






"""simple docstring"""







import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

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


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

							from transformers.modeling_flax_pytorch_utils import (
							    convert_pytorch_state_dict_to_flax,
							    load_flax_weights_in_pytorch_model,
							)
							from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
							import torch




class 				A__ :

		def __init__(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE=14 ,	_SCREAMING_SNAKE_CASE=7 ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=99 ,	_SCREAMING_SNAKE_CASE=32 ,	_SCREAMING_SNAKE_CASE=4 ,	_SCREAMING_SNAKE_CASE=4 ,	_SCREAMING_SNAKE_CASE=4 ,	_SCREAMING_SNAKE_CASE=37 ,	_SCREAMING_SNAKE_CASE="gelu" ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=5_12 ,	_SCREAMING_SNAKE_CASE=0.02 ,	):
			__lowerCAmelCase    :		Any		    =   parent
			__lowerCAmelCase    :		Tuple		    =   batch_size
			__lowerCAmelCase    :		Optional[int]		    =   seq_length
			__lowerCAmelCase    :		int		    =   is_training
			__lowerCAmelCase    :		Dict		    =   use_input_mask
			__lowerCAmelCase    :		int		    =   use_token_type_ids
			__lowerCAmelCase    :		List[str]		    =   use_labels
			__lowerCAmelCase    :		Optional[Any]		    =   vocab_size
			__lowerCAmelCase    :		Tuple		    =   hidden_size
			__lowerCAmelCase    :		Tuple		    =   rotary_dim
			__lowerCAmelCase    :		Any		    =   num_hidden_layers
			__lowerCAmelCase    :		List[Any]		    =   num_attention_heads
			__lowerCAmelCase    :		Optional[Any]		    =   intermediate_size
			__lowerCAmelCase    :		Optional[Any]		    =   hidden_act
			__lowerCAmelCase    :		List[str]		    =   hidden_dropout_prob
			__lowerCAmelCase    :		Optional[Any]		    =   attention_probs_dropout_prob
			__lowerCAmelCase    :		Dict		    =   max_position_embeddings
			__lowerCAmelCase    :		Dict		    =   initializer_range
			__lowerCAmelCase    :		int		    =   None
			__lowerCAmelCase    :		Optional[Any]		    =   vocab_size - 1
			__lowerCAmelCase    :		int		    =   vocab_size - 1
			__lowerCAmelCase    :		Tuple		    =   vocab_size - 1

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[Any]		    =   ids_tensor([self.batch_size, self.seq_length] ,	self.vocab_size				)

			__lowerCAmelCase    :		List[Any]		    =   None
			if self.use_input_mask:
				__lowerCAmelCase    :		List[str]		    =   random_attention_mask([self.batch_size, self.seq_length]				)

			__lowerCAmelCase    :		Dict		    =   GPTJConfig(
			    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 ,	use_cache=_SCREAMING_SNAKE_CASE ,	bos_token_id=self.bos_token_id ,	eos_token_id=self.eos_token_id ,	pad_token_id=self.pad_token_id ,	rotary_dim=self.rotary_dim ,	)

			return (config, input_ids, input_mask)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Union[str, Any]		    =   self.prepare_config_and_inputs()
			__lowerCAmelCase    :		Optional[int]		    =   config_and_inputs
			__lowerCAmelCase    :		List[str]		    =   {'input_ids': input_ids, 'attention_mask': attention_mask}
			return config, inputs_dict

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Tuple		    =   20
			__lowerCAmelCase    :		int		    =   model_class_name(_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		List[str]		    =   model.init_cache(input_ids.shape[0] ,	_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Any		    =   jnp.ones((input_ids.shape[0], max_decoder_length) ,	dtype='i4'				)

