





import argparse
import logging
import os
from datetime import datetime

import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm

from transformers import GPTaLMHeadModel


_UpperCAmelCase       : Tuple			     =       logging.getLogger(__name__)


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase				)    ->  str:







    if os.path.exists(__snake_case				):
        if os.path.exists(os.path.join(__snake_case		,			'config.json'				)				) and os.path.isfile(
            os.path.join(__snake_case		,			'config.json'				)				):
            os.remove(os.path.join(__snake_case		,			'config.json'				)				)
        if os.path.exists(os.path.join(__snake_case		,			'pytorch_model.bin'				)				) and os.path.isfile(
            os.path.join(__snake_case		,			'pytorch_model.bin'				)				):
            os.remove(os.path.join(__snake_case		,			'pytorch_model.bin'				)				)
    else:
        os.makedirs(__snake_case				)
    model.save_pretrained(__snake_case				)


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase=False				)    ->  List[str]:
    lowerCamelCase__					:   Optional[int]      				=	2
    if unlogit:
        lowerCamelCase__					:   Union[str, Any]      				=	torch.pow(__snake_case		,			__snake_case				)
    lowerCamelCase__					:   List[Any]      				=	p * torch.log(__snake_case				)
    lowerCamelCase__					:   Dict      				=	0
    return -plogp.sum(dim=-1				)


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase				)    ->  int:
    logger.info('lv, h >\t' + '\t'.join(F"""{x + 1}""" for x in range(len(__snake_case				)				)				)				)
    for row in range(len(__snake_case				)				):
        if tensor.dtype != torch.long:
            logger.info(F"""layer {row + 1}:\t""" + '\t'.join(F"""{x:.5f}""" for x in tensor[row].cpu().data				)				)
        else:
            logger.info(F"""layer {row + 1}:\t""" + '\t'.join(F"""{x:d}""" for x in tensor[row].cpu().data				)				)


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase=True		,			_UpperCAmelCase=True		,			_UpperCAmelCase=None		,			_UpperCAmelCase=False				)    ->  int:
    lowerCamelCase__				,							lowerCamelCase__					:   Dict      				=	model.config.num_hidden_layers, model.config.num_attention_heads
    lowerCamelCase__					:   Tuple      				=	torch.zeros(__snake_case		,			__snake_case				).to(args.device				)
    lowerCamelCase__					:   int      				=	torch.zeros(__snake_case		,			__snake_case				).to(args.device				)

    if head_mask is None:
        lowerCamelCase__					:   Tuple      				=	torch.ones(__snake_case		,			__snake_case				).to(args.device				)

    head_mask.requires_grad_(requires_grad=__snake_case				)
    # If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
    if actually_pruned:
        lowerCamelCase__					:   List[str]      				=	None

    lowerCamelCase__					:   Any      				=	0.0
    lowerCamelCase__					:   str      				=	0.0
    for step, inputs in enumerate(tqdm(__snake_case		,			desc='Iteration'		,			disable=args.local_rank not in [-1, 0]				)				):
        lowerCamelCase__					:   int      				=	tuple(t.to(args.device				) for t in inputs				)
        ((lowerCamelCase__			)				,										)					:   List[str]      				=	inputs

        # Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
        lowerCamelCase__					:   List[str]      				=	model(__snake_case		,			labels=__snake_case		,			head_mask=__snake_case				)
        #  (loss), lm_logits, presents, (all hidden_states), (attentions)
        lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__					:   Dict      				=	(
            outputs[0],
            outputs[1],
            outputs[-1],
        )  # Loss and logits are the first, attention the last
        loss.backward()  # Backpropagate to populate the gradients in the head mask
        total_loss += loss.detach().cpu().numpy()
        if compute_entropy:
            for layer, attn in enumerate(__snake_case				):
                lowerCamelCase__					:   List[Any]      				=	entropy(attn.detach()		,			__snake_case				)
                attn_entropy[layer] += masked_entropy.sum(-1				).sum(0				).sum(0				).detach()

        if compute_importance:
            head_importance += head_mask.grad.abs().detach()
        tot_tokens += torch.ones_like(__snake_case				).float().detach().sum().data

