





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


SCREAMING_SNAKE_CASE_						       =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_						       =	{
    """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json""",
    # See all Marian models at https://huggingface.co/models?filter=marian
}

class 		UpperCamelCase__			(				lowerCAmelCase_       ):



     '''simple docstring'''

     __snake_case   :     Dict		     =      "marian"
     __snake_case   :     Tuple		     =      ["past_key_values"]
     __snake_case   :     Optional[Any]		     =      {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






     def __init__(							self						:   Optional[int]						,lowerCamelCase__						:   Union[str, Any]=58101						,lowerCamelCase__						:   int=None						,lowerCamelCase__						:   List[str]=1024						,lowerCamelCase__						:   str=12						,lowerCamelCase__						:   List[str]=4096						,lowerCamelCase__						:   str=16						,lowerCamelCase__						:   str=12						,lowerCamelCase__						:   str=4096						,lowerCamelCase__						:   Optional[Any]=16						,lowerCamelCase__						:   Dict=0.0						,lowerCamelCase__						:   List[str]=0.0						,lowerCamelCase__						:   Optional[int]=True						,lowerCamelCase__						:   Tuple=True						,lowerCamelCase__						:   Union[str, Any]="gelu"						,lowerCamelCase__						:   Tuple=1024						,lowerCamelCase__						:   List[str]=0.1						,lowerCamelCase__						:   Dict=0.0						,lowerCamelCase__						:   Union[str, Any]=0.0						,lowerCamelCase__						:   Dict=0.02						,lowerCamelCase__						:   int=58100						,lowerCamelCase__						:   str=False						,lowerCamelCase__						:   Tuple=58100						,lowerCamelCase__						:   Union[str, Any]=0						,lowerCamelCase__						:   Tuple=0						,lowerCamelCase__						:   Tuple=True						,**lowerCamelCase__						:   List[Any]						,)					->		Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						vocab_size
           SCREAMING_SNAKE_CASE		=						decoder_vocab_size or vocab_size
           SCREAMING_SNAKE_CASE		=						max_position_embeddings
           SCREAMING_SNAKE_CASE		=						d_model
           SCREAMING_SNAKE_CASE		=						encoder_ffn_dim
           SCREAMING_SNAKE_CASE		=						encoder_layers
           SCREAMING_SNAKE_CASE		=						encoder_attention_heads
           SCREAMING_SNAKE_CASE		=						decoder_ffn_dim
           SCREAMING_SNAKE_CASE		=						decoder_layers
           SCREAMING_SNAKE_CASE		=						decoder_attention_heads
           SCREAMING_SNAKE_CASE		=						dropout
           SCREAMING_SNAKE_CASE		=						attention_dropout
           SCREAMING_SNAKE_CASE		=						activation_dropout
           SCREAMING_SNAKE_CASE		=						activation_function
           SCREAMING_SNAKE_CASE		=						init_std
           SCREAMING_SNAKE_CASE		=						encoder_layerdrop
           SCREAMING_SNAKE_CASE		=						decoder_layerdrop
           SCREAMING_SNAKE_CASE		=						use_cache
           SCREAMING_SNAKE_CASE		=						encoder_layers
           SCREAMING_SNAKE_CASE		=						scale_embedding  # scale factor will be sqrt(d_model) if True
           SCREAMING_SNAKE_CASE		=						share_encoder_decoder_embeddings
           super().__init__(
               pad_token_id=lowerCamelCase__						,eos_token_id=lowerCamelCase__						,is_encoder_decoder=lowerCamelCase__						,decoder_start_token_id=lowerCamelCase__						,forced_eos_token_id=lowerCamelCase__						,**lowerCamelCase__						,)

class 		UpperCamelCase__			(				lowerCAmelCase_       ):



     '''simple docstring'''

     @property
     # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
     def     SCREAMING_SNAKE_CASE__  (							self						:   Dict	)					->		Mapping[str, Mapping[int, str]]:




