



'''simple docstring'''

import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
							import os

							# The slow tests are often failing with OOM error on GPU
							# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
							# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
							__lowerCAmelCase						      =				'''platform'''

							import jax
							import jax.numpy as jnp

							from transformers import BlenderbotTokenizer
							from transformers.models.blenderbot.modeling_flax_blenderbot import (
							    FlaxBlenderbotForConditionalGeneration,
							    FlaxBlenderbotModel,
							    shift_tokens_right,
							)



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_=None						,      lowerCAmelCase_=None						,      lowerCAmelCase_=None						,      lowerCAmelCase_=None						,      lowerCAmelCase_=None						,      lowerCAmelCase_=None						,      )      ->				List[Any]:

		if attention_mask is None:
				_a					:				Dict				=							np.where(input_ids != config.pad_token_id						,      1						,      0  )
		if decoder_attention_mask is None:
				_a					:				Optional[Any]				=							np.where(decoder_input_ids != config.pad_token_id						,      1						,      0  )
		if head_mask is None:
				_a					:				List[Any]				=							np.ones((config.encoder_layers, config.encoder_attention_heads)  )
		if decoder_head_mask is None:
				_a					:				Any				=							np.ones((config.decoder_layers, config.decoder_attention_heads)  )
		if cross_attn_head_mask is None:
				_a					:				Any				=							np.ones((config.decoder_layers, config.decoder_attention_heads)  )
		return {
		    "input_ids": input_ids,
		    "decoder_input_ids": decoder_input_ids,
		    "attention_mask": attention_mask,
		    "decoder_attention_mask": attention_mask,
		}

class      __magic_name__						:







							def __init__(							self :	List[str]     ,_UpperCAmelCase :	Tuple     ,_UpperCAmelCase :	List[str]=13     ,_UpperCAmelCase :	int=7     ,_UpperCAmelCase :	Any=True     ,_UpperCAmelCase :	Tuple=False     ,_UpperCAmelCase :	Tuple=99     ,_UpperCAmelCase :	Optional[int]=16     ,_UpperCAmelCase :	Any=2     ,_UpperCAmelCase :	Optional[Any]=4     ,_UpperCAmelCase :	List[Any]=4     ,_UpperCAmelCase :	Tuple="gelu"     ,_UpperCAmelCase :	str=0.1     ,_UpperCAmelCase :	List[str]=0.1     ,_UpperCAmelCase :	List[Any]=32     ,_UpperCAmelCase :	str=2     ,_UpperCAmelCase :	str=1     ,_UpperCAmelCase :	Union[str, Any]=0     ,_UpperCAmelCase :	Tuple=0.02     ,):
									_a					:				Tuple				=							parent
									_a					:				Tuple				=							batch_size
									_a					:				Tuple				=							seq_length
									_a					:				Tuple				=							is_training
									_a					:				Union[str, Any]				=							use_labels
									_a					:				int				=							vocab_size
									_a					:				int				=							hidden_size
									_a					:				List[str]				=							num_hidden_layers
									_a					:				Tuple				=							num_attention_heads
									_a					:				Any				=							intermediate_size
									_a					:				List[Any]				=							hidden_act
									_a					:				Dict				=							hidden_dropout_prob
									_a					:				str				=							attention_probs_dropout_prob
									_a					:				Any				=							max_position_embeddings
									_a					:				Dict				=							eos_token_id
									_a					:				Tuple				=							pad_token_id
									_a					:				Union[str, Any]				=							bos_token_id
									_a					:				Optional[int]				=							initializer_range







							def 							__lowercase      (							self :	Optional[Any]		):
									_a					:				str				=							np.clip(ids_tensor([self.batch_size, self.seq_length - 1]     ,self.vocab_size		)     ,3     ,self.vocab_size		)
									_a					:				Tuple				=							np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)     ,dtype=np.intaa		))     ,-1		)

									_a					:				Any				=							shift_tokens_right(lowercase_     ,1     ,2		)

