





import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
       import torch


if is_vision_available():
       from PIL import Image

       from transformers import MgpstrProcessor, ViTImageProcessor




@require_torch
@require_vision
class        SCREAMING_SNAKE_CASE__					(		unittest.TestCase ):
 __SCREAMING_SNAKE_CASE							=      ViTImageProcessor if is_vision_available() else None



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



 def 							UpperCamelCase     (				self					):
        A__		   =		(3, 32, 128)
        A__		   =		tempfile.mkdtemp()

        # fmt: off
        A__		   =		['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
        # fmt: on
        A__		   =		dict(zip(__lowerCamelCase,range(len(__lowerCamelCase					)					)					)					)

        A__		   =		os.path.join(self.tmpdirname,VOCAB_FILES_NAMES['''vocab_file''']					)
        with open(self.vocab_file,'''w''',encoding='''utf-8'''					) as fp:
               fp.write(json.dumps(__lowerCamelCase					) + '''\n'''					)

        A__		   =		{
            '''do_normalize''': False,
            '''do_resize''': True,
            '''image_processor_type''': '''ViTImageProcessor''',
            '''resample''': 3,
            '''size''': {'''height''': 32, '''width''': 128},
        }
        A__		   =		os.path.join(self.tmpdirname,__lowerCamelCase					)
        with open(self.image_processor_file,'''w''',encoding='''utf-8'''					) as fp:
               json.dump(__lowerCamelCase,__lowerCamelCase					)



 def 							UpperCamelCase     (				self,**__lowerCamelCase					):
        return MgpstrTokenizer.from_pretrained(self.tmpdirname,**__lowerCamelCase					)



 def 							UpperCamelCase     (				self,**__lowerCamelCase					):
        return ViTImageProcessor.from_pretrained(self.tmpdirname,**__lowerCamelCase					)



 def 							UpperCamelCase     (				self					):
        shutil.rmtree(self.tmpdirname					)



 def 							UpperCamelCase     (				self					):
        A__		   =		np.random.randint(255,size=(3, 30, 400),dtype=np.uinta					)

        A__		   =		Image.fromarray(np.moveaxis(__lowerCamelCase,0,-1					)					)

        return image_input



 def 							UpperCamelCase     (				self					):
        A__		   =		self.get_tokenizer()
        A__		   =		self.get_image_processor()

        A__		   =		MgpstrProcessor(tokenizer=__lowerCamelCase,image_processor=__lowerCamelCase					)
        processor.save_pretrained(self.tmpdirname					)
        A__		   =		MgpstrProcessor.from_pretrained(self.tmpdirname,use_fast=__lowerCamelCase					)

        self.assertEqual(processor.char_tokenizer.get_vocab(),tokenizer.get_vocab()					)
        self.assertIsInstance(processor.char_tokenizer,__lowerCamelCase					)

        self.assertEqual(processor.image_processor.to_json_string(),image_processor.to_json_string()					)
        self.assertIsInstance(processor.image_processor,__lowerCamelCase					)



 def 							UpperCamelCase     (				self					):
        A__		   =		self.get_tokenizer()
        A__		   =		self.get_image_processor()

        A__		   =		MgpstrProcessor(tokenizer=__lowerCamelCase,image_processor=__lowerCamelCase					)
        processor.save_pretrained(self.tmpdirname					)

        A__		   =		self.get_tokenizer(bos_token='''(BOS)''',eos_token='''(EOS)'''					)
        A__		   =		self.get_image_processor(do_normalize=__lowerCamelCase,padding_value=1.0					)

        A__		   =		MgpstrProcessor.from_pretrained(
            self.tmpdirname,bos_token='''(BOS)''',eos_token='''(EOS)''',do_normalize=__lowerCamelCase,padding_value=1.0					)

        self.assertEqual(processor.char_tokenizer.get_vocab(),tokenizer_add_kwargs.get_vocab()					)
        self.assertIsInstance(processor.char_tokenizer,__lowerCamelCase					)

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



 def 							UpperCamelCase     (				self					):
        A__		   =		self.get_image_processor()
        A__		   =		self.get_tokenizer()

        A__		   =		MgpstrProcessor(tokenizer=__lowerCamelCase,image_processor=__lowerCamelCase					)

        A__		   =		self.prepare_image_inputs()

        A__		   =		image_processor(__lowerCamelCase,return_tensors='''np'''					)
        A__		   =		processor(images=__lowerCamelCase,return_tensors='''np'''					)

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



 def 							UpperCamelCase     (				self					):
        A__		   =		self.get_image_processor()
        A__		   =		self.get_tokenizer()

        A__		   =		MgpstrProcessor(tokenizer=__lowerCamelCase,image_processor=__lowerCamelCase					)