           '''simple docstring'''


           if self.task in ["default", "seq2seq-lm"]:
                 SCREAMING_SNAKE_CASE		=						OrderedDict(
                     [
                         ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                         ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                     ]	)

                 if self.use_past:
                       SCREAMING_SNAKE_CASE		=						{0: """batch"""}
                       SCREAMING_SNAKE_CASE		=						{0: """batch""", 1: """past_decoder_sequence + sequence"""}
                 else:
                       SCREAMING_SNAKE_CASE		=						{0: """batch""", 1: """decoder_sequence"""}
                       SCREAMING_SNAKE_CASE		=						{0: """batch""", 1: """decoder_sequence"""}

                 if self.use_past:
                       self.fill_with_past_key_values_(lowerCamelCase__						,direction="""inputs"""	)
           elif self.task == "causal-lm":
                 # TODO: figure this case out.
                 SCREAMING_SNAKE_CASE		=						OrderedDict(
                     [
                         ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                         ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                     ]	)
                 if self.use_past:
                       SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						self.num_layers
                       for i in range(lowerCamelCase__	):
                             SCREAMING_SNAKE_CASE		=						{0: """batch""", 2: """past_sequence + sequence"""}
                             SCREAMING_SNAKE_CASE		=						{0: """batch""", 2: """past_sequence + sequence"""}
           else:
                 SCREAMING_SNAKE_CASE		=						OrderedDict(
                     [
                         ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                         ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                         ("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
                         ("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
                     ]	)

           return common_inputs






     @property
     # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
     def     SCREAMING_SNAKE_CASE__  (							self						:   Tuple	)					->		Mapping[str, Mapping[int, str]]:




           '''simple docstring'''


           if self.task in ["default", "seq2seq-lm"]:
                 SCREAMING_SNAKE_CASE		=						super().outputs
           else:
                 SCREAMING_SNAKE_CASE		=						super(lowerCamelCase__						,self	).outputs
                 if self.use_past:
                       SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						self.num_layers
                       for i in range(lowerCamelCase__	):
                             SCREAMING_SNAKE_CASE		=						{0: """batch""", 2: """past_sequence + sequence"""}
                             SCREAMING_SNAKE_CASE		=						{0: """batch""", 2: """past_sequence + sequence"""}
           return common_outputs






     def     SCREAMING_SNAKE_CASE__  (							self						:   int						,lowerCamelCase__						:   PreTrainedTokenizer						,lowerCamelCase__						:   int = -1						,lowerCamelCase__						:   int = -1						,lowerCamelCase__						:   bool = False						,lowerCamelCase__						:   Optional[TensorType] = None						,)					->		Mapping[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self._generate_dummy_inputs_for_encoder_and_decoder(
               lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__	)

           # Generate decoder inputs
           SCREAMING_SNAKE_CASE		=						seq_length if not self.use_past else 1
           SCREAMING_SNAKE_CASE		=						self._generate_dummy_inputs_for_encoder_and_decoder(
               lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						{F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
           SCREAMING_SNAKE_CASE		=						dict(**lowerCamelCase__						,**lowerCamelCase__	)

           if self.use_past:
                 if not is_torch_available():
                       raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""	)
                 else:
                       import torch
                 SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						common_inputs["""input_ids"""].shape
                 SCREAMING_SNAKE_CASE		=						common_inputs["""decoder_input_ids"""].shape[1]
                 SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						self.num_attention_heads
                 SCREAMING_SNAKE_CASE		=						(
                     batch,
                     num_encoder_attention_heads,
                     encoder_seq_length,
                     self._config.hidden_size // num_encoder_attention_heads,
                 )
                 SCREAMING_SNAKE_CASE		=						decoder_seq_length + 3
                 SCREAMING_SNAKE_CASE		=						(
                     batch,
                     num_decoder_attention_heads,
                     decoder_past_length,
                     self._config.hidden_size // num_decoder_attention_heads,
                 )

                 SCREAMING_SNAKE_CASE		=						torch.cat(
                     [common_inputs["""decoder_attention_mask"""], torch.ones(lowerCamelCase__						,lowerCamelCase__	)]						,dim=1	)

