import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
     from PIL import Image

     from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast







@require_vision
class      SCREAMING_SNAKE_CASE__					(  unittest.TestCase    ):

       def 			snake_case__    (							self					:     str			):
           __snake_case       :				Union[str, Any] 			=    tempfile.mkdtemp()

           __snake_case       :				Optional[Any] 			=    CLIPImageProcessor()
           __snake_case       :				Optional[int] 			=    BertTokenizer.from_pretrained(
               """hf-internal-testing/tiny-random-BertModel"""			,			model_input_names=["""input_ids""", """attention_mask"""]			)

           __snake_case       :				Optional[int] 			=    GitProcessor(_lowerCAmelCase			,			_lowerCAmelCase			)

           processor.save_pretrained(self.tmpdirname			)

       def 			snake_case__    (							self					:     Optional[Any]			,			**_lowerCAmelCase					:     Tuple			):
           return AutoProcessor.from_pretrained(self.tmpdirname			,			**_lowerCAmelCase			).tokenizer

       def 			snake_case__    (							self					:     Dict			,			**_lowerCAmelCase					:     List[str]			):
           return AutoProcessor.from_pretrained(self.tmpdirname			,			**_lowerCAmelCase			).image_processor

       def 			snake_case__    (							self					:     int			):
           shutil.rmtree(self.tmpdirname			)

       def 			snake_case__    (							self					:     Optional[Any]			):
           __snake_case       :				Optional[Any] 			=    [np.random.randint(2_55			,			size=(3, 30, 4_00)			,			dtype=np.uinta			)]

           __snake_case       :				Tuple 			=    [Image.fromarray(np.moveaxis(_lowerCAmelCase			,			0			,			-1			)			) for x in image_inputs]

           return image_inputs

       def 			snake_case__    (							self					:     Tuple			):
           __snake_case       :				Tuple 			=    GitProcessor(tokenizer=self.get_tokenizer()			,			image_processor=self.get_image_processor()			)
           processor.save_pretrained(self.tmpdirname			)

           __snake_case       :				Union[str, Any] 			=    self.get_tokenizer(bos_token="""(BOS)"""			,			eos_token="""(EOS)"""			)
           __snake_case       :				int 			=    self.get_image_processor(do_normalize=_lowerCAmelCase			,			padding_value=1.0			)

           __snake_case       :				Union[str, Any] 			=    GitProcessor.from_pretrained(
               self.tmpdirname			,			bos_token="""(BOS)"""			,			eos_token="""(EOS)"""			,			do_normalize=_lowerCAmelCase			,			padding_value=1.0			)

           self.assertEqual(processor.tokenizer.get_vocab()			,			tokenizer_add_kwargs.get_vocab()			)
           self.assertIsInstance(processor.tokenizer			,			_lowerCAmelCase			)

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

       def 			snake_case__    (							self					:     List[str]			):
           __snake_case       :				Optional[int] 			=    self.get_image_processor()
           __snake_case       :				Union[str, Any] 			=    self.get_tokenizer()

           __snake_case       :				List[str] 			=    GitProcessor(tokenizer=_lowerCAmelCase			,			image_processor=_lowerCAmelCase			)

           __snake_case       :				Union[str, Any] 			=    self.prepare_image_inputs()

           __snake_case       :				Optional[Any] 			=    image_processor(_lowerCAmelCase			,			return_tensors="""np"""			)
           __snake_case       :				str 			=    processor(images=_lowerCAmelCase			,			return_tensors="""np"""			)

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

       def 			snake_case__    (							self					:     Optional[int]			):
           __snake_case       :				List[Any] 			=    self.get_image_processor()
           __snake_case       :				Optional[Any] 			=    self.get_tokenizer()

           __snake_case       :				Optional[Any] 			=    GitProcessor(tokenizer=_lowerCAmelCase			,			image_processor=_lowerCAmelCase			)

           __snake_case       :				Any 			=    """lower newer"""

           __snake_case       :				Tuple 			=    processor(text=_lowerCAmelCase			)

           __snake_case       :				int 			=    tokenizer(_lowerCAmelCase			,			return_token_type_ids=_lowerCAmelCase			)

           for key in encoded_tok.keys():
               self.assertListEqual(encoded_tok[key]			,			encoded_processor[key]			)

       def 			snake_case__    (							self					:     Any			):
           __snake_case       :				Any 			=    self.get_image_processor()
           __snake_case       :				Tuple 			=    self.get_tokenizer()

