
import inspect
import unittest

import requests

from transformers import MgpstrConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch
				from torch import nn

				from transformers import MgpstrForSceneTextRecognition


if is_vision_available():
				from PIL import Image

				from transformers import MgpstrProcessor






class 							SCREAMING_SNAKE_CASE__     :

							"""simple docstring"""






							def __init__(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_=False  ,						SCREAMING_SNAKE_CASE_=13  ,						SCREAMING_SNAKE_CASE_=(32, 128)  ,						SCREAMING_SNAKE_CASE_=4  ,						SCREAMING_SNAKE_CASE_=3  ,						SCREAMING_SNAKE_CASE_=27  ,						SCREAMING_SNAKE_CASE_=38  ,						SCREAMING_SNAKE_CASE_=99  ,						SCREAMING_SNAKE_CASE_=99  ,						SCREAMING_SNAKE_CASE_=32  ,						SCREAMING_SNAKE_CASE_=5  ,						SCREAMING_SNAKE_CASE_=4  ,						SCREAMING_SNAKE_CASE_=4.0  ,						SCREAMING_SNAKE_CASE_=257  ,						SCREAMING_SNAKE_CASE_=None  ,						)-> str:





													'''simple docstring'''






													__UpperCamelCase  	=      parent
													__UpperCamelCase  	=      is_training
													__UpperCamelCase  	=      batch_size
													__UpperCamelCase  	=      image_size
													__UpperCamelCase  	=      patch_size
													__UpperCamelCase  	=      num_channels
													__UpperCamelCase  	=      max_token_length
													__UpperCamelCase  	=      num_character_labels
													__UpperCamelCase  	=      num_bpe_labels
													__UpperCamelCase  	=      num_wordpiece_labels
													__UpperCamelCase  	=      hidden_size
													__UpperCamelCase  	=      num_hidden_layers
													__UpperCamelCase  	=      num_attention_heads
													__UpperCamelCase  	=      mlp_ratio
													__UpperCamelCase  	=      patch_embeds_hidden_size
													__UpperCamelCase  	=      output_hidden_states



							def      A__		(	self      )-> str:





													'''simple docstring'''






													__UpperCamelCase  	=      floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]      )
													__UpperCamelCase  	=      self.get_config()
													return config, pixel_values



							def      A__		(	self      )-> Tuple:





													'''simple docstring'''






													return MgpstrConfig(
													    image_size=self.image_size  ,						patch_size=self.patch_size  ,						num_channels=self.num_channels  ,						max_token_length=self.max_token_length  ,						num_character_labels=self.num_character_labels  ,						num_bpe_labels=self.num_bpe_labels  ,						num_wordpiece_labels=self.num_wordpiece_labels  ,						hidden_size=self.hidden_size  ,						num_hidden_layers=self.num_hidden_layers  ,						num_attention_heads=self.num_attention_heads  ,						mlp_ratio=self.mlp_ratio  ,						output_hidden_states=self.output_hidden_states  ,						)



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )-> Dict:





													'''simple docstring'''






													__UpperCamelCase  	=      MgpstrForSceneTextRecognition(SCREAMING_SNAKE_CASE_      )
													model.to(SCREAMING_SNAKE_CASE_      )
													model.eval()
													with torch.no_grad():
																			__UpperCamelCase  	=      model(SCREAMING_SNAKE_CASE_      )
													self.parent.assertEqual(
													    generated_ids[0][0].shape  ,						(self.batch_size, self.max_token_length, self.num_character_labels)      )



							def      A__		(	self      )-> Optional[int]:





													'''simple docstring'''






													__UpperCamelCase  	=      self.prepare_config_and_inputs()
													__UpperCamelCase	,				__UpperCamelCase  	=      config_and_inputs
													__UpperCamelCase  	=      {'''pixel_values''': pixel_values}
													return config, inputs_dict






@require_torch
class 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_ ,		unittest.TestCase     ):

							"""simple docstring"""






							_snake_case  		=	(MgpstrForSceneTextRecognition,) if is_torch_available() else ()
							_snake_case  		=	{'feature-extraction': MgpstrForSceneTextRecognition} if is_torch_available() else {}
							_snake_case  		=	False

