





import argparse
import json
import re
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


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





def   lowerCamelCase__       (       UpperCamelCase__     :    int  )     ->	List[str]:



					'''simple docstring'''




					_snake_case													=			MobileNetVaConfig(layer_norm_eps=0.001  )

					if "_quant" in model_name:
										raise ValueError('Quantized models are not supported.'  )

					_snake_case													=			re.match(R'^mobilenet_v1_([^_]*)_([^_]*)$'    ,      UpperCamelCase__  )
					if matches:
										_snake_case													=			float(matches[1]  )
										_snake_case													=			int(matches[2]  )

					# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
					# the usual 1000. The first class (index 0) is "background".
					_snake_case													=			1_001
					_snake_case													=			'imagenet-1k-id2label.json'
					_snake_case													=			'huggingface/label-files'
					_snake_case													=			json.load(open(hf_hub_download(UpperCamelCase__    ,      UpperCamelCase__    ,      repo_type='dataset'  )    ,      'r'  )  )
					_snake_case													=			{int(UpperCamelCase__  ) + 1: v for k, v in idalabel.items()}
					_snake_case													=			'background'
					_snake_case													=			idalabel
					_snake_case													=			{v: k for k, v in idalabel.items()}

					return config





def   lowerCamelCase__       (       )     ->	int:



					'''simple docstring'''




					_snake_case													=			'http://images.cocodataset.org/val2017/000000039769.jpg'
					_snake_case													=			Image.open(requests.get(UpperCamelCase__    ,      stream=UpperCamelCase__  ).raw  )
					return im





@torch.no_grad()
def   lowerCamelCase__       (       UpperCamelCase__     :    List[Any]    ,      UpperCamelCase__     :    Dict    ,      UpperCamelCase__     :    Any    ,      UpperCamelCase__     :    Union[str, Any]=False  )     ->	List[str]:



					'''simple docstring'''




					_snake_case													=			get_mobilenet_va_config(UpperCamelCase__  )

					# Load 🤗 model
					_snake_case													=			MobileNetVaForImageClassification(UpperCamelCase__  ).eval()

					# Load weights from TensorFlow checkpoint
					load_tf_weights_in_mobilenet_va(UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__  )

					# Check outputs on an image, prepared by MobileNetV1ImageProcessor
					_snake_case													=			MobileNetVaImageProcessor(
					    crop_size={'width': config.image_size, 'height': config.image_size}    ,      size={'shortest_edge': config.image_size + 32}    ,      )
					_snake_case													=			image_processor(images=prepare_img()    ,      return_tensors='pt'  )
					_snake_case													=			model(**UpperCamelCase__  )
					_snake_case													=			outputs.logits

					assert logits.shape == (1, 1_001)

					if model_name == "mobilenet_v1_1.0_224":
										_snake_case													=			torch.tensor([-4.1739, -1.1233, 3.1205]  )
					elif model_name == "mobilenet_v1_0.75_192":
										_snake_case													=			torch.tensor([-3.9440, -2.3141, -0.3333]  )
					else:
										_snake_case													=			None

					if expected_logits is not None:
										assert torch.allclose(logits[0, :3]    ,      UpperCamelCase__    ,      atol=1e-4  )

					Path(UpperCamelCase__  ).mkdir(exist_ok=UpperCamelCase__  )
					print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''  )
					model.save_pretrained(UpperCamelCase__  )
					print(F'''Saving image processor to {pytorch_dump_folder_path}'''  )
					image_processor.save_pretrained(UpperCamelCase__  )

					if push_to_hub:
										print('Pushing to the hub...'  )
										_snake_case													=			'google/' + model_name
										image_processor.push_to_hub(UpperCamelCase__  )
										model.push_to_hub(UpperCamelCase__  )


if __name__ == "__main__":
					UpperCAmelCase_ 							=       argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--model_name""",
					    default="""mobilenet_v1_1.0_224""",
					    type=str,
					    help="""Name of the MobileNetV1 model you'd like to convert. Should in the form 'mobilenet_v1_<depth>_<size>'.""",
					)
					parser.add_argument(
					    """--checkpoint_path""", required=True, type=str, help="""Path to the original TensorFlow checkpoint (.ckpt file)."""
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
					)
					parser.add_argument(
					    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
					)

					UpperCAmelCase_ 							=       parser.parse_args()
					convert_movilevit_checkpoint(
					    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
					)










import inspect
import unittest
from typing import List

import numpy as np

from transformers import EfficientFormerConfig
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 (
					    TFEfficientFormerForImageClassification,
					    TFEfficientFormerForImageClassificationWithTeacher,
					    TFEfficientFormerModel,
					)
					from transformers.models.efficientformer.modeling_tf_efficientformer import (
					    TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
					)


if is_vision_available():
					from PIL import Image

					from transformers import EfficientFormerImageProcessor
class      UpperCamelCase_						:




