
import shutil
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_tf_cross_test,
    require_tf,
    require_torch,
    require_torchvision,
    require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available


if is_vision_available():
			from PIL import Image

			from transformers import AutoProcessor, SamImageProcessor, SamProcessor

if is_torch_available():
			import torch

if is_tf_available():
			import tensorflow as tf




@require_vision
@require_torchvision
class    SCREAMING_SNAKE_CASE__     (	unittest.TestCase					):





	'''simple docstring'''



	def  A							(  self	:    Optional[Any]	):


				'''simple docstring'''


				_snake_case		=			tempfile.mkdtemp()
				_snake_case		=			SamImageProcessor()
				_snake_case		=			SamProcessor(lowercase	)
				processor.save_pretrained(self.tmpdirname	)



	def  A							(  self	:    int			,  **lowercase	:    str	):


				'''simple docstring'''


				return AutoProcessor.from_pretrained(self.tmpdirname			,  **lowercase	).image_processor



	def  A							(  self	:    Any	):


				'''simple docstring'''


				shutil.rmtree(self.tmpdirname	)



	def  A							(  self	:    Optional[Any]	):


				'''simple docstring'''


				_snake_case		=			[np.random.randint(255			,  size=(3, 30, 400)			,  dtype=np.uinta	)]

				_snake_case		=			[Image.fromarray(np.moveaxis(lowercase			,  0			,  -1	)	) for x in image_inputs]

				return image_inputs



	def  A							(  self	:    Dict	):


				'''simple docstring'''


				_snake_case		=			SamProcessor(image_processor=self.get_image_processor()	)
				processor.save_pretrained(self.tmpdirname	)

				_snake_case		=			self.get_image_processor(do_normalize=lowercase			,  padding_value=1.0	)

				_snake_case		=			SamProcessor.from_pretrained(self.tmpdirname			,  do_normalize=lowercase			,  padding_value=1.0	)

				self.assertEqual(processor.image_processor.to_json_string()			,  image_processor_add_kwargs.to_json_string()	)
				self.assertIsInstance(processor.image_processor			,  lowercase	)



	def  A							(  self	:    Dict	):


				'''simple docstring'''


				_snake_case		=			self.get_image_processor()

				_snake_case		=			SamProcessor(image_processor=lowercase	)

				_snake_case		=			self.prepare_image_inputs()

				_snake_case		=			image_processor(lowercase			,  return_tensors='np'	)
				_snake_case		=			processor(images=lowercase			,  return_tensors='np'	)

				input_feat_extract.pop('original_sizes'	)  # pop original_sizes as it is popped in the processor
				input_feat_extract.pop('reshaped_input_sizes'	)  # pop original_sizes as it is popped in the processor

				for key in input_feat_extract.keys():
							self.assertAlmostEqual(input_feat_extract[key].sum()			,  input_processor[key].sum()			,  delta=1E-2	)





	@require_torch
	def  A							(  self	:    Optional[int]	):


				'''simple docstring'''


				_snake_case		=			self.get_image_processor()

				_snake_case		=			SamProcessor(image_processor=lowercase	)
				_snake_case		=			[torch.ones((1, 3, 5, 5)	)]

				_snake_case		=			[[1_764, 2_646]]

				_snake_case		=			[[683, 1_024]]
				_snake_case		=			processor.post_process_masks(lowercase			,  lowercase			,  lowercase	)
				self.assertEqual(masks[0].shape			,  (1, 3, 1_764, 2_646)	)

				_snake_case		=			processor.post_process_masks(
				    lowercase			,  torch.tensor(lowercase	)			,  torch.tensor(lowercase	)	)
				self.assertEqual(masks[0].shape			,  (1, 3, 1_764, 2_646)	)

				# should also work with np
				_snake_case		=			[np.ones((1, 3, 5, 5)	)]
				_snake_case		=			processor.post_process_masks(lowercase			,  np.array(lowercase	)			,  np.array(lowercase	)	)

				self.assertEqual(masks[0].shape			,  (1, 3, 1_764, 2_646)	)