									_a					:				List[str]				=							BlenderbotConfig(
									    vocab_size=self.vocab_size     ,d_model=self.hidden_size     ,encoder_layers=self.num_hidden_layers     ,decoder_layers=self.num_hidden_layers     ,encoder_attention_heads=self.num_attention_heads     ,decoder_attention_heads=self.num_attention_heads     ,encoder_ffn_dim=self.intermediate_size     ,decoder_ffn_dim=self.intermediate_size     ,dropout=self.hidden_dropout_prob     ,attention_dropout=self.attention_probs_dropout_prob     ,max_position_embeddings=self.max_position_embeddings     ,eos_token_id=self.eos_token_id     ,bos_token_id=self.bos_token_id     ,pad_token_id=self.pad_token_id     ,initializer_range=self.initializer_range     ,use_cache=lowercase_     ,)
									_a					:				Tuple				=							prepare_blenderbot_inputs_dict(lowercase_     ,lowercase_     ,lowercase_		)
									return config, inputs_dict







							def 							__lowercase      (							self :	int		):
									_a	, _a					:				Any				=							self.prepare_config_and_inputs()
									return config, inputs_dict







							def 							__lowercase      (							self :	str     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	Union[str, Any]     ,_UpperCAmelCase :	Optional[int]		):
									_a					:				Tuple				=							20
									_a					:				Union[str, Any]				=							model_class_name(lowercase_		)

									_a					:				Any				=							model.encode(inputs_dict['input_ids']		)

									_a	, _a					:				Union[str, Any]				=							(
									    inputs_dict['decoder_input_ids'],
									    inputs_dict['decoder_attention_mask'],
									)

									_a					:				Tuple				=							model.init_cache(decoder_input_ids.shape[0]     ,lowercase_     ,lowercase_		)
									_a					:				List[Any]				=							jnp.ones((decoder_input_ids.shape[0], max_decoder_length)     ,dtype='i4'		)

									_a					:				Any				=							jnp.broadcast_to(
									    jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :]     ,(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)     ,)
									_a					:				Union[str, Any]				=							model.decode(
									    decoder_input_ids[:, :-1]     ,lowercase_     ,decoder_attention_mask=lowercase_     ,past_key_values=lowercase_     ,decoder_position_ids=lowercase_     ,)

									_a					:				Tuple				=							jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]     ,dtype='i4'		)
									_a					:				List[str]				=							model.decode(
									    decoder_input_ids[:, -1:]     ,lowercase_     ,decoder_attention_mask=lowercase_     ,past_key_values=outputs_cache.past_key_values     ,decoder_position_ids=lowercase_     ,)

									_a					:				Optional[int]				=							model.decode(lowercase_     ,lowercase_		)

									_a					:				Dict				=							np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])		)		)
									self.parent.assertTrue(diff < 1E-3     ,msg=F"""Max diff is {diff}"""		)







							def 							__lowercase      (							self :	Dict     ,_UpperCAmelCase :	List[Any]     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	Optional[Any]		):
									_a					:				Any				=							20
									_a					:				List[str]				=							model_class_name(lowercase_		)

									_a					:				List[Any]				=							model.encode(inputs_dict['input_ids']		)

									_a	, _a					:				Union[str, Any]				=							(
									    inputs_dict['decoder_input_ids'],
									    inputs_dict['decoder_attention_mask'],
									)

									_a					:				Any				=							jnp.concatenate(
									    [
									        decoder_attention_mask,
									        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])		),
									    ]     ,axis=-1     ,)

									_a					:				Dict				=							model.init_cache(decoder_input_ids.shape[0]     ,lowercase_     ,lowercase_		)
									_a					:				Any				=							jnp.broadcast_to(
									    jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :]     ,(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)     ,)