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_ = 13			,  lowerCAmelCase_ = 64			,  lowerCAmelCase_ = 2			,  lowerCAmelCase_ = 3			,  lowerCAmelCase_ = 3			,  lowerCAmelCase_ = True			,  lowerCAmelCase_ = True			,  lowerCAmelCase_ = 128			,  lowerCAmelCase_=[16, 32, 64, 128]			,  lowerCAmelCase_ = 7			,  lowerCAmelCase_ = 4			,  lowerCAmelCase_ = 37			,  lowerCAmelCase_ = "gelu"			,  lowerCAmelCase_ = 0.1			,  lowerCAmelCase_ = 0.1			,  lowerCAmelCase_ = 10			,  lowerCAmelCase_ = 0.02			,  lowerCAmelCase_ = 2			,  lowerCAmelCase_ = 1			,  lowerCAmelCase_ = 128			,  lowerCAmelCase_ = [2, 2, 2, 2]			,  lowerCAmelCase_ = 2			,  lowerCAmelCase_ = 2			,  )						->							Dict:
										_snake_case													=			parent
										_snake_case													=			batch_size
										_snake_case													=			image_size
										_snake_case													=			patch_size
										_snake_case													=			num_channels
										_snake_case													=			is_training
										_snake_case													=			use_labels
										_snake_case													=			hidden_size
										_snake_case													=			num_hidden_layers
										_snake_case													=			num_attention_heads
										_snake_case													=			intermediate_size
										_snake_case													=			hidden_act
										_snake_case													=			hidden_dropout_prob
										_snake_case													=			attention_probs_dropout_prob
										_snake_case													=			type_sequence_label_size
										_snake_case													=			initializer_range
										_snake_case													=			encoder_stride
										_snake_case													=			num_attention_outputs
										_snake_case													=			embed_dim
										_snake_case													=			embed_dim + 1
										_snake_case													=			resolution
										_snake_case													=			depths
										_snake_case													=			hidden_sizes
										_snake_case													=			dim
										_snake_case													=			mlp_expansion_ratio




					def 		lowerCAmelCase     (						self					)						->							Optional[Any]:
										_snake_case													=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

										_snake_case													=			self.get_config()

										return config, pixel_values, labels




					def 		lowerCAmelCase     (						self					)						->							Tuple:
										return EfficientFormerConfig(
										    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=lowerCAmelCase_			,  initializer_range=self.initializer_range			,  encoder_stride=self.encoder_stride			,  resolution=self.resolution			,  depths=self.depths			,  hidden_sizes=self.hidden_sizes			,  dim=self.dim			,  mlp_expansion_ratio=self.mlp_expansion_ratio			,  )




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							List[Any]:
										_snake_case													=			TFEfficientFormerModel(config=lowerCAmelCase_					)
										_snake_case													=			model(lowerCAmelCase_			,  training=lowerCAmelCase_					)
										self.parent.assertEqual(result.last_hidden_state.shape			,  (self.batch_size, self.seq_length, self.hidden_size)					)




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							Any:
										_snake_case													=			self.type_sequence_label_size
										_snake_case													=			TFEfficientFormerForImageClassification(lowerCAmelCase_					)
										_snake_case													=			model(lowerCAmelCase_			,  labels=lowerCAmelCase_			,  training=lowerCAmelCase_					)
										self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.type_sequence_label_size)					)

										# test greyscale images
										_snake_case													=			1
										_snake_case													=			TFEfficientFormerForImageClassification(lowerCAmelCase_					)

										_snake_case													=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
										_snake_case													=			model(lowerCAmelCase_			,  labels=lowerCAmelCase_					)
										self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.type_sequence_label_size)					)






					def 		lowerCAmelCase     (						self					)						->							List[str]:
										_snake_case													=			self.prepare_config_and_inputs()
										_snake_case							,       _snake_case							,       _snake_case													=			config_and_inputs
										_snake_case													=			{'pixel_values': pixel_values}
										return config, inputs_dict