    # Normalize
    attn_entropy /= tot_tokens
    head_importance /= tot_tokens
    # Layerwise importance normalization
    if not args.dont_normalize_importance_by_layer:
        lowerCamelCase__					:   Union[str, Any]      				=	2
        lowerCamelCase__					:   Dict      				=	torch.pow(torch.pow(__snake_case		,			__snake_case				).sum(-1				)		,			1 / exponent				)
        head_importance /= norm_by_layer.unsqueeze(-1				) + 1e-20

    if not args.dont_normalize_global_importance:
        lowerCamelCase__					:   List[Any]      				=	(head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

    # Print matrices
    if compute_entropy:
        logger.info('Attention entropies'				)
        print_ad_tensor(__snake_case				)
    if compute_importance:
        logger.info('Head importance scores'				)
        print_ad_tensor(__snake_case				)
    logger.info('Head ranked by importance scores'				)
    lowerCamelCase__					:   str      				=	torch.zeros(head_importance.numel()		,			dtype=torch.long		,			device=args.device				)
    lowerCamelCase__					:   str      				=	torch.arange(
        head_importance.numel()		,			device=args.device				)
    lowerCamelCase__					:   Dict      				=	head_ranks.view_as(__snake_case				)
    print_ad_tensor(__snake_case				)
    return attn_entropy, head_importance, total_loss


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)    ->  Union[str, Any]:
    lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__					:   Tuple      				=	compute_heads_importance(__snake_case		,			__snake_case		,			__snake_case		,			compute_entropy=__snake_case				)
    lowerCamelCase__					:   str      				=	1 / loss  # instead of downsteam score use the LM loss
    logger.info('Pruning: original score: %f, threshold: %f'		,			__snake_case		,			original_score * args.masking_threshold				)

    lowerCamelCase__					:   Tuple      				=	torch.ones_like(__snake_case				)
    lowerCamelCase__					:   Union[str, Any]      				=	max(1		,			int(new_head_mask.numel() * args.masking_amount				)				)

    lowerCamelCase__					:   Optional[Any]      				=	original_score
    while current_score >= original_score * args.masking_threshold:
        lowerCamelCase__					:   List[Any]      				=	new_head_mask.clone().detach()  # save current head mask
        # heads from least important to most - keep only not-masked heads
        lowerCamelCase__					:   List[Any]      				=	float('Inf'				)
        lowerCamelCase__					:   Union[str, Any]      				=	head_importance.view(-1				).sort()[1]

        if len(__snake_case				) <= num_to_mask:
            print('BREAK BY num_to_mask'				)
            break

        # mask heads
        lowerCamelCase__					:   Dict      				=	current_heads_to_mask[:num_to_mask]
        logger.info('Heads to mask: %s'		,			str(current_heads_to_mask.tolist()				)				)
        lowerCamelCase__					:   Optional[Any]      				=	new_head_mask.view(-1				)
        lowerCamelCase__					:   List[Any]      				=	0.0
        lowerCamelCase__					:   int      				=	new_head_mask.view_as(__snake_case				)
        lowerCamelCase__					:   Dict      				=	new_head_mask.clone().detach()
        print_ad_tensor(__snake_case				)

        # Compute metric and head importance again
        lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__					:   Optional[Any]      				=	compute_heads_importance(
            __snake_case		,			__snake_case		,			__snake_case		,			compute_entropy=__snake_case		,			head_mask=__snake_case				)
        lowerCamelCase__					:   Any      				=	1 / loss
        logger.info(
            'Masking: current score: %f, remaining heads %d (%.1f percents)'		,			__snake_case		,			new_head_mask.sum()		,			new_head_mask.sum() / new_head_mask.numel() * 100		,			)

    logger.info('Final head mask'				)
    print_ad_tensor(__snake_case				)
    np.save(os.path.join(args.output_dir		,			'head_mask.npy'				)		,			head_mask.detach().cpu().numpy()				)

    return head_mask


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)    ->  Dict:
    lowerCamelCase__					:   List[str]      				=	datetime.now()
    lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__					:   List[str]      				=	compute_heads_importance(
        __snake_case		,			__snake_case		,			__snake_case		,			compute_entropy=__snake_case		,			compute_importance=__snake_case		,			head_mask=__snake_case				)
    lowerCamelCase__					:   int      				=	1 / loss
    lowerCamelCase__					:   Dict      				=	datetime.now() - before_time

    lowerCamelCase__					:   Any      				=	sum(p.numel() for p in model.parameters()				)
    lowerCamelCase__					:   int      				=	{
        layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(__snake_case				)				)
    }

    for k, v in heads_to_prune.items():
        if isinstance(__snake_case		,			__snake_case				):
            lowerCamelCase__					:   str      				=	[
                v,
            ]