           __snake_case       :				Optional[int] 			=    GitProcessor(tokenizer=_lowerCAmelCase			,			image_processor=_lowerCAmelCase			)

           __snake_case       :				Dict 			=    """lower newer"""
           __snake_case       :				Any 			=    self.prepare_image_inputs()

           __snake_case       :				Tuple 			=    processor(text=_lowerCAmelCase			,			images=_lowerCAmelCase			)

           self.assertListEqual(list(inputs.keys()			)			,			["""input_ids""", """attention_mask""", """pixel_values"""]			)

           # test if it raises when no input is passed
           with pytest.raises(_lowerCAmelCase			):
               processor()

       def 			snake_case__    (							self					:     List[str]			):
           __snake_case       :				Any 			=    self.get_image_processor()
           __snake_case       :				Optional[Any] 			=    self.get_tokenizer()

           __snake_case       :				List[Any] 			=    GitProcessor(tokenizer=_lowerCAmelCase			,			image_processor=_lowerCAmelCase			)

           __snake_case       :				str 			=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           __snake_case       :				Optional[int] 			=    processor.batch_decode(_lowerCAmelCase			)
           __snake_case       :				Dict 			=    tokenizer.batch_decode(_lowerCAmelCase			)

           self.assertListEqual(_lowerCAmelCase			,			_lowerCAmelCase			)





       def 			snake_case__    (							self					:     Union[str, Any]			):
           __snake_case       :				int 			=    self.get_image_processor()
           __snake_case       :				int 			=    self.get_tokenizer()

           __snake_case       :				Dict 			=    GitProcessor(tokenizer=_lowerCAmelCase			,			image_processor=_lowerCAmelCase			)

           __snake_case       :				List[str] 			=    """lower newer"""
           __snake_case       :				int 			=    self.prepare_image_inputs()

           __snake_case       :				List[Any] 			=    processor(text=_lowerCAmelCase			,			images=_lowerCAmelCase			)

           # For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
           self.assertListEqual(list(inputs.keys()			)			,			["""input_ids""", """attention_mask""", """pixel_values"""]			)




import torch
from torch import autograd







class      SCREAMING_SNAKE_CASE__					(  autograd.Function    ):

       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     Any			,			_lowerCAmelCase					:     torch.tensor			,			_lowerCAmelCase					:     float			,			_lowerCAmelCase					:     bool			):
           __snake_case       :				Optional[int] 			=    inputs.numel()
           __snake_case       :				str 			=    int(0.005 * nb_elems			) + 1
           if sigmoid:
               __snake_case       :				Union[str, Any] 			=    (torch.sigmoid(_lowerCAmelCase			) > threshold).type(inputs.type()			)
           else:
               __snake_case       :				Any 			=    (inputs > threshold).type(inputs.type()			)
           if mask.sum() < nb_min:
               # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
               __snake_case       :				Dict 			=    inputs.flatten().kthvalue(max(nb_elems - nb_min			,			1			)			).values
               __snake_case       :				List[str] 			=    (inputs > k_threshold).type(inputs.type()			)
           return mask





       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     str			,			_lowerCAmelCase					:     Tuple			):
           return gradOutput, None, None







class      SCREAMING_SNAKE_CASE__					(  autograd.Function    ):

       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     Optional[int]			,			_lowerCAmelCase					:     torch.tensor			,			_lowerCAmelCase					:     float			):
           __snake_case       :				str 			=    inputs.clone()
           __snake_case			, __snake_case       :				Dict 			=    inputs.flatten().sort(descending=_lowerCAmelCase			)
           __snake_case       :				Optional[Any] 			=    int(threshold * inputs.numel()			)

           # flat_out and mask access the same memory.
           __snake_case       :				Optional[int] 			=    mask.flatten()
           __snake_case       :				Dict 			=    0
           __snake_case       :				Optional[Any] 			=    1
           return mask





       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     Any			,			_lowerCAmelCase					:     Optional[Any]			):
           return gradOutput, None







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):

       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     torch.tensor			,			_lowerCAmelCase					:     float			):
           __snake_case       :				List[str] 			=    inputs.clone()
           __snake_case			, __snake_case       :				str 			=    inputs.abs().flatten().sort(descending=_lowerCAmelCase			)
           __snake_case       :				List[Any] 			=    int(threshold * inputs.numel()			)

           # flat_out and mask access the same memory.
           __snake_case       :				List[Any] 			=    mask.flatten()
           __snake_case       :				int 			=    0
           __snake_case       :				Optional[int] 			=    1
           return mask



