
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


A		: Any								=     logging.get_logger(__name__)

A		: Union[str, Any]								=     {
    '''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    a_ (			_a    ):
			a	:			Any					   =							'''gpt_neo'''
			a	:			Optional[Any]					   =							['''past_key_values''']
			a	:			Any					   =							{'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}






			def __init__(      self				,			__UpperCamelCase=50_257				,			__UpperCamelCase=2_048				,			__UpperCamelCase=2_048				,			__UpperCamelCase=24				,			__UpperCamelCase=[[["global", "local"], 12]]				,			__UpperCamelCase=16				,			__UpperCamelCase=None				,			__UpperCamelCase=256				,			__UpperCamelCase="gelu_new"				,			__UpperCamelCase=0.0				,			__UpperCamelCase=0.0				,			__UpperCamelCase=0.0				,			__UpperCamelCase=0.1				,			__UpperCamelCase=1E-5				,			__UpperCamelCase=0.02				,			__UpperCamelCase=True				,			__UpperCamelCase=50_256				,			__UpperCamelCase=50_256				,			**__UpperCamelCase				,			):
									_lowercase					       =					vocab_size
									_lowercase					       =					max_position_embeddings
									_lowercase					       =					hidden_size
									_lowercase					       =					num_layers
									_lowercase					       =					num_heads
									_lowercase					       =					intermediate_size
									_lowercase					       =					window_size
									_lowercase					       =					activation_function
									_lowercase					       =					resid_dropout
									_lowercase					       =					embed_dropout
									_lowercase					       =					attention_dropout
									_lowercase					       =					classifier_dropout
									_lowercase					       =					layer_norm_epsilon
									_lowercase					       =					initializer_range
									_lowercase					       =					use_cache

									_lowercase					       =					bos_token_id
									_lowercase					       =					eos_token_id

									_lowercase					       =					attention_types
									_lowercase					       =					self.expand_attention_types_params(__UpperCamelCase  )

									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=__UpperCamelCase				,			eos_token_id=__UpperCamelCase				,			**__UpperCamelCase  )






			@staticmethod
			def    UpperCamelCase_				(      __UpperCamelCase  ):
									_lowercase					       =					[]
									for item in attention_types:
															for _ in range(item[1]  ):
																					attentions.extend(item[0]  )
									return attentions







def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	List[Any]		,   SCREAMING_SNAKE_CASE_	:	str		,   SCREAMING_SNAKE_CASE_	:	Tuple		,   SCREAMING_SNAKE_CASE_	:	Tuple       )  ->     Optional[int]:
						import torch

						_lowercase					       =					input.size()
						_lowercase					       =					len(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					shape[dimension]

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

						_lowercase					       =					[slice(SCREAMING_SNAKE_CASE_       )] * rank
						_lowercase					       =					indices
						_lowercase					       =					input[s]

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

						return sliced.permute(SCREAMING_SNAKE_CASE_       )




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	List[str]		,   SCREAMING_SNAKE_CASE_	:	Union[str, Any]       )  ->     Optional[int]:
						import torch

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



class    a_ (			_a    ):






			@property
			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}  )
									if self.use_past:
															self.fill_with_past_key_values_(__UpperCamelCase				,			direction="""inputs"""  )
															_lowercase					       =					{0: """batch""", 1: """past_sequence + sequence"""}
									else:
															_lowercase					       =					{0: """batch""", 1: """sequence"""}

									return common_inputs






			@property
			def    UpperCamelCase_				(      self  ):
									return self._config.num_heads






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase = -1				,			__UpperCamelCase = -1				,			__UpperCamelCase = False				,			__UpperCamelCase = None				,			):
									_lowercase					       =					super(__UpperCamelCase				,			self  ).generate_dummy_inputs(
									    __UpperCamelCase				,			batch_size=__UpperCamelCase				,			seq_length=__UpperCamelCase				,			is_pair=__UpperCamelCase				,			framework=__UpperCamelCase  )

									# We need to order the input in the way they appears in the forward()
									_lowercase					       =					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

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

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

									return ordered_inputs






			@property
			def    UpperCamelCase_				(      self  ):
									return 13

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import VivitImageProcessor



class    a_ (			unittest.TestCase    ):






			def __init__(      self				,			__UpperCamelCase				,			__UpperCamelCase=7				,			__UpperCamelCase=3				,			__UpperCamelCase=10				,			__UpperCamelCase=18				,			__UpperCamelCase=30				,			__UpperCamelCase=400				,			__UpperCamelCase=True				,			__UpperCamelCase=None				,			__UpperCamelCase=True				,			__UpperCamelCase=[0.5, 0.5, 0.5]				,			__UpperCamelCase=[0.5, 0.5, 0.5]				,			__UpperCamelCase=None				,			):
									_lowercase					       =					size if size is not None else {"""shortest_edge""": 18}
									_lowercase					       =					crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

