





"""simple docstring"""
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    _UpperCAmelCase  :


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


					def       lowerCamelCase			(   self     :int						):
												A						=    floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]						)
												A						=    self.get_config()
												return config, pixel_values


					def       lowerCamelCase			(   self     :Dict						):
												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       lowerCamelCase			(   self     :str	,			__UpperCamelCase     :Optional[int]	,			__UpperCamelCase     :Dict						):
												A						=    MgpstrForSceneTextRecognition(_UpperCAmelCase						)
												model.to(_UpperCAmelCase						)
												model.eval()
												with torch.no_grad():
																			A						=    model(_UpperCAmelCase						)
												self.parent.assertEqual(
												    generated_ids[0][0].shape	,			(self.batch_size, self.max_token_length, self.num_character_labels)						)


					def       lowerCamelCase			(   self     :Optional[int]						):
												A						=    self.prepare_config_and_inputs()
												A						=    config_and_inputs
												A						=    {'''pixel_values''': pixel_values}
												return config, inputs_dict







@require_torch
class    _UpperCAmelCase  (      UpperCamelCase__	,				UpperCamelCase__	,				unittest.TestCase					):
					UpperCamelCase						=				(MgpstrForSceneTextRecognition,) if is_torch_available() else ()
					UpperCamelCase						=				{"feature-extraction": MgpstrForSceneTextRecognition} if is_torch_available() else {}
					UpperCamelCase						=				False

					UpperCamelCase						=				False
					UpperCamelCase						=				False
					UpperCamelCase						=				False
					UpperCamelCase						=				False


					def       lowerCamelCase			(   self     :Optional[Any]						):
												A						=    MgpstrModelTester(self						)
												A						=    ConfigTester(self	,			config_class=_UpperCAmelCase	,			has_text_modality=_UpperCAmelCase						)


					def       lowerCamelCase			(   self     :Optional[Any]						):
												self.config_tester.run_common_tests()


					def       lowerCamelCase			(   self     :int						):
												A						=    self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*_UpperCAmelCase						)


					@unittest.skip(reason="MgpstrModel does not use inputs_embeds"						)
					def       lowerCamelCase			(   self     :Optional[int]						):
												pass


					def       lowerCamelCase			(   self     :str						):
												A						=    self.model_tester.prepare_config_and_inputs_for_common()

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


					def       lowerCamelCase			(   self     :Dict						):
												A						=    self.model_tester.prepare_config_and_inputs_for_common()

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

																			A						=    ['''pixel_values''']
																			self.assertListEqual(arg_names[:1]	,			_UpperCAmelCase						)


					@unittest.skip(reason="MgpstrModel does not support feedforward chunking"						)
					def       lowerCamelCase			(   self     :List[Any]						):
												pass


					def       lowerCamelCase			(   self     :int						):
												A						=    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

																			A						=    True
																			A						=    model_class(_UpperCAmelCase						)
																			self.assertTrue(model.is_gradient_checkpointing						)


					def       lowerCamelCase			(   self     :Any						):
												def check_hidden_states_output(__UpperCamelCase     :int	,			__UpperCamelCase     :Any	,			__UpperCamelCase     :Optional[int]						):
																			A						=    model_class(_UpperCAmelCase						)
																			model.to(_UpperCAmelCase						)
																			model.eval()

																			with torch.no_grad():
																										A						=    model(**self._prepare_for_class(_UpperCAmelCase	,			_UpperCAmelCase						)						)

																			A						=    outputs.hidden_states

																			A						=    getattr(
																			    self.model_tester	,			"expected_num_hidden_layers"	,			self.model_tester.num_hidden_layers + 1						)
																			self.assertEqual(len(_UpperCAmelCase						)	,			_UpperCAmelCase						)

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

												A						=    self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			A						=    True
																			check_hidden_states_output(_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase						)

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

																			check_hidden_states_output(_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase						)


					def       lowerCamelCase			(   self     :List[Any]						):
												A						=    self.model_tester.prepare_config_and_inputs_for_common()

												A						=    _config_zero_init(_UpperCAmelCase						)
												for model_class in self.all_model_classes:
																			A						=    model_class(config=_UpperCAmelCase						)
																			for name, param in model.named_parameters():
																										if isinstance(_UpperCAmelCase	,			(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       lowerCamelCase			(   self     :Union[str, Any]						):
												pass







def    A__							(     ):
							A						=    '''https://i.postimg.cc/ZKwLg2Gw/367-14.png'''
							A						=    Image.open(requests.get(a_   ,    stream=a_			).raw			).convert("RGB"			)
							return im






