
"""simple docstring"""


from __future__ import annotations

import unittest

from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
						import numpy
						import tensorflow as tf

						from transformers.models.esm.modeling_tf_esm import (
						    TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFEsmForMaskedLM,
						    TFEsmForSequenceClassification,
						    TFEsmForTokenClassification,
						    TFEsmModel,
						)




class  __lowerCamelCase  :




				def __init__(				self							,						UpperCAmelCase							,						):
							lowerCamelCase_					=  parent
							lowerCamelCase_					=  13
							lowerCamelCase_					=  7
							lowerCamelCase_					=  True
							lowerCamelCase_					=  True
							lowerCamelCase_					=  True
							lowerCamelCase_					=  99
							lowerCamelCase_					=  32
							lowerCamelCase_					=  2
							lowerCamelCase_					=  4
							lowerCamelCase_					=  37
							lowerCamelCase_					=  '''gelu'''
							lowerCamelCase_					=  0.1
							lowerCamelCase_					=  0.1
							lowerCamelCase_					=  512
							lowerCamelCase_					=  16
							lowerCamelCase_					=  2
							lowerCamelCase_					=  0.0_2
							lowerCamelCase_					=  3
							lowerCamelCase_					=  4
							lowerCamelCase_					=  None




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  ids_tensor([self.batch_size, self.seq_length]							,						self.vocab_size   )

							lowerCamelCase_					=  None
							if self.use_input_mask:
										lowerCamelCase_					=  random_attention_mask([self.batch_size, self.seq_length]   )

							lowerCamelCase_					=  None
							lowerCamelCase_					=  None
							lowerCamelCase_					=  None
							if self.use_labels:
										lowerCamelCase_					=  ids_tensor([self.batch_size]							,						self.type_sequence_label_size   )
										lowerCamelCase_					=  ids_tensor([self.batch_size, self.seq_length]							,						self.num_labels   )
										lowerCamelCase_					=  ids_tensor([self.batch_size]							,						self.num_choices   )

							lowerCamelCase_					=  EsmConfig(
							    vocab_size=self.vocab_size							,						hidden_size=self.hidden_size							,						num_hidden_layers=self.num_hidden_layers							,						pad_token_id=1							,						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							,						max_position_embeddings=self.max_position_embeddings							,						type_vocab_size=self.type_vocab_size							,						initializer_range=self.initializer_range							,						)

							return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels




				def        UpperCAmelCase__						(				self   ):
							(
							    (
							    lowerCamelCase_
							)		, (
							    lowerCamelCase_
							)		, (
							    lowerCamelCase_
							)		, (
							    lowerCamelCase_
							)		, (
							    lowerCamelCase_
							)		, (
							    lowerCamelCase_
							)		, 
							)					=  self.prepare_config_and_inputs()

							lowerCamelCase_					=  True
							lowerCamelCase_					=  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
							lowerCamelCase_					=  ids_tensor([self.batch_size, self.seq_length]							,						vocab_size=2   )

							return (
							    config,
							    input_ids,
							    input_mask,
							    sequence_labels,
							    token_labels,
							    choice_labels,
							    encoder_hidden_states,
							    encoder_attention_mask,
							)




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   ):
							lowerCamelCase_					=  TFEsmModel(config=UpperCAmelCase   )
							lowerCamelCase_					=  {'''input_ids''': input_ids, '''attention_mask''': input_mask}
							lowerCamelCase_					=  model(UpperCAmelCase   )

							lowerCamelCase_					=  [input_ids, input_mask]
							lowerCamelCase_					=  model(UpperCAmelCase   )

							lowerCamelCase_					=  model(UpperCAmelCase   )

							self.parent.assertEqual(result.last_hidden_state.shape							,						(self.batch_size, self.seq_length, self.hidden_size)   )




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						):
							lowerCamelCase_					=  True

							lowerCamelCase_					=  TFEsmModel(config=UpperCAmelCase   )
							lowerCamelCase_					=  {
							    '''input_ids''': input_ids,
							    '''attention_mask''': input_mask,
							    '''encoder_hidden_states''': encoder_hidden_states,
							    '''encoder_attention_mask''': encoder_attention_mask,
							}
							lowerCamelCase_					=  model(UpperCAmelCase   )

							lowerCamelCase_					=  [input_ids, input_mask]
							lowerCamelCase_					=  model(UpperCAmelCase							,						encoder_hidden_states=UpperCAmelCase   )

							# Also check the case where encoder outputs are not passed
							lowerCamelCase_					=  model(UpperCAmelCase							,						attention_mask=UpperCAmelCase   )

							self.parent.assertEqual(result.last_hidden_state.shape							,						(self.batch_size, self.seq_length, self.hidden_size)   )




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   ):
							lowerCamelCase_					=  TFEsmForMaskedLM(config=UpperCAmelCase   )
							lowerCamelCase_					=  model([input_ids, input_mask]   )
							self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.seq_length, self.vocab_size)   )




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   ):
							lowerCamelCase_					=  self.num_labels
							lowerCamelCase_					=  TFEsmForTokenClassification(config=UpperCAmelCase   )
							lowerCamelCase_					=  {'''input_ids''': input_ids, '''attention_mask''': input_mask}
							lowerCamelCase_					=  model(UpperCAmelCase   )
							self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.seq_length, self.num_labels)   )





