






"""simple docstring"""
import inspect
import unittest

from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
			from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import AutoImageProcessor







class     UpperCamelCase_ :





	def __init__(   self      :				Dict			,			lowerCAmelCase_      :				Optional[Any]			,			lowerCAmelCase_      :				Optional[int]=13			,			lowerCAmelCase_      :				Optional[int]=32			,			lowerCAmelCase_      :				str=3			,			lowerCAmelCase_      :				Optional[int]=4			,			lowerCAmelCase_      :				Tuple=[10, 20, 30, 40]			,			lowerCAmelCase_      :				str=[2, 2, 3, 2]			,			lowerCAmelCase_      :				Any=True			,			lowerCAmelCase_      :				Dict=True			,			lowerCAmelCase_      :				int=37			,			lowerCAmelCase_      :				Union[str, Any]="gelu"			,			lowerCAmelCase_      :				Union[str, Any]=10			,			lowerCAmelCase_      :				Tuple=0.0_2			,			lowerCAmelCase_      :				str=["stage2", "stage3", "stage4"]			,			lowerCAmelCase_      :				Optional[Any]=[2, 3, 4]			,			lowerCAmelCase_      :				List[Any]=None			,			)		->    int:
				UpperCAmelCase_  :				List[str]      =      parent
				UpperCAmelCase_  :				Optional[Any]      =      batch_size
				UpperCAmelCase_  :				List[str]      =      image_size
				UpperCAmelCase_  :				str      =      num_channels
				UpperCAmelCase_  :				Optional[Any]      =      num_stages
				UpperCAmelCase_  :				Tuple      =      hidden_sizes
				UpperCAmelCase_  :				Optional[Any]      =      depths
				UpperCAmelCase_  :				Optional[int]      =      is_training
				UpperCAmelCase_  :				Union[str, Any]      =      use_labels
				UpperCAmelCase_  :				Union[str, Any]      =      intermediate_size
				UpperCAmelCase_  :				Any      =      hidden_act
				UpperCAmelCase_  :				str      =      num_labels
				UpperCAmelCase_  :				Any      =      initializer_range
				UpperCAmelCase_  :				str      =      out_features
				UpperCAmelCase_  :				Optional[Any]      =      out_indices
				UpperCAmelCase_  :				List[str]      =      scope





	def   _SCREAMING_SNAKE_CASE   (   self      :				int		)		->    List[str]:
				UpperCAmelCase_  :				List[str]      =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

				UpperCAmelCase_  :				Any      =      None
				if self.use_labels:
							UpperCAmelCase_  :				int      =      ids_tensor([self.batch_size]			,			self.num_labels		)

				UpperCAmelCase_  :				Dict      =      self.get_config()

				return config, pixel_values, labels





	def   _SCREAMING_SNAKE_CASE   (   self      :				str		)		->    List[str]:
				return ConvNextVaConfig(
				    num_channels=self.num_channels			,			hidden_sizes=self.hidden_sizes			,			depths=self.depths			,			num_stages=self.num_stages			,			hidden_act=self.hidden_act			,			is_decoder=lowerCAmelCase_			,			initializer_range=self.initializer_range			,			out_features=self.out_features			,			out_indices=self.out_indices			,			num_labels=self.num_labels			,			)





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]			,			lowerCAmelCase_      :				Tuple			,			lowerCAmelCase_      :				Union[str, Any]			,			lowerCAmelCase_      :				Optional[int]		)		->    List[str]:
				UpperCAmelCase_  :				Tuple      =      ConvNextVaModel(config=lowerCAmelCase_		)
				model.to(lowerCAmelCase_		)
				model.eval()
				UpperCAmelCase_  :				Union[str, Any]      =      model(lowerCAmelCase_		)
				# expected last hidden states: B, C, H // 32, W // 32
				self.parent.assertEqual(
				    result.last_hidden_state.shape			,			(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)			,			)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]			,			lowerCAmelCase_      :				str			,			lowerCAmelCase_      :				str			,			lowerCAmelCase_      :				int		)		->    Dict:
				UpperCAmelCase_  :				Tuple      =      ConvNextVaForImageClassification(lowerCAmelCase_		)
				model.to(lowerCAmelCase_		)
				model.eval()
				UpperCAmelCase_  :				int      =      model(lowerCAmelCase_			,			labels=lowerCAmelCase_		)
				self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.num_labels)		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				str			,			lowerCAmelCase_      :				Tuple			,			lowerCAmelCase_      :				str			,			lowerCAmelCase_      :				str		)		->    List[Any]:
				UpperCAmelCase_  :				Dict      =      ConvNextVaBackbone(config=lowerCAmelCase_		)
				model.to(lowerCAmelCase_		)
				model.eval()
				UpperCAmelCase_  :				List[Any]      =      model(lowerCAmelCase_		)