							_snake_case  		=	False
							_snake_case  		=	False
							_snake_case  		=	False
							_snake_case  		=	False



							def      A__		(	self      )-> Tuple:





													'''simple docstring'''






													__UpperCamelCase  	=      MgpstrModelTester(self      )
													__UpperCamelCase  	=      ConfigTester(self  ,						config_class=SCREAMING_SNAKE_CASE_  ,						has_text_modality=SCREAMING_SNAKE_CASE_      )



							def      A__		(	self      )-> Any:





													'''simple docstring'''






													self.config_tester.run_common_tests()



							def      A__		(	self      )-> List[Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_      )



							@unittest.skip(reason='''MgpstrModel does not use inputs_embeds'''      )
							def      A__		(	self      )-> List[str]:





													'''simple docstring'''






													pass



							def      A__		(	self      )-> str:





													'''simple docstring'''






													__UpperCamelCase	,				__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			__UpperCamelCase  	=      model_class(SCREAMING_SNAKE_CASE_      )
																			self.assertIsInstance(model.get_input_embeddings()  ,						(nn.Module)      )
																			__UpperCamelCase  	=      model.get_output_embeddings()
																			self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE_  ,						nn.Linear      )      )



							def      A__		(	self      )-> List[Any]:





													'''simple docstring'''






													__UpperCamelCase	,				__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			__UpperCamelCase  	=      model_class(SCREAMING_SNAKE_CASE_      )
																			__UpperCamelCase  	=      inspect.signature(model.forward      )
																			# signature.parameters is an OrderedDict => so arg_names order is deterministic
																			__UpperCamelCase  	=      [*signature.parameters.keys()]

																			__UpperCamelCase  	=      ['''pixel_values''']
																			self.assertListEqual(arg_names[:1]  ,						SCREAMING_SNAKE_CASE_      )



							@unittest.skip(reason='''MgpstrModel does not support feedforward chunking'''      )
							def      A__		(	self      )-> Optional[Any]:





													'''simple docstring'''






													pass



							def      A__		(	self      )-> int:





													'''simple docstring'''






													__UpperCamelCase	,				__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			if not model_class.supports_gradient_checkpointing:
																									continue

																			__UpperCamelCase  	=      True
																			__UpperCamelCase  	=      model_class(SCREAMING_SNAKE_CASE_      )
																			self.assertTrue(model.is_gradient_checkpointing      )



							def      A__		(	self      )-> str:





													'''simple docstring'''






													def check_hidden_states_output(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      ):
																			__UpperCamelCase  	=      model_class(SCREAMING_SNAKE_CASE_      )
																			model.to(SCREAMING_SNAKE_CASE_      )
																			model.eval()

																			with torch.no_grad():
																									__UpperCamelCase  	=      model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )      )

																			__UpperCamelCase  	=      outputs.hidden_states

																			__UpperCamelCase  	=      getattr(
																			    self.model_tester  ,						'''expected_num_hidden_layers'''  ,						self.model_tester.num_hidden_layers + 1      )
																			self.assertEqual(len(SCREAMING_SNAKE_CASE_      )  ,						SCREAMING_SNAKE_CASE_      )

																			self.assertListEqual(
																			    list(hidden_states[0].shape[-2:]      )  ,						[self.model_tester.patch_embeds_hidden_size, self.model_tester.hidden_size]  ,						)

													__UpperCamelCase	,				__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			__UpperCamelCase  	=      True
																			check_hidden_states_output(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )

																			# check that output_hidden_states also work using config
																			del inputs_dict["output_hidden_states"]
																			__UpperCamelCase  	=      True

																			check_hidden_states_output(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )



							def      A__		(	self      )-> Optional[int]:





													'''simple docstring'''






