



"""simple docstring"""







import gc
import unittest

from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
							    CTRLForSequenceClassification,
							    CTRLLMHeadModel,
							    CTRLModel,
							)






class      UpperCAmelCase__		:



		def __init__( self  :    Union[str, Any]      ,							snake_case  :    int      ,							snake_case  :    Union[str, Any]=14      ,							snake_case  :    Any=7      ,							snake_case  :    Optional[int]=True      ,							snake_case  :    Dict=True      ,							snake_case  :    int=True      ,							snake_case  :    Optional[int]=True      ,							snake_case  :    int=True      ,							snake_case  :    Any=99      ,							snake_case  :    Optional[Any]=32      ,							snake_case  :    Union[str, Any]=5      ,							snake_case  :    str=4      ,							snake_case  :    Union[str, Any]=37      ,							snake_case  :    Any="gelu"      ,							snake_case  :    Tuple=0.1      ,							snake_case  :    int=0.1      ,							snake_case  :    Any=512      ,							snake_case  :    int=16      ,							snake_case  :    Union[str, Any]=2      ,							snake_case  :    int=0.02      ,							snake_case  :    Tuple=3      ,							snake_case  :    Optional[int]=4      ,							snake_case  :    Dict=None      ,							)			->						Any:






									'''simple docstring'''

									A         =					parent
									A         =					batch_size
									A         =					seq_length
									A         =					is_training
									A         =					use_token_type_ids
									A         =					use_input_mask
									A         =					use_labels
									A         =					use_mc_token_ids
									A         =					vocab_size
									A         =					hidden_size
									A         =					num_hidden_layers
									A         =					num_attention_heads
									A         =					intermediate_size
									A         =					hidden_act
									A         =					hidden_dropout_prob
									A         =					attention_probs_dropout_prob
									A         =					max_position_embeddings
									A         =					type_vocab_size
									A         =					type_sequence_label_size
									A         =					initializer_range
									A         =					num_labels
									A         =					num_choices
									A         =					scope
									A         =					self.vocab_size - 1



		def 					A_		( self  :    Any				)			->						List[str]:






									'''simple docstring'''

									A         =					ids_tensor([self.batch_size, self.seq_length]      ,							self.vocab_size				)

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

									A         =					None
									if self.use_token_type_ids:
																A         =					ids_tensor([self.batch_size, self.seq_length]      ,							self.type_vocab_size				)

									A         =					None
									if self.use_mc_token_ids:
																A         =					ids_tensor([self.batch_size, self.num_choices]      ,							self.seq_length				)

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

									A         =					self.get_config()

									A         =					ids_tensor([self.num_hidden_layers, self.num_attention_heads]      ,							2				)

									return (
									    config,
									    input_ids,
									    input_mask,
									    head_mask,
									    token_type_ids,
									    mc_token_ids,
									    sequence_labels,
									    token_labels,
									    choice_labels,
									)



		def 					A_		( self  :    Union[str, Any]				)			->						List[Any]:






									'''simple docstring'''

									return CTRLConfig(
									    vocab_size=self.vocab_size      ,							n_embd=self.hidden_size      ,							n_layer=self.num_hidden_layers      ,							n_head=self.num_attention_heads      ,							n_positions=self.max_position_embeddings      ,							pad_token_id=self.pad_token_id      ,							)



		def 					A_		( self  :    str      ,							snake_case  :    List[str]      ,							snake_case  :    Tuple      ,							snake_case  :    str      ,							snake_case  :    int      ,							snake_case  :    Tuple      ,							*snake_case  :    Optional[int]				)			->						Optional[int]:






									'''simple docstring'''

									A         =					CTRLModel(config=snake_case				)
									model.to(snake_case				)
									model.eval()

									model(snake_case      ,							token_type_ids=snake_case      ,							head_mask=snake_case				)
									model(snake_case      ,							token_type_ids=snake_case				)
									A         =					model(snake_case				)
									self.parent.assertEqual(result.last_hidden_state.shape      ,							(self.batch_size, self.seq_length, self.hidden_size)				)
									self.parent.assertEqual(len(result.past_key_values				)      ,							config.n_layer				)



		def 					A_		( self  :    Optional[Any]      ,							snake_case  :    str      ,							snake_case  :    Union[str, Any]      ,							snake_case  :    Optional[Any]      ,							snake_case  :    str      ,							snake_case  :    Union[str, Any]      ,							*snake_case  :    Dict				)			->						Tuple:






									'''simple docstring'''

									A         =					CTRLLMHeadModel(snake_case				)
									model.to(snake_case				)
									model.eval()

									A         =					model(snake_case      ,							token_type_ids=snake_case      ,							labels=snake_case				)
									self.parent.assertEqual(result.loss.shape      ,							()				)
									self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.seq_length, self.vocab_size)				)



		def 					A_		( self  :    List[Any]				)			->						Dict:






									'''simple docstring'''

									A         =					self.prepare_config_and_inputs()

									(
									    (
									    A
									)      ,  (
									    A
									)      ,  (
									    A
									)      ,  (
									    A
									)      ,  (
									    A
									)      ,  (
									    A
									)      ,  (
									    A
									)      ,  (
									    A
									)      ,  (
									    A
									)      ,  
									)         =					config_and_inputs

									A         =					{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'head_mask': head_mask}

									return config, inputs_dict



		def 					A_		( self  :    str      ,							snake_case  :    List[str]      ,							snake_case  :    List[str]      ,							snake_case  :    Any      ,							snake_case  :    Union[str, Any]      ,							*snake_case  :    Any				)			->						Optional[int]:






									'''simple docstring'''