			__lowerCAmelCase    :		Optional[int]		    =   jnp.broadcast_to(
			    jnp.arange(input_ids.shape[-1] - 1				)[None, :] ,	(input_ids.shape[0], input_ids.shape[-1] - 1)				)
			__lowerCAmelCase    :		List[Any]		    =   model(
			    input_ids[:, :-1] ,	attention_mask=_SCREAMING_SNAKE_CASE ,	past_key_values=_SCREAMING_SNAKE_CASE ,	position_ids=_SCREAMING_SNAKE_CASE ,	)

			__lowerCAmelCase    :		Dict		    =   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,	dtype='i4'				)
			__lowerCAmelCase    :		List[Any]		    =   model(
			    input_ids[:, -1:] ,	attention_mask=_SCREAMING_SNAKE_CASE ,	past_key_values=outputs_cache.past_key_values ,	position_ids=_SCREAMING_SNAKE_CASE ,	)

			__lowerCAmelCase    :		List[Any]		    =   model(_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		int		    =   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 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Union[str, Any]		    =   20
			__lowerCAmelCase    :		str		    =   model_class_name(_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Dict		    =   jnp.concatenate(
			    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])				)] ,	axis=-1 ,	)

			__lowerCAmelCase    :		Optional[int]		    =   model.init_cache(input_ids.shape[0] ,	_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		str		    =   jnp.broadcast_to(
			    jnp.arange(input_ids.shape[-1] - 1				)[None, :] ,	(input_ids.shape[0], input_ids.shape[-1] - 1)				)

			__lowerCAmelCase    :		Tuple		    =   model(
			    input_ids[:, :-1] ,	attention_mask=_SCREAMING_SNAKE_CASE ,	past_key_values=_SCREAMING_SNAKE_CASE ,	position_ids=_SCREAMING_SNAKE_CASE ,	)
			__lowerCAmelCase    :		Dict		    =   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,	dtype='i4'				)
			__lowerCAmelCase    :		str		    =   model(
			    input_ids[:, -1:] ,	past_key_values=outputs_cache.past_key_values ,	attention_mask=_SCREAMING_SNAKE_CASE ,	position_ids=_SCREAMING_SNAKE_CASE ,	)

			__lowerCAmelCase    :		Optional[Any]		    =   model(_SCREAMING_SNAKE_CASE ,	attention_mask=_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Tuple		    =   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 				A__ (			_lowerCamelCase ,  _lowerCamelCase ,  unittest.TestCase):
		A_		: Union[str, Any]            =			(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
		A_		: List[str]            =			(FlaxGPTJForCausalLM,) if is_flax_available() else ()

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		str		    =   FlaxGPTJModelTester(self				)

		def 							__lowerCamelCase							(   self				):
			for model_class_name in self.all_model_classes:
				__lowerCAmelCase    :		Tuple		    =   self.model_tester.prepare_config_and_inputs()
				self.model_tester.check_use_cache_forward(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self				):
			for model_class_name in self.all_model_classes:
				__lowerCAmelCase    :		Tuple		    =   self.model_tester.prepare_config_and_inputs()
				self.model_tester.check_use_cache_forward_with_attn_mask(
				    _SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

		@tooslow
		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Dict		    =   GPTaTokenizer.from_pretrained('gpt2' ,	pad_token='<|endoftext|>' ,	padding_side='left'				)
			__lowerCAmelCase    :		Dict		    =   tokenizer(['Hello this is a long string', 'Hey'] ,	return_tensors='np' ,	padding=_SCREAMING_SNAKE_CASE ,	truncation=_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Tuple		    =   FlaxGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B'				)
			__lowerCAmelCase    :		str		    =   False
			__lowerCAmelCase    :		List[str]		    =   model.config.eos_token_id

			__lowerCAmelCase    :		List[Any]		    =   jax.jit(model.generate				)

			__lowerCAmelCase    :		Tuple		    =   jit_generate(
			    inputs['input_ids'] ,	attention_mask=inputs['attention_mask'] ,	pad_token_id=tokenizer.pad_token_id				).sequences