    assert sum(len(__snake_case				) for h in heads_to_prune.values()				) == (1 - head_mask.long()).sum().item()
    model.prune_heads(__snake_case				)
    lowerCamelCase__					:   Union[str, Any]      				=	sum(p.numel() for p in model.parameters()				)

    lowerCamelCase__					:   Optional[int]      				=	datetime.now()
    lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__					:   int      				=	compute_heads_importance(
        __snake_case		,			__snake_case		,			__snake_case		,			compute_entropy=__snake_case		,			compute_importance=__snake_case		,			head_mask=__snake_case		,			actually_pruned=__snake_case		,			)

    lowerCamelCase__					:   str      				=	1 / loss
    lowerCamelCase__					:   Tuple      				=	datetime.now() - before_time

    logger.info(
        'Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)'		,			__snake_case		,			__snake_case		,			pruned_num_params / original_num_params * 100		,			)
    logger.info('Pruning: score with masking: %f score with pruning: %f'		,			__snake_case		,			__snake_case				)
    logger.info('Pruning: speed ratio (original timing / new timing): %f percents'		,			original_time / new_time * 100				)
    save_model(__snake_case		,			args.output_dir				)


def  SCREAMING_SNAKE_CASE   ( )    ->  Optional[Any]:
    lowerCamelCase__					:   Optional[Any]      				=	argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--data_dir'		,			default=__snake_case		,			type=__snake_case		,			required=__snake_case		,			help='The input data dir. Should contain the .tsv files (or other data files) for the task.'		,			)
    parser.add_argument(
        '--model_name_or_path'		,			default=__snake_case		,			type=__snake_case		,			required=__snake_case		,			help='Path to pretrained model or model identifier from huggingface.co/models'		,			)
    parser.add_argument(
        '--output_dir'		,			default=__snake_case		,			type=__snake_case		,			required=__snake_case		,			help='The output directory where the model predictions and checkpoints will be written.'		,			)

    # Other parameters
    parser.add_argument(
        '--config_name'		,			default=''		,			type=__snake_case		,			help='Pretrained config name or path if not the same as model_name_or_path'		,			)
    parser.add_argument(
        '--tokenizer_name'		,			default=''		,			type=__snake_case		,			help='Pretrained tokenizer name or path if not the same as model_name_or_path'		,			)
    parser.add_argument(
        '--cache_dir'		,			default=__snake_case		,			type=__snake_case		,			help='Where do you want to store the pre-trained models downloaded from s3'		,			)
    parser.add_argument(
        '--data_subset'		,			type=__snake_case		,			default=-1		,			help='If > 0: limit the data to a subset of data_subset instances.'				)
    parser.add_argument(
        '--overwrite_output_dir'		,			action='store_true'		,			help='Whether to overwrite data in output directory'				)
    parser.add_argument(
        '--overwrite_cache'		,			action='store_true'		,			help='Overwrite the cached training and evaluation sets'				)

    parser.add_argument(
        '--dont_normalize_importance_by_layer'		,			action='store_true'		,			help='Don\'t normalize importance score by layers'				)
    parser.add_argument(
        '--dont_normalize_global_importance'		,			action='store_true'		,			help='Don\'t normalize all importance scores between 0 and 1'		,			)

    parser.add_argument(
        '--try_masking'		,			action='store_true'		,			help='Whether to try to mask head until a threshold of accuracy.'				)
    parser.add_argument(
        '--masking_threshold'		,			default=0.9		,			type=__snake_case		,			help='masking threshold in term of metrics (stop masking when metric < threshold * original metric value).'		,			)
    parser.add_argument(
        '--masking_amount'		,			default=0.1		,			type=__snake_case		,			help='Amount to heads to masking at each masking step.'				)
    parser.add_argument('--metric_name'		,			default='acc'		,			type=__snake_case		,			help='Metric to use for head masking.'				)

    parser.add_argument(
        '--max_seq_length'		,			default=128		,			type=__snake_case		,			help=(
            'The maximum total input sequence length after WordPiece tokenization. \n'
            'Sequences longer than this will be truncated, sequences shorter padded.'
        )		,			)
    parser.add_argument('--batch_size'		,			default=1		,			type=__snake_case		,			help='Batch size.'				)

