
"""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    __lowerCamelCase     :




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





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




@require_torch
class    __lowerCamelCase     (   lowerCAmelCase	,  lowerCAmelCase	,  unittest.TestCase ):
				a__:			Dict									=						(MgpstrForSceneTextRecognition,) if is_torch_available() else ()
				a__:			Optional[int]									=						{'feature-extraction': MgpstrForSceneTextRecognition} if is_torch_available() else {}
				a__:			int									=						False

				a__:			Any									=						False
				a__:			Union[str, Any]									=						False
				a__:			str									=						False
				a__:			List[str]									=						False




				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     MgpstrModelTester(self      )
							lowerCamelCase_ 		=     ConfigTester(self       ,	config_class=UpperCAmelCase       ,	has_text_modality=UpperCAmelCase      )




				def 					UpperCAmelCase__	(							self      ):
							self.config_tester.run_common_tests()




				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     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      ):
							pass




				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_		,   lowerCamelCase_ 		=     self.model_tester.prepare_config_and_inputs_for_common()

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




				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_		,   lowerCamelCase_ 		=     self.model_tester.prepare_config_and_inputs_for_common()

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




				@unittest.skip(reason='''MgpstrModel does not support feedforward chunking'''      )
				def 					UpperCAmelCase__	(							self      ):
							pass




				def 					UpperCAmelCase__	(							self      ):
							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(UpperCAmelCase      )
										self.assertTrue(model.is_gradient_checkpointing      )




				def 					UpperCAmelCase__	(							self      ):
							def check_hidden_states_output(UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase      ):
										lowerCamelCase_ 		=     model_class(UpperCAmelCase      )
										model.to(UpperCAmelCase      )
										model.eval()

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

										lowerCamelCase_ 		=     outputs.hidden_states

										lowerCamelCase_ 		=     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]       ,	)

							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(UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase      )

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

										check_hidden_states_output(UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase      )




				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_		,   lowerCamelCase_ 		=     self.model_tester.prepare_config_and_inputs_for_common()

							lowerCamelCase_ 		=     _config_zero_init(UpperCAmelCase      )
							for model_class in self.all_model_classes:
										lowerCamelCase_ 		=     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      ):
							pass






def    lowercase	(  ):
			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 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     '''alibaba-damo/mgp-str-base'''
							lowerCamelCase_ 		=     MgpstrForSceneTextRecognition.from_pretrained(UpperCAmelCase      ).to(UpperCAmelCase      )
							lowerCamelCase_ 		=     MgpstrProcessor.from_pretrained(UpperCAmelCase      )

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

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

							# 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]       ,	UpperCAmelCase      )

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

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





"""simple docstring"""


import glob
import os

from get_test_info import get_tester_classes


if __name__ == "__main__":
						A_						       =				[]

						A_						       =				os.path.join("""tests""", """models""", """**""", """test_modeling_*.py""")
						A_						       =				glob.glob(pattern)
						# TODO: deal with TF/Flax too
						A_						       =				[
						    x for x in test_files if not (x.startswith("""test_modeling_tf_""") or x.startswith("""test_modeling_flax_"""))
						]

						for test_file in test_files:
												A_						       =				get_tester_classes(test_file)
												for tester_class in tester_classes:
																		# A few tester classes don't have `parent` parameter in `__init__`.
																		# TODO: deal this better
																		try:
																								A_						       =				tester_class(parent=None)
																		except Exception:
																								continue
																		if hasattr(tester, """get_config"""):
																								A_						       =				tester.get_config()
																								for k, v in config.to_dict().items():
																														if isinstance(v, int):
																																				A_						       =				None
																																				if k in ["vocab_size"]:
																																										A_						       =				100
																																				elif k in ["max_position_embeddings"]:
																																										A_						       =				128
																																				elif k in ["hidden_size", "d_model"]:
																																										A_						       =				40
																																				elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
																																										A_						       =				5
																																				if target is not None and v > target:
																																										failures.append(
																																										    f"{tester_class.__name__} will produce a `config` of type `{config.__class__.__name__}`"
																																										    f" with config[\"{k}\"] = {v} which is too large for testing! Set its value to be smaller"
																																										    f" than {target}."
																																										)

						if len(failures) > 0:
												raise Exception(f"There were {len(failures)} failures:\n" + """\n""".join(failures))



