






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				:





	"""simple docstring"""






	def __init__(							self ,					UpperCAmelCase ,					UpperCAmelCase=False ,					UpperCAmelCase=13 ,					UpperCAmelCase=(32, 128) ,					UpperCAmelCase=4 ,					UpperCAmelCase=3 ,					UpperCAmelCase=27 ,					UpperCAmelCase=38 ,					UpperCAmelCase=99 ,					UpperCAmelCase=99 ,					UpperCAmelCase=32 ,					UpperCAmelCase=5 ,					UpperCAmelCase=4 ,					UpperCAmelCase=4.0 ,					UpperCAmelCase=257 ,					UpperCAmelCase=None ,					):
							"""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  UpperCamelCase					(							self					):
							"""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  UpperCamelCase					(							self					):
							"""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  UpperCamelCase					(							self ,					UpperCAmelCase ,					UpperCAmelCase					):
							"""simple docstring"""







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

	def  UpperCamelCase					(							self					):
							"""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 __lowerCamelCase				(   snake_case__  , snake_case__  , unittest.TestCase):





	"""simple docstring"""






	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  UpperCamelCase					(							self					):
							"""simple docstring"""







							_UpperCAmelCase             =    MgpstrModelTester(self					)
							_UpperCAmelCase             =    ConfigTester(self ,					config_class=UpperCAmelCase ,					has_text_modality=UpperCAmelCase					)

	def  UpperCamelCase					(							self					):
							"""simple docstring"""







							self.config_tester.run_common_tests()

	def  UpperCamelCase					(							self					):
							"""simple docstring"""







							_UpperCAmelCase             =    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  UpperCamelCase					(							self					):
							"""simple docstring"""







							pass

	def  UpperCamelCase					(							self					):
							"""simple docstring"""







							_UpperCAmelCase      ,				_UpperCAmelCase             =    self.model_tester.prepare_config_and_inputs_for_common()

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

	def  UpperCamelCase					(							self					):
							"""simple docstring"""







							_UpperCAmelCase      ,				_UpperCAmelCase             =    self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													_UpperCAmelCase             =    model_class(UpperCAmelCase					)
													_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] ,					UpperCAmelCase					)

	@unittest.skip(reason='MgpstrModel does not support feedforward chunking'					)
	def  UpperCamelCase					(							self					):
							"""simple docstring"""







							pass

	def  UpperCamelCase					(							self					):
							"""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(UpperCAmelCase					)
													self.assertTrue(model.is_gradient_checkpointing					)

	def  UpperCamelCase					(							self					):
							"""simple docstring"""







							def check_hidden_states_output(UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase					):
													_UpperCAmelCase             =    model_class(UpperCAmelCase					)
													model.to(UpperCAmelCase					)
													model.eval()

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

													_UpperCAmelCase             =    outputs.hidden_states

													_UpperCAmelCase             =    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] ,					)

							_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(UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase					)

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

													check_hidden_states_output(UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase					)

	def  UpperCamelCase					(							self					):
							"""simple docstring"""







							_UpperCAmelCase      ,				_UpperCAmelCase             =    self.model_tester.prepare_config_and_inputs_for_common()

							_UpperCAmelCase             =    _config_zero_init(UpperCAmelCase					)
							for model_class in self.all_model_classes:
													_UpperCAmelCase             =    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  UpperCamelCase					(							self					):
							"""simple docstring"""







							pass



def __A					(   )->						str:





						"""simple docstring"""



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





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





	"""simple docstring"""






	@slow
	def  UpperCamelCase					(							self					):
							"""simple docstring"""







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

							_UpperCAmelCase             =    prepare_img()
							_UpperCAmelCase             =    processor(images=UpperCAmelCase ,					return_tensors='pt'					).pixel_values.to(UpperCAmelCase					)

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

							# 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] ,					UpperCAmelCase					)

							_UpperCAmelCase             =    torch.tensor(
							    [[[-39.53_97, -44.40_24, -36.18_44], [-61.47_09, -63.86_39, -58.34_54], [-74.02_25, -68.54_94, -71.21_64]]] ,					device=UpperCAmelCase ,					)

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








from typing import Any





class __lowerCamelCase				:





	"""simple docstring"""






	def __init__(							self					):
							"""simple docstring"""







							_UpperCAmelCase             =    []
							_UpperCAmelCase             =    0

	def __str__(							self					):
							"""simple docstring"""







							_UpperCAmelCase             =    '<' + str(self.stack					)[1:-1] + '>'
							return printed

	def  UpperCamelCase					(							self ,					UpperCAmelCase					):
							"""simple docstring"""







							self.stack.append(UpperCAmelCase					)
							_UpperCAmelCase             =    self.length + 1

	def  UpperCamelCase					(							self					):
							"""simple docstring"""







							self.rotate(1					)
							_UpperCAmelCase             =    self.stack[self.length - 1]
							_UpperCAmelCase             =    self.stack[:-1]
							self.rotate(self.length - 1					)
							_UpperCAmelCase             =    self.length - 1
							return dequeued

	def  UpperCamelCase					(							self ,					UpperCAmelCase					):
							"""simple docstring"""







							for _ in range(UpperCAmelCase					):
													_UpperCAmelCase             =    self.stack[0]
													_UpperCAmelCase             =    self.stack[1:]
													self.put(UpperCAmelCase					)
													_UpperCAmelCase             =    self.length - 1

	def  UpperCamelCase					(							self					):
							"""simple docstring"""







							_UpperCAmelCase             =    self.get()
							self.put(UpperCAmelCase					)
							self.rotate(self.length - 1					)
							return front

	def  UpperCamelCase					(							self					):
							"""simple docstring"""







							return self.length