    parser.add_argument('--seed'		,			type=__snake_case		,			default=42				)
    parser.add_argument('--local_rank'		,			type=__snake_case		,			default=-1		,			help='local_rank for distributed training on gpus'				)
    parser.add_argument('--no_cuda'		,			action='store_true'		,			help='Whether not to use CUDA when available'				)
    parser.add_argument('--server_ip'		,			type=__snake_case		,			default=''		,			help='Can be used for distant debugging.'				)
    parser.add_argument('--server_port'		,			type=__snake_case		,			default=''		,			help='Can be used for distant debugging.'				)
    lowerCamelCase__					:   Union[str, Any]      				=	parser.parse_args()

    if args.server_ip and args.server_port:
        # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
        import ptvsd

        print('Waiting for debugger attach'				)
        ptvsd.enable_attach(address=(args.server_ip, args.server_port)		,			redirect_output=__snake_case				)
        ptvsd.wait_for_attach()

    # Setup devices and distributed training
    if args.local_rank == -1 or args.no_cuda:
        lowerCamelCase__					:   Any      				=	torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'				)
        lowerCamelCase__					:   Any      				=	0 if args.no_cuda else torch.cuda.device_count()
    else:
        torch.cuda.set_device(args.local_rank				)
        lowerCamelCase__					:   Optional[int]      				=	torch.device('cuda'		,			args.local_rank				)
        lowerCamelCase__					:   Dict      				=	1
        torch.distributed.init_process_group(backend='nccl'				)  # Initializes the distributed backend

    # Setup logging
    logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN				)
    logger.info('device: {} n_gpu: {}, distributed: {}'.format(args.device		,			args.n_gpu		,			bool(args.local_rank != -1				)				)				)

    lowerCamelCase__					:   Any      				=	GPTaLMHeadModel.from_pretrained(args.model_name_or_path				)

    # Distributed and parallel training
    model.to(args.device				)
    if args.local_rank != -1:
        lowerCamelCase__					:   Optional[int]      				=	nn.parallel.DistributedDataParallel(
            __snake_case		,			device_ids=[args.local_rank]		,			output_device=args.local_rank		,			find_unused_parameters=__snake_case				)
    elif args.n_gpu > 1:
        lowerCamelCase__					:   Optional[int]      				=	nn.DataParallel(__snake_case				)

    # Print/save training arguments
    os.makedirs(args.output_dir		,			exist_ok=__snake_case				)
    torch.save(__snake_case		,			os.path.join(args.output_dir		,			'run_args.bin'				)				)
    logger.info('Training/evaluation parameters %s'		,			__snake_case				)

    # Prepare dataset
    lowerCamelCase__					:   Optional[int]      				=	np.concatenate(
        [
            np.loadtxt(args.data_dir		,			dtype=np.intaa				),
        ]				)
    lowerCamelCase__					:   Dict      				=	(torch.from_numpy(__snake_case				),)
    lowerCamelCase__					:   Tuple      				=	TensorDataset(*__snake_case				)
    lowerCamelCase__					:   Union[str, Any]      				=	RandomSampler(__snake_case				)
    lowerCamelCase__					:   str      				=	DataLoader(__snake_case		,			sampler=__snake_case		,			batch_size=args.batch_size				)

    # Compute head entropy and importance score
    compute_heads_importance(__snake_case		,			__snake_case		,			__snake_case				)

    # Try head masking (set heads to zero until the score goes under a threshole)
    # and head pruning (remove masked heads and see the effect on the network)
    if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
        lowerCamelCase__					:   Optional[int]      				=	mask_heads(__snake_case		,			__snake_case		,			__snake_case				)
        prune_heads(__snake_case		,			__snake_case		,			__snake_case		,			__snake_case				)


if __name__ == "__main__":
       main()












import inspect
import unittest

from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
    import jax
    import jax.numpy as jnp