			__lowerCAmelCase    :		List[Any]		    =   tokenizer.batch_decode(_SCREAMING_SNAKE_CASE ,	skip_special_tokens=_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		int		    =   [
			    'Hello this is a long string of text.\n\nI\'m trying to get the text of the',
			    'Hey, I\'m a little late to the party. I\'m going to',
			]

			self.assertListEqual(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

		@is_pt_flax_cross_test
		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		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__				):
					# prepare inputs
					__lowerCAmelCase    :		Optional[int]		    =   self._prepare_for_class(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)
					__lowerCAmelCase    :		int		    =   {k: torch.tensor(v.tolist()				) for k, v in prepared_inputs_dict.items()}

					# load corresponding PyTorch class
					__lowerCAmelCase    :		str		    =   model_class.__name__[4:]  # Skip the "Flax" at the beginning
					__lowerCAmelCase    :		Union[str, Any]		    =   getattr(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

					__lowerCAmelCase    :		int		    =   pt_inputs['input_ids'].shape
					__lowerCAmelCase    :		int		    =   np.random.randint(0 ,	seq_length - 1 ,	size=(batch_size,)				)
					for batch_idx, start_index in enumerate(_SCREAMING_SNAKE_CASE				):
						__lowerCAmelCase    :		int		    =   0
						__lowerCAmelCase    :		Any		    =   1
						__lowerCAmelCase    :		List[str]		    =   0
						__lowerCAmelCase    :		Optional[int]		    =   1
					__lowerCAmelCase    :		Optional[Any]		    =   pt_model_class(_SCREAMING_SNAKE_CASE				).eval()
					__lowerCAmelCase    :		Tuple		    =   model_class(_SCREAMING_SNAKE_CASE ,	dtype=jnp.floataa				)

					__lowerCAmelCase    :		Union[str, Any]		    =   convert_pytorch_state_dict_to_flax(pt_model.state_dict() ,	_SCREAMING_SNAKE_CASE				)
					__lowerCAmelCase    :		List[Any]		    =   fx_state

					with torch.no_grad():
						__lowerCAmelCase    :		Tuple		    =   pt_model(**_SCREAMING_SNAKE_CASE				).to_tuple()

					__lowerCAmelCase    :		List[Any]		    =   fx_model(**_SCREAMING_SNAKE_CASE				).to_tuple()
					self.assertEqual(len(_SCREAMING_SNAKE_CASE				) ,	len(_SCREAMING_SNAKE_CASE				) ,	'Output lengths differ between Flax and PyTorch'				)
					for fx_output, pt_output in zip(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
						self.assert_almost_equals(fx_output[:, -1] ,	pt_output[:, -1].numpy() ,	4E-2				)

					with tempfile.TemporaryDirectory() as tmpdirname:
						pt_model.save_pretrained(_SCREAMING_SNAKE_CASE				)
						__lowerCAmelCase    :		Optional[int]		    =   model_class.from_pretrained(_SCREAMING_SNAKE_CASE ,	from_pt=_SCREAMING_SNAKE_CASE				)

					__lowerCAmelCase    :		Union[str, Any]		    =   fx_model_loaded(**_SCREAMING_SNAKE_CASE				).to_tuple()
					self.assertEqual(
					    len(_SCREAMING_SNAKE_CASE				) ,	len(_SCREAMING_SNAKE_CASE				) ,	'Output lengths differ between Flax and PyTorch'				)
					for fx_output_loaded, pt_output in zip(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
						self.assert_almost_equals(fx_output_loaded[:, -1] ,	pt_output[:, -1].numpy() ,	4E-2				)

		@is_pt_flax_cross_test
		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Dict		    =   self.model_tester.prepare_config_and_inputs_for_common()
			for model_class in self.all_model_classes:
				with self.subTest(model_class.__name__				):
					# prepare inputs
					__lowerCAmelCase    :		Dict		    =   self._prepare_for_class(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)
					__lowerCAmelCase    :		Any		    =   {k: torch.tensor(v.tolist()				) for k, v in prepared_inputs_dict.items()}

