

import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    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 torch import nn

							from transformers import ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
							from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import ViTImageProcessor







class 						lowercase    :


		"""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=5	,       __snake_case=4	,       __snake_case=37	,       __snake_case="gelu"	,       __snake_case=0.1	,       __snake_case=0.1	,       __snake_case=10	,       __snake_case=0.0_2	,       __snake_case=None	,       __snake_case=2	,       ):
			_UpperCamelCase   :		Dict			=						parent
			_UpperCamelCase   :		str			=						batch_size
			_UpperCamelCase   :		int			=						image_size
			_UpperCamelCase   :		Optional[Any]			=						patch_size
			_UpperCamelCase   :		List[Any]			=						num_channels
			_UpperCamelCase   :		List[str]			=						is_training
			_UpperCamelCase   :		List[str]			=						use_labels
			_UpperCamelCase   :		Union[str, Any]			=						hidden_size
			_UpperCamelCase   :		Optional[Any]			=						num_hidden_layers
			_UpperCamelCase   :		Union[str, Any]			=						num_attention_heads
			_UpperCamelCase   :		Optional[int]			=						intermediate_size
			_UpperCamelCase   :		Tuple			=						hidden_act
			_UpperCamelCase   :		Optional[Any]			=						hidden_dropout_prob
			_UpperCamelCase   :		Optional[int]			=						attention_probs_dropout_prob
			_UpperCamelCase   :		Union[str, Any]			=						type_sequence_label_size
			_UpperCamelCase   :		Optional[int]			=						initializer_range
			_UpperCamelCase   :		Dict			=						scope
			_UpperCamelCase   :		List[str]			=						encoder_stride

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


		def 		A__					(				self):
			_UpperCamelCase   :		Tuple			=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

			_UpperCamelCase   :		int			=						None
			if self.use_labels:
				_UpperCamelCase   :		Optional[Any]			=						ids_tensor([self.batch_size]	,       self.type_sequence_label_size)

			_UpperCamelCase   :		Optional[Any]			=						self.get_config()

			return config, pixel_values, labels


		def 		A__					(				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=__snake_case	,       initializer_range=self.initializer_range	,       encoder_stride=self.encoder_stride	,       )


		def 		A__					(				self	,       __snake_case	,       __snake_case	,       __snake_case):
			_UpperCamelCase   :		List[str]			=						ViTModel(config=__snake_case)
			model.to(__snake_case)
			model.eval()
			_UpperCamelCase   :		List[Any]			=						model(__snake_case)
			self.parent.assertEqual(result.last_hidden_state.shape	,       (self.batch_size, self.seq_length, self.hidden_size))


		def 		A__					(				self	,       __snake_case	,       __snake_case	,       __snake_case):
			_UpperCamelCase   :		Dict			=						ViTForMaskedImageModeling(config=__snake_case)
			model.to(__snake_case)
			model.eval()
			_UpperCamelCase   :		Any			=						model(__snake_case)
			self.parent.assertEqual(
			    result.reconstruction.shape	,       (self.batch_size, self.num_channels, self.image_size, self.image_size))

			# test greyscale images
			_UpperCamelCase   :		Dict			=						1
			_UpperCamelCase   :		Optional[Any]			=						ViTForMaskedImageModeling(__snake_case)
			model.to(__snake_case)
			model.eval()

			_UpperCamelCase   :		Tuple			=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
			_UpperCamelCase   :		Union[str, Any]			=						model(__snake_case)
			self.parent.assertEqual(result.reconstruction.shape	,       (self.batch_size, 1, self.image_size, self.image_size))


		def 		A__					(				self	,       __snake_case	,       __snake_case	,       __snake_case):
			_UpperCamelCase   :		Optional[Any]			=						self.type_sequence_label_size
			_UpperCamelCase   :		Optional[int]			=						ViTForImageClassification(__snake_case)
			model.to(__snake_case)
			model.eval()
			_UpperCamelCase   :		Any			=						model(__snake_case	,       labels=__snake_case)
			self.parent.assertEqual(result.logits.shape	,       (self.batch_size, self.type_sequence_label_size))

			# test greyscale images
			_UpperCamelCase   :		Dict			=						1
			_UpperCamelCase   :		List[Any]			=						ViTForImageClassification(__snake_case)
			model.to(__snake_case)
			model.eval()

			_UpperCamelCase   :		Union[str, Any]			=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
			_UpperCamelCase   :		Union[str, Any]			=						model(__snake_case)
			self.parent.assertEqual(result.logits.shape	,       (self.batch_size, self.type_sequence_label_size))






		def 		A__					(				self):
			_UpperCamelCase   :		Any			=						self.prepare_config_and_inputs()
			(
			    _UpperCamelCase		
			)   :		Optional[Any]			=						config_and_inputs
			_UpperCamelCase   :		str			=						{'pixel_values': pixel_values}
			return config, inputs_dict







@require_torch
class 						lowercase    (				_lowercase    ,							_lowercase    ,							unittest.TestCase							):