				# verify hidden states
				self.parent.assertEqual(len(result.feature_maps		)			,			len(config.out_features		)		)
				self.parent.assertListEqual(list(result.feature_maps[0].shape		)			,			[self.batch_size, self.hidden_sizes[1], 4, 4]		)

				# verify channels
				self.parent.assertEqual(len(model.channels		)			,			len(config.out_features		)		)
				self.parent.assertListEqual(model.channels			,			config.hidden_sizes[1:]		)

				# verify backbone works with out_features=None
				UpperCAmelCase_  :				List[Any]      =      None
				UpperCAmelCase_  :				int      =      ConvNextVaBackbone(config=lowerCAmelCase_		)
				model.to(lowerCAmelCase_		)
				model.eval()
				UpperCAmelCase_  :				Union[str, Any]      =      model(lowerCAmelCase_		)

				# verify feature maps
				self.parent.assertEqual(len(result.feature_maps		)			,			1		)
				self.parent.assertListEqual(list(result.feature_maps[0].shape		)			,			[self.batch_size, self.hidden_sizes[-1], 1, 1]		)

				# verify channels
				self.parent.assertEqual(len(model.channels		)			,			1		)
				self.parent.assertListEqual(model.channels			,			[config.hidden_sizes[-1]]		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    List[Any]:
				UpperCAmelCase_  :				Optional[int]      =      self.prepare_config_and_inputs()
				UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  :				Dict      =      config_and_inputs
				UpperCAmelCase_  :				Any      =      {"pixel_values": pixel_values}
				return config, inputs_dict





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any		)		->    Optional[int]:
				UpperCAmelCase_  :				str      =      self.prepare_config_and_inputs()
				UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  :				Optional[Any]      =      config_and_inputs
				UpperCAmelCase_  :				int      =      {"pixel_values": pixel_values, "labels": labels}
				return config, inputs_dict







@require_torch
class     UpperCamelCase_ (__A       ,		__A       ,		unittest.TestCase  ):
	__magic_name__			   =       (
	    (
	        ConvNextVaModel,
	        ConvNextVaForImageClassification,
	        ConvNextVaBackbone,
	    )
	    if is_torch_available()
	    else ()
	)
	__magic_name__			   =       (
	    {'''feature-extraction''': ConvNextVaModel, '''image-classification''': ConvNextVaForImageClassification}
	    if is_torch_available()
	    else {}
	)

	__magic_name__			   =       False
	__magic_name__			   =       False
	__magic_name__			   =       False
	__magic_name__			   =       False
	__magic_name__			   =       False





	def   _SCREAMING_SNAKE_CASE   (   self      :				str		)		->    List[str]:
				UpperCAmelCase_  :				List[str]      =      ConvNextVaModelTester(self		)
				UpperCAmelCase_  :				List[Any]      =      ConfigTester(self			,			config_class=lowerCAmelCase_			,			has_text_modality=lowerCAmelCase_			,			hidden_size=37		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]		)		->    Tuple:
				self.create_and_test_config_common_properties()
				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      :				int		)		->    Optional[Any]:
				return





	@unittest.skip(reason="ConvNextV2 does not use inputs_embeds"		)
	def   _SCREAMING_SNAKE_CASE   (   self      :				int		)		->    Union[str, Any]:
				pass