									_lowercase					       =					parent
									_lowercase					       =					batch_size
									_lowercase					       =					num_channels
									_lowercase					       =					num_frames
									_lowercase					       =					image_size
									_lowercase					       =					min_resolution
									_lowercase					       =					max_resolution
									_lowercase					       =					do_resize
									_lowercase					       =					size
									_lowercase					       =					do_normalize
									_lowercase					       =					image_mean
									_lowercase					       =					image_std
									_lowercase					       =					crop_size






			def    UpperCamelCase_				(      self  ):
									return {
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_normalize": self.do_normalize,
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "crop_size": self.crop_size,
									}







@require_torch
@require_vision
class    a_ (			_a					,			unittest.TestCase    ):
			a	:			str					   =							VivitImageProcessor if is_vision_available() else None






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					VivitImageProcessingTester(self  )






			@property
			def    UpperCamelCase_				(      self  ):
									return self.image_processor_tester.prepare_image_processor_dict()






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.image_processing_class(**self.image_processor_dict  )
									self.assertTrue(hasattr(__UpperCamelCase				,			"""image_mean"""  )  )
									self.assertTrue(hasattr(__UpperCamelCase				,			"""image_std"""  )  )
									self.assertTrue(hasattr(__UpperCamelCase				,			"""do_normalize"""  )  )
									self.assertTrue(hasattr(__UpperCamelCase				,			"""do_resize"""  )  )
									self.assertTrue(hasattr(__UpperCamelCase				,			"""do_center_crop"""  )  )
									self.assertTrue(hasattr(__UpperCamelCase				,			"""size"""  )  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.image_processing_class.from_dict(self.image_processor_dict  )
									self.assertEqual(image_processor.size				,			{"""shortest_edge""": 18}  )
									self.assertEqual(image_processor.crop_size				,			{"""height""": 18, """width""": 18}  )

									_lowercase					       =					self.image_processing_class.from_dict(self.image_processor_dict				,			size=42				,			crop_size=84  )
									self.assertEqual(image_processor.size				,			{"""shortest_edge""": 42}  )
									self.assertEqual(image_processor.crop_size				,			{"""height""": 84, """width""": 84}  )






			def    UpperCamelCase_				(      self  ):
									# Initialize image_processing
									_lowercase					       =					self.image_processing_class(**self.image_processor_dict  )
									# create random PIL videos
									_lowercase					       =					prepare_video_inputs(self.image_processor_tester				,			equal_resolution=__UpperCamelCase  )
									for video in video_inputs:
															self.assertIsInstance(__UpperCamelCase				,			__UpperCamelCase  )
															self.assertIsInstance(video[0]				,			Image.Image  )

									# Test not batched input
									_lowercase					       =					image_processing(video_inputs[0]				,			return_tensors="""pt"""  ).pixel_values
									self.assertEqual(
									    encoded_videos.shape				,			(
									        1,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )				,			)

									# Test batched
									_lowercase					       =					image_processing(__UpperCamelCase				,			return_tensors="""pt"""  ).pixel_values
									self.assertEqual(
									    encoded_videos.shape				,			(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )				,			)






			def    UpperCamelCase_				(      self  ):
									# Initialize image_processing
									_lowercase					       =					self.image_processing_class(**self.image_processor_dict  )
									# create random numpy tensors
									_lowercase					       =					prepare_video_inputs(self.image_processor_tester				,			equal_resolution=__UpperCamelCase				,			numpify=__UpperCamelCase  )
									for video in video_inputs:
															self.assertIsInstance(__UpperCamelCase				,			__UpperCamelCase  )
															self.assertIsInstance(video[0]				,			np.ndarray  )

									# Test not batched input
									_lowercase					       =					image_processing(video_inputs[0]				,			return_tensors="""pt"""  ).pixel_values
									self.assertEqual(
									    encoded_videos.shape				,			(
									        1,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )				,			)

									# Test batched
									_lowercase					       =					image_processing(__UpperCamelCase				,			return_tensors="""pt"""  ).pixel_values
									self.assertEqual(
									    encoded_videos.shape				,			(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )				,			)






			def    UpperCamelCase_				(      self  ):
									# Initialize image_processing
									_lowercase					       =					self.image_processing_class(**self.image_processor_dict  )
									# create random PyTorch tensors
									_lowercase					       =					prepare_video_inputs(self.image_processor_tester				,			equal_resolution=__UpperCamelCase				,			torchify=__UpperCamelCase  )
									for video in video_inputs:
															self.assertIsInstance(__UpperCamelCase				,			__UpperCamelCase  )
															self.assertIsInstance(video[0]				,			torch.Tensor  )

									# Test not batched input
									_lowercase					       =					image_processing(video_inputs[0]				,			return_tensors="""pt"""  ).pixel_values
									self.assertEqual(
									    encoded_videos.shape				,			(
									        1,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )				,			)

									# Test batched
									_lowercase					       =					image_processing(__UpperCamelCase				,			return_tensors="""pt"""  ).pixel_values
									self.assertEqual(
									    encoded_videos.shape				,			(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )				,			)