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 	_a  :



	"""simple docstring"""



	def __init__(   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_=False       ,  lowerCAmelCase_=13       ,  lowerCAmelCase_=(32, 128)       ,  lowerCAmelCase_=4       ,  lowerCAmelCase_=3       ,  lowerCAmelCase_=27       ,  lowerCAmelCase_=38       ,  lowerCAmelCase_=99       ,  lowerCAmelCase_=99       ,  lowerCAmelCase_=32       ,  lowerCAmelCase_=5       ,  lowerCAmelCase_=4       ,  lowerCAmelCase_=4.0       ,  lowerCAmelCase_=257       ,  lowerCAmelCase_=None       ,  ):
		_lowercase   =parent
		_lowercase   =is_training
		_lowercase   =batch_size
		_lowercase   =image_size
		_lowercase   =patch_size
		_lowercase   =num_channels
		_lowercase   =max_token_length
		_lowercase   =num_character_labels
		_lowercase   =num_bpe_labels
		_lowercase   =num_wordpiece_labels
		_lowercase   =hidden_size
		_lowercase   =num_hidden_layers
		_lowercase   =num_attention_heads
		_lowercase   =mlp_ratio
		_lowercase   =patch_embeds_hidden_size
		_lowercase   =output_hidden_states


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


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


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.prepare_config_and_inputs()
		_lowercase   ,  _lowercase   =config_and_inputs
		_lowercase   ={"pixel_values": pixel_values}
		return config, inputs_dict

@require_torch
class 	_a  (							lowerCamelCase_							,		lowerCamelCase_							,		unittest.TestCase					):



	"""simple docstring"""



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

	__SCREAMING_SNAKE_CASE              =					False
	__SCREAMING_SNAKE_CASE              =					False
	__SCREAMING_SNAKE_CASE              =					False
	__SCREAMING_SNAKE_CASE              =					False


	def __lowerCAmelCase      (   self       ):
		_lowercase   =MgpstrModelTester(self       )
		_lowercase   =ConfigTester(self       ,  config_class=lowerCAmelCase_       ,  has_text_modality=lowerCAmelCase_       )


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


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*lowerCAmelCase_       )


	@unittest.skip(reason="MgpstrModel does not use inputs_embeds"       )
	def __lowerCAmelCase      (   self       ):
		pass


	def __lowerCAmelCase      (   self       ):
		_lowercase   ,  _lowercase   =self.model_tester.prepare_config_and_inputs_for_common()

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


	def __lowerCAmelCase      (   self       ):
		_lowercase   ,  _lowercase   =self.model_tester.prepare_config_and_inputs_for_common()

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

			_lowercase   =["pixel_values"]
			self.assertListEqual(arg_names[:1]       ,  lowerCAmelCase_       )


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


	def __lowerCAmelCase      (   self       ):
		_lowercase   ,  _lowercase   =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

			_lowercase   =True
			_lowercase   =model_class(lowerCAmelCase_       )
			self.assertTrue(model.is_gradient_checkpointing       )


	def __lowerCAmelCase      (   self       ):
		def check_hidden_states_output(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
			_lowercase   =model_class(lowerCAmelCase_       )
			model.to(lowerCAmelCase_       )
			model.eval()

			with torch.no_grad():
				_lowercase   =model(**self._prepare_for_class(lowerCAmelCase_       ,  lowerCAmelCase_       )       )

			_lowercase   =outputs.hidden_states

			_lowercase   =getattr(
			    self.model_tester       ,  "expected_num_hidden_layers"       ,  self.model_tester.num_hidden_layers + 1       )
			self.assertEqual(len(lowerCAmelCase_       )       ,  lowerCAmelCase_       )

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

		_lowercase   ,  _lowercase   =self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			_lowercase   =True
			check_hidden_states_output(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       )

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

			check_hidden_states_output(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   ,  _lowercase   =self.model_tester.prepare_config_and_inputs_for_common()

		_lowercase   =_config_zero_init(lowerCAmelCase_       )
		for model_class in self.all_model_classes:
			_lowercase   =model_class(config=lowerCAmelCase_       )
			for name, param in model.named_parameters():
				if isinstance(lowerCAmelCase_       ,  (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       ):
		pass

def 	__lowerCamelCase							(				)      ->       Tuple:
	_lowercase   ="https://i.postimg.cc/ZKwLg2Gw/367-14.png"
	_lowercase   =Image.open(requests.get(__a      ,							stream=__a		).raw		).convert("RGB"		)
	return im
@require_vision
@require_torch
class 	_a  (							unittest.TestCase					):



	"""simple docstring"""



	@slow
	def __lowerCAmelCase      (   self       ):
		_lowercase   ="alibaba-damo/mgp-str-base"
		_lowercase   =MgpstrForSceneTextRecognition.from_pretrained(lowerCAmelCase_       ).to(lowerCAmelCase_       )
		_lowercase   =MgpstrProcessor.from_pretrained(lowerCAmelCase_       )

		_lowercase   =prepare_img()
		_lowercase   =processor(images=lowerCAmelCase_       ,  return_tensors="pt"       ).pixel_values.to(lowerCAmelCase_       )

		# forward pass
		with torch.no_grad():
			_lowercase   =model(lowerCAmelCase_       )

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

		_lowercase   =processor.batch_decode(outputs.logits       )
		_lowercase   ="ticket"

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

		_lowercase   =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=lowerCAmelCase_       ,  )

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












'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowerCamelCase_					:			Any     						=				{
    '''configuration_sam''': [
        '''SAM_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SamConfig''',
        '''SamMaskDecoderConfig''',
        '''SamPromptEncoderConfig''',
        '''SamVisionConfig''',
    ],
    '''processing_sam''': ['''SamProcessor'''],
}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_					:			Union[str, Any]     						=				[
					    '''SAM_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''SamModel''',
					    '''SamPreTrainedModel''',
					]
try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_					:			Optional[Any]     						=				[
					    '''TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFSamModel''',
					    '''TFSamPreTrainedModel''',
					]
try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_					:			Optional[Any]     						=				['''SamImageProcessor''']


if TYPE_CHECKING:
					from .configuration_sam import (
					    SAM_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    SamConfig,
					    SamMaskDecoderConfig,
					    SamPromptEncoderConfig,
					    SamVisionConfig,
					)
					from .processing_sam import SamProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_sam import SAM_PRETRAINED_MODEL_ARCHIVE_LIST, SamModel, SamPreTrainedModel

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_sam import TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST, TFSamModel, TFSamPreTrainedModel

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_sam import SamImageProcessor

else:
					import sys

					lowerCamelCase_					:			Optional[Any]     						=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