					# load corresponding PyTorch class
					__lowerCAmelCase    :		int		    =   model_class.__name__[4:]  # Skip the "Flax" at the beginning
					__lowerCAmelCase    :		Any		    =   getattr(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

					__lowerCAmelCase    :		List[Any]		    =   pt_model_class(_SCREAMING_SNAKE_CASE				).eval()
					__lowerCAmelCase    :		Optional[int]		    =   model_class(_SCREAMING_SNAKE_CASE ,	dtype=jnp.floataa				)

					__lowerCAmelCase    :		List[str]		    =   load_flax_weights_in_pytorch_model(_SCREAMING_SNAKE_CASE ,	fx_model.params				)
					__lowerCAmelCase    :		List[Any]		    =   pt_inputs['input_ids'].shape
					__lowerCAmelCase    :		Optional[Any]		    =   np.random.randint(0 ,	seq_length - 1 ,	size=(batch_size,)				)
					for batch_idx, start_index in enumerate(_SCREAMING_SNAKE_CASE				):
						__lowerCAmelCase    :		List[str]		    =   0
						__lowerCAmelCase    :		Optional[Any]		    =   1
						__lowerCAmelCase    :		Union[str, Any]		    =   0
						__lowerCAmelCase    :		Optional[int]		    =   1

					# make sure weights are tied in PyTorch
					pt_model.tie_weights()

					with torch.no_grad():
						__lowerCAmelCase    :		Dict		    =   pt_model(**_SCREAMING_SNAKE_CASE				).to_tuple()

					__lowerCAmelCase    :		int		    =   fx_model(**_SCREAMING_SNAKE_CASE				).to_tuple()
					self.assertEqual(len(_SCREAMING_SNAKE_CASE				) ,	len(_SCREAMING_SNAKE_CASE				) ,	'Output lengths differ between Flax and PyTorch'				)
					for fx_output, pt_output in zip(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
						self.assert_almost_equals(fx_output[:, -1] ,	pt_output[:, -1].numpy() ,	4E-2				)

					with tempfile.TemporaryDirectory() as tmpdirname:
						fx_model.save_pretrained(_SCREAMING_SNAKE_CASE				)
						__lowerCAmelCase    :		Optional[int]		    =   pt_model_class.from_pretrained(_SCREAMING_SNAKE_CASE ,	from_flax=_SCREAMING_SNAKE_CASE				)

					with torch.no_grad():
						__lowerCAmelCase    :		Optional[Any]		    =   pt_model_loaded(**_SCREAMING_SNAKE_CASE				).to_tuple()

					self.assertEqual(
					    len(_SCREAMING_SNAKE_CASE				) ,	len(_SCREAMING_SNAKE_CASE				) ,	'Output lengths differ between Flax and PyTorch'				)
					for fx_output, pt_output in zip(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
						self.assert_almost_equals(fx_output[:, -1] ,	pt_output[:, -1].numpy() ,	4E-2				)







		@tooslow
		def 							__lowerCamelCase							(   self				):
			for model_class_name in self.all_model_classes:
				__lowerCAmelCase    :		List[str]		    =   model_class_name.from_pretrained('EleutherAI/gpt-j-6B'				)
				__lowerCAmelCase    :		str		    =   model(np.ones((1, 1)				)				)
				self.assertIsNotNone(_SCREAMING_SNAKE_CASE				)

"""simple docstring"""







import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch
							from torch import nn

							from transformers import RegNetForImageClassification, RegNetModel
							from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor




class 				A__ :