    from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor

class      lowerCAmelCase							(      unittest.TestCase    ):
 def __init__(   self						:   List[Any]    ,  UpperCAmelCase						:   Optional[int]    ,  UpperCAmelCase						:   Optional[Any]=3    ,  UpperCAmelCase						:   str=32    ,  UpperCAmelCase						:   Union[str, Any]=3    ,  UpperCAmelCase						:   Dict=10    ,  UpperCAmelCase						:   List[str]=[10, 20, 30, 40]    ,  UpperCAmelCase						:   Any=[1, 1, 2, 1]    ,  UpperCAmelCase						:   List[str]=True    ,  UpperCAmelCase						:   List[str]=True    ,  UpperCAmelCase						:   Dict="relu"    ,  UpperCAmelCase						:   Tuple=3    ,  UpperCAmelCase						:   Dict=None    ,  )    ->			Optional[Any]:
     lowerCamelCase__					:   Union[str, Any]      				=	parent
     lowerCamelCase__					:   Optional[Any]      				=	batch_size
     lowerCamelCase__					:   Dict      				=	image_size
     lowerCamelCase__					:   int      				=	num_channels
     lowerCamelCase__					:   int      				=	embeddings_size
     lowerCamelCase__					:   str      				=	hidden_sizes
     lowerCamelCase__					:   Any      				=	depths
     lowerCamelCase__					:   str      				=	is_training
     lowerCamelCase__					:   List[Any]      				=	use_labels
     lowerCamelCase__					:   Union[str, Any]      				=	hidden_act
     lowerCamelCase__					:   Dict      				=	num_labels
     lowerCamelCase__					:   Dict      				=	scope
     lowerCamelCase__					:   List[str]      				=	len(UpperCAmelCase			)
 def   A_			(   self						:   Optional[int]			)    ->			Optional[int]:
     lowerCamelCase__					:   int      				=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

     lowerCamelCase__					:   str      				=	self.get_config()

     return config, pixel_values
 def   A_			(   self						:   Optional[int]			)    ->			Dict:
     return RegNetConfig(
         num_channels=self.num_channels    ,  embeddings_size=self.embeddings_size    ,  hidden_sizes=self.hidden_sizes    ,  depths=self.depths    ,  hidden_act=self.hidden_act    ,  num_labels=self.num_labels    ,  image_size=self.image_size    ,  )
 def   A_			(   self						:   str    ,  UpperCAmelCase						:   Optional[int]    ,  UpperCAmelCase						:   Any			)    ->			Optional[Any]:
     lowerCamelCase__					:   Optional[Any]      				=	FlaxRegNetModel(config=UpperCAmelCase			)
     lowerCamelCase__					:   Optional[Any]      				=	model(UpperCAmelCase			)

     # Output shape (b, c, h, w)
     self.parent.assertEqual(
         result.last_hidden_state.shape    ,  (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)    ,  )
 def   A_			(   self						:   str    ,  UpperCAmelCase						:   int    ,  UpperCAmelCase						:   Tuple			)    ->			Tuple:
     lowerCamelCase__					:   List[str]      				=	self.num_labels
     lowerCamelCase__					:   str      				=	FlaxRegNetForImageClassification(config=UpperCAmelCase			)
     lowerCamelCase__					:   Union[str, Any]      				=	model(UpperCAmelCase			)
     self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.num_labels)			)







 def   A_			(   self						:   Dict			)    ->			str:
     lowerCamelCase__					:   Optional[int]      				=	self.prepare_config_and_inputs()
     lowerCamelCase__				,							lowerCamelCase__					:   Any      				=	config_and_inputs
     lowerCamelCase__					:   Dict      				=	{'pixel_values': pixel_values}
     return config, inputs_dict


@require_flax
class      lowerCAmelCase							(      __UpperCamelCase,						unittest.TestCase    ):
 UpperCAmelCase__									=	(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

 UpperCAmelCase__									=	False
 UpperCAmelCase__									=	False
 UpperCAmelCase__									=	False
 def   A_			(   self						:   List[Any]			)    ->			None:
     lowerCamelCase__					:   List[Any]      				=	FlaxRegNetModelTester(self			)
     lowerCamelCase__					:   Union[str, Any]      				=	ConfigTester(self    ,  config_class=UpperCAmelCase    ,  has_text_modality=UpperCAmelCase			)
 def   A_			(   self						:   int			)    ->			Tuple:
     self.create_and_test_config_common_properties()
     self.config_tester.create_and_test_config_to_json_string()
     self.config_tester.create_and_test_config_to_json_file()
     self.config_tester.create_and_test_config_from_and_save_pretrained()
     self.config_tester.create_and_test_config_with_num_labels()
     self.config_tester.check_config_can_be_init_without_params()
     self.config_tester.check_config_arguments_init()
 def   A_			(   self						:   Any			)    ->			str:
     return
 def   A_			(   self						:   Optional[Any]			)    ->			Optional[Any]:
     lowerCamelCase__					:   Union[str, Any]      				=	self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*UpperCAmelCase			)
 def   A_			(   self						:   Dict			)    ->			Optional[Any]:
     lowerCamelCase__					:   Any      				=	self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase			)
 @unittest.skip(reason='RegNet does not use inputs_embeds'			)
 def   A_			(   self						:   Dict			)    ->			Dict:
     pass
 @unittest.skip(reason='RegNet does not support input and output embeddings'			)
 def   A_			(   self						:   Any			)    ->			Tuple:
     pass
 def   A_			(   self						:   Union[str, Any]			)    ->			Union[str, Any]:
     lowerCamelCase__				,							lowerCamelCase__					:   Tuple      				=	self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         lowerCamelCase__					:   Optional[int]      				=	model_class(UpperCAmelCase			)
         lowerCamelCase__					:   Optional[Any]      				=	inspect.signature(model.__call__			)
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         lowerCamelCase__					:   List[Any]      				=	[*signature.parameters.keys()]

