
from __future__ import annotations

import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers import TFViTForImageClassification, TFViTModel


if is_vision_available():
	from PIL import Image

	from transformers import ViTImageProcessor







class        A_   :




	'''simple docstring'''


	def __init__(       self		,    snake_case		,    snake_case=13		,    snake_case=30		,    snake_case=2		,    snake_case=3		,    snake_case=True		,    snake_case=True		,    snake_case=32		,    snake_case=2		,    snake_case=4		,    snake_case=37		,    snake_case="gelu"		,    snake_case=0.1		,    snake_case=0.1		,    snake_case=10		,    snake_case=0.02		,    snake_case=3		,    snake_case=None		,    ):
		lowercase       =    parent
		lowercase       =    batch_size
		lowercase       =    image_size
		lowercase       =    patch_size
		lowercase       =    num_channels
		lowercase       =    is_training
		lowercase       =    use_labels
		lowercase       =    hidden_size
		lowercase       =    num_hidden_layers
		lowercase       =    num_attention_heads
		lowercase       =    intermediate_size
		lowercase       =    hidden_act
		lowercase       =    hidden_dropout_prob
		lowercase       =    attention_probs_dropout_prob
		lowercase       =    type_sequence_label_size
		lowercase       =    initializer_range
		lowercase       =    scope

		# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
		lowercase       =    (image_size // patch_size) ** 2
		lowercase       =    num_patches + 1





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

		lowercase       =    None
		if self.use_labels:
			lowercase       =    ids_tensor([self.batch_size]		,    self.type_sequence_label_size       )

		lowercase       =    self.get_config()

		return config, pixel_values, labels





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		return ViTConfig(
		    image_size=self.image_size		,    patch_size=self.patch_size		,    num_channels=self.num_channels		,    hidden_size=self.hidden_size		,    num_hidden_layers=self.num_hidden_layers		,    num_attention_heads=self.num_attention_heads		,    intermediate_size=self.intermediate_size		,    hidden_act=self.hidden_act		,    hidden_dropout_prob=self.hidden_dropout_prob		,    attention_probs_dropout_prob=self.attention_probs_dropout_prob		,    is_decoder=_A		,    initializer_range=self.initializer_range		,    )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case       ):
		lowercase       =    TFViTModel(config=_A       )
		lowercase       =    model(_A		,    training=_A       )
		self.parent.assertEqual(result.last_hidden_state.shape		,    (self.batch_size, self.seq_length, self.hidden_size)       )

		# Test with an image with different size than the one specified in config.
		lowercase       =    self.image_size // 2
		lowercase       =    pixel_values[:, :, :image_size, :image_size]
		lowercase       =    model(_A		,    interpolate_pos_encoding=_A		,    training=_A       )
		lowercase       =    (image_size // self.patch_size) ** 2 + 1
		self.parent.assertEqual(result.last_hidden_state.shape		,    (self.batch_size, seq_length, self.hidden_size)       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case       ):
		lowercase       =    self.type_sequence_label_size
		lowercase       =    TFViTForImageClassification(_A       )
		lowercase       =    model(_A		,    labels=_A		,    training=_A       )
		self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.type_sequence_label_size)       )

		# Test with an image with different size than the one specified in config.
		lowercase       =    self.image_size // 2
		lowercase       =    pixel_values[:, :, :image_size, :image_size]
		lowercase       =    model(_A		,    interpolate_pos_encoding=_A		,    training=_A       )
		self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.type_sequence_label_size)       )

		# test greyscale images
		lowercase       =    1
		lowercase       =    TFViTForImageClassification(_A       )
		lowercase       =    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
		lowercase       =    model(_A       )
		self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.type_sequence_label_size)       )





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







@require_tf
class        A_   (			a_      ,	a_      ,	unittest.TestCase   ):




	'''simple docstring'''


	_UpperCamelCase     :     Optional[int]							 =       (TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
	_UpperCamelCase     :     Dict							 =       (
	    {"""feature-extraction""": TFViTModel, """image-classification""": TFViTForImageClassification}
	    if is_tf_available()
	    else {}
	)

	_UpperCamelCase     :     Optional[Any]							 =       False
	_UpperCamelCase     :     Optional[Any]							 =       False
	_UpperCamelCase     :     Optional[Any]							 =       False





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    TFViTModelTester(self       )
		lowercase       =    ConfigTester(self		,    config_class=_A		,    has_text_modality=_A		,    hidden_size=37       )





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





	@unittest.skip(reason='ViT does not use inputs_embeds'       )
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		pass





	@unittest.skip(reason='ViT does not use inputs_embeds'       )
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		pass





