

'''simple docstring'''





import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


A											=     [
    # tf -> hf
    ('/', '.'),
    ('layer_', 'layers.'),
    ('kernel', 'weight'),
    ('beta', 'bias'),
    ('gamma', 'weight'),
    ('pegasus', 'model'),
]
A											=     [
    ('.output.dense', '.fc2'),
    ('intermediate.LayerNorm', 'final_layer_norm'),
    ('intermediate.dense', 'fc1'),
]

A											=     (
    INIT_COMMON
    + [
        ('attention.self.LayerNorm', 'self_attn_layer_norm'),
        ('attention.output.dense', 'self_attn.out_proj'),
        ('attention.self', 'self_attn'),
        ('attention.encdec.LayerNorm', 'encoder_attn_layer_norm'),
        ('attention.encdec_output.dense', 'encoder_attn.out_proj'),
        ('attention.encdec', 'encoder_attn'),
        ('key', 'k_proj'),
        ('value', 'v_proj'),
        ('query', 'q_proj'),
        ('decoder.LayerNorm', 'decoder.layernorm_embedding'),
    ]
    + END_COMMON
)

A											=     (
    INIT_COMMON
    + [
        ('embeddings.word_embeddings', 'shared.weight'),
        ('embeddings.position_embeddings', 'embed_positions.weight'),
        ('attention.self.LayerNorm', 'self_attn_layer_norm'),
        ('attention.output.dense', 'self_attn.output'),
        ('attention.self', 'self_attn.self'),
        ('encoder.LayerNorm', 'encoder.layernorm_embedding'),
    ]
    + END_COMMON
)

A											=     [
    'encdec/key/bias',
    'encdec/query/bias',
    'encdec/value/bias',
    'self/key/bias',
    'self/query/bias',
    'self/value/bias',
    'encdec_output/dense/bias',
    'attention/output/dense/bias',
]





def a(lowercase__			, lowercase__	):





	'''simple docstring'''
	for tf_name, hf_name in patterns:
		snake_case_      		= k.replace(lowercase__			, lowercase__	)
	return k





def a(lowercase__			, lowercase__	):





	'''simple docstring'''
	snake_case_      		= BigBirdPegasusConfig(**lowercase__	)
	snake_case_      		= BigBirdPegasusForConditionalGeneration(lowercase__	)
	snake_case_      		= torch_model.state_dict()
	snake_case_      		= {}

	# separating decoder weights
	snake_case_      		= {k: tf_weights[k] for k in tf_weights if k.startswith('pegasus/decoder'	)}
	snake_case_      		= {k: tf_weights[k] for k in tf_weights if not k.startswith('pegasus/decoder'	)}

	for k, v in tqdm(decoder_weights.items()			, 'tf -> hf conversion'	):
		snake_case_      		= [k.endswith(lowercase__	) for ending in KEYS_TO_IGNORE]
		if any(lowercase__	):
			continue
		snake_case_      		= DECODER_PATTERNS
		snake_case_      		= rename_state_dict_key(lowercase__			, lowercase__	)
		if new_k not in state_dict:
			raise ValueError(f"""could not find new key {new_k} in state dict. (converted from {k})"""	)
		if any(True if i in k else False for i in ['dense', 'query', 'key', 'value']	):
			snake_case_      		= v.T
		snake_case_      		= torch.from_numpy(lowercase__	)
		assert v.shape == state_dict[new_k].shape, f"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""

	for k, v in tqdm(remaining_weights.items()			, 'tf -> hf conversion'	):
		snake_case_      		= [k.endswith(lowercase__	) for ending in KEYS_TO_IGNORE]
		if any(lowercase__	):
			continue
		snake_case_      		= REMAINING_PATTERNS
		snake_case_      		= rename_state_dict_key(lowercase__			, lowercase__	)
		if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
			raise ValueError(f"""could not find new key {new_k} in state dict. (converted from {k})"""	)
		if any(True if i in k else False for i in ['dense', 'query', 'key', 'value']	):
			snake_case_      		= v.T
		snake_case_      		= torch.from_numpy(lowercase__	)
		if k != "pegasus/embeddings/position_embeddings":
			assert v.shape == state_dict[new_k].shape, f"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""