				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.prepare_config_and_inputs()
							(
							    (
							    lowerCamelCase_
							)		, (
							    lowerCamelCase_
							)		, (
							    lowerCamelCase_
							)		, (
							    lowerCamelCase_
							)		, (
							    lowerCamelCase_
							)		, (
							    lowerCamelCase_
							)		, 
							)					=  config_and_inputs
							lowerCamelCase_					=  {'''input_ids''': input_ids, '''attention_mask''': input_mask}
							return config, inputs_dict




@require_tf
class  __lowerCamelCase  ( lowerCAmelCase						,	lowerCAmelCase						,	unittest.TestCase			):
				a__:      Optional[Any]          =    (
				    (
				        TFEsmModel,
				        TFEsmForMaskedLM,
				        TFEsmForSequenceClassification,
				        TFEsmForTokenClassification,
				    )
				    if is_tf_available()
				    else ()
				)
				a__:      Tuple          =    (
				    {
				        'feature-extraction': TFEsmModel,
				        'fill-mask': TFEsmForMaskedLM,
				        'text-classification': TFEsmForSequenceClassification,
				        'token-classification': TFEsmForTokenClassification,
				        'zero-shot': TFEsmForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)
				a__:      str          =    False
				a__:      Tuple          =    False




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  TFEsmModelTester(self   )
							lowerCamelCase_					=  ConfigTester(self							,						config_class=UpperCAmelCase							,						hidden_size=37   )




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




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.model_tester.prepare_config_and_inputs_for_decoder()
							self.model_tester.create_and_check_model_as_decoder(*UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase   )




				@slow
				def        UpperCAmelCase__						(				self   ):
							for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowerCamelCase_					=  TFEsmModel.from_pretrained(UpperCAmelCase   )
										self.assertIsNotNone(UpperCAmelCase   )




				@unittest.skip('''Protein models do not support embedding resizing.'''   )
				def        UpperCAmelCase__						(				self   ):
							pass




				@unittest.skip('''Protein models do not support embedding resizing.'''   )
				def        UpperCAmelCase__						(				self   ):
							pass





				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_		, lowerCamelCase_					=  self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
										lowerCamelCase_					=  model_class(UpperCAmelCase   )
										assert isinstance(model.get_input_embeddings()							,						tf.keras.layers.Layer   )
										if model_class is TFEsmForMaskedLM:
													# Output embedding test differs from the main test because they're a matrix, not a layer
													lowerCamelCase_					=  model.get_bias()
													assert isinstance(UpperCAmelCase							,						UpperCAmelCase   )
													for k, v in name.items():
																assert isinstance(UpperCAmelCase							,						tf.Variable   )
										else:
													lowerCamelCase_					=  model.get_output_embeddings()
													assert x is None
													lowerCamelCase_					=  model.get_bias()
													assert name is None




@require_tf
class  __lowerCamelCase  ( unittest.TestCase			):




				@slow
				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  TFEsmForMaskedLM.from_pretrained('''facebook/esm2_t6_8M_UR50D'''   )

							lowerCamelCase_					=  tf.constant([[0, 1, 2, 3, 4, 5]]   )
							lowerCamelCase_					=  model(UpperCAmelCase   )[0]
							lowerCamelCase_					=  [1, 6, 33]
							self.assertEqual(list(output.numpy().shape   )							,						UpperCAmelCase   )
							# compare the actual values for a slice.
							lowerCamelCase_					=  tf.constant(
							    [
							        [
							            [8.9_2_1_5_1_8, -1_0.5_8_9_8_1_4, -6.4_6_7_1_3_0_7],
							            [-6.3_9_6_7_1_5_6, -1_3.9_1_1_3_7_7, -1.1_2_1_1_9_1_5],
							            [-7.7_8_1_2_4_7, -1_3.9_5_1_5_5_7, -3.7_4_0_5_9_2],
							        ]
							    ]   )
							self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()							,						expected_slice.numpy()							,						atol=1e-2   )   )





				@slow
				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  TFEsmModel.from_pretrained('''facebook/esm2_t6_8M_UR50D'''   )

							lowerCamelCase_					=  tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]   )
							lowerCamelCase_					=  model(UpperCAmelCase   )[0]
							# compare the actual values for a slice.
							lowerCamelCase_					=  tf.constant(
							    [
							        [
							            [0.1_4_4_4_3_0_9_2, 0.5_4_1_2_5_3_2_7, 0.3_2_4_7_7_3_9],
							            [0.3_0_3_4_0_4_8_4, 0.0_0_5_2_6_6_7_6, 0.3_1_0_7_7_7_2_2],
							            [0.3_2_2_7_8_0_4_3, -0.2_4_9_8_7_0_9_6, 0.3_4_1_4_6_2_8],
							        ]
							    ]   )
							self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()							,						expected_slice.numpy()							,						atol=1e-4   )   )







import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase:						List[str]   				=       logging.get_logger()






