
'''simple docstring'''

import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
		import torch

		from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin






class  A  :





	def __init__(       self      :							Dict   ,				__magic_name__      :							List[str]   ,				__magic_name__      :							Dict=None   ,				__magic_name__      :							List[Any]=None   ,				__magic_name__      :							List[Any]=None   ,				__magic_name__      :							int="resnet50"   ,				__magic_name__      :							List[Any]=3   ,				__magic_name__      :							Dict=32   ,				__magic_name__      :							int=3   ,				__magic_name__      :							List[str]=True   ,				__magic_name__      :							Any=True   ,				):


				"""simple docstring"""

				lowerCAmelCase__										=				parent
				lowerCAmelCase__										=				out_indices if out_indices is not None else [4]
				lowerCAmelCase__										=				stage_names
				lowerCAmelCase__										=				out_features
				lowerCAmelCase__										=				backbone
				lowerCAmelCase__										=				batch_size
				lowerCAmelCase__										=				image_size
				lowerCAmelCase__										=				num_channels
				lowerCAmelCase__										=				use_pretrained_backbone
				lowerCAmelCase__										=				is_training





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[int]				):


				"""simple docstring"""

				lowerCAmelCase__										=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)
				lowerCAmelCase__										=				self.get_config()

				return config, pixel_values





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple				):


				"""simple docstring"""

				return TimmBackboneConfig(
				    image_size=self.image_size   ,				num_channels=self.num_channels   ,				out_features=self.out_features   ,				out_indices=self.out_indices   ,				stage_names=self.stage_names   ,				use_pretrained_backbone=self.use_pretrained_backbone   ,				backbone=self.backbone   ,				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Dict   ,				__magic_name__      :							Optional[int]   ,				__magic_name__      :							Optional[Any]				):


				"""simple docstring"""

				lowerCAmelCase__										=				TimmBackbone(config=__magic_name__				)
				model.to(__magic_name__				)
				model.eval()
				with torch.no_grad():
							lowerCAmelCase__										=				model(__magic_name__				)
				self.parent.assertEqual(
				    result.feature_map[-1].shape   ,				(self.batch_size, model.channels[-1], 14, 14)   ,				)







	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[int]				):


				"""simple docstring"""

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






@require_torch
@require_timm
class  A  (						SCREAMING_SNAKE_CASE__	,   SCREAMING_SNAKE_CASE__	,   SCREAMING_SNAKE_CASE__	,   unittest.TestCase     ):
	snake_case__   :Any   =  (TimmBackbone,) if is_torch_available() else ()
	snake_case__   :Optional[int]   =  {'feature-extraction': TimmBackbone} if is_torch_available() else {}
	snake_case__   :Optional[Any]   =  False
	snake_case__   :Union[str, Any]   =  False
	snake_case__   :Tuple   =  False
	snake_case__   :int   =  False





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Any				):


				"""simple docstring"""

				lowerCAmelCase__										=				TimmBackboneModelTester(self				)
				lowerCAmelCase__										=				ConfigTester(self   ,				config_class=__magic_name__   ,				has_text_modality=__magic_name__				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[str]				):


				"""simple docstring"""

				self.config_tester.create_and_test_config_to_json_string()
				self.config_tester.create_and_test_config_to_json_file()
				self.config_tester.create_and_test_config_from_and_save_pretrained()
				self.config_tester.create_and_test_config_with_num_labels()
				self.config_tester.check_config_can_be_init_without_params()
				self.config_tester.check_config_arguments_init()





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Union[str, Any]				):


				"""simple docstring"""

				lowerCAmelCase__										=				"resnet18"
				lowerCAmelCase__										=				"microsoft/resnet-18"

				lowerCAmelCase__										=				AutoBackbone.from_pretrained(__magic_name__   ,				use_timm_backbone=__magic_name__				)
				lowerCAmelCase__										=				AutoBackbone.from_pretrained(__magic_name__				)

				self.assertEqual(len(timm_model.out_features				)   ,				len(transformers_model.out_features				)				)
				self.assertEqual(len(timm_model.stage_names				)   ,				len(transformers_model.stage_names				)				)
				self.assertEqual(timm_model.channels   ,				transformers_model.channels				)
				# Out indices are set to the last layer by default. For timm models, we don't know
				# the number of layers in advance, so we set it to (-1,), whereas for transformers
				# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
				self.assertEqual(timm_model.out_indices   ,				(-1,)				)
				self.assertEqual(transformers_model.out_indices   ,				[len(timm_model.stage_names				) - 1]				)

				lowerCAmelCase__										=				AutoBackbone.from_pretrained(__magic_name__   ,				use_timm_backbone=__magic_name__   ,				out_indices=[1, 2, 3]				)
				lowerCAmelCase__										=				AutoBackbone.from_pretrained(__magic_name__   ,				out_indices=[1, 2, 3]				)

				self.assertEqual(timm_model.out_indices   ,				transformers_model.out_indices				)
				self.assertEqual(len(timm_model.out_features				)   ,				len(transformers_model.out_features				)				)
				self.assertEqual(timm_model.channels   ,				transformers_model.channels				)





	@unittest.skip("TimmBackbone doesn't support feed forward chunking"				)
	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[Any]				):


				"""simple docstring"""

				pass





	@unittest.skip("TimmBackbone doesn't have num_hidden_layers attribute"				)
	def 				__SCREAMING_SNAKE_CASE	(       self      :							str				):


				"""simple docstring"""

				pass





	@unittest.skip("TimmBackbone initialization is managed on the timm side"				)
	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[Any]				):