									_a					:				Optional[int]				=							model.decode(
									    decoder_input_ids[:, :-1]     ,lowercase_     ,decoder_attention_mask=lowercase_     ,past_key_values=lowercase_     ,decoder_position_ids=lowercase_     ,)
									_a					:				List[str]				=							jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]     ,dtype='i4'		)
									_a					:				str				=							model.decode(
									    decoder_input_ids[:, -1:]     ,lowercase_     ,past_key_values=outputs_cache.past_key_values     ,decoder_attention_mask=lowercase_     ,decoder_position_ids=lowercase_     ,)

									_a					:				Optional[int]				=							model.decode(lowercase_     ,lowercase_     ,decoder_attention_mask=lowercase_		)

									_a					:				Optional[Any]				=							np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])		)		)
									self.parent.assertTrue(diff < 1E-3     ,msg=F"""Max diff is {diff}"""		)

@require_flax
class      __magic_name__						(   unittest.TestCase       ):
							lowerCAmelCase	:   Optional[int]           = 9_9







							def 							__lowercase      (							self :	List[str]		):
									_a					:				List[Any]				=							np.array(
									    [
									        [71, 82, 18, 33, 46, 91, 2],
									        [68, 34, 26, 58, 30, 82, 2],
									        [5, 97, 17, 39, 94, 40, 2],
									        [76, 83, 94, 25, 70, 78, 2],
									        [87, 59, 41, 35, 48, 66, 2],
									        [55, 13, 16, 58, 5, 2, 1],  # note padding
									        [64, 27, 31, 51, 12, 75, 2],
									        [52, 64, 86, 17, 83, 39, 2],
									        [48, 61, 9, 24, 71, 82, 2],
									        [26, 1, 60, 48, 22, 13, 2],
									        [21, 5, 62, 28, 14, 76, 2],
									        [45, 98, 37, 86, 59, 48, 2],
									        [70, 70, 50, 9, 28, 0, 2],
									    ]     ,dtype=np.intaa     ,)

									_a					:				Union[str, Any]				=							input_ids.shape[0]
									_a					:				str				=							BlenderbotConfig(
									    vocab_size=self.vocab_size     ,d_model=24     ,encoder_layers=2     ,decoder_layers=2     ,encoder_attention_heads=2     ,decoder_attention_heads=2     ,encoder_ffn_dim=32     ,decoder_ffn_dim=32     ,max_position_embeddings=48     ,eos_token_id=2     ,pad_token_id=1     ,bos_token_id=0     ,)
									return config, input_ids, batch_size







							def 							__lowercase      (							self :	Union[str, Any]		):
									_a	, _a	, _a					:				Dict				=							self._get_config_and_data()
									_a					:				int				=							FlaxBlenderbotForConditionalGeneration(lowercase_		)
									_a					:				Optional[Any]				=							lm_model(input_ids=lowercase_		)
									_a					:				List[Any]				=							(batch_size, input_ids.shape[1], config.vocab_size)
									self.assertEqual(outputs['logits'].shape     ,lowercase_		)







							def 							__lowercase      (							self :	Optional[Any]		):
									_a					:				Tuple				=							BlenderbotConfig(
									    vocab_size=self.vocab_size     ,d_model=14     ,encoder_layers=2     ,decoder_layers=2     ,encoder_attention_heads=2     ,decoder_attention_heads=2     ,encoder_ffn_dim=8     ,decoder_ffn_dim=8     ,max_position_embeddings=48     ,)
									_a					:				List[Any]				=							FlaxBlenderbotForConditionalGeneration(lowercase_		)
									_a					:				str				=							np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]     ,dtype=np.intaa		)
									_a					:				Any				=							np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]     ,dtype=np.intaa		)
									_a					:				str				=							lm_model(input_ids=lowercase_     ,decoder_input_ids=lowercase_		)
									_a					:				Optional[Any]				=							(*summary.shape, config.vocab_size)
									self.assertEqual(outputs['logits'].shape     ,lowercase_		)







							def 							__lowercase      (							self :	Tuple		):
									_a					:				Optional[int]				=							np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]     ,dtype=np.intaa		)
									_a					:				Union[str, Any]				=							shift_tokens_right(lowercase_     ,1     ,2		)
									_a					:				Dict				=							np.equal(lowercase_     ,1		).astype(np.floataa		).sum()
									_a					:				Tuple				=							np.equal(lowercase_     ,1		).astype(np.floataa		).sum()
									self.assertEqual(shifted.shape     ,input_ids.shape		)
									self.assertEqual(lowercase_     ,n_pad_before - 1		)
									self.assertTrue(np.equal(shifted[:, 0]     ,2		).all()		)