													__UpperCamelCase	,				__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs_for_common()

													__UpperCamelCase  	=      _config_zero_init(SCREAMING_SNAKE_CASE_      )
													for model_class in self.all_model_classes:
																			__UpperCamelCase  	=      model_class(config=SCREAMING_SNAKE_CASE_      )
																			for name, param in model.named_parameters():
																									if isinstance(SCREAMING_SNAKE_CASE_  ,						(nn.Linear, nn.Convad, nn.LayerNorm)      ):
																															if param.requires_grad:
																																					self.assertIn(
																																					    ((param.data.mean() * 1E9).round() / 1E9).item()  ,						[0.0, 1.0]  ,						msg=F"Parameter {name} of model {model_class} seems not properly initialized"  ,						)



							@unittest.skip(reason='''Retain_grad is tested in individual model tests'''      )
							def      A__		(	self      )-> List[Any]:





													'''simple docstring'''






													pass





def 						A_			(				)   ->     Dict:



						'''simple docstring'''

						__UpperCamelCase  	=      '''https://i.postimg.cc/ZKwLg2Gw/367-14.png'''
						__UpperCamelCase  	=      Image.open(requests.get(snake_case			,   stream=snake_case			).raw			).convert('''RGB'''			)
						return im






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

							"""simple docstring"""






							@slow
							def      A__		(	self      )-> Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      '''alibaba-damo/mgp-str-base'''
													__UpperCamelCase  	=      MgpstrForSceneTextRecognition.from_pretrained(SCREAMING_SNAKE_CASE_      ).to(SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      MgpstrProcessor.from_pretrained(SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      prepare_img()
													__UpperCamelCase  	=      processor(images=SCREAMING_SNAKE_CASE_  ,						return_tensors='''pt'''      ).pixel_values.to(SCREAMING_SNAKE_CASE_      )

													# forward pass
													with torch.no_grad():
																			__UpperCamelCase  	=      model(SCREAMING_SNAKE_CASE_      )

													# verify the logits
													self.assertEqual(outputs.logits[0].shape  ,						torch.Size((1, 27, 38)      )      )

													__UpperCamelCase  	=      processor.batch_decode(outputs.logits      )
													__UpperCamelCase  	=      '''ticket'''

													self.assertEqual(out_strs['''generated_text'''][0]  ,						SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      torch.tensor(
													    [[[-3_9.5_3_9_7, -4_4.4_0_2_4, -3_6.1_8_4_4], [-6_1.4_7_0_9, -6_3.8_6_3_9, -5_8.3_4_5_4], [-7_4.0_2_2_5, -6_8.5_4_9_4, -7_1.2_1_6_4]]]  ,						device=SCREAMING_SNAKE_CASE_  ,						)

													self.assertTrue(torch.allclose(outputs.logits[0][:, 1:4, 1:4]  ,						SCREAMING_SNAKE_CASE_  ,						atol=1E-4      )      )








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


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import TvltImageProcessor






def  _UpperCAmelCase						( __lowerCamelCase			,  __lowerCamelCase=10			,  __lowerCamelCase=10			,  __lowerCamelCase=False			,  __lowerCamelCase=False							)		->Tuple:


 '''simple docstring'''
 snake_case :			Dict							=   []
 for i in range(image_processor_tester.num_frames							):
  video.append(np.random.randint(255			,  size=(image_processor_tester.num_channels, width, height)			,  dtype=np.uinta							)							)

 if not numpify and not torchify:
  # PIL expects the channel dimension as last dimension
  snake_case :			Optional[int]							=   [Image.fromarray(np.moveaxis(__lowerCamelCase			,  0			,  -1							)							) for frame in video]

 if torchify:
  snake_case :			int							=   [torch.from_numpy(__lowerCamelCase							) for frame in video]

 return video






def  _UpperCAmelCase						( __lowerCamelCase			,  __lowerCamelCase=False			,  __lowerCamelCase=False			,  __lowerCamelCase=False							)		->Optional[Any]:


 '''simple docstring'''
 assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

 snake_case :			Any							=   []
 for i in range(image_processor_tester.batch_size							):
  if equal_resolution:
   snake_case :			Optional[int]							=   image_processor_tester.max_resolution
  else:
   snake_case	,       snake_case :			str							=   np.random.choice(
       np.arange(image_processor_tester.min_resolution			,  image_processor_tester.max_resolution							)			,  2							)
   snake_case :			Any							=   prepare_video(
       image_processor_tester=__lowerCamelCase			,  width=__lowerCamelCase			,  height=__lowerCamelCase			,  numpify=__lowerCamelCase			,  torchify=__lowerCamelCase			,  )
  video_inputs.append(__lowerCamelCase							)

 return video_inputs

class   lowerCamelCase						(	unittest.TestCase  ):

       def __init__(      self						,    a__						,    a__=7						,    a__=3						,    a__=4						,    a__=18						,    a__=30						,    a__=4_00						,    a__=True						,    a__=None						,    a__=True						,    a__=[0.5, 0.5, 0.5]						,    a__=[0.5, 0.5, 0.5]						,    a__=True						,    a__=None						,    ):






        '''simple docstring'''






        snake_case :			Tuple							=   size if size is not None else {'''shortest_edge''': 18}
        snake_case :			List[str]							=   crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

        snake_case :			Optional[Any]							=   parent
        snake_case :			List[Any]							=   batch_size
        snake_case :			Optional[int]							=   num_channels
        snake_case :			Union[str, Any]							=   num_frames
        snake_case :			List[Any]							=   image_size
        snake_case :			Tuple							=   min_resolution
        snake_case :			List[Any]							=   max_resolution
        snake_case :			Tuple							=   do_resize
        snake_case :			List[Any]							=   size
        snake_case :			List[Any]							=   do_normalize
        snake_case :			List[Any]							=   image_mean
        snake_case :			List[str]							=   image_std
        snake_case :			Tuple							=   do_center_crop
        snake_case :			str							=   crop_size



       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
        }







@require_torch
@require_vision
class   lowerCamelCase						(	SCREAMING_SNAKE_CASE,      unittest.TestCase  ):
       _UpperCamelCase     		=   TvltImageProcessor if is_vision_available() else None

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Dict							=   TvltImageProcessorTester(self       )

       @property
       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        return self.image_processor_tester.prepare_image_processor_dict()

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Dict							=   self.image_processing_class(**self.image_processor_dict       )
        self.assertTrue(hasattr(a__						,    '''image_mean'''       )       )
        self.assertTrue(hasattr(a__						,    '''image_std'''       )       )
        self.assertTrue(hasattr(a__						,    '''do_normalize'''       )       )
        self.assertTrue(hasattr(a__						,    '''do_resize'''       )       )
        self.assertTrue(hasattr(a__						,    '''do_center_crop'''       )       )
        self.assertTrue(hasattr(a__						,    '''size'''       )       )

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Optional[int]							=   self.image_processing_class(**self.image_processor_dict       )
        # create random PIL videos
        snake_case :			List[str]							=   prepare_video_inputs(self.image_processor_tester						,    equal_resolution=a__       )
        for video in video_inputs:
         self.assertIsInstance(a__						,    a__       )
         self.assertIsInstance(video[0]						,    Image.Image       )

        # Test not batched input
        snake_case :			int							=   image_processor(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
        snake_case :			Optional[Any]							=   image_processor(a__						,    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       ):






        '''simple docstring'''






        snake_case :			Optional[int]							=   self.image_processing_class(**self.image_processor_dict       )
        # create random numpy tensors
        snake_case :			Union[str, Any]							=   prepare_video_inputs(self.image_processor_tester						,    equal_resolution=a__						,    numpify=a__       )
        for video in video_inputs:
         self.assertIsInstance(a__						,    a__       )
         self.assertIsInstance(video[0]						,    np.ndarray       )

        # Test not batched input
        snake_case :			List[str]							=   image_processor(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
        snake_case :			Tuple							=   image_processor(a__						,    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       ):






        '''simple docstring'''






        snake_case :			str							=   self.image_processing_class(**self.image_processor_dict       )
        # create random PyTorch tensors
        snake_case :			str							=   prepare_video_inputs(self.image_processor_tester						,    equal_resolution=a__						,    torchify=a__       )
        for video in video_inputs:
         self.assertIsInstance(a__						,    a__       )
         self.assertIsInstance(video[0]						,    torch.Tensor       )

        # Test not batched input
        snake_case :			Tuple							=   image_processor(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
        snake_case :			Optional[int]							=   image_processor(a__						,    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'''],
            )						,    )