				_snake_case		=			[[1, 0], [0, 1]]
				with self.assertRaises(lowercase	):
							_snake_case		=			processor.post_process_masks(lowercase			,  np.array(lowercase	)			,  np.array(lowercase	)	)




@require_vision
@require_tf
class    SCREAMING_SNAKE_CASE__     (	unittest.TestCase					):





	'''simple docstring'''



	def  A							(  self	:    Tuple	):


				'''simple docstring'''


				_snake_case		=			tempfile.mkdtemp()
				_snake_case		=			SamImageProcessor()
				_snake_case		=			SamProcessor(lowercase	)
				processor.save_pretrained(self.tmpdirname	)



	def  A							(  self	:    str			,  **lowercase	:    int	):


				'''simple docstring'''


				return AutoProcessor.from_pretrained(self.tmpdirname			,  **lowercase	).image_processor



	def  A							(  self	:    List[Any]	):


				'''simple docstring'''


				shutil.rmtree(self.tmpdirname	)



	def  A							(  self	:    Any	):


				'''simple docstring'''


				_snake_case		=			[np.random.randint(255			,  size=(3, 30, 400)			,  dtype=np.uinta	)]

				_snake_case		=			[Image.fromarray(np.moveaxis(lowercase			,  0			,  -1	)	) for x in image_inputs]

				return image_inputs



	def  A							(  self	:    Optional[Any]	):


				'''simple docstring'''


				_snake_case		=			SamProcessor(image_processor=self.get_image_processor()	)
				processor.save_pretrained(self.tmpdirname	)

				_snake_case		=			self.get_image_processor(do_normalize=lowercase			,  padding_value=1.0	)

				_snake_case		=			SamProcessor.from_pretrained(self.tmpdirname			,  do_normalize=lowercase			,  padding_value=1.0	)

				self.assertEqual(processor.image_processor.to_json_string()			,  image_processor_add_kwargs.to_json_string()	)
				self.assertIsInstance(processor.image_processor			,  lowercase	)



	def  A							(  self	:    str	):


				'''simple docstring'''


				_snake_case		=			self.get_image_processor()

				_snake_case		=			SamProcessor(image_processor=lowercase	)

				_snake_case		=			self.prepare_image_inputs()

				_snake_case		=			image_processor(lowercase			,  return_tensors='np'	)
				_snake_case		=			processor(images=lowercase			,  return_tensors='np'	)

				input_feat_extract.pop('original_sizes'	)  # pop original_sizes as it is popped in the processor
				input_feat_extract.pop('reshaped_input_sizes'	)  # pop reshaped_input_sizes as it is popped in the processor

				for key in input_feat_extract.keys():
							self.assertAlmostEqual(input_feat_extract[key].sum()			,  input_processor[key].sum()			,  delta=1E-2	)





	@require_tf
	def  A							(  self	:    Tuple	):


				'''simple docstring'''


				_snake_case		=			self.get_image_processor()

				_snake_case		=			SamProcessor(image_processor=lowercase	)
				_snake_case		=			[tf.ones((1, 3, 5, 5)	)]

				_snake_case		=			[[1_764, 2_646]]

				_snake_case		=			[[683, 1_024]]
				_snake_case		=			processor.post_process_masks(lowercase			,  lowercase			,  lowercase			,  return_tensors='tf'	)
				self.assertEqual(masks[0].shape			,  (1, 3, 1_764, 2_646)	)

				_snake_case		=			processor.post_process_masks(
				    lowercase			,  tf.convert_to_tensor(lowercase	)			,  tf.convert_to_tensor(lowercase	)			,  return_tensors='tf'			,  )
				self.assertEqual(masks[0].shape			,  (1, 3, 1_764, 2_646)	)

				# should also work with np
				_snake_case		=			[np.ones((1, 3, 5, 5)	)]
				_snake_case		=			processor.post_process_masks(
				    lowercase			,  np.array(lowercase	)			,  np.array(lowercase	)			,  return_tensors='tf'	)

				self.assertEqual(masks[0].shape			,  (1, 3, 1_764, 2_646)	)

