




'''simple docstring'''


import tempfile
import unittest

import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, slow

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


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

     from transformers.modeling_flax_pytorch_utils import (
         convert_pytorch_state_dict_to_flax,
         load_flax_weights_in_pytorch_model,
     )
     from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel


if is_torch_available():
     import torch






@require_flax
class 	lowerCAmelCase_  :







  '''simple docstring'''

  def __init__(   self							:       List[Any]				,     SCREAMING_SNAKE_CASE_							:       Optional[int]				,     SCREAMING_SNAKE_CASE_							:       Any=14				,     SCREAMING_SNAKE_CASE_							:       Union[str, Any]=7				,     SCREAMING_SNAKE_CASE_							:       str=True				,     SCREAMING_SNAKE_CASE_							:       Tuple=True				,     SCREAMING_SNAKE_CASE_							:       Any=True				,     SCREAMING_SNAKE_CASE_							:       Dict=99				,     SCREAMING_SNAKE_CASE_							:       Dict=32				,     SCREAMING_SNAKE_CASE_							:       Optional[int]=5				,     SCREAMING_SNAKE_CASE_							:       Tuple=4				,     SCREAMING_SNAKE_CASE_							:       Union[str, Any]=37				,     SCREAMING_SNAKE_CASE_							:       Union[str, Any]="gelu"				,     SCREAMING_SNAKE_CASE_							:       int=0.1				,     SCREAMING_SNAKE_CASE_							:       List[Any]=0.1				,     SCREAMING_SNAKE_CASE_							:       Optional[Any]=5_12				,     SCREAMING_SNAKE_CASE_							:       Dict=0.02				,     SCREAMING_SNAKE_CASE_							:       Dict=None				,     ) ->		Tuple:







       '''simple docstring'''



       A:		Optional[int]        =		parent
       A:		Dict        =		batch_size
       A:		str        =		seq_length
       A:		List[str]        =		is_training
       A:		Tuple        =		use_input_mask
       A:		Optional[Any]        =		use_labels
       A:		int        =		vocab_size
       A:		int        =		d_model
       A:		List[str]        =		num_hidden_layers
       A:		List[str]        =		num_attention_heads
       A:		Dict        =		ffn_dim
       A:		Dict        =		activation_function
       A:		Tuple        =		activation_dropout
       A:		str        =		attention_dropout
       A:		Tuple        =		max_position_embeddings
       A:		List[Any]        =		initializer_range
       A:		int        =		None
       A:		Any        =		0
       A:		Optional[int]        =		2
       A:		Optional[Any]        =		1



  def 				_snake_case    (   self							:       Optional[Any]  ) ->		Union[str, Any]:







       '''simple docstring'''



       A:		List[Any]        =		np.clip(ids_tensor([self.batch_size, self.seq_length]				,     self.vocab_size  )				,     3				,     self.vocab_size  )

       A:		Optional[int]        =		None
       if self.use_input_mask:
            A:		Union[str, Any]        =		random_attention_mask([self.batch_size, self.seq_length]  )

       A:		Optional[int]        =		XGLMConfig(
           vocab_size=self.vocab_size				,     d_model=self.hidden_size				,     num_layers=self.num_hidden_layers				,     attention_heads=self.num_attention_heads				,     ffn_dim=self.ffn_dim				,     activation_function=self.activation_function				,     activation_dropout=self.activation_dropout				,     attention_dropout=self.attention_dropout				,     max_position_embeddings=self.max_position_embeddings				,     initializer_range=self.initializer_range				,     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				,     )

       return (config, input_ids, input_mask)



  def 				_snake_case    (   self							:       Dict  ) ->		str:







       '''simple docstring'''



       A:		Any        =		self.prepare_config_and_inputs()
       A    ,						A    ,						A:		Dict        =		config_and_inputs
       A:		Optional[Any]        =		{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
       return config, inputs_dict



  def 				_snake_case    (   self							:       List[str]  ) ->		int:







       '''simple docstring'''



       A    ,						A    ,						A:		str        =		self.prepare_config_and_inputs()

       A:		Union[str, Any]        =		floats_tensor([self.batch_size, self.seq_length, self.hidden_size]  )
       A:		List[str]        =		ids_tensor([self.batch_size, self.seq_length]				,     vocab_size=2  )

       return (
           config,
           input_ids,
           attention_mask,
           encoder_hidden_states,
           encoder_attention_mask,
       )



  def 				_snake_case    (   self							:       Tuple				,     SCREAMING_SNAKE_CASE_							:       str				,     SCREAMING_SNAKE_CASE_							:       int				,     SCREAMING_SNAKE_CASE_							:       Tuple				,     SCREAMING_SNAKE_CASE_							:       Tuple  ) ->		Dict:







       '''simple docstring'''



       A:		Optional[Any]        =		20
       A:		str        =		model_class_name(SCREAMING_SNAKE_CASE_  )

       A:		Tuple        =		model.init_cache(input_ids.shape[0]				,     SCREAMING_SNAKE_CASE_  )
       A:		Tuple        =		jnp.ones((input_ids.shape[0], max_decoder_length)				,     dtype='''i4'''  )

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

       A:		str        =		jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]				,     dtype='''i4'''  )
       A:		List[str]        =		model(
           input_ids[:, -1:]				,     attention_mask=SCREAMING_SNAKE_CASE_				,     past_key_values=outputs_cache.past_key_values				,     position_ids=SCREAMING_SNAKE_CASE_				,     )

       A:		Tuple        =		model(SCREAMING_SNAKE_CASE_  )

       A:		List[str]        =		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 				_snake_case    (   self							:       List[Any]				,     SCREAMING_SNAKE_CASE_							:       Union[str, Any]				,     SCREAMING_SNAKE_CASE_							:       Optional[Any]				,     SCREAMING_SNAKE_CASE_							:       Optional[int]				,     SCREAMING_SNAKE_CASE_							:       Union[str, Any]  ) ->		Optional[int]:







       '''simple docstring'''



       A:		List[str]        =		20
       A:		Optional[int]        =		model_class_name(SCREAMING_SNAKE_CASE_  )

       A:		Optional[int]        =		jnp.concatenate(
           [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])  )]				,     axis=-1				,     )

       A:		Dict        =		model.init_cache(input_ids.shape[0]				,     SCREAMING_SNAKE_CASE_  )
       A:		List[str]        =		jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1  )[None, :]				,     (input_ids.shape[0], input_ids.shape[-1] - 1)  )

       A:		int        =		model(
           input_ids[:, :-1]				,     attention_mask=SCREAMING_SNAKE_CASE_				,     past_key_values=SCREAMING_SNAKE_CASE_				,     position_ids=SCREAMING_SNAKE_CASE_				,     )
       A:		Optional[int]        =		jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]				,     dtype='''i4'''  )
       A:		Optional[Any]        =		model(
           input_ids[:, -1:]				,     past_key_values=outputs_cache.past_key_values				,     attention_mask=SCREAMING_SNAKE_CASE_				,     position_ids=SCREAMING_SNAKE_CASE_				,     )

       A:		Tuple        =		model(SCREAMING_SNAKE_CASE_				,     attention_mask=SCREAMING_SNAKE_CASE_  )
       A:		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}"""  )






@require_sentencepiece
@require_flax
class 	lowerCAmelCase_  (					UpperCAmelCase_					,				UpperCAmelCase_					,				unittest.TestCase       ):







  '''simple docstring'''

  UpperCamelCase_ :			int        =							(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
  UpperCamelCase_ :			int        =							(FlaxXGLMForCausalLM,) if is_flax_available() else ()



  def 				_snake_case    (   self							:       str  ) ->		int:







       '''simple docstring'''



       A:		List[Any]        =		FlaxXGLMModelTester(self  )



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







       '''simple docstring'''



       for model_class_name in self.all_model_classes:
            A    ,						A    ,						A:		Optional[Any]        =		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 				_snake_case    (   self							:       Optional[Any]  ) ->		Dict:







       '''simple docstring'''



       for model_class_name in self.all_model_classes:
            A    ,						A    ,						A:		Dict        =		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_  )



  @slow
  def 				_snake_case    (   self							:       Dict  ) ->		Tuple:







       '''simple docstring'''



       A:		List[str]        =		XGLMTokenizer.from_pretrained('''XGLM'''				,     padding_side='''left'''  )
       A:		Any        =		tokenizer(['''Hello this is a long string''', '''Hey''']				,     return_tensors='''np'''				,     padding=SCREAMING_SNAKE_CASE_				,     truncation=SCREAMING_SNAKE_CASE_  )

       A:		List[str]        =		FlaxXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''  )
       A:		int        =		1
       A:		Optional[Any]        =		False

       A:		str        =		jax.jit(model.generate  )

       A:		Tuple        =		jit_generate(inputs['''input_ids''']				,     attention_mask=inputs['''attention_mask''']  ).sequences

       A:		str        =		tokenizer.batch_decode(SCREAMING_SNAKE_CASE_				,     skip_special_tokens=SCREAMING_SNAKE_CASE_  )

