"""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 			_snake_case		:







				"""simple docstring"""






				def __init__(   self				:				Tuple   ,     _A				:				Optional[Any]   ,     _A				:				List[Any]=3   ,     _A				:				Any=3_2   ,     _A				:				Union[str, Any]=3   ,     _A				:				Optional[int]=1_0   ,     _A				:				List[Any]=[1_0, 2_0, 3_0, 4_0]   ,     _A				:				Optional[int]=[1, 1, 2, 1]   ,     _A				:				Any=True   ,     _A				:				Union[str, Any]=True   ,     _A				:				List[str]="relu"   ,     _A				:				Union[str, Any]=3   ,     _A				:				Optional[Any]=None   ,     ):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:    int      	=     parent
											_SCREAMING_SNAKE_CASE		:    List[Any]      	=     batch_size
											_SCREAMING_SNAKE_CASE		:    List[Any]      	=     image_size
											_SCREAMING_SNAKE_CASE		:    Optional[int]      	=     num_channels
											_SCREAMING_SNAKE_CASE		:    Optional[Any]      	=     embeddings_size
											_SCREAMING_SNAKE_CASE		:    Dict      	=     hidden_sizes
											_SCREAMING_SNAKE_CASE		:    Optional[Any]      	=     depths
											_SCREAMING_SNAKE_CASE		:    Dict      	=     is_training
											_SCREAMING_SNAKE_CASE		:    Optional[int]      	=     use_labels
											_SCREAMING_SNAKE_CASE		:    int      	=     hidden_act
											_SCREAMING_SNAKE_CASE		:    List[Any]      	=     num_labels
											_SCREAMING_SNAKE_CASE		:    Optional[int]      	=     scope
											_SCREAMING_SNAKE_CASE		:    List[str]      	=     len(lowercase_)





				def 		_lowerCAmelCase		(   self				:				Any):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:    List[Any]      	=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

											_SCREAMING_SNAKE_CASE		:    str      	=     None
											if self.use_labels:
																		_SCREAMING_SNAKE_CASE		:    int      	=     ids_tensor([self.batch_size]   ,     self.num_labels)

											_SCREAMING_SNAKE_CASE		:    Tuple      	=     self.get_config()

											return config, pixel_values, labels





				def 		_lowerCAmelCase		(   self				:				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   ,     )





				def 		_lowerCAmelCase		(   self				:				List[Any]   ,     _A				:				List[Any]   ,     _A				:				Tuple   ,     _A				:				str):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:    Union[str, Any]      	=     RegNetModel(config=lowercase_)
											model.to(lowercase_)
											model.eval()
											_SCREAMING_SNAKE_CASE		:    List[str]      	=     model(lowercase_)
											# 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 // 3_2, self.image_size // 3_2)   ,     )





				def 		_lowerCAmelCase		(   self				:				List[str]   ,     _A				:				Any   ,     _A				:				Union[str, Any]   ,     _A				:				str):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:    List[str]      	=     self.num_labels
											_SCREAMING_SNAKE_CASE		:    Optional[int]      	=     RegNetForImageClassification(lowercase_)
											model.to(lowercase_)
											model.eval()
											_SCREAMING_SNAKE_CASE		:    str      	=     model(lowercase_   ,     labels=lowercase_)
											self.parent.assertEqual(result.logits.shape   ,     (self.batch_size, self.num_labels))





				def 		_lowerCAmelCase		(   self				:				Optional[int]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:    Optional[Any]      	=     self.prepare_config_and_inputs()
											_SCREAMING_SNAKE_CASE	,					_SCREAMING_SNAKE_CASE	,					_SCREAMING_SNAKE_CASE		:    Optional[int]      	=     config_and_inputs
											_SCREAMING_SNAKE_CASE		:    Tuple      	=     {"""pixel_values""": pixel_values}
											return config, inputs_dict




@require_torch
class 			_snake_case		(     __a ,							__a ,							unittest.TestCase			):







				"""simple docstring"""






				a      =    (RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
				a      =    (
				    {"feature-extraction": RegNetModel, "image-classification": RegNetForImageClassification}
				    if is_torch_available()
				    else {}
				)

				a      =    False
				a      =    False
				a      =    False
				a      =    False





				def 		_lowerCAmelCase		(   self				:				str):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:    List[str]      	=     RegNetModelTester(self)
											_SCREAMING_SNAKE_CASE		:    Optional[Any]      	=     ConfigTester(self   ,     config_class=lowercase_   ,     has_text_modality=lowercase_)





				def 		_lowerCAmelCase		(   self				:				List[Any]):