        A__		   =		'''test'''

        A__		   =		processor(text=__lowerCamelCase					)

        A__		   =		tokenizer(__lowerCamelCase					)
        for key in encoded_tok.keys():
               self.assertListEqual(encoded_tok[key],encoded_processor[key]					)



 def 							UpperCamelCase     (				self					):
        A__		   =		self.get_image_processor()
        A__		   =		self.get_tokenizer()

        A__		   =		MgpstrProcessor(tokenizer=__lowerCamelCase,image_processor=__lowerCamelCase					)

        A__		   =		'''test'''
        A__		   =		self.prepare_image_inputs()

        A__		   =		processor(text=__lowerCamelCase,images=__lowerCamelCase					)

        self.assertListEqual(list(inputs.keys()					),['''pixel_values''', '''labels''']					)

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



 def 							UpperCamelCase     (				self					):
        A__		   =		self.get_image_processor()
        A__		   =		self.get_tokenizer()

        A__		   =		MgpstrProcessor(tokenizer=__lowerCamelCase,image_processor=__lowerCamelCase					)

        A__		   =		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

        A__		   =		processor.char_decode(__lowerCamelCase					)
        A__		   =		tokenizer.batch_decode(__lowerCamelCase					)
        A__		   =		[seq.replace(''' ''',''''''					) for seq in decoded_tok]

        self.assertListEqual(__lowerCamelCase,__lowerCamelCase					)



 def 							UpperCamelCase     (				self					):
        A__		   =		self.get_image_processor()
        A__		   =		self.get_tokenizer()

        A__		   =		MgpstrProcessor(tokenizer=__lowerCamelCase,image_processor=__lowerCamelCase					)

        A__		   =		None
        A__		   =		self.prepare_image_inputs()

        A__		   =		processor(text=__lowerCamelCase,images=__lowerCamelCase					)

        self.assertListEqual(list(inputs.keys()					),processor.model_input_names					)




 def 							UpperCamelCase     (				self					):
        A__		   =		self.get_image_processor()
        A__		   =		self.get_tokenizer()

        A__		   =		MgpstrProcessor(tokenizer=__lowerCamelCase,image_processor=__lowerCamelCase					)

        A__		   =		torch.randn(1,27,38					)
        A__		   =		torch.randn(1,27,5_0257					)
        A__		   =		torch.randn(1,27,3_0522					)

        A__		   =		processor.batch_decode([char_input, bpe_input, wp_input]					)

        self.assertListEqual(list(results.keys()					),['''generated_text''', '''scores''', '''char_preds''', '''bpe_preds''', '''wp_preds''']					)












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 ):



 def 							UpperCamelCase     (				self					):
        A__		   =		tempfile.mkdtemp()
        A__		   =		SamImageProcessor()
        A__		   =		SamProcessor(__lowerCamelCase					)
        processor.save_pretrained(self.tmpdirname					)



 def 							UpperCamelCase     (				self,**__lowerCamelCase					):
        return AutoProcessor.from_pretrained(self.tmpdirname,**__lowerCamelCase					).image_processor



 def 							UpperCamelCase     (				self					):
        shutil.rmtree(self.tmpdirname					)



 def 							UpperCamelCase     (				self					):
        A__		   =		[np.random.randint(255,size=(3, 30, 400),dtype=np.uinta					)]

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

        return image_inputs



 def 							UpperCamelCase     (				self					):
        A__		   =		SamProcessor(image_processor=self.get_image_processor()					)
        processor.save_pretrained(self.tmpdirname					)

        A__		   =		self.get_image_processor(do_normalize=__lowerCamelCase,padding_value=1.0					)

        A__		   =		SamProcessor.from_pretrained(self.tmpdirname,do_normalize=__lowerCamelCase,padding_value=1.0					)

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



 def 							UpperCamelCase     (				self					):
        A__		   =		self.get_image_processor()

        A__		   =		SamProcessor(image_processor=__lowerCamelCase					)

        A__		   =		self.prepare_image_inputs()

        A__		   =		image_processor(__lowerCamelCase,return_tensors='''np'''					)
        A__		   =		processor(images=__lowerCamelCase,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 							UpperCamelCase     (				self					):
        A__		   =		self.get_image_processor()

        A__		   =		SamProcessor(image_processor=__lowerCamelCase					)
        A__		   =		[torch.ones((1, 3, 5, 5)					)]

        A__		   =		[[1764, 2646]]

        A__		   =		[[683, 1024]]
        A__		   =		processor.post_process_masks(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase					)
        self.assertEqual(masks[0].shape,(1, 3, 1764, 2646)					)