		def __init__(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE=3 ,	_SCREAMING_SNAKE_CASE=32 ,	_SCREAMING_SNAKE_CASE=3 ,	_SCREAMING_SNAKE_CASE=10 ,	_SCREAMING_SNAKE_CASE=[10, 20, 30, 40] ,	_SCREAMING_SNAKE_CASE=[1, 1, 2, 1] ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE="relu" ,	_SCREAMING_SNAKE_CASE=3 ,	_SCREAMING_SNAKE_CASE=None ,	):
			__lowerCAmelCase    :		Optional[Any]		    =   parent
			__lowerCAmelCase    :		int		    =   batch_size
			__lowerCAmelCase    :		Union[str, Any]		    =   image_size
			__lowerCAmelCase    :		Optional[Any]		    =   num_channels
			__lowerCAmelCase    :		List[Any]		    =   embeddings_size
			__lowerCAmelCase    :		Dict		    =   hidden_sizes
			__lowerCAmelCase    :		Optional[int]		    =   depths
			__lowerCAmelCase    :		Optional[Any]		    =   is_training
			__lowerCAmelCase    :		int		    =   use_labels
			__lowerCAmelCase    :		List[Any]		    =   hidden_act
			__lowerCAmelCase    :		Optional[Any]		    =   num_labels
			__lowerCAmelCase    :		List[str]		    =   scope
			__lowerCAmelCase    :		Optional[Any]		    =   len(_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[Any]		    =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

			__lowerCAmelCase    :		List[str]		    =   None
			if self.use_labels:
				__lowerCAmelCase    :		Tuple		    =   ids_tensor([self.batch_size] ,	self.num_labels				)

			__lowerCAmelCase    :		int		    =   self.get_config()

			return config, pixel_values, labels

		def 							__lowerCamelCase							(   self				):
			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 ,	)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Any		    =   RegNetModel(config=_SCREAMING_SNAKE_CASE				)
			model.to(_SCREAMING_SNAKE_CASE				)
			model.eval()
			__lowerCAmelCase    :		Any		    =   model(_SCREAMING_SNAKE_CASE				)
			# expected last hidden states: B, C, H // 32, W // 32
			self.parent.assertEqual(
			    result.last_hidden_state.shape ,	(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) ,	)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Optional[Any]		    =   self.num_labels
			__lowerCAmelCase    :		str		    =   RegNetForImageClassification(_SCREAMING_SNAKE_CASE				)
			model.to(_SCREAMING_SNAKE_CASE				)
			model.eval()
			__lowerCAmelCase    :		Any		    =   model(_SCREAMING_SNAKE_CASE ,	labels=_SCREAMING_SNAKE_CASE				)
			self.parent.assertEqual(result.logits.shape ,	(self.batch_size, self.num_labels)				)







		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[Any]		    =   self.prepare_config_and_inputs()
			__lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    :		Any		    =   config_and_inputs
			__lowerCAmelCase    :		List[str]		    =   {'pixel_values': pixel_values}
			return config, inputs_dict




@require_torch
class 				A__ (			_lowerCamelCase ,  _lowerCamelCase ,  unittest.TestCase):
		A_		: List[str]            =			(RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
		A_		: List[Any]            =			(
		    {'feature-extraction': RegNetModel, 'image-classification': RegNetForImageClassification}
		    if is_torch_available()
		    else {}
		)

		A_		: Dict            =			False
		A_		: str            =			False
		A_		: int            =			False
		A_		: Union[str, Any]            =			False

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		List[str]		    =   RegNetModelTester(self				)
			__lowerCAmelCase    :		int		    =   ConfigTester(self ,	config_class=_SCREAMING_SNAKE_CASE ,	has_text_modality=_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self				):
			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 							__lowerCamelCase							(   self				):
			return

		@unittest.skip(reason='RegNet does not use inputs_embeds'				)
		def 							__lowerCamelCase							(   self				):
			pass

		@unittest.skip(reason='RegNet does not support input and output embeddings'				)
		def 							__lowerCamelCase							(   self				):
			pass

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    ,     __lowerCAmelCase    :		List[Any]		    =   self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
				__lowerCAmelCase    :		Optional[int]		    =   model_class(_SCREAMING_SNAKE_CASE				)
				__lowerCAmelCase    :		Dict		    =   inspect.signature(model.forward				)
				# signature.parameters is an OrderedDict => so arg_names order is deterministic
				__lowerCAmelCase    :		Tuple		    =   [*signature.parameters.keys()]