                 SCREAMING_SNAKE_CASE		=						[]
                 # If the number of encoder and decoder layers are present in the model configuration, both are considered
                 SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						self.num_layers
                 SCREAMING_SNAKE_CASE		=						min(lowerCamelCase__						,lowerCamelCase__	)
                 SCREAMING_SNAKE_CASE		=						max(lowerCamelCase__						,lowerCamelCase__	) - min_num_layers
                 SCREAMING_SNAKE_CASE		=						"""encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

                 for _ in range(lowerCamelCase__	):
                       common_inputs["past_key_values"].append(
                           (
                               torch.zeros(lowerCamelCase__	),
                               torch.zeros(lowerCamelCase__	),
                               torch.zeros(lowerCamelCase__	),
                               torch.zeros(lowerCamelCase__	),
                           )	)
                 # TODO: test this.
                 SCREAMING_SNAKE_CASE		=						encoder_shape if remaining_side_name == """encoder""" else decoder_shape
                 for _ in range(lowerCamelCase__						,lowerCamelCase__	):
                       common_inputs["past_key_values"].append((torch.zeros(lowerCamelCase__	), torch.zeros(lowerCamelCase__	))	)
           return common_inputs






     def     SCREAMING_SNAKE_CASE__  (							self						:   int						,lowerCamelCase__						:   PreTrainedTokenizer						,lowerCamelCase__						:   int = -1						,lowerCamelCase__						:   int = -1						,lowerCamelCase__						:   bool = False						,lowerCamelCase__						:   Optional[TensorType] = None						,)					->		Mapping[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self._generate_dummy_inputs_for_encoder_and_decoder(
               lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__	)

           if self.use_past:
                 if not is_torch_available():
                       raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""	)
                 else:
                       import torch
                 SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						common_inputs["""input_ids"""].shape
                 # Not using the same length for past_key_values
                 SCREAMING_SNAKE_CASE		=						seqlen + 2
                 SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						self.num_layers
                 SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						self.num_attention_heads
                 SCREAMING_SNAKE_CASE		=						(
                     batch,
                     num_encoder_attention_heads,
                     past_key_values_length,
                     self._config.hidden_size // num_encoder_attention_heads,
                 )

                 SCREAMING_SNAKE_CASE		=						common_inputs["""attention_mask"""].dtype
                 SCREAMING_SNAKE_CASE		=						torch.cat(
                     [common_inputs["""attention_mask"""], torch.ones(lowerCamelCase__						,lowerCamelCase__						,dtype=lowerCamelCase__	)]						,dim=1	)
                 SCREAMING_SNAKE_CASE		=						[
                     (torch.zeros(lowerCamelCase__	), torch.zeros(lowerCamelCase__	)) for _ in range(lowerCamelCase__	)
                 ]
           return common_inputs






     def     SCREAMING_SNAKE_CASE__  (							self						:   str						,lowerCamelCase__						:   PreTrainedTokenizer						,lowerCamelCase__						:   int = -1						,lowerCamelCase__						:   int = -1						,lowerCamelCase__						:   bool = False						,lowerCamelCase__						:   Optional[TensorType] = None						,)					->		Mapping[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						compute_effective_axis_dimension(
               lowerCamelCase__						,fixed_dimension=OnnxConfig.default_fixed_batch						,num_token_to_add=0	)

           # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
           SCREAMING_SNAKE_CASE		=						tokenizer.num_special_tokens_to_add(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						compute_effective_axis_dimension(
               lowerCamelCase__						,fixed_dimension=OnnxConfig.default_fixed_sequence						,num_token_to_add=lowerCamelCase__	)

           # Generate dummy inputs according to compute batch and sequence
           SCREAMING_SNAKE_CASE		=						[""" """.join([tokenizer.unk_token]	) * seq_length] * batch_size
           SCREAMING_SNAKE_CASE		=						dict(tokenizer(lowerCamelCase__						,return_tensors=lowerCamelCase__	)	)
           return common_inputs






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[Any]						,lowerCamelCase__						:   PreTrainedTokenizer						,lowerCamelCase__						:   int = -1						,lowerCamelCase__						:   int = -1						,lowerCamelCase__						:   bool = False						,lowerCamelCase__						:   Optional[TensorType] = None						,)					->		Mapping[str, Any]:




           '''simple docstring'''


           if self.task in ["default", "seq2seq-lm"]:
                 SCREAMING_SNAKE_CASE		=						self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                     lowerCamelCase__						,batch_size=lowerCamelCase__						,seq_length=lowerCamelCase__						,is_pair=lowerCamelCase__						,framework=lowerCamelCase__	)

           else:
                 SCREAMING_SNAKE_CASE		=						self._generate_dummy_inputs_for_causal_lm(
                     lowerCamelCase__						,batch_size=lowerCamelCase__						,seq_length=lowerCamelCase__						,is_pair=lowerCamelCase__						,framework=lowerCamelCase__	)

           return common_inputs






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[Any]						,lowerCamelCase__						:   List[Any]						,lowerCamelCase__						:   int						,lowerCamelCase__						:   Union[str, Any]						,lowerCamelCase__						:   str	)					->		List[str]:




           '''simple docstring'''


           if self.task in ["default", "seq2seq-lm"]:
                 SCREAMING_SNAKE_CASE		=						super()._flatten_past_key_values_(lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__	)
           else:
                 SCREAMING_SNAKE_CASE		=						super(lowerCamelCase__						,self	)._flatten_past_key_values_(
                     lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__	)






     @property
     def     SCREAMING_SNAKE_CASE__  (							self						:   List[Any]	)					->		float:




           '''simple docstring'''


           return 1e-4









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 		UpperCamelCase__			(				unittest.TestCase       ):



     '''simple docstring'''

     def __init__(							self						:   Optional[int]						,lowerCamelCase__						:   List[str]						,lowerCamelCase__						:   Optional[Any]=3						,lowerCamelCase__						:   List[str]=32						,lowerCamelCase__						:   List[Any]=3						,lowerCamelCase__						:   str=10						,lowerCamelCase__						:   Any=[10, 20, 30, 40]						,lowerCamelCase__						:   Optional[Any]=[1, 1, 2, 1]						,lowerCamelCase__						:   Union[str, Any]=True						,lowerCamelCase__						:   int=True						,lowerCamelCase__						:   Tuple="relu"						,lowerCamelCase__						:   Dict=3						,lowerCamelCase__						:   Optional[int]=None						,)					->		Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						parent
           SCREAMING_SNAKE_CASE		=						batch_size
           SCREAMING_SNAKE_CASE		=						image_size
           SCREAMING_SNAKE_CASE		=						num_channels
           SCREAMING_SNAKE_CASE		=						embeddings_size
           SCREAMING_SNAKE_CASE		=						hidden_sizes
           SCREAMING_SNAKE_CASE		=						depths
           SCREAMING_SNAKE_CASE		=						is_training
           SCREAMING_SNAKE_CASE		=						use_labels
           SCREAMING_SNAKE_CASE		=						hidden_act
           SCREAMING_SNAKE_CASE		=						num_labels
           SCREAMING_SNAKE_CASE		=						scope
           SCREAMING_SNAKE_CASE		=						len(lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Tuple	)					->		Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

           SCREAMING_SNAKE_CASE		=						self.get_config()

           return config, pixel_values






     def     SCREAMING_SNAKE_CASE__  (							self						:   str	)					->		Optional[int]:




           '''simple docstring'''


           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     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]						,lowerCamelCase__						:   int						,lowerCamelCase__						:   Union[str, Any]	)					->		Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						FlaxRegNetModel(config=lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						model(lowerCamelCase__	)

           # 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     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]						,lowerCamelCase__						:   Optional[int]						,lowerCamelCase__						:   Optional[Any]	)					->		Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.num_labels
           SCREAMING_SNAKE_CASE		=						FlaxRegNetForImageClassification(config=lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						model(lowerCamelCase__	)
           self.parent.assertEqual(result.logits.shape						,(self.batch_size, self.num_labels)	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   str	)					->		int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.prepare_config_and_inputs()
           SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						config_and_inputs
           SCREAMING_SNAKE_CASE		=						{"""pixel_values""": pixel_values}
           return config, inputs_dict