        A__		   =		processor.post_process_masks(
            __lowerCamelCase,torch.tensor(__lowerCamelCase					),torch.tensor(__lowerCamelCase					)					)
        self.assertEqual(masks[0].shape,(1, 3, 1764, 2646)					)

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

        self.assertEqual(masks[0].shape,(1, 3, 1764, 2646)					)

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




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



 def 							UpperCamelCase     (				self					):
        A__		   =		tempfile.mkdtemp()
        A__		   =		SamImageProcessor()
        A__		   =		SamProcessor(__lowerCamelCase					)
        processor.save_pretrained(self.tmpdirname					)



 def 							UpperCamelCase     (				self,**__lowerCamelCase					):
        return AutoProcessor.from_pretrained(self.tmpdirname,**__lowerCamelCase					).image_processor



 def 							UpperCamelCase     (				self					):
        shutil.rmtree(self.tmpdirname					)



 def 							UpperCamelCase     (				self					):
        A__		   =		[np.random.randint(255,size=(3, 30, 400),dtype=np.uinta					)]

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

        return image_inputs



 def 							UpperCamelCase     (				self					):
        A__		   =		SamProcessor(image_processor=self.get_image_processor()					)
        processor.save_pretrained(self.tmpdirname					)

        A__		   =		self.get_image_processor(do_normalize=__lowerCamelCase,padding_value=1.0					)

        A__		   =		SamProcessor.from_pretrained(self.tmpdirname,do_normalize=__lowerCamelCase,padding_value=1.0					)

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



 def 							UpperCamelCase     (				self					):
        A__		   =		self.get_image_processor()

        A__		   =		SamProcessor(image_processor=__lowerCamelCase					)

        A__		   =		self.prepare_image_inputs()

        A__		   =		image_processor(__lowerCamelCase,return_tensors='''np'''					)
        A__		   =		processor(images=__lowerCamelCase,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 							UpperCamelCase     (				self					):
        A__		   =		self.get_image_processor()

        A__		   =		SamProcessor(image_processor=__lowerCamelCase					)
        A__		   =		[tf.ones((1, 3, 5, 5)					)]

        A__		   =		[[1764, 2646]]

        A__		   =		[[683, 1024]]
        A__		   =		processor.post_process_masks(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,return_tensors='''tf'''					)
        self.assertEqual(masks[0].shape,(1, 3, 1764, 2646)					)

        A__		   =		processor.post_process_masks(
            __lowerCamelCase,tf.convert_to_tensor(__lowerCamelCase					),tf.convert_to_tensor(__lowerCamelCase					),return_tensors='''tf''',)
        self.assertEqual(masks[0].shape,(1, 3, 1764, 2646)					)

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

        self.assertEqual(masks[0].shape,(1, 3, 1764, 2646)					)

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




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



 def 							UpperCamelCase     (				self					):
        A__		   =		tempfile.mkdtemp()
        A__		   =		SamImageProcessor()
        A__		   =		SamProcessor(__lowerCamelCase					)
        processor.save_pretrained(self.tmpdirname					)



 def 							UpperCamelCase     (				self,**__lowerCamelCase					):
        return AutoProcessor.from_pretrained(self.tmpdirname,**__lowerCamelCase					).image_processor



 def 							UpperCamelCase     (				self					):
        shutil.rmtree(self.tmpdirname					)



 def 							UpperCamelCase     (				self					):
        A__		   =		[np.random.randint(255,size=(3, 30, 400),dtype=np.uinta					)]

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

        return image_inputs



 @is_pt_tf_cross_test
 def 							UpperCamelCase     (				self					):
        A__		   =		self.get_image_processor()

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

        A__		   =		[[1764, 2646]]

        A__		   =		[[683, 1024]]
        A__		   =		processor.post_process_masks(
            __lowerCamelCase,__lowerCamelCase,__lowerCamelCase,return_tensors='''tf'''					)
        A__		   =		processor.post_process_masks(
            __lowerCamelCase,__lowerCamelCase,__lowerCamelCase,return_tensors='''pt'''					)

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




 @is_pt_tf_cross_test
 def 							UpperCamelCase     (				self					):
        A__		   =		self.get_image_processor()

        A__		   =		SamProcessor(image_processor=__lowerCamelCase					)

        A__		   =		self.prepare_image_inputs()

        A__		   =		image_processor(__lowerCamelCase,return_tensors='''pt'''					)['''pixel_values'''].numpy()
        A__		   =		processor(images=__lowerCamelCase,return_tensors='''pt'''					)['''pixel_values'''].numpy()

        A__		   =		image_processor(__lowerCamelCase,return_tensors='''tf'''					)['''pixel_values'''].numpy()
        A__		   =		processor(images=__lowerCamelCase,return_tensors='''tf'''					)['''pixel_values'''].numpy()

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