		"""simple docstring"""




		a__					       =  (
		    (
		        ViTModel,
		        ViTForImageClassification,
		        ViTForMaskedImageModeling,
		    )
		    if is_torch_available()
		    else ()
		)
		a__					       =  (
		    {"feature-extraction": ViTModel, "image-classification": ViTForImageClassification}
		    if is_torch_available()
		    else {}
		)
		a__					       =  True

		a__					       =  False
		a__					       =  False
		a__					       =  False


		def 		A__					(				self):
			_UpperCamelCase   :		Any			=						ViTModelTester(self)
			_UpperCamelCase   :		Any			=						ConfigTester(self	,       config_class=__snake_case	,       has_text_modality=__snake_case	,       hidden_size=37)


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


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


		def 		A__					(				self):
			_UpperCamelCase   :		Union[str, Any]			=						self.model_tester.prepare_config_and_inputs_for_common()

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


		def 		A__					(				self):
			_UpperCamelCase   :		Any			=						self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
				_UpperCamelCase   :		Tuple			=						model_class(__snake_case)
				_UpperCamelCase   :		int			=						inspect.signature(model.forward)
				# signature.parameters is an OrderedDict => so arg_names order is deterministic
				_UpperCamelCase   :		List[str]			=						[*signature.parameters.keys()]

				_UpperCamelCase   :		Optional[int]			=						['pixel_values']
				self.assertListEqual(arg_names[:1]	,       __snake_case)


		def 		A__					(				self):
			_UpperCamelCase   :		Any			=						self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*__snake_case)


		def 		A__					(				self):
			_UpperCamelCase   :		Tuple			=						self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_masked_image_modeling(*__snake_case)


		def 		A__					(				self):
			_UpperCamelCase   :		Tuple			=						self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_image_classification(*__snake_case)






		@slow
		def 		A__					(				self):
			for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
				_UpperCamelCase   :		Any			=						ViTModel.from_pretrained(__snake_case)
				self.assertIsNotNone(__snake_case)






def 					lowerCamelCase_     (							)						->				List[str]:




	'''simple docstring'''




	_UpperCamelCase   :		Optional[int]			=						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
	return image







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


		"""simple docstring"""




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


		@slow
		def 		A__					(				self):
			_UpperCamelCase   :		List[str]			=						ViTForImageClassification.from_pretrained('google/vit-base-patch16-224').to(__snake_case)

			_UpperCamelCase   :		Optional[int]			=						self.default_image_processor
			_UpperCamelCase   :		List[str]			=						prepare_img()
			_UpperCamelCase   :		Optional[Any]			=						image_processor(images=__snake_case	,       return_tensors='pt').to(__snake_case)

			# forward pass
			with torch.no_grad():
				_UpperCamelCase   :		List[str]			=						model(**__snake_case)

			# verify the logits
			_UpperCamelCase   :		Optional[int]			=						torch.Size((1, 10_00))
			self.assertEqual(outputs.logits.shape	,       __snake_case)

			_UpperCamelCase   :		int			=						torch.tensor([-0.2_7_4_4, 0.8_2_1_5, -0.0_8_3_6]).to(__snake_case)

			self.assertTrue(torch.allclose(outputs.logits[0, :3]	,       __snake_case	,       atol=1e-4))


		@slow
		def 		A__					(				self):
			# ViT models have an `interpolate_pos_encoding` argument in their forward method,
			# allowing to interpolate the pre-trained position embeddings in order to use
			# the model on higher resolutions. The DINO model by Facebook AI leverages this
			# to visualize self-attention on higher resolution images.
			_UpperCamelCase   :		Tuple			=						ViTModel.from_pretrained('facebook/dino-vits8').to(__snake_case)

			_UpperCamelCase   :		Optional[int]			=						ViTImageProcessor.from_pretrained('facebook/dino-vits8'	,       size=4_80)
			_UpperCamelCase   :		Tuple			=						prepare_img()
			_UpperCamelCase   :		Union[str, Any]			=						image_processor(images=__snake_case	,       return_tensors='pt')
			_UpperCamelCase   :		str			=						inputs.pixel_values.to(__snake_case)

			# forward pass
			with torch.no_grad():
				_UpperCamelCase   :		Optional[Any]			=						model(__snake_case	,       interpolate_pos_encoding=__snake_case)

			# verify the logits
			_UpperCamelCase   :		Dict			=						torch.Size((1, 36_01, 3_84))
			self.assertEqual(outputs.last_hidden_state.shape	,       __snake_case)

			_UpperCamelCase   :		Any			=						torch.tensor(
			    [[4.2_3_4_0, 4.3_9_0_6, -6.6_6_9_2], [4.5_4_6_3, 1.8_9_2_8, -6.7_2_5_7], [4.4_4_2_9, 0.8_4_9_6, -5.8_5_8_5]]).to(__snake_case)

			self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]	,       __snake_case	,       atol=1e-4))






		@slow
		@require_accelerate
		@require_torch_gpu
		def 		A__					(				self):
			_UpperCamelCase   :		Any			=						ViTModel.from_pretrained('facebook/dino-vits8'	,       torch_dtype=torch.floataa	,       device_map='auto')
			_UpperCamelCase   :		Union[str, Any]			=						self.default_image_processor