@require_flax
class      __magic_name__						(   _UpperCamelCase      ,						unittest.TestCase      ,						_UpperCamelCase       ):
							lowerCAmelCase	:   Optional[Any]           = True
							lowerCAmelCase	:   Dict           = (
							    (
							        FlaxBlenderbotModel,
							        FlaxBlenderbotForConditionalGeneration,
							    )
							    if is_flax_available()
							    else ()
							)
							lowerCAmelCase	:   Any           = (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()







							def 							__lowercase      (							self :	Any		):
									_a					:				Dict				=							FlaxBlenderbotModelTester(self		)







							def 							__lowercase      (							self :	int		):
									_a	, _a					:				int				=							self.model_tester.prepare_config_and_inputs()
									for model_class in self.all_model_classes:
											self.model_tester.check_use_cache_forward(lowercase_     ,lowercase_     ,lowercase_		)







							def 							__lowercase      (							self :	Dict		):
									_a	, _a					:				List[Any]				=							self.model_tester.prepare_config_and_inputs()
									for model_class in self.all_model_classes:
											self.model_tester.check_use_cache_forward_with_attn_mask(lowercase_     ,lowercase_     ,lowercase_		)







							def 							__lowercase      (							self :	int		):
									_a	, _a					:				Tuple				=							self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__		):
													_a					:				str				=							self._prepare_for_class(lowercase_     ,lowercase_		)
													_a					:				Optional[Any]				=							model_class(lowercase_		)

													@jax.jit
													def encode_jitted(_UpperCAmelCase :	int     ,_UpperCAmelCase :	Any=None     ,**_UpperCAmelCase :	str		):
															return model.encode(input_ids=lowercase_     ,attention_mask=lowercase_		)

													with self.subTest('JIT Enabled'		):
															_a					:				List[str]				=							encode_jitted(**lowercase_		).to_tuple()

													with self.subTest('JIT Disabled'		):
															with jax.disable_jit():
																	_a					:				Tuple				=							encode_jitted(**lowercase_		).to_tuple()

													self.assertEqual(len(lowercase_		)     ,len(lowercase_		)		)
													for jitted_output, output in zip(lowercase_     ,lowercase_		):
															self.assertEqual(jitted_output.shape     ,output.shape		)







							def 							__lowercase      (							self :	int		):
									_a	, _a					:				Optional[int]				=							self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__		):
													_a					:				Optional[Any]				=							model_class(lowercase_		)
													_a					:				Any				=							model.encode(inputs_dict['input_ids']     ,inputs_dict['attention_mask']		)

													_a					:				Optional[Any]				=							{
													    'decoder_input_ids': inputs_dict['decoder_input_ids'],
													    'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
													    'encoder_outputs': encoder_outputs,
													}

													@jax.jit
													def decode_jitted(_UpperCAmelCase :	int     ,_UpperCAmelCase :	Tuple     ,_UpperCAmelCase :	Any		):
															return model.decode(
															    decoder_input_ids=lowercase_     ,decoder_attention_mask=lowercase_     ,encoder_outputs=lowercase_     ,)

													with self.subTest('JIT Enabled'		):
															_a					:				int				=							decode_jitted(**lowercase_		).to_tuple()

													with self.subTest('JIT Disabled'		):
															with jax.disable_jit():
																	_a					:				Optional[int]				=							decode_jitted(**lowercase_		).to_tuple()

													self.assertEqual(len(lowercase_		)     ,len(lowercase_		)		)
													for jitted_output, output in zip(lowercase_     ,lowercase_		):
															self.assertEqual(jitted_output.shape     ,output.shape		)