def   _lowercase(					__a				:						int    ,     __a				:						str    ,     __a				:						LevitConfig    ,     __a				:						Path    ,     __a				:						bool = True     ):
							print(f"""Converting {name}..."""     )

							with torch.no_grad():
														if hidden_sizes == 128:
																					if name[-1] == "S":
																												a__      =timm.create_model('levit_128s'    ,     pretrained=__a     )
																					else:
																												a__      =timm.create_model('levit_128'    ,     pretrained=__a     )
														if hidden_sizes == 192:
																					a__      =timm.create_model('levit_192'    ,     pretrained=__a     )
														if hidden_sizes == 256:
																					a__      =timm.create_model('levit_256'    ,     pretrained=__a     )
														if hidden_sizes == 384:
																					a__      =timm.create_model('levit_384'    ,     pretrained=__a     )

														from_model.eval()
														a__      =LevitForImageClassificationWithTeacher(__a     ).eval()
														a__      =OrderedDict()

														a__      =from_model.state_dict()
														a__      =list(from_model.state_dict().keys()     )
														a__      =list(our_model.state_dict().keys()     )
														print(len(__a     )    ,     len(__a     )     )
														for i in range(len(__a     )     ):
																					a__      =weights[og_keys[i]]
														our_model.load_state_dict(__a     )

														a__      =torch.randn((2, 3, 224, 224)     )
														a__      =from_model(__a     )
														a__      =our_model(__a     ).logits

							assert torch.allclose(__a    ,     __a     ), "The model logits don't match the original one."

							a__      =name
							print(__a     )

							if push_to_hub:
														our_model.save_pretrained(save_directory / checkpoint_name     )
														a__      =LevitImageProcessor()
														image_processor.save_pretrained(save_directory / checkpoint_name     )

														print(f"""Pushed {checkpoint_name}"""     )






def   _lowercase(					__a				:						Path    ,     __a				:						str = None    ,     __a				:						bool = True     ):
							a__      ='imagenet-1k-id2label.json'
							a__      =1000
							a__      =(1, num_labels)

							a__      ='huggingface/label-files'
							a__      =num_labels
							a__      =json.load(open(hf_hub_download(__a    ,     __a    ,     repo_type='dataset'     )    ,     'r'     )     )
							a__      ={int(__a     ): v for k, v in idalabel.items()}

							a__      =idalabel
							a__      ={v: k for k, v in idalabel.items()}

							a__      =partial(__a    ,     num_labels=__a    ,     idalabel=__a    ,     labelaid=__a     )

							a__      ={
							    'levit-128S': 128,
							    'levit-128': 128,
							    'levit-192': 192,
							    'levit-256': 256,
							    'levit-384': 384,
							}

							a__      ={
							    'levit-128S': ImageNetPreTrainedConfig(
							        hidden_sizes=[128, 256, 384]    ,     num_attention_heads=[4, 6, 8]    ,     depths=[2, 3, 4]    ,     key_dim=[16, 16, 16]    ,     drop_path_rate=0    ,     ),
							    'levit-128': ImageNetPreTrainedConfig(
							        hidden_sizes=[128, 256, 384]    ,     num_attention_heads=[4, 8, 12]    ,     depths=[4, 4, 4]    ,     key_dim=[16, 16, 16]    ,     drop_path_rate=0    ,     ),
							    'levit-192': ImageNetPreTrainedConfig(
							        hidden_sizes=[192, 288, 384]    ,     num_attention_heads=[3, 5, 6]    ,     depths=[4, 4, 4]    ,     key_dim=[32, 32, 32]    ,     drop_path_rate=0    ,     ),
							    'levit-256': ImageNetPreTrainedConfig(
							        hidden_sizes=[256, 384, 512]    ,     num_attention_heads=[4, 6, 8]    ,     depths=[4, 4, 4]    ,     key_dim=[32, 32, 32]    ,     drop_path_rate=0    ,     ),
							    'levit-384': ImageNetPreTrainedConfig(
							        hidden_sizes=[384, 512, 768]    ,     num_attention_heads=[6, 9, 12]    ,     depths=[4, 4, 4]    ,     key_dim=[32, 32, 32]    ,     drop_path_rate=0.1    ,     ),
							}

							if model_name:
														convert_weight_and_push(
														    names_to_hidden_sizes[model_name]    ,     __a    ,     names_to_config[model_name]    ,     __a    ,     __a     )
							else:
														for model_name, config in names_to_config.items():
																					convert_weight_and_push(names_to_hidden_sizes[model_name]    ,     __a    ,     __a    ,     __a    ,     __a     )
							return config, expected_shape


if __name__ == "__main__":
	_lowerCAmelCase:						Any   				=       argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--model_name',
	    default=None,
	    type=str,
	    help='The name of the model you wish to convert, it must be one of the supported Levit* architecture,',
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path',
	    default='levit-dump-folder/',
	    type=Path,
	    required=False,
	    help='Path to the output PyTorch model directory.',
	)
	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',
	)

	_lowerCAmelCase:						Union[str, Any]   				=       parser.parse_args()
	_lowerCAmelCase:						Path   				=       args.pytorch_dump_folder_path
	pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
	convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)