				__lowerCAmelCase    :		Optional[int]		    =   ['pixel_values']
				self.assertListEqual(arg_names[:1] ,	_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[Any]		    =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    ,     __lowerCAmelCase    :		List[str]		    =   self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
				__lowerCAmelCase    :		Union[str, Any]		    =   model_class(config=_SCREAMING_SNAKE_CASE				)
				for name, module in model.named_modules():
					if isinstance(_SCREAMING_SNAKE_CASE ,	(nn.BatchNormad, nn.GroupNorm)				):
						self.assertTrue(
						    torch.all(module.weight == 1				) ,	msg=f"Parameter {name} of model {model_class} seems not properly initialized" ,	)
						self.assertTrue(
						    torch.all(module.bias == 0				) ,	msg=f"Parameter {name} of model {model_class} seems not properly initialized" ,	)

		def 							__lowerCamelCase							(   self				):
			def check_hidden_states_output(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
				__lowerCAmelCase    :		Any		    =   model_class(_SCREAMING_SNAKE_CASE				)
				model.to(_SCREAMING_SNAKE_CASE				)
				model.eval()

				with torch.no_grad():
					__lowerCAmelCase    :		List[str]		    =   model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)				)

				__lowerCAmelCase    :		Any		    =   outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

				__lowerCAmelCase    :		List[str]		    =   self.model_tester.num_stages
				self.assertEqual(len(_SCREAMING_SNAKE_CASE				) ,	expected_num_stages + 1				)

				# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
				self.assertListEqual(
				    list(hidden_states[0].shape[-2:]				) ,	[self.model_tester.image_size // 2, self.model_tester.image_size // 2] ,	)

			__lowerCAmelCase    ,     __lowerCAmelCase    :		Dict		    =   self.model_tester.prepare_config_and_inputs_for_common()
			__lowerCAmelCase    :		List[Any]		    =   ['basic', 'bottleneck']
			for model_class in self.all_model_classes:
				for layer_type in layers_type:
					__lowerCAmelCase    :		List[Any]		    =   layer_type
					__lowerCAmelCase    :		Tuple		    =   True
					check_hidden_states_output(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

					# check that output_hidden_states also work using config
					del inputs_dict["output_hidden_states"]
					__lowerCAmelCase    :		Dict		    =   True

					check_hidden_states_output(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		List[Any]		    =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE				)







		@slow
		def 							__lowerCamelCase							(   self				):
			for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
				__lowerCAmelCase    :		Dict		    =   RegNetModel.from_pretrained(_SCREAMING_SNAKE_CASE				)
				self.assertIsNotNone(_SCREAMING_SNAKE_CASE				)



def 					__lowerCAmelCase       ():
	__lowerCAmelCase    :		str		    =   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
	return image




@require_torch
@require_vision
class 				A__ (			unittest.TestCase):

		@cached_property
		def 							__lowerCamelCase							(   self				):
			return (
			    AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)
			    if is_vision_available()
			    else None
			)







		@slow
		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		List[str]		    =   RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				).to(_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		str		    =   self.default_image_processor
			__lowerCAmelCase    :		Any		    =   prepare_img()
			__lowerCAmelCase    :		Any		    =   image_processor(images=_SCREAMING_SNAKE_CASE ,	return_tensors='pt'				).to(_SCREAMING_SNAKE_CASE				)

			# forward pass
			with torch.no_grad():
				__lowerCAmelCase    :		List[str]		    =   model(**_SCREAMING_SNAKE_CASE				)

			# verify the logits
			__lowerCAmelCase    :		Union[str, Any]		    =   torch.Size((1, 10_00)				)
			self.assertEqual(outputs.logits.shape ,	_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Optional[int]		    =   torch.tensor([-0.4180, -1.5051, -3.4836]				).to(_SCREAMING_SNAKE_CASE				)

			self.assertTrue(torch.allclose(outputs.logits[0, :3] ,	_SCREAMING_SNAKE_CASE ,	atol=1E-4				)				)