				_snake_case		=			[[1, 0], [0, 1]]
				with self.assertRaises(tf.errors.InvalidArgumentError	):
							_snake_case		=			processor.post_process_masks(
							    lowercase			,  np.array(lowercase	)			,  np.array(lowercase	)			,  return_tensors='tf'	)




@require_vision
@require_torchvision
class    SCREAMING_SNAKE_CASE__     (	unittest.TestCase					):





	'''simple docstring'''



	def  A							(  self	:    Optional[Any]	):


				'''simple docstring'''


				_snake_case		=			tempfile.mkdtemp()
				_snake_case		=			SamImageProcessor()
				_snake_case		=			SamProcessor(lowercase	)
				processor.save_pretrained(self.tmpdirname	)



	def  A							(  self	:    Any			,  **lowercase	:    List[str]	):


				'''simple docstring'''


				return AutoProcessor.from_pretrained(self.tmpdirname			,  **lowercase	).image_processor



	def  A							(  self	:    Dict	):


				'''simple docstring'''


				shutil.rmtree(self.tmpdirname	)



	def  A							(  self	:    Tuple	):


				'''simple docstring'''


				_snake_case		=			[np.random.randint(255			,  size=(3, 30, 400)			,  dtype=np.uinta	)]

				_snake_case		=			[Image.fromarray(np.moveaxis(lowercase			,  0			,  -1	)	) for x in image_inputs]

				return image_inputs



	@is_pt_tf_cross_test
	def  A							(  self	:    List[Any]	):


				'''simple docstring'''


				_snake_case		=			self.get_image_processor()

				_snake_case		=			SamProcessor(image_processor=lowercase	)
				_snake_case		=			np.random.randint(0			,  2			,  size=(1, 3, 5, 5)	).astype(np.floataa	)
				_snake_case		=			[tf.convert_to_tensor(lowercase	)]
				_snake_case		=			[torch.tensor(lowercase	)]

				_snake_case		=			[[1_764, 2_646]]

				_snake_case		=			[[683, 1_024]]
				_snake_case		=			processor.post_process_masks(
				    lowercase			,  lowercase			,  lowercase			,  return_tensors='tf'	)
				_snake_case		=			processor.post_process_masks(
				    lowercase			,  lowercase			,  lowercase			,  return_tensors='pt'	)

				self.assertTrue(np.all(tf_masks[0].numpy() == pt_masks[0].numpy()	)	)





	@is_pt_tf_cross_test
	def  A							(  self	:    Optional[int]	):


				'''simple docstring'''


				_snake_case		=			self.get_image_processor()

				_snake_case		=			SamProcessor(image_processor=lowercase	)

				_snake_case		=			self.prepare_image_inputs()

				_snake_case		=			image_processor(lowercase			,  return_tensors='pt'	)['pixel_values'].numpy()
				_snake_case		=			processor(images=lowercase			,  return_tensors='pt'	)['pixel_values'].numpy()

				_snake_case		=			image_processor(lowercase			,  return_tensors='tf'	)['pixel_values'].numpy()
				_snake_case		=			processor(images=lowercase			,  return_tensors='tf'	)['pixel_values'].numpy()

				self.assertTrue(np.allclose(lowercase			,  lowercase	)	)
				self.assertTrue(np.allclose(lowercase			,  lowercase	)	)
				self.assertTrue(np.allclose(lowercase			,  lowercase	)	)

from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
			from ...onnx.config import PatchingSpec
			from ...tokenization_utils_base import PreTrainedTokenizerBase


_lowerCamelCase       :				List[Any]       =			logging.get_logger(__name__)

_lowerCamelCase       :				Union[str, Any]       =			{
    '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json''',
    '''allenai/longformer-large-4096''': '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json''',
    '''allenai/longformer-large-4096-finetuned-triviaqa''': (
        '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json'''
    ),
    '''allenai/longformer-base-4096-extra.pos.embd.only''': (
        '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json'''
    ),
    '''allenai/longformer-large-4096-extra.pos.embd.only''': (
        '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json'''
    ),
}




class    SCREAMING_SNAKE_CASE__     (	UpperCAmelCase					):





