image-captioning"			)
													self.tool.setup()
													_UpperCAmelCase  :			Optional[Any]	       =		load_tool("image-captioning"	,						remote=_lowerCamelCase			)

						def 						a__ (					self		:			int			):
													_UpperCAmelCase  :			Optional[int]	       =		Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"			)			) / "000000039769.png"			)
													_UpperCAmelCase  :			Optional[int]	       =		self.tool(_lowerCamelCase			)
													self.assertEqual(_lowerCamelCase	,						"two cats sleeping on a couch"			)

						def 						a__ (					self		:			str			):
													_UpperCAmelCase  :			List[str]	       =		Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"			)			) / "000000039769.png"			)
													_UpperCAmelCase  :			Optional[Any]	       =		self.remote_tool(_lowerCamelCase			)
													self.assertEqual(_lowerCamelCase	,						"two cats sleeping on a couch"			)

						def 						a__ (					self		:			Union[str, Any]			):
													_UpperCAmelCase  :			Any	       =		Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"			)			) / "000000039769.png"			)
													_UpperCAmelCase  :			Union[str, Any]	       =		self.tool(image=_lowerCamelCase			)
													self.assertEqual(_lowerCamelCase	,						"two cats sleeping on a couch"			)






						def 						a__ (					self		:			Optional[int]			):
													_UpperCAmelCase  :			str	       =		Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"			)			) / "000000039769.png"			)
													_UpperCAmelCase  :			str	       =		self.remote_tool(image=_lowerCamelCase			)
													self.assertEqual(_lowerCamelCase	,						"two cats sleeping on a couch"			)