       A:		Tuple        =		[
           '''Hello this is a long string of questions, but I\'m not sure if I\'m''',
           '''Hey, I\'m a newbie to the forum and I\'''',
       ]

       self.assertListEqual(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_  )



  @is_pt_flax_cross_test
  def 				_snake_case    (   self							:       List[str]  ) ->		Tuple:







       '''simple docstring'''



       A    ,						A:		Union[str, Any]        =		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
                 A:		Optional[Any]        =		self._prepare_for_class(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_  )
                 A:		Union[str, Any]        =		{k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}

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

                 A    ,						A:		Tuple        =		pt_inputs['''input_ids'''].shape
                 A:		List[str]        =		np.random.randint(0				,     seq_length - 1				,     size=(batch_size,)  )
                 for batch_idx, start_index in enumerate(SCREAMING_SNAKE_CASE_  ):
                      A:		str        =		0
                      A:		Optional[Any]        =		1
                      A:		int        =		0
                      A:		int        =		1
                 A:		Tuple        =		pt_model_class(SCREAMING_SNAKE_CASE_  ).eval()
                 # Flax models don't use the `use_cache` option and cache is not returned as a default.
                 # So we disable `use_cache` here for PyTorch model.
                 A:		Optional[int]        =		False
                 A:		Optional[Any]        =		model_class(SCREAMING_SNAKE_CASE_				,     dtype=jnp.floataa  )
                 A:		Union[str, Any]        =		convert_pytorch_state_dict_to_flax(pt_model.state_dict()				,     SCREAMING_SNAKE_CASE_  )
                 A:		str        =		fx_state

                 with torch.no_grad():
                      A:		List[str]        =		pt_model(**SCREAMING_SNAKE_CASE_  ).to_tuple()

                 A:		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_  )
                      A:		Tuple        =		model_class.from_pretrained(SCREAMING_SNAKE_CASE_				,     from_pt=SCREAMING_SNAKE_CASE_  )

                 A:		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 				_snake_case    (   self							:       Any  ) ->		Union[str, Any]:







       '''simple docstring'''



       A    ,						A:		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
                 A:		Union[str, Any]        =		self._prepare_for_class(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_  )
                 A:		Dict        =		{k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}

                 # load corresponding PyTorch class
                 A:		Optional[Any]        =		model_class.__name__[4:]  # Skip the "Flax" at the beginning
                 A:		Dict        =		getattr(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_  )

                 A:		Tuple        =		pt_model_class(SCREAMING_SNAKE_CASE_  ).eval()
                 A:		List[str]        =		False
                 A:		Dict        =		model_class(SCREAMING_SNAKE_CASE_				,     dtype=jnp.floataa  )

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

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

                 with torch.no_grad():
                      A:		int        =		pt_model(**SCREAMING_SNAKE_CASE_  ).to_tuple()

                 A:		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:
                      fx_model.save_pretrained(SCREAMING_SNAKE_CASE_  )
                      A:		Union[str, Any]        =		pt_model_class.from_pretrained(SCREAMING_SNAKE_CASE_				,     from_flax=SCREAMING_SNAKE_CASE_  )

                 with torch.no_grad():
                      A:		Dict        =		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  )




  @slow
  def 				_snake_case    (   self							:       Union[str, Any]  ) ->		str:







       '''simple docstring'''



       for model_class_name in self.all_model_classes:
            A:		str        =		model_class_name.from_pretrained('''facebook/xglm-564M'''  )
            A:		Union[str, Any]        =		model(np.ones((1, 1)  )  )
            self.assertIsNotNone(SCREAMING_SNAKE_CASE_  )












'''simple docstring'''


import inspect
import unittest

from transformers import EfficientNetConfig
from transformers.testing_utils import is_pipeline_test, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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 transformers import EfficientNetForImageClassification, EfficientNetModel
     from transformers.models.efficientnet.modeling_efficientnet import EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor






class 	lowerCAmelCase_  :







  '''simple docstring'''

  def __init__(   self							:       List[str]				,     SCREAMING_SNAKE_CASE_							:       Tuple				,     SCREAMING_SNAKE_CASE_							:       Tuple=13				,     SCREAMING_SNAKE_CASE_							:       int=32				,     SCREAMING_SNAKE_CASE_							:       int=3				,     SCREAMING_SNAKE_CASE_							:       Dict=[3, 3, 5]				,     SCREAMING_SNAKE_CASE_							:       int=[32, 16, 24]				,     SCREAMING_SNAKE_CASE_							:       List[Any]=[16, 24, 40]				,     SCREAMING_SNAKE_CASE_							:       List[str]=[1, 1, 2]				,     SCREAMING_SNAKE_CASE_							:       Optional[Any]=[1, 1, 2]				,     SCREAMING_SNAKE_CASE_							:       List[Any]=[1, 6, 6]				,     SCREAMING_SNAKE_CASE_							:       Dict=True				,     SCREAMING_SNAKE_CASE_							:       Union[str, Any]=True				,     SCREAMING_SNAKE_CASE_							:       str=37				,     SCREAMING_SNAKE_CASE_							:       List[Any]="gelu"				,     SCREAMING_SNAKE_CASE_							:       Any=10				,     ) ->		Tuple:







       '''simple docstring'''



       A:		Optional[Any]        =		parent
       A:		int        =		batch_size
       A:		List[str]        =		image_size
       A:		List[str]        =		num_channels
       A:		Any        =		kernel_sizes
       A:		Optional[int]        =		in_channels
       A:		Any        =		out_channels
       A:		Optional[Any]        =		strides
       A:		str        =		num_block_repeats
       A:		List[Any]        =		expand_ratios
       A:		List[Any]        =		is_training
       A:		Any        =		hidden_act
       A:		List[Any]        =		num_labels
       A:		Tuple        =		use_labels



  def 				_snake_case    (   self							:       List[str]  ) ->		List[str]:







       '''simple docstring'''



       A:		str        =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

       A:		Optional[int]        =		None
       if self.use_labels:
            A:		List[Any]        =		ids_tensor([self.batch_size]				,     self.num_labels  )

       A:		int        =		self.get_config()
       return config, pixel_values, labels



  def 				_snake_case    (   self							:       List[str]  ) ->		str:







       '''simple docstring'''



       return EfficientNetConfig(
           num_channels=self.num_channels				,     kernel_sizes=self.kernel_sizes				,     in_channels=self.in_channels				,     out_channels=self.out_channels				,     strides=self.strides				,     num_block_repeats=self.num_block_repeats				,     expand_ratios=self.expand_ratios				,     hidden_act=self.hidden_act				,     num_labels=self.num_labels				,     )



  def 				_snake_case    (   self							:       int				,     SCREAMING_SNAKE_CASE_							:       Any				,     SCREAMING_SNAKE_CASE_							:       List[Any]				,     SCREAMING_SNAKE_CASE_							:       List[str]  ) ->		Dict:







       '''simple docstring'''



       A:		int        =		EfficientNetModel(config=SCREAMING_SNAKE_CASE_  )
       model.to(SCREAMING_SNAKE_CASE_  )
       model.eval()
       A:		List[Any]        =		model(SCREAMING_SNAKE_CASE_  )
       # expected last hidden states: B, C, H // 4, W // 4
       self.parent.assertEqual(
           result.last_hidden_state.shape				,     (self.batch_size, config.hidden_dim, self.image_size // 4, self.image_size // 4)				,     )



  def 				_snake_case    (   self							:       Union[str, Any]				,     SCREAMING_SNAKE_CASE_							:       str				,     SCREAMING_SNAKE_CASE_							:       List[str]				,     SCREAMING_SNAKE_CASE_							:       List[Any]  ) ->		Tuple:







       '''simple docstring'''



       A:		Optional[Any]        =		EfficientNetForImageClassification(SCREAMING_SNAKE_CASE_  )
       model.to(SCREAMING_SNAKE_CASE_  )
       model.eval()
       A:		Union[str, Any]        =		model(SCREAMING_SNAKE_CASE_				,     labels=SCREAMING_SNAKE_CASE_  )
       self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.num_labels)  )




  def 				_snake_case    (   self							:       Dict  ) ->		Optional[int]:







       '''simple docstring'''



       A:		Any        =		self.prepare_config_and_inputs()
       A    ,						A    ,						A:		str        =		config_and_inputs
       A:		Optional[int]        =		{'''pixel_values''': pixel_values}
       return config, inputs_dict






@require_torch
class 	lowerCAmelCase_  (					UpperCAmelCase_					,				UpperCAmelCase_					,				unittest.TestCase       ):







  '''simple docstring'''

  UpperCamelCase_ :			Dict        =							(EfficientNetModel, EfficientNetForImageClassification) if is_torch_available() else ()
  UpperCamelCase_ :			Dict        =							(
      {"""feature-extraction""": EfficientNetModel, """image-classification""": EfficientNetForImageClassification}
      if is_torch_available()
      else {}
  )

  UpperCamelCase_ :			List[Any]        =							False
  UpperCamelCase_ :			Union[str, Any]        =							False
  UpperCamelCase_ :			Union[str, Any]        =							False
  UpperCamelCase_ :			List[Any]        =							False
  UpperCamelCase_ :			str        =							False



  def 				_snake_case    (   self							:       Any  ) ->		str:







       '''simple docstring'''



       A:		List[Any]        =		EfficientNetModelTester(self  )
       A:		List[Any]        =		ConfigTester(
           self				,     config_class=SCREAMING_SNAKE_CASE_				,     has_text_modality=SCREAMING_SNAKE_CASE_				,     hidden_size=37  )



  def 				_snake_case    (   self							:       Tuple  ) ->		int:







       '''simple docstring'''



       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 				_snake_case    (   self							:       List[Any]  ) ->		str:







       '''simple docstring'''



       return



  @unittest.skip(reason='''EfficientNet does not use inputs_embeds'''  )
  def 				_snake_case    (   self							:       Tuple  ) ->		Any:







       '''simple docstring'''



       pass



  @unittest.skip(reason='''EfficientNet does not support input and output embeddings'''  )
  def 				_snake_case    (   self							:       List[str]  ) ->		Union[str, Any]:







       '''simple docstring'''



       pass



  @unittest.skip(reason='''EfficientNet does not use feedforward chunking'''  )
  def 				_snake_case    (   self							:       Union[str, Any]  ) ->		int:







       '''simple docstring'''



       pass



  def 				_snake_case    (   self							:       str  ) ->		int:







       '''simple docstring'''



       A    ,						A:		List[Any]        =		self.model_tester.prepare_config_and_inputs_for_common()

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

            A:		Any        =		['''pixel_values''']
            self.assertListEqual(arg_names[:1]				,     SCREAMING_SNAKE_CASE_  )



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







       '''simple docstring'''



       A:		Union[str, Any]        =		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_  )



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







       '''simple docstring'''



       def check_hidden_states_output(SCREAMING_SNAKE_CASE_							:       int				,     SCREAMING_SNAKE_CASE_							:       Any				,     SCREAMING_SNAKE_CASE_							:       int  ):
            A:		int        =		model_class(SCREAMING_SNAKE_CASE_  )
            model.to(SCREAMING_SNAKE_CASE_  )
            model.eval()

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

            A:		Tuple        =		outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
            A:		Optional[Any]        =		sum(config.num_block_repeats  ) * 4
            self.assertEqual(len(SCREAMING_SNAKE_CASE_  )				,     SCREAMING_SNAKE_CASE_  )

            # EfficientNet'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]				,     )

       A    ,						A:		List[Any]        =		self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            A:		Union[str, Any]        =		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"]
            A:		Optional[Any]        =		True

            check_hidden_states_output(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_  )



  def 				_snake_case    (   self							:       str  ) ->		List[str]:







       '''simple docstring'''



       A:		Dict        =		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_  )



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







       '''simple docstring'''



       pass



  @slow
  def 				_snake_case    (   self							:       Any  ) ->		int:







       '''simple docstring'''



       for model_name in EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            A:		str        =		EfficientNetModel.from_pretrained(SCREAMING_SNAKE_CASE_  )
            self.assertIsNotNone(SCREAMING_SNAKE_CASE_  )




  @is_pipeline_test
  @require_vision
  @slow
  def 				_snake_case    (   self							:       int  ) ->		str:







       '''simple docstring'''



       super().test_pipeline_image_classification()






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






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







  '''simple docstring'''

  @cached_property
  def 				_snake_case    (   self							:       Dict  ) ->		List[str]:







       '''simple docstring'''



       return AutoImageProcessor.from_pretrained('''google/efficientnet-b7'''  ) if is_vision_available() else None




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







       '''simple docstring'''



       A:		str        =		EfficientNetForImageClassification.from_pretrained('''google/efficientnet-b7'''  ).to(SCREAMING_SNAKE_CASE_  )

       A:		Optional[int]        =		self.default_image_processor
       A:		Tuple        =		prepare_img()
       A:		List[Any]        =		image_processor(images=SCREAMING_SNAKE_CASE_				,     return_tensors='''pt'''  ).to(SCREAMING_SNAKE_CASE_  )

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

       # verify the logits
       A:		List[str]        =		torch.Size((1, 10_00)  )
       self.assertEqual(outputs.logits.shape				,     SCREAMING_SNAKE_CASE_  )

       A:		Union[str, Any]        =		torch.tensor([0.0001, 0.0002, 0.0002]  ).to(SCREAMING_SNAKE_CASE_  )
       self.assertTrue(torch.allclose(outputs.logits[0, :3]				,     SCREAMING_SNAKE_CASE_				,     atol=1E-4  )  )