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase  ,					lowercase       =    self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			lowercase       =    model_class(_A       )
			self.assertIsInstance(model.get_input_embeddings()		,    (tf.keras.layers.Layer)       )
			lowercase       =    model.get_output_embeddings()
			self.assertTrue(x is None or isinstance(_A		,    tf.keras.layers.Layer       )       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase  ,					lowercase       =    self.model_tester.prepare_config_and_inputs_for_common()

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

			lowercase       =    ['pixel_values']
			self.assertListEqual(arg_names[:1]		,    _A       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*_A       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_image_classification(*_A       )





	@slow
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    TFViTModel.from_pretrained('google/vit-base-patch16-224'       )
		self.assertIsNotNone(_A       )






def 							UpperCAmelCase_		(							):
	lowercase       =    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
	return image







@require_tf
@require_vision
class        A_   (			unittest.TestCase   ):




	'''simple docstring'''


	@cached_property
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		return ViTImageProcessor.from_pretrained('google/vit-base-patch16-224'       ) if is_vision_available() else None





	@slow
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    TFViTForImageClassification.from_pretrained('google/vit-base-patch16-224'       )

		lowercase       =    self.default_image_processor
		lowercase       =    prepare_img()
		lowercase       =    image_processor(images=_A		,    return_tensors='tf'       )

		# forward pass
		lowercase       =    model(**_A       )

		# verify the logits
		lowercase       =    tf.TensorShape((1, 1000)       )
		self.assertEqual(outputs.logits.shape		,    _A       )

		lowercase       =    tf.constant([-0.2_744, 0.8_215, -0.0_836]       )

		tf.debugging.assert_near(outputs.logits[0, :3]		,    _A		,    atol=1E-4       )








import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				Union[str, Any]:







			'''simple docstring'''

			if "model" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''model.'''			,			'''''' )
			if "norm1" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''norm1'''			,			'''attention.output.LayerNorm''' )
			if "norm2" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''norm2'''			,			'''output.LayerNorm''' )
			if "norm" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''norm'''			,			'''LayerNorm''' )
			if "transformer" in orig_key:
						UpperCAmelCase   		=				orig_key.split('''.''' )[0].split('''_''' )[-1]
						UpperCAmelCase   		=				orig_key.replace(F"""transformer_{layer_num}"""			,			F"""encoder.layer.{layer_num}""" )
			if "mha.attn" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''mha.attn'''			,			'''attention.self''' )
			if "mha" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''mha'''			,			'''attention''' )
			if "W_q" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''W_q'''			,			'''self.query''' )
			if "W_k" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''W_k'''			,			'''self.key''' )
			if "W_v" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''W_v'''			,			'''self.value''' )
			if "ff1" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''ff1'''			,			'''intermediate.dense''' )
			if "ff2" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''ff2'''			,			'''output.dense''' )
			if "ff" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''ff'''			,			'''output.dense''' )
			if "mlm_class" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''mlm.mlm_class'''			,			'''cls.predictions.decoder''' )
			if "mlm" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''mlm'''			,			'''cls.predictions.transform''' )
			if "cls" not in orig_key:
						UpperCAmelCase   		=				'''yoso.''' + orig_key

			return orig_key





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__ )  ->				Dict:







			'''simple docstring'''

			for key in orig_state_dict.copy().keys():
						UpperCAmelCase   		=				orig_state_dict.pop(UpperCamelCase__ )

						if ("pooler" in key) or ("sen_class" in key):
									continue
						else:
									UpperCAmelCase   		=				val

			UpperCAmelCase   		=				orig_state_dict['''cls.predictions.decoder.bias''']
			UpperCAmelCase   		=				torch.arange(UpperCamelCase__ ).expand((1, -1) ) + 2

			return orig_state_dict





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__ )  ->				int:







			'''simple docstring'''

			UpperCAmelCase   		=				torch.load(UpperCamelCase__			,			map_location='''cpu''' )['''model_state_dict''']
			UpperCAmelCase   		=				YosoConfig.from_json_file(UpperCamelCase__ )
			UpperCAmelCase   		=				YosoForMaskedLM(UpperCamelCase__ )

			UpperCAmelCase   		=				convert_checkpoint_helper(config.max_position_embeddings			,			UpperCamelCase__ )

			print(model.load_state_dict(UpperCamelCase__ ) )
			model.eval()
			model.save_pretrained(UpperCamelCase__ )

			print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}""" )


if __name__ == "__main__":
				__A				:    Any       =   argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--pytorch_model_path", default=None, type=str, required=True, help="Path to YOSO pytorch checkpoint."
				)
				parser.add_argument(
				    "--config_file",
				    default=None,
				    type=str,
				    required=True,
				    help="The json file for YOSO model config.",
				)
				parser.add_argument(
				    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
				)
				__A				:    List[str]       =   parser.parse_args()
				convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)



