






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     __SCREAMING_SNAKE_CASE					:






						def __init__(	self     ,						_UpperCamelCase     ,						_UpperCamelCase=None     ,						_UpperCamelCase=None     ,						_UpperCamelCase=None     ,						_UpperCamelCase="resnet50"     ,						_UpperCamelCase=3     ,						_UpperCamelCase=32     ,						_UpperCamelCase=3     ,						_UpperCamelCase=True     ,						_UpperCamelCase=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       UpperCamelCase__      (	self   ):






												"""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       UpperCamelCase__      (	self   ):






												"""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       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase   ):






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







						def       UpperCamelCase__      (	self   ):






												"""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     __SCREAMING_SNAKE_CASE					(   __lowercase		,					__lowercase		,					__lowercase		,					unittest.TestCase):
						_SCREAMING_SNAKE_CASE	:       Union[str, Any]   =					(TimmBackbone,) if is_torch_available() else ()
						_SCREAMING_SNAKE_CASE	:       Union[str, Any]   =					{'''feature-extraction''': TimmBackbone} if is_torch_available() else {}
						_SCREAMING_SNAKE_CASE	:       Tuple   =					False
						_SCREAMING_SNAKE_CASE	:       Optional[int]   =					False
						_SCREAMING_SNAKE_CASE	:       Dict   =					False
						_SCREAMING_SNAKE_CASE	:       Any   =					False






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						TimmBackboneModelTester(self   )
												lowerCAmelCase__    						=						ConfigTester(self     ,						config_class=_UpperCamelCase     ,						has_text_modality=_UpperCamelCase   )






						def       UpperCamelCase__      (	self   ):






												"""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       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						'resnet18'
												lowerCAmelCase__    						=						'microsoft/resnet-18'

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

												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(_UpperCamelCase     ,						use_timm_backbone=_UpperCamelCase     ,						out_indices=[1, 2, 3]   )
												lowerCAmelCase__    						=						AutoBackbone.from_pretrained(_UpperCamelCase     ,						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       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												pass






						@unittest.skip('TimmBackbone doesn\'t have num_hidden_layers attribute'   )
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												pass






						@unittest.skip('TimmBackbone initialization is managed on the timm side'   )
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												pass






						@unittest.skip('TimmBackbone models doesn\'t have inputs_embeds'   )
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												pass






						@unittest.skip('TimmBackbone models doesn\'t have inputs_embeds'   )
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												pass






						@unittest.skip('TimmBackbone model cannot be created without specifying a backbone checkpoint'   )
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												pass






						@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone'   )
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												pass






						@unittest.skip('model weights aren\'t tied in TimmBackbone.'   )
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												pass






						@unittest.skip('model weights aren\'t tied in TimmBackbone.'   )
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												pass






						@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone'   )
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												pass






						@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone'   )
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												pass






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






												"""simple docstring"""
												pass






						@unittest.skip('TimmBackbone doesn\'t support output_attentions.'   )
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												pass






						@unittest.skip('Safetensors is not supported by timm.'   )
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												pass






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






												"""simple docstring"""
												pass






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												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   )






						def       UpperCamelCase__      (	self   ):






												"""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(_UpperCamelCase   )
												model.to(_UpperCamelCase   )

												lowerCAmelCase__    						=						self._prepare_for_class(_UpperCamelCase     ,						_UpperCamelCase   )
												lowerCAmelCase__    						=						model(**_UpperCamelCase   )
												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=_UpperCamelCase   )

												self.assertIsNotNone(hidden_states.grad   )

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







						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__						,       lowerCAmelCase__    						=						self.model_tester.prepare_config_and_inputs_for_common()

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

																		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(_UpperCamelCase   )
																		lowerCAmelCase__    						=						None
																		lowerCAmelCase__    						=						model_class(_UpperCamelCase   )
																		model.to(_UpperCamelCase   )
																		model.eval()
																		lowerCAmelCase__    						=						model(**_UpperCamelCase   )

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

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













# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

__snake_case    :    Optional[int]         =     """facebook/wmt19-en-de"""
__snake_case    :    Optional[Any]         =     FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
__snake_case    :    List[str]         =     FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

__snake_case    :    Optional[Any]         =     FSMTForConditionalGeneration(config)
print(f'num of params {tiny_model.num_parameters()}')

# Test
__snake_case    :    List[Any]         =     tokenizer(["""Making tiny model"""], return_tensors="""pt""")
__snake_case    :    Any         =     tiny_model(**batch)

print("""test output:""", len(outputs.logits[0]))

# Save
__snake_case    :    Any         =     """tiny-wmt19-en-de"""
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f'Generated {mname_tiny}')

# Upload
# transformers-cli upload tiny-wmt19-en-de