											"""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 		_lowerCAmelCase		(   self				:				List[str]):



											"""simple docstring"""







											return





				@unittest.skip(reason="""RegNet does not use inputs_embeds""")
				def 		_lowerCAmelCase		(   self				:				Any):



											"""simple docstring"""







											pass





				@unittest.skip(reason="""RegNet does not support input and output embeddings""")
				def 		_lowerCAmelCase		(   self				:				int):



											"""simple docstring"""







											pass





				def 		_lowerCAmelCase		(   self				:				Union[str, Any]):



											"""simple docstring"""







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

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

																		_SCREAMING_SNAKE_CASE		:    List[Any]      	=     ["""pixel_values"""]
																		self.assertListEqual(arg_names[:1]   ,     lowercase_)





				def 		_lowerCAmelCase		(   self				:				Union[str, Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:    List[str]      	=     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*lowercase_)





				def 		_lowerCAmelCase		(   self				:				List[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE	,					_SCREAMING_SNAKE_CASE		:    Any      	=     self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		_SCREAMING_SNAKE_CASE		:    Tuple      	=     model_class(config=lowercase_)
																		for name, module in model.named_modules():
																									if isinstance(lowercase_   ,     (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				:				List[str]):



											"""simple docstring"""







											def check_hidden_states_output(_A				:				Optional[Any]   ,     _A				:				Union[str, Any]   ,     _A				:				Tuple):
																		_SCREAMING_SNAKE_CASE		:    Dict      	=     model_class(lowercase_)
																		model.to(lowercase_)
																		model.eval()

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

																		_SCREAMING_SNAKE_CASE		:    List[Any]      	=     outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																		_SCREAMING_SNAKE_CASE		:    Union[str, Any]      	=     self.model_tester.num_stages
																		self.assertEqual(len(lowercase_)   ,     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]   ,     )

											_SCREAMING_SNAKE_CASE	,					_SCREAMING_SNAKE_CASE		:    Dict      	=     self.model_tester.prepare_config_and_inputs_for_common()
											_SCREAMING_SNAKE_CASE		:    Tuple      	=     ["""basic""", """bottleneck"""]
											for model_class in self.all_model_classes:
																		for layer_type in layers_type:
																									_SCREAMING_SNAKE_CASE		:    Optional[int]      	=     layer_type
																									_SCREAMING_SNAKE_CASE		:    Union[str, Any]      	=     True
																									check_hidden_states_output(lowercase_   ,     lowercase_   ,     lowercase_)

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

																									check_hidden_states_output(lowercase_   ,     lowercase_   ,     lowercase_)





				def 		_lowerCAmelCase		(   self				:				Union[str, Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:    List[Any]      	=     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*lowercase_)





				@slow
				def 		_lowerCAmelCase		(   self				:				Dict):



											"""simple docstring"""







											for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		_SCREAMING_SNAKE_CASE		:    Any      	=     RegNetModel.from_pretrained(lowercase_)
																		self.assertIsNotNone(lowercase_)



def   lowerCamelCase_()->     Optional[Any]:
							_SCREAMING_SNAKE_CASE		:    List[str]      	=     Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
							return image




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







				"""simple docstring"""






				@cached_property
				def 		_lowerCAmelCase		(   self				:				str):



											"""simple docstring"""







											return (
											    AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0])
											    if is_vision_available()
											    else None
											)





				@slow
				def 		_lowerCAmelCase		(   self				:				Dict):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:    List[Any]      	=     RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(lowercase_)

											_SCREAMING_SNAKE_CASE		:    List[str]      	=     self.default_image_processor
											_SCREAMING_SNAKE_CASE		:    int      	=     prepare_img()
											_SCREAMING_SNAKE_CASE		:    List[Any]      	=     image_processor(images=lowercase_   ,     return_tensors="""pt""").to(lowercase_)

											# forward pass
											with torch.no_grad():
																		_SCREAMING_SNAKE_CASE		:    int      	=     model(**lowercase_)

											# verify the logits
											_SCREAMING_SNAKE_CASE		:    List[Any]      	=     torch.Size((1, 1_0_0_0))
											self.assertEqual(outputs.logits.shape   ,     lowercase_)

											_SCREAMING_SNAKE_CASE		:    Dict      	=     torch.tensor([-0.4_180, -1.5_051, -3.4_836]).to(lowercase_)