			_UpperCamelCase   :		Union[str, Any]			=						prepare_img()
			_UpperCamelCase   :		Tuple			=						image_processor(images=__snake_case	,       return_tensors='pt')
			_UpperCamelCase   :		List[Any]			=						inputs.pixel_values.to(__snake_case)

			# forward pass to make sure inference works in fp16
			with torch.no_grad():
				_UpperCamelCase   :		str			=						model(__snake_case)







import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__								=		logging.get_logger(__name__)

lowerCAmelCase__								=		[
    ["""attention""", """attn"""],
    ["""encoder_attention""", """encoder_attn"""],
    ["""q_lin""", """q_proj"""],
    ["""k_lin""", """k_proj"""],
    ["""v_lin""", """v_proj"""],
    ["""out_lin""", """out_proj"""],
    ["""norm_embeddings""", """layernorm_embedding"""],
    ["""position_embeddings""", """embed_positions"""],
    ["""embeddings""", """embed_tokens"""],
    ["""ffn.lin""", """fc"""],
]






def 					lowerCamelCase_     (							UpperCAmelCase_							:     List[Any]	)						->				Optional[int]:




	'''simple docstring'''




	if k == "embeddings.weight":
		return "shared.weight"

	for parlai_name, hf_name in PATTERNS:
		_UpperCamelCase   :		List[Any]			=						k.replace(UpperCAmelCase_							,       UpperCAmelCase_	)

	if k.startswith('encoder'	):
		_UpperCamelCase   :		Optional[Any]			=						k.replace('.attn'							,       '.self_attn'	)
		_UpperCamelCase   :		Optional[int]			=						k.replace('norm1'							,       'self_attn_layer_norm'	)
		_UpperCamelCase   :		Tuple			=						k.replace('norm2'							,       'final_layer_norm'	)
	elif k.startswith('decoder'	):
		_UpperCamelCase   :		Any			=						k.replace('norm1'							,       'self_attn_layer_norm'	)
		_UpperCamelCase   :		Tuple			=						k.replace('norm2'							,       'encoder_attn_layer_norm'	)
		_UpperCamelCase   :		Tuple			=						k.replace('norm3'							,       'final_layer_norm'	)
	return k






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Dict	)						->				Optional[Any]:




	'''simple docstring'''




	_UpperCamelCase   :		Union[str, Any]			=						[
	    'model.encoder.layernorm_embedding.weight',
	    'model.encoder.layernorm_embedding.bias',
	    'model.decoder.layernorm_embedding.weight',
	    'model.decoder.layernorm_embedding.bias',
	]
	for k in keys:
		_UpperCamelCase   :		Optional[int]			=						sd.pop(UpperCAmelCase_	)
		_UpperCamelCase   :		str			=						k.replace('layernorm_embedding'							,       'layer_norm'	)
		assert new_k not in sd
		_UpperCamelCase   :		Tuple			=						v


lowerCAmelCase__								=		["""START"""]






@torch.no_grad()
def 					lowerCamelCase_     (							UpperCAmelCase_							:     Tuple							,       UpperCAmelCase_							:     Optional[Any]							,       UpperCAmelCase_							:     Any	)						->				List[str]:




	'''simple docstring'''




	_UpperCamelCase   :		Optional[Any]			=						torch.load(UpperCAmelCase_							,       map_location='cpu'	)
	_UpperCamelCase   :		int			=						model['model']
	_UpperCamelCase   :		List[Any]			=						BlenderbotConfig.from_json_file(UpperCAmelCase_	)
	_UpperCamelCase   :		Any			=						BlenderbotForConditionalGeneration(UpperCAmelCase_	)
	_UpperCamelCase   :		int			=						m.model.state_dict().keys()
	_UpperCamelCase   :		Union[str, Any]			=						[]
	_UpperCamelCase   :		int			=						{}
	for k, v in sd.items():
		if k in IGNORE_KEYS:
			continue

		_UpperCamelCase   :		Optional[int]			=						rename_state_dict_key(UpperCAmelCase_	)
		if new_k not in valid_keys:
			failures.append([k, new_k]	)
		else:
			_UpperCamelCase   :		int			=						v
	if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
		rename_layernorm_keys(UpperCAmelCase_	)
	m.model.load_state_dict(UpperCAmelCase_							,       strict=UpperCAmelCase_	)
	m.half()
	m.save_pretrained(UpperCAmelCase_	)


if __name__ == "__main__":
							lowerCAmelCase__								=		argparse.ArgumentParser()
							# Required parameters
							parser.add_argument("""--src_path""", type=str, help="""like blenderbot-model.bin""")
							parser.add_argument("""--save_dir""", default="""hf_blenderbot""", type=str, help="""Where to save converted model.""")
							parser.add_argument(
							    """--hf_config_json""", default="""blenderbot-3b-config.json""", type=str, help="""Path to config to use"""
							)
							lowerCAmelCase__								=		parser.parse_args()
							convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)