         lowerCamelCase__					:   int      				=	['pixel_values']
         self.assertListEqual(arg_names[:1]    ,  UpperCAmelCase			)
 def   A_			(   self						:   int			)    ->			List[str]:
     def check_hidden_states_output(UpperCAmelCase						:   Union[str, Any]    ,  UpperCAmelCase						:   List[str]    ,  UpperCAmelCase						:   Optional[Any]			):
         lowerCamelCase__					:   Any      				=	model_class(UpperCAmelCase			)

         lowerCamelCase__					:   Optional[Any]      				=	model(**self._prepare_for_class(UpperCAmelCase    ,  UpperCAmelCase			)			)

         lowerCamelCase__					:   Union[str, Any]      				=	outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

         lowerCamelCase__					:   Optional[int]      				=	self.model_tester.num_stages
         self.assertEqual(len(UpperCAmelCase			)    ,  expected_num_stages + 1			)

     lowerCamelCase__				,							lowerCamelCase__					:   Optional[Any]      				=	self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         lowerCamelCase__					:   List[str]      				=	True
         check_hidden_states_output(UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase			)

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         lowerCamelCase__					:   List[Any]      				=	True

         check_hidden_states_output(UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase			)







 def   A_			(   self						:   Dict			)    ->			int:
     lowerCamelCase__				,							lowerCamelCase__					:   int      				=	self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__			):
             lowerCamelCase__					:   int      				=	self._prepare_for_class(UpperCAmelCase    ,  UpperCAmelCase			)
             lowerCamelCase__					:   Tuple      				=	model_class(UpperCAmelCase			)

             @jax.jit
             def model_jitted(UpperCAmelCase						:   Tuple    ,  **UpperCAmelCase						:   Tuple			):
                 return model(pixel_values=UpperCAmelCase    ,  **UpperCAmelCase			)

             with self.subTest('JIT Enabled'			):
                 lowerCamelCase__					:   Dict      				=	model_jitted(**UpperCAmelCase			).to_tuple()

             with self.subTest('JIT Disabled'			):
                 with jax.disable_jit():
                     lowerCamelCase__					:   Optional[int]      				=	model_jitted(**UpperCAmelCase			).to_tuple()

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


def  SCREAMING_SNAKE_CASE   ( )    ->  Optional[Any]:
    lowerCamelCase__					:   Optional[int]      				=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
    return image


@require_flax
class      lowerCAmelCase							(      unittest.TestCase    ):
 @cached_property
 def   A_			(   self						:   Any			)    ->			List[Any]:
     return AutoImageProcessor.from_pretrained('facebook/regnet-y-040'			) if is_vision_available() else None







 @slow
 def   A_			(   self						:   Dict			)    ->			Tuple:
     lowerCamelCase__					:   Union[str, Any]      				=	FlaxRegNetForImageClassification.from_pretrained('facebook/regnet-y-040'			)

     lowerCamelCase__					:   Dict      				=	self.default_image_processor
     lowerCamelCase__					:   List[Any]      				=	prepare_img()
     lowerCamelCase__					:   List[str]      				=	image_processor(images=UpperCAmelCase    ,  return_tensors='np'			)

     lowerCamelCase__					:   List[Any]      				=	model(**UpperCAmelCase			)

     # verify the logits
     lowerCamelCase__					:   Union[str, Any]      				=	(1, 1000)
     self.assertEqual(outputs.logits.shape    ,  UpperCAmelCase			)

     lowerCamelCase__					:   Tuple      				=	jnp.array([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6]			)

     self.assertTrue(jnp.allclose(outputs.logits[0, :3]    ,  UpperCAmelCase    ,  atol=1e-4			)			)