	'''simple docstring'''



	_UpperCAmelCase       :		Dict       =					"longformer"



	def __init__(  self	:    Optional[Any]			,  lowercase	:    Union[List[int], int] = 512			,  lowercase	:    int = 2			,  lowercase	:    int = 1			,  lowercase	:    int = 0			,  lowercase	:    int = 2			,  lowercase	:    int = 30_522			,  lowercase	:    int = 768			,  lowercase	:    int = 12			,  lowercase	:    int = 12			,  lowercase	:    int = 3_072			,  lowercase	:    str = "gelu"			,  lowercase	:    float = 0.1			,  lowercase	:    float = 0.1			,  lowercase	:    int = 512			,  lowercase	:    int = 2			,  lowercase	:    float = 0.02			,  lowercase	:    float = 1E-12			,  lowercase	:    bool = False			,  **lowercase	:    Optional[Any]			,  ):


				'''simple docstring'''


				super().__init__(pad_token_id=lowercase			,  **lowercase	)

				_snake_case		=			attention_window
				_snake_case		=			sep_token_id
				_snake_case		=			bos_token_id
				_snake_case		=			eos_token_id
				_snake_case		=			vocab_size
				_snake_case		=			hidden_size
				_snake_case		=			num_hidden_layers
				_snake_case		=			num_attention_heads
				_snake_case		=			hidden_act
				_snake_case		=			intermediate_size
				_snake_case		=			hidden_dropout_prob
				_snake_case		=			attention_probs_dropout_prob
				_snake_case		=			max_position_embeddings
				_snake_case		=			type_vocab_size
				_snake_case		=			initializer_range
				_snake_case		=			layer_norm_eps
				_snake_case		=			onnx_export




class    SCREAMING_SNAKE_CASE__     (	UpperCAmelCase					):





	'''simple docstring'''



	def __init__(  self	:    int			,  lowercase	:    "PretrainedConfig"			,  lowercase	:    str = "default"			,  lowercase	:    "List[PatchingSpec]" = None	):


				'''simple docstring'''


				super().__init__(lowercase			,  lowercase			,  lowercase	)
				_snake_case		=			True



	@property
	def  A							(  self	:    Union[str, Any]	):


				'''simple docstring'''


				if self.task == "multiple-choice":
							_snake_case		=			{0: 'batch', 1: 'choice', 2: 'sequence'}
				else:
							_snake_case		=			{0: 'batch', 1: 'sequence'}
				return OrderedDict(
				    [
				        ('input_ids', dynamic_axis),
				        ('attention_mask', dynamic_axis),
				        ('global_attention_mask', dynamic_axis),
				    ]	)



	@property
	def  A							(  self	:    int	):


				'''simple docstring'''


				_snake_case		=			super().outputs
				if self.task == "default":
							_snake_case		=			{0: 'batch'}
				return outputs



	@property
	def  A							(  self	:    List[Any]	):


				'''simple docstring'''


				return 1E-4



	@property
	def  A							(  self	:    List[str]	):


				'''simple docstring'''


				return max(super().default_onnx_opset			,  14	)





	def  A							(  self	:    str			,  lowercase	:    "PreTrainedTokenizerBase"			,  lowercase	:    int = -1			,  lowercase	:    int = -1			,  lowercase	:    bool = False			,  lowercase	:    Optional[TensorType] = None			,  ):


				'''simple docstring'''


				_snake_case		=			super().generate_dummy_inputs(
				    preprocessor=lowercase			,  batch_size=lowercase			,  seq_length=lowercase			,  is_pair=lowercase			,  framework=lowercase	)
				import torch

				# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
				# makes the export fail randomly
				_snake_case		=			torch.zeros_like(inputs['input_ids']	)
				# make every second token global
				_snake_case		=			1

				return inputs