
import logging

# standard libraries
import sys

import numpy as np
import pytest  # type: ignore

# Custom/local libraries
from matrix import matrix_operation as matop

SCREAMING_SNAKE_CASE__     :    Optional[Any]      							=					[[12, 10], [3, 9]]
SCREAMING_SNAKE_CASE__     :    Union[str, Any]      							=					[[3, 4], [7, 4]]
SCREAMING_SNAKE_CASE__     :    List[Any]      							=					[[3, 0, 2], [2, 0, -2], [0, 1, 1]]
SCREAMING_SNAKE_CASE__     :    int      							=					[[3, 0, -2], [2, 0, 2], [0, 1, 1]]
SCREAMING_SNAKE_CASE__     :    List[Any]      							=					[[3, 0, 2], [2, 0, -2], [0, 1, 1], [2, 0, -2]]
SCREAMING_SNAKE_CASE__     :    Dict      							=					[1]
SCREAMING_SNAKE_CASE__     :    List[str]      							=					[2]

SCREAMING_SNAKE_CASE__     :    Union[str, Any]      							=					logging.getLogger()
SCREAMING_SNAKE_CASE__     :    Dict      							=					logging.DEBUG
SCREAMING_SNAKE_CASE__     :    str      							=					logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)




@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ('''mat1''', '''mat2''')	,						[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]							)
def 				__magic_name__				(     __lowerCAmelCase			:		str	,						__lowerCAmelCase			:		Dict							)    ->     List[Any]:
				if (np.array(__lowerCAmelCase							)).shape < (2, 2) or (np.array(__lowerCAmelCase							)).shape < (2, 2):
								with pytest.raises(__lowerCAmelCase							):
												logger.info(f'''\n\t{test_addition.__name__} returned integer'''							)
												matop.add(__lowerCAmelCase	,						__lowerCAmelCase							)
				elif (np.array(__lowerCAmelCase							)).shape == (np.array(__lowerCAmelCase							)).shape:
								logger.info(f'''\n\t{test_addition.__name__} with same matrix dims'''							)
								__lowerCamelCase					      =			(np.array(__lowerCAmelCase							) + np.array(__lowerCAmelCase							)).tolist()
								__lowerCamelCase					      =			matop.add(__lowerCAmelCase	,						__lowerCAmelCase							)
								assert theo == act
				else:
								with pytest.raises(__lowerCAmelCase							):
												logger.info(f'''\n\t{test_addition.__name__} with different matrix dims'''							)
												matop.add(__lowerCAmelCase	,						__lowerCAmelCase							)




@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ('''mat1''', '''mat2''')	,						[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]							)
def 				__magic_name__				(     __lowerCAmelCase			:		Union[str, Any]	,						__lowerCAmelCase			:		Tuple							)    ->     str:
				if (np.array(__lowerCAmelCase							)).shape < (2, 2) or (np.array(__lowerCAmelCase							)).shape < (2, 2):
								with pytest.raises(__lowerCAmelCase							):
												logger.info(f'''\n\t{test_subtraction.__name__} returned integer'''							)
												matop.subtract(__lowerCAmelCase	,						__lowerCAmelCase							)
				elif (np.array(__lowerCAmelCase							)).shape == (np.array(__lowerCAmelCase							)).shape:
								logger.info(f'''\n\t{test_subtraction.__name__} with same matrix dims'''							)
								__lowerCamelCase					      =			(np.array(__lowerCAmelCase							) - np.array(__lowerCAmelCase							)).tolist()
								__lowerCamelCase					      =			matop.subtract(__lowerCAmelCase	,						__lowerCAmelCase							)
								assert theo == act
				else:
								with pytest.raises(__lowerCAmelCase							):
												logger.info(f'''\n\t{test_subtraction.__name__} with different matrix dims'''							)
												assert matop.subtract(__lowerCAmelCase	,						__lowerCAmelCase							)




@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ('''mat1''', '''mat2''')	,						[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]							)
def 				__magic_name__				(     __lowerCAmelCase			:		Optional[Any]	,						__lowerCAmelCase			:		Any							)    ->     Optional[int]:
				if (np.array(__lowerCAmelCase							)).shape < (2, 2) or (np.array(__lowerCAmelCase							)).shape < (2, 2):
								logger.info(f'''\n\t{test_multiplication.__name__} returned integer'''							)
								with pytest.raises(__lowerCAmelCase							):
												matop.add(__lowerCAmelCase	,						__lowerCAmelCase							)
				elif (np.array(__lowerCAmelCase							)).shape == (np.array(__lowerCAmelCase							)).shape:
								logger.info(f'''\n\t{test_multiplication.__name__} meets dim requirements'''							)
								__lowerCamelCase					      =			(np.matmul(__lowerCAmelCase	,						__lowerCAmelCase							)).tolist()
								__lowerCamelCase					      =			matop.multiply(__lowerCAmelCase	,						__lowerCAmelCase							)
								assert theo == act
				else:
								with pytest.raises(__lowerCAmelCase							):
												logger.info(
												    f'''\n\t{test_multiplication.__name__} does not meet dim requirements'''							)
												assert matop.subtract(__lowerCAmelCase	,						__lowerCAmelCase							)