@require_tf
class      UpperCamelCase_						(					_lowerCamelCase							,					_lowerCamelCase							,					unittest.TestCase   ):
					lowerCAmelCase_							=		(
					    (
					        TFEfficientFormerModel,
					        TFEfficientFormerForImageClassificationWithTeacher,
					        TFEfficientFormerForImageClassification,
					    )
					    if is_tf_available()
					    else ()
					)
					lowerCAmelCase_							=		(
					    {
					        '''feature-extraction''': TFEfficientFormerModel,
					        '''image-classification''': (
					            TFEfficientFormerForImageClassification,
					            TFEfficientFormerForImageClassificationWithTeacher,
					        ),
					    }
					    if is_tf_available()
					    else {}
					)

					lowerCAmelCase_							=		False

					lowerCAmelCase_							=		False
					lowerCAmelCase_							=		False
					lowerCAmelCase_							=		False
					lowerCAmelCase_							=		False




					def 		lowerCAmelCase     (						self					)						->							str:
										_snake_case													=			TFEfficientFormerModelTester(self					)
										_snake_case													=			ConfigTester(
										    self			,  config_class=lowerCAmelCase_			,  has_text_modality=lowerCAmelCase_			,  hidden_size=37					)




					def 		lowerCAmelCase     (						self					)						->							str:
										self.config_tester.run_common_tests()




					@unittest.skip(reason='EfficientFormer does not use inputs_embeds'					)
					def 		lowerCAmelCase     (						self					)						->							int:
										pass




					@unittest.skip(reason='EfficientFormer does not support input and output embeddings'					)
					def 		lowerCAmelCase     (						self					)						->							Optional[Any]:
										pass




					def 		lowerCAmelCase     (						self					)						->							str:
										_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(lowerCAmelCase_					)
															_snake_case													=			inspect.signature(model.call					)
															# 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]			,  lowerCAmelCase_					)




					def 		lowerCAmelCase     (						self					)						->							Optional[Any]:
										def check_hidden_states_output(lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					):
															_snake_case													=			model_class(lowerCAmelCase_					)

															_snake_case													=			model(**self._prepare_for_class(lowerCAmelCase_			,  lowerCAmelCase_					)			,  training=lowerCAmelCase_					)
															_snake_case													=			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

															_snake_case													=			getattr(
															    self.model_tester			,  'expected_num_hidden_layers'			,  self.model_tester.num_hidden_layers + 1					)
															self.assertEqual(len(lowerCAmelCase_					)			,  lowerCAmelCase_					)

															if hasattr(self.model_tester			,  'encoder_seq_length'					):
																				_snake_case													=			self.model_tester.encoder_seq_length
																				if hasattr(self.model_tester			,  'chunk_length'					) and self.model_tester.chunk_length > 1:
																									_snake_case													=			seq_length * self.model_tester.chunk_length
															else:
																				_snake_case													=			self.model_tester.seq_length

															self.assertListEqual(
															    list(hidden_states[-1].shape[-2:]					)			,  [seq_length, self.model_tester.hidden_size]			,  )

															if config.is_encoder_decoder:
																				_snake_case													=			outputs.decoder_hidden_states

																				self.asseretIsInstance(lowerCAmelCase_			,  (list, tuple)					)
																				self.assertEqual(len(lowerCAmelCase_					)			,  lowerCAmelCase_					)
																				_snake_case													=			getattr(self.model_tester			,  'seq_length'			,  lowerCAmelCase_					)
																				_snake_case													=			getattr(self.model_tester			,  'decoder_seq_length'			,  lowerCAmelCase_					)

																				self.assertListEqual(
																				    list(hidden_states[-1].shape[-2:]					)			,  [decoder_seq_length, self.model_tester.hidden_size]			,  )

										_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(lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					)

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

															check_hidden_states_output(lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					)




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_=False					)						->							List[Any]:
										_snake_case													=			super()._prepare_for_class(lowerCAmelCase_			,  lowerCAmelCase_			,  return_labels=lowerCAmelCase_					)

										if return_labels:
															if model_class.__name__ == "TFEfficientFormerForImageClassificationWithTeacher":
																				del inputs_dict["labels"]

										return inputs_dict




					def 		lowerCAmelCase     (						self					)						->							Dict:
										_snake_case													=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase_					)




					@unittest.skip(reason='EfficientFormer does not implement masked image modeling yet'					)
					def 		lowerCAmelCase     (						self					)						->							Dict:
										_snake_case													=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_image_modeling(*lowerCAmelCase_					)




					def 		lowerCAmelCase     (						self					)						->							List[Any]:
										_snake_case													=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_					)




					@slow
					def 		lowerCAmelCase     (						self					)						->							str:
										for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															_snake_case													=			TFEfficientFormerModel.from_pretrained(lowerCAmelCase_					)
															self.assertIsNotNone(lowerCAmelCase_					)




					def 		lowerCAmelCase     (						self					)						->							List[str]:
										_snake_case							,       _snake_case													=			self.model_tester.prepare_config_and_inputs_for_common()
										_snake_case													=			True