	snake_case_      		= mapping['model.embed_positions.weight']
	snake_case_      		= mapping.pop('model.embed_positions.weight'	)
	snake_case_		,       snake_case_      		= torch_model.load_state_dict(lowercase__			, strict=lowercase__	)
	snake_case_      		= [
	    k
	    for k in missing
	    if k
	    not in [
	        'final_logits_bias',
	        'model.encoder.embed_tokens.weight',
	        'model.decoder.embed_tokens.weight',
	        'lm_head.weight',
	    ]
	]
	assert unexpected_missing == [], f"""no matches found for the following torch keys {unexpected_missing}"""
	assert extra == [], f"""no matches found for the following tf keys {extra}"""
	return torch_model





def a(lowercase__	):





	'''simple docstring'''
	snake_case_      		= tf.train.list_variables(lowercase__	)
	snake_case_      		= {}
	snake_case_      		= ['global_step']
	for name, shape in tqdm(lowercase__			, desc='converting tf checkpoint to dict'	):
		snake_case_      		= any(pat in name for pat in ignore_name	)
		if skip_key:
			continue
		snake_case_      		= tf.train.load_variable(lowercase__			, lowercase__	)
		snake_case_      		= array
	return tf_weights





def a(lowercase__			, lowercase__			, lowercase__	):





	'''simple docstring'''
	snake_case_      		= get_tf_weights_as_numpy(lowercase__	)
	snake_case_      		= convert_bigbird_pegasus(lowercase__			, lowercase__	)
	torch_model.save_pretrained(lowercase__	)


if __name__ == "__main__":
		A											=     argparse.ArgumentParser()
		parser.add_argument('--tf_ckpt_path', type=str, help='passed to tf.train.list_variables')
		parser.add_argument('--save_dir', default=None, type=str, help='Path to the output PyTorch model.')
		A											=     parser.parse_args()
		A											=     {}
		convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)



import inspect
import unittest

from transformers import MobileViTConfig
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 MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
		from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import MobileViTImageProcessor


class SCREAMING_SNAKE_CASE  (		__snake_case							):






			"""simple docstring"""




			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.config_class(**self.inputs_dict							)
				self.parent.assertTrue(hasattr(__UpperCamelCase ,				'hidden_sizes'							)							)
				self.parent.assertTrue(hasattr(__UpperCamelCase ,				'neck_hidden_sizes'							)							)
				self.parent.assertTrue(hasattr(__UpperCamelCase ,				'num_attention_heads'							)							)







class SCREAMING_SNAKE_CASE  :






			"""simple docstring"""




			def __init__(    self ,				__UpperCamelCase ,				__UpperCamelCase=13 ,				__UpperCamelCase=32 ,				__UpperCamelCase=2 ,				__UpperCamelCase=3 ,				__UpperCamelCase=6_40 ,				__UpperCamelCase=4 ,				__UpperCamelCase="silu" ,				__UpperCamelCase=3 ,				__UpperCamelCase=32 ,				__UpperCamelCase=0.1 ,				__UpperCamelCase=0.1 ,				__UpperCamelCase=0.1 ,				__UpperCamelCase=0.02 ,				__UpperCamelCase=True ,				__UpperCamelCase=True ,				__UpperCamelCase=10 ,				__UpperCamelCase=None ,				):

				"""simple docstring"""


				snake_case_      		= parent
				snake_case_      		= batch_size
				snake_case_      		= image_size
				snake_case_      		= patch_size
				snake_case_      		= num_channels
				snake_case_      		= last_hidden_size
				snake_case_      		= num_attention_heads
				snake_case_      		= hidden_act
				snake_case_      		= conv_kernel_size
				snake_case_      		= output_stride
				snake_case_      		= hidden_dropout_prob
				snake_case_      		= attention_probs_dropout_prob
				snake_case_      		= classifier_dropout_prob
				snake_case_      		= use_labels
				snake_case_      		= is_training
				snake_case_      		= num_labels
				snake_case_      		= initializer_range
				snake_case_      		= scope


