





"""simple docstring"""





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


UpperCamelCase_					:						str				= logging.get_logger(__name__)

UpperCamelCase_					:						int				= {
    '''EleutherAI/gpt-neo-1.3B''': '''https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json''',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}



class __lowerCAmelCase				(					_lowercase      ):




			"""simple docstring"""







			snake_case	      =				"gpt_neo"
			snake_case	      =				["past_key_values"]
			snake_case	      =				{"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}




			def __init__(				self						:					Dict       ,   _snake_case						:					int=50_257       ,   _snake_case						:					Optional[Any]=2_048       ,   _snake_case						:					List[Any]=2_048       ,   _snake_case						:					int=24       ,   _snake_case						:					List[str]=[[["global", "local"], 12]]       ,   _snake_case						:					int=16       ,   _snake_case						:					Tuple=None       ,   _snake_case						:					Optional[int]=256       ,   _snake_case						:					str="gelu_new"       ,   _snake_case						:					Union[str, Any]=0.0       ,   _snake_case						:					Dict=0.0       ,   _snake_case						:					int=0.0       ,   _snake_case						:					Union[str, Any]=0.1       ,   _snake_case						:					Optional[Any]=1e-5       ,   _snake_case						:					Tuple=0.0_2       ,   _snake_case						:					Any=True       ,   _snake_case						:					Tuple=50_256       ,   _snake_case						:					Union[str, Any]=50_256       ,   **_snake_case						:					Optional[Any]       ,   )      -> Any:







								"""simple docstring"""
								A_				 =						vocab_size
								A_				 =						max_position_embeddings
								A_				 =						hidden_size
								A_				 =						num_layers
								A_				 =						num_heads
								A_				 =						intermediate_size
								A_				 =						window_size
								A_				 =						activation_function
								A_				 =						resid_dropout
								A_				 =						embed_dropout
								A_				 =						attention_dropout
								A_				 =						classifier_dropout
								A_				 =						layer_norm_epsilon
								A_				 =						initializer_range
								A_				 =						use_cache

								A_				 =						bos_token_id
								A_				 =						eos_token_id

								A_				 =						attention_types
								A_				 =						self.expand_attention_types_params(_snake_case	)

								if len(self.attention_layers	) != self.num_layers:
													raise ValueError(
													    "Configuration for convolutional module is incorrect. "
													    "It is required that `len(config.attention_layers)` == `config.num_layers` "
													    F'but is `len(config.attention_layers) = {len(self.attention_layers	)}`, '
													    F'`config.num_layers = {self.num_layers}`. '
													    "`config.attention_layers` is prepared using `config.attention_types`. "
													    "Please verify the value of `config.attention_types` argument."	)

								super().__init__(bos_token_id=_snake_case       ,   eos_token_id=_snake_case       ,   **_snake_case	)






			@staticmethod
			def      lowerCamelCase__     (				_snake_case						:					str	)      -> Optional[int]:







								"""simple docstring"""
								A_				 =						[]
								for item in attention_types:
													for _ in range(item[1]	):
																		attentions.extend(item[0]	)
								return attentions





def 							A_     (__a				,   __a				,   __a				,   __a				):

					'''simple docstring'''





					import torch

					A_				 =						input.size()
					A_				 =						len(__a				)
					A_				 =						shape[dimension]

					A_				 =						torch.arange(0				,   __a				,   __a				)
					A_				 =						torch.div(sizedim - size				,   __a				,   rounding_mode="floor"				) + 1
					A_				 =						torch.arange(__a				) + low_indices[:min_length][:, None]

					A_				 =						[slice(__a				)] * rank
					A_				 =						indices
					A_				 =						input[s]

					A_				 =						list(range(0				,   rank + 1				)				)
					perm.append(perm.pop(dimension + 1				)				)

					return sliced.permute(__a				)
def 							A_     (__a				,   __a				):

					'''simple docstring'''





					import torch

					A_				 =						torch.arange(1				,   __a				)
					A_				 =						torch.remainder(__a				,   __a				)
					A_				 =						remainders == 0
					A_				 =						candidates[divisor_indices]
					A_				 =						torch.max(__a				)
					return largest_divisor, torch.div(__a				,   __a				,   rounding_mode="floor"				)




class __lowerCAmelCase				(					_lowercase      ):




			"""simple docstring"""







			@property
			def      lowerCamelCase__     (				self						:					Dict	)      -> Mapping[str, Mapping[int, str]]:







								"""simple docstring"""
								A_				 =						OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}	)
								if self.use_past:
													self.fill_with_past_key_values_(_snake_case       ,   direction="inputs"	)
													A_				 =						{0: "batch", 1: "past_sequence + sequence"}
								else:
													A_				 =						{0: "batch", 1: "sequence"}

								return common_inputs




			@property
			def      lowerCamelCase__     (				self						:					List[Any]	)      -> int:







								"""simple docstring"""
								return self._config.num_heads




			def      lowerCamelCase__     (				self						:					Union[str, Any]       ,   _snake_case						:					Union[str, Any]       ,   _snake_case						:					List[str] = -1       ,   _snake_case						:					str = -1       ,   _snake_case						:					Dict = False       ,   _snake_case						:					Union[str, Any] = None       ,   )      -> Mapping[str, Any]:







								"""simple docstring"""
								A_				 =						super(_snake_case       ,   self	).generate_dummy_inputs(
								    _snake_case       ,   batch_size=_snake_case       ,   seq_length=_snake_case       ,   is_pair=_snake_case       ,   framework=_snake_case	)

								# We need to order the input in the way they appears in the forward()
								A_				 =						OrderedDict({"input_ids": common_inputs["input_ids"]}	)

								# Need to add the past_keys
								if self.use_past:
													if not is_torch_available():
																		raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."	)
													else:
																		import torch

																		A_ ,    A_				 =						common_inputs["input_ids"].shape
																		# Not using the same length for past_key_values
																		A_				 =						seqlen + 2
																		A_				 =						(
																		    batch,
																		    self.num_attention_heads,
																		    past_key_values_length,
																		    self._config.hidden_size // self.num_attention_heads,
																		)
																		A_				 =						[
																		    (torch.zeros(_snake_case	), torch.zeros(_snake_case	)) for _ in range(self.num_layers	)
																		]

								A_				 =						common_inputs["attention_mask"]
								if self.use_past:
													A_				 =						ordered_inputs["attention_mask"].dtype
													A_				 =						torch.cat(
													    [ordered_inputs["attention_mask"], torch.ones(_snake_case       ,   _snake_case       ,   dtype=_snake_case	)]       ,   dim=1	)

								return ordered_inputs






			@property
			def      lowerCamelCase__     (				self						:					Dict	)      -> int:







								"""simple docstring"""
								return 13






"""simple docstring"""





import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
		from PIL import Image
else:



		class __lowerCAmelCase				:




					"""simple docstring"""







					@staticmethod
					def      lowerCamelCase__     (				*_snake_case						:					int       ,   **_snake_case						:					Optional[Any]	)      -> str:







										"""simple docstring"""
										pass
def 							A_     (__a				):

					'''simple docstring'''





					A_				 =						hashlib.mda(image.tobytes()				)
					return m.hexdigest()[:10]
def 							A_     (__a				):

					'''simple docstring'''





					A_				 =						np.array(__a				)
					A_				 =						npimg.shape
					return {"hash": hashimage(__a				), "shape": shape}




@is_pipeline_test
@require_vision
@require_torch
class __lowerCAmelCase				(					unittest.TestCase      ):




			"""simple docstring"""







			snake_case	      =				dict(
			    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()      ) if MODEL_FOR_MASK_GENERATION_MAPPING else [])      )
			snake_case	      =				dict(
			    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()      ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])      )




			def      lowerCamelCase__     (				self						:					List[Any]       ,   _snake_case						:					List[str]       ,   _snake_case						:					Dict       ,   _snake_case						:					int	)      -> str:







								"""simple docstring"""
								A_				 =						MaskGenerationPipeline(model=_snake_case       ,   image_processor=_snake_case	)
								return image_segmenter, [
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								]




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







								"""simple docstring"""
								pass




			@require_tf
			@unittest.skip("Image segmentation not implemented in TF"	)
			def      lowerCamelCase__     (				self						:					Optional[Any]	)      -> Tuple:







								"""simple docstring"""
								pass




			@slow
			@require_torch
			def      lowerCamelCase__     (				self						:					Tuple	)      -> int:







								"""simple docstring"""
								A_				 =						pipeline("mask-generation"       ,   model="facebook/sam-vit-huge"	)

								A_				 =						image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg"       ,   points_per_batch=256	)

								# Shortening by hashing
								A_				 =						[]
								for i, o in enumerate(outputs["masks"]	):
													new_outupt += [{"mask": mask_to_test_readable(_snake_case	), "scores": outputs["scores"][i]}]