@pytest.mark.mat_ops
def 				__magic_name__				(     )    ->     str:
				__lowerCamelCase					      =			(3.5 * np.array(__lowerCAmelCase							)).tolist()
				__lowerCamelCase					      =			matop.scalar_multiply(__lowerCAmelCase	,						3.5							)
				assert theo == act




@pytest.mark.mat_ops
def 				__magic_name__				(     )    ->     Tuple:
				__lowerCamelCase					      =			(np.identity(5							)).tolist()
				__lowerCamelCase					      =			matop.identity(5							)
				assert theo == act







@pytest.mark.mat_ops
@pytest.mark.parametrize('''mat'''	,						[mat_a, mat_b, mat_c, mat_d, mat_e, mat_f]							)
def 				__magic_name__				(     __lowerCAmelCase			:		str							)    ->     Any:
				if (np.array(__lowerCAmelCase							)).shape < (2, 2):
								with pytest.raises(__lowerCAmelCase							):
												logger.info(f'''\n\t{test_transpose.__name__} returned integer'''							)
												matop.transpose(__lowerCAmelCase							)
				else:
								__lowerCamelCase					      =			(np.transpose(__lowerCAmelCase							)).tolist()
								__lowerCamelCase					      =			matop.transpose(__lowerCAmelCase	,						return_map=__lowerCAmelCase							)
								assert theo == act





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      lowerCAmelCase__  :






		def __init__(	self	:							int							,    SCREAMING_SNAKE_CASE__	:							Union[str, Any]							,    SCREAMING_SNAKE_CASE__	:							List[Any]=False							,    SCREAMING_SNAKE_CASE__	:							Optional[int]=13							,    SCREAMING_SNAKE_CASE__	:							Any=(32, 1_28)							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]=4							,    SCREAMING_SNAKE_CASE__	:							List[Any]=3							,    SCREAMING_SNAKE_CASE__	:							Union[str, Any]=27							,    SCREAMING_SNAKE_CASE__	:							Union[str, Any]=38							,    SCREAMING_SNAKE_CASE__	:							Dict=99							,    SCREAMING_SNAKE_CASE__	:							int=99							,    SCREAMING_SNAKE_CASE__	:							Tuple=32							,    SCREAMING_SNAKE_CASE__	:							Any=5							,    SCREAMING_SNAKE_CASE__	:							str=4							,    SCREAMING_SNAKE_CASE__	:							List[str]=4.0							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]=2_57							,    SCREAMING_SNAKE_CASE__	:							Tuple=None							,    )  ->							List[Any]:
						__lowerCamelCase					      =			parent
						__lowerCamelCase					      =			is_training
						__lowerCamelCase					      =			batch_size
						__lowerCamelCase					      =			image_size
						__lowerCamelCase					      =			patch_size
						__lowerCamelCase					      =			num_channels
						__lowerCamelCase					      =			max_token_length
						__lowerCamelCase					      =			num_character_labels
						__lowerCamelCase					      =			num_bpe_labels
						__lowerCamelCase					      =			num_wordpiece_labels
						__lowerCamelCase					      =			hidden_size
						__lowerCamelCase					      =			num_hidden_layers
						__lowerCamelCase					      =			num_attention_heads
						__lowerCamelCase					      =			mlp_ratio
						__lowerCamelCase					      =			patch_embeds_hidden_size
						__lowerCamelCase					      =			output_hidden_states






		def 				__A (	self	:							Any						)  ->							Optional[int]:
						__lowerCamelCase					      =			floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]						)
						__lowerCamelCase					      =			self.get_config()
						return config, pixel_values






		def 				__A (	self	:							int						)  ->							Union[str, Any]:
						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	:							List[str]							,    SCREAMING_SNAKE_CASE__	:							List[str]							,    SCREAMING_SNAKE_CASE__	:							Union[str, Any]						)  ->							List[Any]:
						__lowerCamelCase					      =			MgpstrForSceneTextRecognition(SCREAMING_SNAKE_CASE__						)
						model.to(SCREAMING_SNAKE_CASE__						)
						model.eval()
						with torch.no_grad():
										__lowerCamelCase					      =			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	:							Dict						)  ->							Union[str, Any]:
						__lowerCamelCase					      =			self.prepare_config_and_inputs()
						__lowerCamelCase      ,					__lowerCamelCase					      =			config_and_inputs
						__lowerCamelCase					      =			{'''pixel_values''': pixel_values}
						return config, inputs_dict