							@slow
							def 							__lowercase      (							self :	Any		):







									for model_class_name in self.all_model_classes:
											_a					:				Optional[int]				=							model_class_name.from_pretrained('facebook/blenderbot-400M-distill'		)
											# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
											_a					:				Any				=							np.ones((1, 1)		) * model.config.eos_token_id
											_a					:				Optional[Any]				=							model(lowercase_		)
											self.assertIsNotNone(lowercase_		)







							@unittest.skipUnless(jax_device != 'cpu'     ,'3B test too slow on CPU.'		)
							@slow
							def 							__lowercase      (							self :	Tuple		):
									_a					:				str				=							{'num_beams': 1, 'early_stopping': True, 'min_length': 15, 'max_length': 25}
									_a					:				List[str]				=							{'skip_special_tokens': True, 'clean_up_tokenization_spaces': True}

									_a					:				str				=							FlaxBlenderbotForConditionalGeneration.from_pretrained('facebook/blenderbot-3B'     ,from_pt=lowercase_		)
									_a					:				List[Any]				=							BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B'		)

									_a					:				Tuple				=							['Sam']
									_a					:				Union[str, Any]				=							tokenizer(lowercase_     ,return_tensors='jax'		)

									_a					:				str				=							model.generate(**lowercase_     ,**lowercase_		)
									_a					:				List[Any]				=							'Sam is a great name. It means \"sun\" in Gaelic.'

									_a					:				Any				=							tokenizer.batch_decode(lowercase_     ,**lowercase_		)
									assert generated_txt[0].strip() == tgt_text









'''simple docstring'''

import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				int:
		_a					:				Optional[int]				=							old_name

		if "patch_embed" in old_name:
				_a	, _a	, _a					:				int				=							old_name.split('.'  )

				if layer == "0":
						_a					:				Dict				=							old_name.replace('0'						,      'convolution1'  )
				elif layer == "1":
						_a					:				List[str]				=							old_name.replace('1'						,      'batchnorm_before'  )
				elif layer == "3":
						_a					:				List[str]				=							old_name.replace('3'						,      'convolution2'  )
				else:
						_a					:				Union[str, Any]				=							old_name.replace('4'						,      'batchnorm_after'  )

		if "network" in old_name and re.search(r'\d\.\d'						,      lowerCAmelCase_  ):
				_a					:				Dict				=							r'\b\d{2}\b'
				if bool(re.search(lowerCAmelCase_						,      lowerCAmelCase_  )  ):
						_a					:				Dict				=							re.search(r'\d\.\d\d.'						,      lowerCAmelCase_  ).group()
				else:
						_a					:				Optional[int]				=							re.search(r'\d\.\d.'						,      lowerCAmelCase_  ).group()
				if int(match[0]  ) < 6:
						_a					:				int				=							old_name.replace(lowerCAmelCase_						,      ''  )
						_a					:				Union[str, Any]				=							trimmed_name.replace('network'						,      match[0] + '.meta4D_layers.blocks.' + match[2:-1]  )
						_a					:				int				=							'intermediate_stages.' + trimmed_name
				else:
						_a					:				Tuple				=							old_name.replace(lowerCAmelCase_						,      ''  )
						if int(match[2]  ) < num_meta4D_last_stage:
								_a					:				str				=							trimmed_name.replace('network'						,      'meta4D_layers.blocks.' + match[2]  )
						else:
								_a					:				int				=							str(int(match[2]  ) - num_meta4D_last_stage  )
								_a					:				List[str]				=							trimmed_name.replace('network'						,      'meta3D_layers.blocks.' + layer_index  )
								if "norm1" in old_name:
										_a					:				Union[str, Any]				=							trimmed_name.replace('norm1'						,      'layernorm1'  )
								elif "norm2" in old_name:
										_a					:				List[Any]				=							trimmed_name.replace('norm2'						,      'layernorm2'  )
								elif "fc1" in old_name:
										_a					:				Dict				=							trimmed_name.replace('fc1'						,      'linear_in'  )
								elif "fc2" in old_name:
										_a					:				List[Any]				=							trimmed_name.replace('fc2'						,      'linear_out'  )