								# fmt: off
								self.assertEqual(
								    nested_simplify(_snake_case       ,   decimals=4	)       ,   [
								        {"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.0_4_4_4},
								        {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.0_2_1},
								        {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.0_1_6_7},
								        {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.0_1_3_2},
								        {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.0_0_5_3},
								        {"mask": {"hash": "e2d0b7a0b7", "shape": (480, 640)}, "scores": 0.9_9_6_7},
								        {"mask": {"hash": "453c7844bd", "shape": (480, 640)}, "scores": 0.9_9_3},
								        {"mask": {"hash": "3d44f2926d", "shape": (480, 640)}, "scores": 0.9_9_0_9},
								        {"mask": {"hash": "64033ddc3f", "shape": (480, 640)}, "scores": 0.9_8_7_9},
								        {"mask": {"hash": "801064ff79", "shape": (480, 640)}, "scores": 0.9_8_3_4},
								        {"mask": {"hash": "6172f276ef", "shape": (480, 640)}, "scores": 0.9_7_1_6},
								        {"mask": {"hash": "b49e60e084", "shape": (480, 640)}, "scores": 0.9_6_1_2},
								        {"mask": {"hash": "a811e775fd", "shape": (480, 640)}, "scores": 0.9_5_9_9},
								        {"mask": {"hash": "a6a8ebcf4b", "shape": (480, 640)}, "scores": 0.9_5_5_2},
								        {"mask": {"hash": "9d8257e080", "shape": (480, 640)}, "scores": 0.9_5_3_2},
								        {"mask": {"hash": "32de6454a8", "shape": (480, 640)}, "scores": 0.9_5_1_6},
								        {"mask": {"hash": "af3d4af2c8", "shape": (480, 640)}, "scores": 0.9_4_9_9},
								        {"mask": {"hash": "3c6db475fb", "shape": (480, 640)}, "scores": 0.9_4_8_3},
								        {"mask": {"hash": "c290813fb9", "shape": (480, 640)}, "scores": 0.9_4_6_4},
								        {"mask": {"hash": "b6f0b8f606", "shape": (480, 640)}, "scores": 0.9_4_3},
								        {"mask": {"hash": "92ce16bfdf", "shape": (480, 640)}, "scores": 0.9_4_3},
								        {"mask": {"hash": "c749b25868", "shape": (480, 640)}, "scores": 0.9_4_0_8},
								        {"mask": {"hash": "efb6cab859", "shape": (480, 640)}, "scores": 0.9_3_3_5},
								        {"mask": {"hash": "1ff2eafb30", "shape": (480, 640)}, "scores": 0.9_3_2_6},
								        {"mask": {"hash": "788b798e24", "shape": (480, 640)}, "scores": 0.9_2_6_2},
								        {"mask": {"hash": "abea804f0e", "shape": (480, 640)}, "scores": 0.8_9_9_9},
								        {"mask": {"hash": "7b9e8ddb73", "shape": (480, 640)}, "scores": 0.8_9_8_6},
								        {"mask": {"hash": "cd24047c8a", "shape": (480, 640)}, "scores": 0.8_9_8_4},
								        {"mask": {"hash": "6943e6bcbd", "shape": (480, 640)}, "scores": 0.8_8_7_3},
								        {"mask": {"hash": "b5f47c9191", "shape": (480, 640)}, "scores": 0.8_8_7_1}
								    ]       ,   )
								# fmt: on






			@require_torch
			@slow
			def      lowerCamelCase__     (				self						:					List[Any]	)      -> Optional[int]:







								"""simple docstring"""
								A_				 =						"facebook/sam-vit-huge"
								A_				 =						pipeline("mask-generation"       ,   model=_snake_case	)

								A_				 =						image_segmenter(
								    "http://images.cocodataset.org/val2017/000000039769.jpg"       ,   pred_iou_thresh=1       ,   points_per_batch=256	)

								# Shortening by hashing
								A_				 =						[]
								for i, o in enumerate(outputs["masks"]	):
													new_outupt += [{"mask": mask_to_test_readable(_snake_case	), "scores": outputs["scores"][i]}]

								self.assertEqual(
								    nested_simplify(_snake_case       ,   decimals=4	)       ,   [
								        {"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.0_4_4_4},
								        {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.0_2_1_0},
								        {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.0_1_6_7},
								        {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.0_1_3_2},
								        {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.0_0_5_3},
								    ]       ,   )