									A         =					self.num_labels
									A         =					CTRLForSequenceClassification(snake_case				)
									model.to(snake_case				)
									model.eval()
									A         =					ids_tensor([self.batch_size]      ,							self.type_sequence_label_size				)
									A         =					model(snake_case      ,							token_type_ids=snake_case      ,							labels=snake_case				)
									self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.num_labels)				)






@require_torch
class      UpperCAmelCase__		(      UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,unittest.TestCase     ):
		lowerCAmelCase_       :	Optional[int] 							=     (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
		lowerCAmelCase_       :	str 							=     (CTRLLMHeadModel,) if is_torch_available() else ()
		lowerCAmelCase_       :	List[str] 							=     (
		    {
		        """feature-extraction""": CTRLModel,
		        """text-classification""": CTRLForSequenceClassification,
		        """text-generation""": CTRLLMHeadModel,
		        """zero-shot""": CTRLForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		lowerCAmelCase_       :	List[str] 							=     True
		lowerCAmelCase_       :	Union[str, Any] 							=     False
		lowerCAmelCase_       :	str 							=     False



		def 					A_		( self  :    Any      ,							snake_case  :    Tuple      ,							snake_case  :    List[str]      ,							snake_case  :    Dict      ,							snake_case  :    int      ,							snake_case  :    Optional[int]				)			->						str:






									'''simple docstring'''

									if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
																# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
																# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
																# config could not be created.
																return True

									return False



		def 					A_		( self  :    Optional[Any]				)			->						Optional[int]:






									'''simple docstring'''

									A         =					CTRLModelTester(self				)
									A         =					ConfigTester(self      ,							config_class=snake_case      ,							n_embd=37				)



		def 					A_		( self  :    Dict				)			->						Union[str, Any]:






									'''simple docstring'''

									super().tearDown()
									# clean-up as much as possible GPU memory occupied by PyTorch
									gc.collect()
									torch.cuda.empty_cache()



		def 					A_		( self  :    Union[str, Any]				)			->						int:






									'''simple docstring'''

									self.config_tester.run_common_tests()



		def 					A_		( self  :    Any				)			->						Dict:






									'''simple docstring'''

									A         =					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_ctrl_model(*snake_case				)



		def 					A_		( self  :    Tuple				)			->						List[str]:






									'''simple docstring'''

									A         =					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_lm_head_model(*snake_case				)



		@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'				)
		def 					A_		( self  :    Optional[int]				)			->						Dict:






									'''simple docstring'''

									pass



		@slow
		def 					A_		( self  :    Optional[Any]				)			->						Optional[Any]:






									'''simple docstring'''

									for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																A         =					CTRLModel.from_pretrained(snake_case				)
																self.assertIsNotNone(snake_case				)



		@unittest.skip('The model doesn\'t support left padding'				)  # and it's not used enough to be worth fixing :)
		def 					A_		( self  :    Tuple				)			->						Tuple:






									'''simple docstring'''

									pass






@require_torch
class      UpperCAmelCase__		(      unittest.TestCase     ):



		def 					A_		( self  :    str				)			->						str:






									'''simple docstring'''

									super().tearDown()
									# clean-up as much as possible GPU memory occupied by PyTorch
									gc.collect()
									torch.cuda.empty_cache()



		@slow
		def 					A_		( self  :    str				)			->						Any:






									'''simple docstring'''

									A         =					CTRLLMHeadModel.from_pretrained('ctrl'				)
									model.to(snake_case				)
									A         =					torch.tensor(
									    [[11_859, 0, 1_611, 8]]      ,							dtype=torch.long      ,							device=snake_case				)  # Legal the president is
									A         =					[
									    11_859,
									    0,
									    1_611,
									    8,
									    5,
									    150,
									    26_449,
									    2,
									    19,
									    348,
									    469,
									    3,
									    2_595,
									    48,
									    20_740,
									    246_533,
									    246_533,
									    19,
									    30,
									    5,
									]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

									A         =					model.generate(snake_case      ,							do_sample=snake_case				)
									self.assertListEqual(output_ids[0].tolist()      ,							snake_case				)








"""simple docstring"""







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()
A    =			logging.get_logger(__name__)

def   lowerCAmelCase__			(     lowerCamelCase__			) ->							Union[str, Any]:
							A         =					MobileNetVaConfig(layer_norm_eps=0.0_01			)

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

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

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

							return config

def   lowerCAmelCase__			(     ) ->							List[Any]:
							A         =					'http://images.cocodataset.org/val2017/000000039769.jpg'
							A         =					Image.open(requests.get(lowerCamelCase__				,      stream=lowerCamelCase__			).raw			)
							return im

@torch.no_grad()
def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__=False			) ->							Dict:
							A         =					get_mobilenet_va_config(lowerCamelCase__			)

							# Load 🤗 model
							A         =					MobileNetVaForImageClassification(lowerCamelCase__			).eval()

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

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

							assert logits.shape == (1, 1001)

							if model_name == "mobilenet_v1_1.0_224":
														A         =					torch.tensor([-4.17_39, -1.12_33, 3.12_05]			)
							elif model_name == "mobilenet_v1_0.75_192":
														A         =					torch.tensor([-3.94_40, -2.31_41, -0.33_33]			)
							else:
														A         =					None

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

							Path(lowerCamelCase__			).mkdir(exist_ok=lowerCamelCase__			)
							print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""			)
							model.save_pretrained(lowerCamelCase__			)
							print(f"""Saving image processor to {pytorch_dump_folder_path}"""			)
							image_processor.save_pretrained(lowerCamelCase__			)

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


if __name__ == "__main__":
							A    =			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.'
							)

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