@require_torch
class      lowerCAmelCase__  (			__lowercase						,			__lowercase						,			unittest.TestCase    ):
		a__ :		Dict   						=	(MgpstrForSceneTextRecognition,) if is_torch_available() else ()
		a__ :		Optional[int]   						=	{"""feature-extraction""": MgpstrForSceneTextRecognition} if is_torch_available() else {}
		a__ :		Any   						=	False

		a__ :		List[Any]   						=	False
		a__ :		Any   						=	False
		a__ :		int   						=	False
		a__ :		Tuple   						=	False






		def 				__A (	self	:							Optional[Any]						)  ->							str:
						__lowerCamelCase					      =			MgpstrModelTester(self						)
						__lowerCamelCase					      =			ConfigTester(self							,    config_class=SCREAMING_SNAKE_CASE__							,    has_text_modality=SCREAMING_SNAKE_CASE__						)






		def 				__A (	self	:							List[Any]						)  ->							Tuple:
						self.config_tester.run_common_tests()






		def 				__A (	self	:							List[str]						)  ->							Any:
						__lowerCamelCase					      =			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	:							Optional[int]						)  ->							Dict:
						pass






		def 				__A (	self	:							Tuple						)  ->							Tuple:
						__lowerCamelCase      ,					__lowerCamelCase					      =			self.model_tester.prepare_config_and_inputs_for_common()

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






		def 				__A (	self	:							Tuple						)  ->							Optional[Any]:
						__lowerCamelCase      ,					__lowerCamelCase					      =			self.model_tester.prepare_config_and_inputs_for_common()

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

										__lowerCamelCase					      =			['''pixel_values''']
										self.assertListEqual(arg_names[:1]							,    SCREAMING_SNAKE_CASE__						)






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






		def 				__A (	self	:							str						)  ->							Tuple:
						__lowerCamelCase      ,					__lowerCamelCase					      =			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

										__lowerCamelCase					      =			True
										__lowerCamelCase					      =			model_class(SCREAMING_SNAKE_CASE__						)
										self.assertTrue(model.is_gradient_checkpointing						)






		def 				__A (	self	:							List[str]						)  ->							List[str]:
						def check_hidden_states_output(SCREAMING_SNAKE_CASE__	:							Optional[Any]							,    SCREAMING_SNAKE_CASE__	:							List[Any]							,    SCREAMING_SNAKE_CASE__	:							Union[str, Any]						):
										__lowerCamelCase					      =			model_class(SCREAMING_SNAKE_CASE__						)
										model.to(SCREAMING_SNAKE_CASE__						)
										model.eval()

										with torch.no_grad():
														__lowerCamelCase					      =			model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)						)

										__lowerCamelCase					      =			outputs.hidden_states

										__lowerCamelCase					      =			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]							,    )

						__lowerCamelCase      ,					__lowerCamelCase					      =			self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										__lowerCamelCase					      =			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"]
										__lowerCamelCase					      =			True

										check_hidden_states_output(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)






		def 				__A (	self	:							Tuple						)  ->							int:
						__lowerCamelCase      ,					__lowerCamelCase					      =			self.model_tester.prepare_config_and_inputs_for_common()

						__lowerCamelCase					      =			_config_zero_init(SCREAMING_SNAKE_CASE__						)
						for model_class in self.all_model_classes:
										__lowerCamelCase					      =			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	:							int						)  ->							Union[str, Any]:
						pass





def 				__magic_name__				(     )    ->     Optional[Any]:
				__lowerCamelCase					      =			'''https://i.postimg.cc/ZKwLg2Gw/367-14.png'''
				__lowerCamelCase					      =			Image.open(requests.get(__lowerCAmelCase	,						stream=__lowerCAmelCase							).raw							).convert('''RGB'''							)
				return im







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






		@slow
		def 				__A (	self	:							List[str]						)  ->							Optional[int]:
						__lowerCamelCase					      =			'''alibaba-damo/mgp-str-base'''
						__lowerCamelCase					      =			MgpstrForSceneTextRecognition.from_pretrained(SCREAMING_SNAKE_CASE__						).to(SCREAMING_SNAKE_CASE__						)
						__lowerCamelCase					      =			MgpstrProcessor.from_pretrained(SCREAMING_SNAKE_CASE__						)

						__lowerCamelCase					      =			prepare_img()
						__lowerCamelCase					      =			processor(images=SCREAMING_SNAKE_CASE__							,    return_tensors='''pt'''						).pixel_values.to(SCREAMING_SNAKE_CASE__						)

						# forward pass
						with torch.no_grad():
										__lowerCamelCase					      =			model(SCREAMING_SNAKE_CASE__						)

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

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

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

						__lowerCamelCase					      =			torch.tensor(
						    [[[-39.5397, -44.4024, -36.1844], [-61.4709, -63.8639, -58.3454], [-74.0225, -68.5494, -71.2164]]]							,    device=SCREAMING_SNAKE_CASE__							,    )

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