						_a					:				Dict				=							'last_stage.' + trimmed_name

		elif "network" in old_name and re.search(r'.\d.'						,      lowerCAmelCase_  ):
				_a					:				List[str]				=							old_name.replace('network'						,      'intermediate_stages'  )

		if "fc" in new_name:
				_a					:				Optional[int]				=							new_name.replace('fc'						,      'convolution'  )
		elif ("norm1" in new_name) and ("layernorm1" not in new_name):
				_a					:				Tuple				=							new_name.replace('norm1'						,      'batchnorm_before'  )
		elif ("norm2" in new_name) and ("layernorm2" not in new_name):
				_a					:				Union[str, Any]				=							new_name.replace('norm2'						,      'batchnorm_after'  )
		if "proj" in new_name:
				_a					:				Tuple				=							new_name.replace('proj'						,      'projection'  )
		if "dist_head" in new_name:
				_a					:				List[Any]				=							new_name.replace('dist_head'						,      'distillation_classifier'  )
		elif "head" in new_name:
				_a					:				Dict				=							new_name.replace('head'						,      'classifier'  )
		elif "patch_embed" in new_name:
				_a					:				Tuple				=							'efficientformer.' + new_name
		elif new_name == "norm.weight" or new_name == "norm.bias":
				_a					:				int				=							new_name.replace('norm'						,      'layernorm'  )
				_a					:				Any				=							'efficientformer.' + new_name
		else:
				_a					:				Optional[int]				=							'efficientformer.encoder.' + new_name

		return new_name



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				Union[str, Any]:
		for key in checkpoint.copy().keys():
				_a					:				List[str]				=							checkpoint.pop(lowerCAmelCase_  )
				_a					:				List[str]				=							val

		return checkpoint



def 					__lowerCamelCase					(				)      ->				Any:
		_a					:				int				=							'http://images.cocodataset.org/val2017/000000039769.jpg'
		_a					:				List[str]				=							Image.open(requests.get(lowerCAmelCase_						,      stream=lowerCAmelCase_  ).raw  )

		return image



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )      ->				List[Any]:
		_a					:				int				=							torch.load(lowerCAmelCase_						,      map_location='cpu'  )['model']
		_a					:				Any				=							EfficientFormerConfig.from_json_file(lowerCAmelCase_  )
		_a					:				Optional[int]				=							EfficientFormerForImageClassificationWithTeacher(lowerCAmelCase_  )
		_a					:				List[Any]				=							'_'.join(checkpoint_path.split('/'  )[-1].split('.'  )[0].split('_'  )[:-1]  )

		_a					:				Dict				=							config.depths[-1] - config.num_metaad_blocks + 1
		_a					:				int				=							convert_torch_checkpoint(lowerCAmelCase_						,      lowerCAmelCase_  )

		model.load_state_dict(lowerCAmelCase_  )
		model.eval()

		_a					:				Any				=							{
		    'bilinear': PILImageResampling.BILINEAR,
		    'bicubic': PILImageResampling.BICUBIC,
		    'nearest': PILImageResampling.NEAREST,
		}

		# prepare image
		_a					:				Any				=							prepare_img()
		_a					:				Dict				=							256
		_a					:				Dict				=							224
		_a					:				int				=							EfficientFormerImageProcessor(
		    size={'shortest_edge': image_size}						,      crop_size={'height': crop_size, 'width': crop_size}						,      resample=pillow_resamplings['bicubic']						,      )
		_a					:				Optional[int]				=							processor(images=lowerCAmelCase_						,      return_tensors='pt'  ).pixel_values