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


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

				snake_case_      		= None
				snake_case_      		= None
				if self.use_labels:
					snake_case_      		= ids_tensor([self.batch_size] ,				self.num_labels							)
					snake_case_      		= ids_tensor([self.batch_size, self.image_size, self.image_size] ,				self.num_labels							)

				snake_case_      		= self.get_config()

				return config, pixel_values, labels, pixel_labels


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				return MobileViTConfig(
				    image_size=self.image_size ,				patch_size=self.patch_size ,				num_channels=self.num_channels ,				num_attention_heads=self.num_attention_heads ,				hidden_act=self.hidden_act ,				conv_kernel_size=self.conv_kernel_size ,				output_stride=self.output_stride ,				hidden_dropout_prob=self.hidden_dropout_prob ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob ,				classifier_dropout_prob=self.classifier_dropout_prob ,				initializer_range=self.initializer_range ,				)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= MobileViTModel(config=__UpperCamelCase							)
				model.to(__UpperCamelCase							)
				model.eval()
				snake_case_      		= model(__UpperCamelCase							)
				self.parent.assertEqual(
				    result.last_hidden_state.shape ,				(
				        self.batch_size,
				        self.last_hidden_size,
				        self.image_size // self.output_stride,
				        self.image_size // self.output_stride,
				    ) ,				)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= self.num_labels
				snake_case_      		= MobileViTForImageClassification(__UpperCamelCase							)
				model.to(__UpperCamelCase							)
				model.eval()
				snake_case_      		= model(__UpperCamelCase ,				labels=__UpperCamelCase							)
				self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_labels)							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= self.num_labels
				snake_case_      		= MobileViTForSemanticSegmentation(__UpperCamelCase							)
				model.to(__UpperCamelCase							)
				model.eval()
				snake_case_      		= model(__UpperCamelCase							)
				self.parent.assertEqual(
				    result.logits.shape ,				(
				        self.batch_size,
				        self.num_labels,
				        self.image_size // self.output_stride,
				        self.image_size // self.output_stride,
				    ) ,				)
				snake_case_      		= model(__UpperCamelCase ,				labels=__UpperCamelCase							)
				self.parent.assertEqual(
				    result.logits.shape ,				(
				        self.batch_size,
				        self.num_labels,
				        self.image_size // self.output_stride,
				        self.image_size // self.output_stride,
				    ) ,				)







			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


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







@require_torch
class SCREAMING_SNAKE_CASE  (		__snake_case							,						__snake_case							,						unittest.TestCase							):






			"""simple docstring"""




			__A						  =  (
			    (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
			    if is_torch_available()
			    else ()
			)
			__A						  =  (
			    {
			        """feature-extraction""": MobileViTModel,
			        """image-classification""": MobileViTForImageClassification,
			        """image-segmentation""": MobileViTForSemanticSegmentation,
			    }
			    if is_torch_available()
			    else {}
			)

			__A						  =  False
			__A						  =  False
			__A						  =  False
			__A						  =  False


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= MobileViTModelTester(self							)
				snake_case_      		= MobileViTConfigTester(self ,				config_class=__UpperCamelCase ,				has_text_modality=__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				self.config_tester.run_common_tests()


			@unittest.skip(reason='MobileViT does not use inputs_embeds'							)
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				pass


			@unittest.skip(reason='MobileViT does not support input and output embeddings'							)
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				pass


			@unittest.skip(reason='MobileViT does not output attentions'							)
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				pass


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_		,       snake_case_      		= self.model_tester.prepare_config_and_inputs_for_common()

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

					snake_case_      		= ['pixel_values']
					self.assertListEqual(arg_names[:1] ,				__UpperCamelCase							)