				"""simple docstring"""

				pass





	@unittest.skip("TimmBackbone models doesn't have inputs_embeds"				)
	def 				__SCREAMING_SNAKE_CASE	(       self      :							int				):


				"""simple docstring"""

				pass





	@unittest.skip("TimmBackbone models doesn't have inputs_embeds"				)
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[Any]				):


				"""simple docstring"""

				pass





	@unittest.skip("TimmBackbone model cannot be created without specifying a backbone checkpoint"				)
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Dict				):


				"""simple docstring"""

				pass





	@unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone"				)
	def 				__SCREAMING_SNAKE_CASE	(       self      :							str				):


				"""simple docstring"""

				pass





	@unittest.skip("model weights aren't tied in TimmBackbone."				)
	def 				__SCREAMING_SNAKE_CASE	(       self      :							int				):


				"""simple docstring"""

				pass





	@unittest.skip("model weights aren't tied in TimmBackbone."				)
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[Any]				):


				"""simple docstring"""

				pass





	@unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone"				)
	def 				__SCREAMING_SNAKE_CASE	(       self      :							int				):


				"""simple docstring"""

				pass





	@unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone"				)
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Any				):


				"""simple docstring"""

				pass





	@unittest.skip("TimmBackbone doesn't have hidden size info in its configuration."				)
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Any				):


				"""simple docstring"""

				pass





	@unittest.skip("TimmBackbone doesn't support output_attentions."				)
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple				):


				"""simple docstring"""

				pass





	@unittest.skip("Safetensors is not supported by timm."				)
	def 				__SCREAMING_SNAKE_CASE	(       self      :							str				):


				"""simple docstring"""

				pass





	@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."				)
	def 				__SCREAMING_SNAKE_CASE	(       self      :							str				):


				"""simple docstring"""

				pass





	def 				__SCREAMING_SNAKE_CASE	(       self      :							str				):


				"""simple docstring"""

				lowerCAmelCase__						,lowerCAmelCase__										=				self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							lowerCAmelCase__										=				model_class(__magic_name__				)
							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]   ,				__magic_name__				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[str]				):


				"""simple docstring"""

				lowerCAmelCase__						,lowerCAmelCase__										=				self.model_tester.prepare_config_and_inputs_for_common()
				lowerCAmelCase__										=				True
				lowerCAmelCase__										=				self.has_attentions

				# no need to test all models as different heads yield the same functionality
				lowerCAmelCase__										=				self.all_model_classes[0]
				lowerCAmelCase__										=				model_class(__magic_name__				)
				model.to(__magic_name__				)

				lowerCAmelCase__										=				self._prepare_for_class(__magic_name__   ,				__magic_name__				)
				lowerCAmelCase__										=				model(**__magic_name__				)
				lowerCAmelCase__										=				outputs[0][-1]

				# Encoder-/Decoder-only models
				lowerCAmelCase__										=				outputs.hidden_states[0]
				hidden_states.retain_grad()

				if self.has_attentions:
							lowerCAmelCase__										=				outputs.attentions[0]
							attentions.retain_grad()

				output.flatten()[0].backward(retain_graph=__magic_name__				)

				self.assertIsNotNone(hidden_states.grad				)

				if self.has_attentions:
							self.assertIsNotNone(attentions.grad				)







	def 				__SCREAMING_SNAKE_CASE	(       self      :							Dict				):


				"""simple docstring"""

				lowerCAmelCase__						,lowerCAmelCase__										=				self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							lowerCAmelCase__										=				model_class(__magic_name__				)
							model.to(__magic_name__				)
							model.eval()
							lowerCAmelCase__										=				model(**__magic_name__				)

							self.assertEqual(len(result.feature_maps				)   ,				len(config.out_indices				)				)
							self.assertEqual(len(model.channels				)   ,				len(config.out_indices				)				)

							# Check output of last stage is taken if out_features=None, out_indices=None
							lowerCAmelCase__										=				copy.deepcopy(__magic_name__				)
							lowerCAmelCase__										=				None
							lowerCAmelCase__										=				model_class(__magic_name__				)
							model.to(__magic_name__				)
							model.eval()
							lowerCAmelCase__										=				model(**__magic_name__				)

							self.assertEqual(len(result.feature_maps				)   ,				1				)
							self.assertEqual(len(model.channels				)   ,				1				)

							# Check backbone can be initialized with fresh weights
							lowerCAmelCase__										=				copy.deepcopy(__magic_name__				)
							lowerCAmelCase__										=				False
							lowerCAmelCase__										=				model_class(__magic_name__				)
							model.to(__magic_name__				)
							model.eval()
							lowerCAmelCase__										=				model(**__magic_name__				)







"""simple docstring"""




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
   from transformers import AutoModelForSeqaSeqLM, AutoTokenizer

@require_torch
@require_sentencepiece
@require_tokenizers
class 						a__		( unittest.TestCase		):
   @slow
   def      __magic_name__     (     self							):
          lowercase       :							Tuple									=				AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"      ,       return_dict=_a							).to(_a							)
          lowercase       :							Union[str, Any]									=				AutoTokenizer.from_pretrained("google/mt5-small"							)

          lowercase       :							Optional[Any]									=				tokenizer("Hello there"      ,       return_tensors="pt"							).input_ids
          lowercase       :							Union[str, Any]									=				tokenizer("Hi I am"      ,       return_tensors="pt"							).input_ids

          lowercase       :							int									=				model(input_ids.to(_a							)      ,       labels=labels.to(_a							)							).loss
          lowercase       :							str									=				-(labels.shape[-1] * loss.item())

          lowercase       :							List[str]									=				-8_4.9_1_2_7
          self.assertTrue(abs(mtf_score - EXPECTED_SCORE							) < 1E-4							)