@require_flax
class 		UpperCamelCase__			(				lowerCAmelCase_							,				unittest.TestCase       ):



     '''simple docstring'''

     __snake_case   :     Union[str, Any]		     =      (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

     __snake_case   :     Tuple		     =      False
     __snake_case   :     int		     =      False
     __snake_case   :     Tuple		     =      False






     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		None:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						FlaxRegNetModelTester(self	)
           SCREAMING_SNAKE_CASE		=						ConfigTester(self						,config_class=lowerCamelCase__						,has_text_modality=lowerCamelCase__	)






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




           '''simple docstring'''


           return






     def     SCREAMING_SNAKE_CASE__  (							self						:   Tuple	)					->		List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[int]	)					->		int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__	)






     @unittest.skip(reason="""RegNet does not use inputs_embeds"""	)
     def     SCREAMING_SNAKE_CASE__  (							self						:   str	)					->		Any:




           '''simple docstring'''


           pass






     @unittest.skip(reason="""RegNet does not support input and output embeddings"""	)
     def     SCREAMING_SNAKE_CASE__  (							self						:   str	)					->		Any:




           '''simple docstring'''


           pass






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs_for_common()

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

                 SCREAMING_SNAKE_CASE		=						["""pixel_values"""]
                 self.assertListEqual(arg_names[:1]						,lowerCamelCase__	)






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




           '''simple docstring'''


           def check_hidden_states_output(lowerCamelCase__						:   Tuple						,lowerCamelCase__						:   Union[str, Any]						,lowerCamelCase__						:   Optional[int]	):
                 SCREAMING_SNAKE_CASE		=						model_class(lowerCamelCase__	)

                 SCREAMING_SNAKE_CASE		=						model(**self._prepare_for_class(lowerCamelCase__						,lowerCamelCase__	)	)

                 SCREAMING_SNAKE_CASE		=						outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                 SCREAMING_SNAKE_CASE		=						self.model_tester.num_stages
                 self.assertEqual(len(lowerCamelCase__	)						,expected_num_stages + 1	)

           SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 SCREAMING_SNAKE_CASE		=						True
                 check_hidden_states_output(lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__	)

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 SCREAMING_SNAKE_CASE		=						True

                 check_hidden_states_output(lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Any	)					->		List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs_for_common()

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

                       @jax.jit
                       def model_jitted(lowerCamelCase__						:   Dict						,**lowerCamelCase__						:   Optional[Any]	):
                             return model(pixel_values=lowerCamelCase__						,**lowerCamelCase__	)

                       with self.subTest("""JIT Enabled"""	):
                             SCREAMING_SNAKE_CASE		=						model_jitted(**lowerCamelCase__	).to_tuple()

                       with self.subTest("""JIT Disabled"""	):
                             with jax.disable_jit():
                                   SCREAMING_SNAKE_CASE		=						model_jitted(**lowerCamelCase__	).to_tuple()

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



def        __lowercase       (				)			->       Union[str, Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     )
      return image



@require_flax
class 		UpperCamelCase__			(				unittest.TestCase       ):



     '''simple docstring'''

     @cached_property
     def     SCREAMING_SNAKE_CASE__  (							self						:   int	)					->		str:




           '''simple docstring'''


           return AutoImageProcessor.from_pretrained("""facebook/regnet-y-040"""	) if is_vision_available() else None






     @slow
     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[int]	)					->		List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						FlaxRegNetForImageClassification.from_pretrained("""facebook/regnet-y-040"""	)

           SCREAMING_SNAKE_CASE		=						self.default_image_processor
           SCREAMING_SNAKE_CASE		=						prepare_img()
           SCREAMING_SNAKE_CASE		=						image_processor(images=lowerCamelCase__						,return_tensors="""np"""	)

           SCREAMING_SNAKE_CASE		=						model(**lowerCamelCase__	)

           # verify the logits
           SCREAMING_SNAKE_CASE		=						(1, 1000)
           self.assertEqual(outputs.logits.shape						,lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						jnp.array([-0.4180, -1.5051, -3.4836]	)

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