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



'''simple docstring'''


import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration

def   A_      (       SCREAMING_SNAKE_CASE_						)			->List[Any]:
  lowercase_     =       [
      """encoder.version""",
      """decoder.version""",
      """model.encoder.version""",
      """model.decoder.version""",
      """decoder.output_projection.weight""",
      """_float_tensor""",
      """encoder.embed_positions._float_tensor""",
      """decoder.embed_positions._float_tensor""",
  ]
  for k in ignore_keys:
    state_dict.pop(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)

def   A_      (       SCREAMING_SNAKE_CASE_						)			->Optional[int]:
  lowercase_     =       list(s_dict.keys()						)
  for key in keys:
    if "transformer_layers" in key:
      lowercase_     =       s_dict.pop(SCREAMING_SNAKE_CASE_						)
    elif "subsample" in key:
      lowercase_     =       s_dict.pop(SCREAMING_SNAKE_CASE_						)

def   A_      (       SCREAMING_SNAKE_CASE_						)			->Dict:
  lowercase_     ,	lowercase_     =       emb.weight.shape
  lowercase_     =       nn.Linear(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	bias=SCREAMING_SNAKE_CASE_						)
  lowercase_     =       emb.weight.data
  return lin_layer

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Any:
  lowercase_     =       torch.load(SCREAMING_SNAKE_CASE_ ,	map_location="""cpu"""						)
  lowercase_     =       mam_aaa["""args"""]
  lowercase_     =       mam_aaa["""model"""]
  lowercase_     =       state_dict["""decoder.output_projection.weight"""]

  remove_ignore_keys_(SCREAMING_SNAKE_CASE_						)
  rename_keys(SCREAMING_SNAKE_CASE_						)

  lowercase_     =       state_dict["""decoder.embed_tokens.weight"""].shape[0]

  lowercase_     =       args.share_decoder_input_output_embed

  lowercase_     =       [int(SCREAMING_SNAKE_CASE_						) for i in args.conv_kernel_sizes.split(""","""						)]
  lowercase_     =       SpeechaTextConfig(
      vocab_size=SCREAMING_SNAKE_CASE_ ,	max_source_positions=args.max_source_positions ,	max_target_positions=args.max_target_positions ,	encoder_layers=args.encoder_layers ,	decoder_layers=args.decoder_layers ,	encoder_attention_heads=args.encoder_attention_heads ,	decoder_attention_heads=args.decoder_attention_heads ,	encoder_ffn_dim=args.encoder_ffn_embed_dim ,	decoder_ffn_dim=args.decoder_ffn_embed_dim ,	d_model=args.encoder_embed_dim ,	dropout=args.dropout ,	attention_dropout=args.attention_dropout ,	activation_dropout=args.activation_dropout ,	activation_function="""relu""" ,	num_conv_layers=len(SCREAMING_SNAKE_CASE_						) ,	conv_channels=args.conv_channels ,	conv_kernel_sizes=SCREAMING_SNAKE_CASE_ ,	input_feat_per_channel=args.input_feat_per_channel ,	input_channels=args.input_channels ,	tie_word_embeddings=SCREAMING_SNAKE_CASE_ ,	num_beams=5 ,	max_length=2_00 ,	use_cache=SCREAMING_SNAKE_CASE_ ,	decoder_start_token_id=2 ,	early_stopping=SCREAMING_SNAKE_CASE_ ,	)

  lowercase_     =       SpeechaTextForConditionalGeneration(SCREAMING_SNAKE_CASE_						)
  lowercase_     ,	lowercase_     =       model.model.load_state_dict(SCREAMING_SNAKE_CASE_ ,	strict=SCREAMING_SNAKE_CASE_						)
  if len(SCREAMING_SNAKE_CASE_						) > 0 and not set(SCREAMING_SNAKE_CASE_						) <= {
      "encoder.embed_positions.weights",
      "decoder.embed_positions.weights",
  }:
    raise ValueError(
        """Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,"""
        f""" but all the following weights are missing {missing}"""						)

  if tie_embeds:
    lowercase_     =       make_linear_from_emb(model.model.decoder.embed_tokens						)
  else:
    lowercase_     =       lm_head_weights

  model.save_pretrained(SCREAMING_SNAKE_CASE_						)


if __name__ == "__main__":
       __snake_case						  =		argparse.ArgumentParser()
       # Required parameters
       parser.add_argument("""--fairseq_path""", type=str, help="""Path to the fairseq model (.pt) file.""")
       parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
       __snake_case						  =		parser.parse_args()
       convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)