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

				"""simple docstring"""


				pass


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				def check_hidden_states_output(__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):
					snake_case_      		= model_class(__UpperCamelCase							)
					model.to(__UpperCamelCase							)
					model.eval()

					with torch.no_grad():
						snake_case_      		= model(**self._prepare_for_class(__UpperCamelCase ,				__UpperCamelCase							)							)

					snake_case_      		= outputs.hidden_states

					snake_case_      		= 5
					self.assertEqual(len(__UpperCamelCase							) ,				__UpperCamelCase							)

					# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
					# with the width and height being successively divided by 2.
					snake_case_      		= 2
					for i in range(len(__UpperCamelCase							)							):
						self.assertListEqual(
						    list(hidden_states[i].shape[-2:]							) ,				[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor] ,				)
						divisor *= 2

					self.assertEqual(self.model_tester.output_stride ,				divisor // 2							)

				snake_case_		,       snake_case_      		= self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					snake_case_      		= True
					check_hidden_states_output(__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							)

					# check that output_hidden_states also work using config
					del inputs_dict["output_hidden_states"]
					snake_case_      		= True

					check_hidden_states_output(__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_semantic_segmentation(*__UpperCamelCase							)







			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					snake_case_      		= MobileViTModel.from_pretrained(__UpperCamelCase							)
					self.assertIsNotNone(__UpperCamelCase							)







def a():





	'''simple docstring'''
	snake_case_      		= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
	return image






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






			"""simple docstring"""




			@cached_property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				return MobileViTImageProcessor.from_pretrained('apple/mobilevit-xx-small'							) if is_vision_available() else None


			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= MobileViTForImageClassification.from_pretrained('apple/mobilevit-xx-small'							).to(__UpperCamelCase							)

				snake_case_      		= self.default_image_processor
				snake_case_      		= prepare_img()
				snake_case_      		= image_processor(images=__UpperCamelCase ,				return_tensors='pt'							).to(__UpperCamelCase							)

				# forward pass
				with torch.no_grad():
					snake_case_      		= model(**__UpperCamelCase							)

				# verify the logits
				snake_case_      		= torch.Size((1, 10_00)							)
				self.assertEqual(outputs.logits.shape ,				__UpperCamelCase							)

				snake_case_      		= torch.tensor([-1.9364, -1.2327, -0.4653]							).to(__UpperCamelCase							)

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


			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small'							)
				snake_case_      		= model.to(__UpperCamelCase							)

				snake_case_      		= MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small'							)

				snake_case_      		= prepare_img()
				snake_case_      		= image_processor(images=__UpperCamelCase ,				return_tensors='pt'							).to(__UpperCamelCase							)

				# forward pass
				with torch.no_grad():
					snake_case_      		= model(**__UpperCamelCase							)
				snake_case_      		= outputs.logits

				# verify the logits
				snake_case_      		= torch.Size((1, 21, 32, 32)							)
				self.assertEqual(logits.shape ,				__UpperCamelCase							)

				snake_case_      		= torch.tensor(
				    [
				        [[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
				        [[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
				        [[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
				    ] ,				device=__UpperCamelCase ,				)

				self.assertTrue(torch.allclose(logits[0, :3, :3, :3] ,				__UpperCamelCase ,				atol=1E-4							)							)







			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small'							)
				snake_case_      		= model.to(__UpperCamelCase							)

				snake_case_      		= MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small'							)

				snake_case_      		= prepare_img()
				snake_case_      		= image_processor(images=__UpperCamelCase ,				return_tensors='pt'							).to(__UpperCamelCase							)

				# forward pass
				with torch.no_grad():
					snake_case_      		= model(**__UpperCamelCase							)

				snake_case_      		= outputs.logits.detach().cpu()

				snake_case_      		= image_processor.post_process_semantic_segmentation(outputs=__UpperCamelCase ,				target_sizes=[(50, 60)]							)
				snake_case_      		= torch.Size((50, 60)							)
				self.assertEqual(segmentation[0].shape ,				__UpperCamelCase							)

				snake_case_      		= image_processor.post_process_semantic_segmentation(outputs=__UpperCamelCase							)
				snake_case_      		= torch.Size((32, 32)							)
				self.assertEqual(segmentation[0].shape ,				__UpperCamelCase							)