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


					@slow
					def       lowerCamelCase			(   self     :str						):
												A						=    '''alibaba-damo/mgp-str-base'''
												A						=    MgpstrForSceneTextRecognition.from_pretrained(_UpperCAmelCase						).to(_UpperCAmelCase						)
												A						=    MgpstrProcessor.from_pretrained(_UpperCAmelCase						)

												A						=    prepare_img()
												A						=    processor(images=_UpperCAmelCase	,			return_tensors="pt"						).pixel_values.to(_UpperCAmelCase						)

												# forward pass
												with torch.no_grad():
																			A						=    model(_UpperCAmelCase						)

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

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

												self.assertEqual(out_strs["generated_text"][0]	,			_UpperCAmelCase						)

												A						=    torch.tensor(
												    [[[-39.5_397, -44.4_024, -36.1_844], [-61.4_709, -63.8_639, -58.3_454], [-74.0_225, -68.5_494, -71.2_164]]]	,			device=_UpperCAmelCase	,			)

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










'''simple docstring'''







import math

import torch
from torch import nn
from torch.nn import init

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer




class 					__UpperCAmelCase (		nn.Linear	):

      '''simple docstring'''





      def __init__(					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase = True  ,		_UpperCAmelCase = "constant"  ,		_UpperCAmelCase = 0.0  ,		_UpperCAmelCase = "topK"  ,		):
             super(_UpperCAmelCase  ,		self						).__init__(in_features=_UpperCAmelCase  ,		out_features=_UpperCAmelCase  ,		bias=_UpperCAmelCase						)
             assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
             UpperCAmelCase__	:   Union[str, Any]												=	pruning_method

             if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
                    UpperCAmelCase__	:   Union[str, Any]												=	mask_scale
                    UpperCAmelCase__	:   Optional[int]												=	mask_init
                    UpperCAmelCase__	:   List[str]												=	nn.Parameter(torch.empty(self.weight.size()						)						)
                    self.init_mask()




      def 							lowerCamelCase     (					self						):
             if self.mask_init == "constant":
                    init.constant_(self.mask_scores  ,		val=self.mask_scale						)
             elif self.mask_init == "uniform":
                    init.uniform_(self.mask_scores  ,		a=-self.mask_scale  ,		b=self.mask_scale						)
             elif self.mask_init == "kaiming":
                    init.kaiming_uniform_(self.mask_scores  ,		a=math.sqrt(5						)						)




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase						):
             # Get the mask
             if self.pruning_method == "topK":
                    UpperCAmelCase__	:   Union[str, Any]												=	TopKBinarizer.apply(self.mask_scores  ,		_UpperCAmelCase						)
             elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
                    UpperCAmelCase__	:   List[Any]												=	'''sigmoied''' in self.pruning_method
                    UpperCAmelCase__	:   Tuple												=	ThresholdBinarizer.apply(self.mask_scores  ,		_UpperCAmelCase  ,		_UpperCAmelCase						)
             elif self.pruning_method == "magnitude":
                    UpperCAmelCase__	:   Optional[int]												=	MagnitudeBinarizer.apply(self.weight  ,		_UpperCAmelCase						)
             elif self.pruning_method == "l0":
                    UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__	:   int												=	-0.1, 1.1, 2 / 3
                    if self.training:
                           UpperCAmelCase__	:   Optional[int]												=	torch.zeros_like(self.mask_scores						).uniform_().clamp(0.00_01  ,		0.99_99						)
                           UpperCAmelCase__	:   Tuple												=	torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b						)
                    else:
                           UpperCAmelCase__	:   Any												=	torch.sigmoid(self.mask_scores						)
                    UpperCAmelCase__	:   List[Any]												=	s * (r - l) + l
                    UpperCAmelCase__	:   Optional[int]												=	s_bar.clamp(min=0.0  ,		max=1.0						)
             # Mask weights with computed mask
             UpperCAmelCase__	:   Union[str, Any]												=	mask * self.weight
             # Compute output (linear layer) with masked weights
             return nn.functional.linear(_UpperCAmelCase  ,		_UpperCAmelCase  ,		self.bias						)