		# original processing pipeline
		_a					:				str				=							Compose(
		    [
		        Resize(lowerCAmelCase_						,      interpolation=pillow_resamplings['bicubic']  ),
		        CenterCrop(lowerCAmelCase_  ),
		        ToTensor(),
		        Normalize(lowerCAmelCase_						,      lowerCAmelCase_  ),
		    ]  )
		_a					:				Any				=							image_transforms(lowerCAmelCase_  ).unsqueeze(0  )

		assert torch.allclose(lowerCAmelCase_						,      lowerCAmelCase_  )

		_a					:				Tuple				=							model(lowerCAmelCase_  )
		_a					:				Tuple				=							outputs.logits

		_a					:				List[Any]				=							(1, 1000)

		if "l1" in model_name:
				_a					:				Optional[Any]				=							torch.Tensor(
				    [-0.1_312, 0.4_353, -1.0_499, -0.5_124, 0.4_183, -0.6_793, -1.3_777, -0.0_893, -0.7_358, -2.4_328]  )
				assert torch.allclose(logits[0, :10]						,      lowerCAmelCase_						,      atol=1E-3  )
				assert logits.shape == expected_shape
		elif "l3" in model_name:
				_a					:				List[Any]				=							torch.Tensor(
				    [-1.3_150, -1.5_456, -1.2_556, -0.8_496, -0.7_127, -0.7_897, -0.9_728, -0.3_052, 0.3_751, -0.3_127]  )
				assert torch.allclose(logits[0, :10]						,      lowerCAmelCase_						,      atol=1E-3  )
				assert logits.shape == expected_shape
		elif "l7" in model_name:
				_a					:				Tuple				=							torch.Tensor(
				    [-1.0_283, -1.4_131, -0.5_644, -1.3_115, -0.5_785, -1.2_049, -0.7_528, 0.1_992, -0.3_822, -0.0_878]  )
				assert logits.shape == expected_shape
		else:
				raise ValueError(
				    f"""Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7"""  )

		# Save Checkpoints
		Path(lowerCAmelCase_  ).mkdir(exist_ok=lowerCAmelCase_  )
		model.save_pretrained(lowerCAmelCase_  )
		print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""  )
		processor.save_pretrained(lowerCAmelCase_  )
		print(f"""Processor successfuly saved at {pytorch_dump_path}"""  )

		if push_to_hub:
				print('Pushing model to the hub...'  )

				model.push_to_hub(
				    repo_id=f"""Bearnardd/{pytorch_dump_path}"""						,      commit_message='Add model'						,      use_temp_dir=lowerCAmelCase_						,      )
				processor.push_to_hub(
				    repo_id=f"""Bearnardd/{pytorch_dump_path}"""						,      commit_message='Add image processor'						,      use_temp_dir=lowerCAmelCase_						,      )


if __name__ == "__main__":
							__lowerCAmelCase						      =       argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '''--pytorch_model_path''',
							    default=None,
							    type=str,
							    required=True,
							    help='''Path to EfficientFormer pytorch checkpoint.''',
							)
							parser.add_argument(
							    '''--config_file''',
							    default=None,
							    type=str,
							    required=True,
							    help='''The json file for EfficientFormer model config.''',
							)
							parser.add_argument(
							    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
							)

							parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')
							parser.add_argument(
							    '''--no-push_to_hub''',
							    dest='''push_to_hub''',
							    action='''store_false''',
							    help='''Do not push model and image processor to the hub''',
							)
							parser.set_defaults(push_to_hub=True)

							__lowerCAmelCase						      =       parser.parse_args()
							convert_efficientformer_checkpoint(
							    checkpoint_path=args.pytorch_model_path,
							    efficientformer_config_file=args.config_file,
							    pytorch_dump_path=args.pytorch_dump_path,
							    push_to_hub=args.push_to_hub,
							)