	@unittest.skip(reason="ConvNextV2 does not support input and output embeddings"		)
	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]		)		->    int:
				pass





	@unittest.skip(reason="ConvNextV2 does not use feedforward chunking"		)
	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple		)		->    Optional[Any]:
				pass





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]		)		->    int:
				if not self.model_tester.is_training:
							return

				for model_class in self.all_model_classes:
							UpperCAmelCase_  ,			UpperCAmelCase_  :				Dict      =      self.model_tester.prepare_config_and_inputs_with_labels()
							UpperCAmelCase_  :				str      =      True

							if model_class.__name__ in [
							    *get_values(lowerCAmelCase_		),
							    *get_values(lowerCAmelCase_		),
							]:
										continue

							UpperCAmelCase_  :				Optional[int]      =      model_class(lowerCAmelCase_		)
							model.to(lowerCAmelCase_		)
							model.train()
							UpperCAmelCase_  :				List[Any]      =      self._prepare_for_class(lowerCAmelCase_			,			lowerCAmelCase_			,			return_labels=lowerCAmelCase_		)
							UpperCAmelCase_  :				str      =      model(**lowerCAmelCase_		).loss
							loss.backward()





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple		)		->    str:
				if not self.model_tester.is_training:
							return

				for model_class in self.all_model_classes:
							UpperCAmelCase_  ,			UpperCAmelCase_  :				List[str]      =      self.model_tester.prepare_config_and_inputs_with_labels()
							UpperCAmelCase_  :				Dict      =      False
							UpperCAmelCase_  :				Optional[int]      =      True

							if (
							    model_class.__name__
							    in [*get_values(lowerCAmelCase_		), *get_values(lowerCAmelCase_		)]
							    or not model_class.supports_gradient_checkpointing
							):
										continue

							UpperCAmelCase_  :				Dict      =      model_class(lowerCAmelCase_		)
							model.to(lowerCAmelCase_		)
							model.gradient_checkpointing_enable()
							model.train()
							UpperCAmelCase_  :				List[Any]      =      self._prepare_for_class(lowerCAmelCase_			,			lowerCAmelCase_			,			return_labels=lowerCAmelCase_		)
							UpperCAmelCase_  :				Tuple      =      model(**lowerCAmelCase_		).loss
							loss.backward()





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    List[Any]:
				UpperCAmelCase_  ,			UpperCAmelCase_  :				Union[str, Any]      =      self.model_tester.prepare_config_and_inputs_for_common()

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

							UpperCAmelCase_  :				Union[str, Any]      =      ["pixel_values"]
							self.assertListEqual(arg_names[:1]			,			lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any		)		->    int:
				UpperCAmelCase_  :				Union[str, Any]      =      self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				int		)		->    Any:
				def check_hidden_states_output(lowerCAmelCase_      :				str			,			lowerCAmelCase_      :				Optional[int]			,			lowerCAmelCase_      :				Dict		):
							UpperCAmelCase_  :				List[str]      =      model_class(lowerCAmelCase_		)
							model.to(lowerCAmelCase_		)
							model.eval()

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

							UpperCAmelCase_  :				Tuple      =      outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

							UpperCAmelCase_  :				Any      =      self.model_tester.num_stages
							self.assertEqual(len(lowerCAmelCase_		)			,			expected_num_stages + 1		)

							# ConvNextV2's feature maps are of shape (batch_size, num_channels, height, width)
							self.assertListEqual(
							    list(hidden_states[0].shape[-2:]		)			,			[self.model_tester.image_size // 4, self.model_tester.image_size // 4]			,			)

				UpperCAmelCase_  ,			UpperCAmelCase_  :				str      =      self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							UpperCAmelCase_  :				List[Any]      =      True
							check_hidden_states_output(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_		)

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							UpperCAmelCase_  :				Any      =      True

							check_hidden_states_output(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]		)		->    str:
				UpperCAmelCase_  :				List[Any]      =      self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_		)