										_snake_case													=			getattr(self.model_tester			,  'seq_length'			,  lowerCAmelCase_					)
										_snake_case													=			getattr(self.model_tester			,  'encoder_seq_length'			,  lowerCAmelCase_					)
										_snake_case													=			getattr(self.model_tester			,  'key_length'			,  lowerCAmelCase_					)
										_snake_case													=			getattr(self.model_tester			,  'chunk_length'			,  lowerCAmelCase_					)

										if chunk_length is not None and hasattr(self.model_tester			,  'num_hashes'					):
															_snake_case													=			encoder_seq_length * self.model_tester.num_hashes

										for model_class in self.all_model_classes:
															_snake_case													=			True
															_snake_case													=			False
															_snake_case													=			True
															_snake_case													=			model_class(lowerCAmelCase_					)

															_snake_case													=			model(**self._prepare_for_class(lowerCAmelCase_			,  lowerCAmelCase_					)			,  training=lowerCAmelCase_					)
															_snake_case													=			outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
															self.assertEqual(len(lowerCAmelCase_					)			,  self.model_tester.num_attention_outputs					)

															# check that output_attentions also work using config
															del inputs_dict["output_attentions"]
															_snake_case													=			True
															_snake_case													=			model_class(lowerCAmelCase_					)
															_snake_case													=			model(**self._prepare_for_class(lowerCAmelCase_			,  lowerCAmelCase_					)			,  training=lowerCAmelCase_					)

															_snake_case													=			outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
															self.assertEqual(len(lowerCAmelCase_					)			,  self.model_tester.num_attention_outputs					)

															if chunk_length is not None:
																				self.assertListEqual(
																				    list(attentions[0].shape[-4:]					)			,  [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]			,  )
															else:
																				self.assertListEqual(
																				    list(attentions[0].shape[-3:]					)			,  [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]			,  )






					def 		lowerCAmelCase     (						self					)						->							Dict:
										# We use a simplified version of this test for EfficientFormer because it requires training=False
										# and Keras refuses to let us force that during functional construction
										_snake_case							,       _snake_case													=			self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
															# Prepare our model
															_snake_case													=			model_class(lowerCAmelCase_					)
															# These are maximally general inputs for the model, with multiple None dimensions
															# Hopefully this will catch any conditionals that fail for flexible shapes
															_snake_case													=			{
															    key: tf.keras.Input(shape=val.shape[1:]			,  dtype=val.dtype			,  name=lowerCAmelCase_					)
															    for key, val in model.input_signature.items()
															    if key in model.dummy_inputs
															}
															_snake_case													=			model(lowerCAmelCase_					)
															self.assertTrue(outputs_dict is not None					)





def   lowerCamelCase__       (       )     ->	List[str]:



					'''simple docstring'''




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




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




					@cached_property
					def 		lowerCAmelCase     (						self					)						->							Dict:
										return (
										    EfficientFormerImageProcessor.from_pretrained('snap-research/efficientformer-l1-300'					)
										    if is_vision_available()
										    else None
										)




					@slow
					def 		lowerCAmelCase     (						self					)						->							Union[str, Any]:
										_snake_case													=			TFEfficientFormerForImageClassification.from_pretrained('snap-research/efficientformer-l1-300'					)
										_snake_case													=			self.default_image_processor
										_snake_case													=			prepare_img()
										_snake_case													=			image_processor(images=lowerCAmelCase_			,  return_tensors='tf'					)
										# forward pass
										_snake_case													=			model(**lowerCAmelCase_			,  training=lowerCAmelCase_					)
										# verify the logits
										_snake_case													=			tf.TensorShape((1, 1000)					)
										self.assertEqual(outputs.logits.shape			,  lowerCAmelCase_					)
										_snake_case													=			tf.constant([-0.05_55, 0.48_25, -0.08_52]					)
										self.assertTrue(np.allclose(outputs.logits[0, :3]			,  lowerCAmelCase_			,  atol=1E-4					)					)






					@slow
					def 		lowerCAmelCase     (						self					)						->							str:
										_snake_case													=			TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
										    'snap-research/efficientformer-l1-300'					)
										_snake_case													=			self.default_image_processor
										_snake_case													=			prepare_img()
										_snake_case													=			image_processor(images=lowerCAmelCase_			,  return_tensors='tf'					)
										# forward pass
										_snake_case													=			model(**lowerCAmelCase_			,  training=lowerCAmelCase_					)
										# verify the logits
										_snake_case													=			tf.TensorShape((1, 1000)					)
										self.assertEqual(outputs.logits.shape			,  lowerCAmelCase_					)
										_snake_case													=			tf.constant([-0.13_12, 0.43_53, -1.04_99]					)
										self.assertTrue(np.allclose(outputs.logits[0, :3]			,  lowerCAmelCase_			,  atol=1E-4					)					)