	@slow
	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]		)		->    Optional[int]:
				for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							UpperCAmelCase_  :				Any      =      ConvNextVaModel.from_pretrained(lowerCAmelCase_		)
							self.assertIsNotNone(lowerCAmelCase_		)





def        snake_case   (     ):
			UpperCAmelCase_  :				int      =      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
			return image







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





	@cached_property
	def   _SCREAMING_SNAKE_CASE   (   self      :				Any		)		->    List[Any]:
				return AutoImageProcessor.from_pretrained("facebook/convnextv2-tiny-1k-224"		) if is_vision_available() else None





	@slow
	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]		)		->    List[str]:
				UpperCAmelCase_  :				List[str]      =      ConvNextVaForImageClassification.from_pretrained("facebook/convnextv2-tiny-1k-224"		).to(lowerCAmelCase_		)

				UpperCAmelCase_  :				Tuple      =      self.default_image_processor
				UpperCAmelCase_  :				int      =      prepare_img()
				UpperCAmelCase_  :				Union[str, Any]      =      preprocessor(images=lowerCAmelCase_			,			return_tensors="pt"		).to(lowerCAmelCase_		)

				# forward pass
				with torch.no_grad():
							UpperCAmelCase_  :				Optional[int]      =      model(**lowerCAmelCase_		)

				# verify the logits
				UpperCAmelCase_  :				Optional[Any]      =      torch.Size((1, 1_000)		)
				self.assertEqual(outputs.logits.shape			,			lowerCAmelCase_		)

				UpperCAmelCase_  :				Optional[Any]      =      torch.tensor([0.9_9_9_6, 0.1_9_6_6, -0.4_3_8_6]		).to(lowerCAmelCase_		)
				self.assertTrue(torch.allclose(outputs.logits[0, :3]			,			lowerCAmelCase_			,			atol=1e-4		)		)











"""simple docstring"""
# 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 vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

lowerCamelCase_       			=   '''tiny-wmt19-en-ru'''

# Build

# borrowed from a test
lowerCamelCase_       			=   [
    '''l''',
    '''o''',
    '''w''',
    '''e''',
    '''r''',
    '''s''',
    '''t''',
    '''i''',
    '''d''',
    '''n''',
    '''w</w>''',
    '''r</w>''',
    '''t</w>''',
    '''lo''',
    '''low''',
    '''er</w>''',
    '''low</w>''',
    '''lowest</w>''',
    '''newer</w>''',
    '''wider</w>''',
    '''<unk>''',
]
lowerCamelCase_       			=   dict(zip(vocab, range(len(vocab))))
lowerCamelCase_       			=   ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

with tempfile.TemporaryDirectory() as tmpdirname:
			lowerCamelCase_       			=   Path(tmpdirname)
			lowerCamelCase_       			=   build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
			lowerCamelCase_       			=   build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
			lowerCamelCase_       			=   build_dir / VOCAB_FILES_NAMES['''merges_file''']
			with open(src_vocab_file, '''w''') as fp:
						fp.write(json.dumps(vocab_tokens))
			with open(tgt_vocab_file, '''w''') as fp:
						fp.write(json.dumps(vocab_tokens))
			with open(merges_file, '''w''') as fp:
						fp.write('''\n'''.join(merges))

			lowerCamelCase_       			=   FSMTTokenizer(
			    langs=['''en''', '''ru'''],
			    src_vocab_size=len(vocab),
			    tgt_vocab_size=len(vocab),
			    src_vocab_file=src_vocab_file,
			    tgt_vocab_file=tgt_vocab_file,
			    merges_file=merges_file,
			)

lowerCamelCase_       			=   FSMTConfig(
    langs=['''ru''', '''en'''],
    src_vocab_size=1000,
    tgt_vocab_size=1000,
    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,
)

lowerCamelCase_       			=   FSMTForConditionalGeneration(config)
print(f'num of params {tiny_model.num_parameters()}')

# Test
lowerCamelCase_       			=   tokenizer(['''Making tiny model'''], return_tensors='''pt''')
lowerCamelCase_       			=   tiny_model(**batch)

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

# Save
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-ru



