

'''simple docstring'''

import inspect
import unittest

from transformers import ViTMSNConfig
from transformers.testing_utils import 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 torch import nn

 from transformers import ViTMSNForImageClassification, ViTMSNModel
 from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import ViTImageProcessor




class 	A_			:






     '''simple docstring'''



     def __init__(		self			:					List[Any]					,      lowercase_			:					Optional[Any]					,      lowercase_			:					Optional[int]=13					,      lowercase_			:					Union[str, Any]=30					,      lowercase_			:					Tuple=2					,      lowercase_			:					Optional[Any]=3					,      lowercase_			:					Optional[int]=True					,      lowercase_			:					str=True					,      lowercase_			:					Tuple=32					,      lowercase_			:					Union[str, Any]=5					,      lowercase_			:					int=4					,      lowercase_			:					List[Any]=37					,      lowercase_			:					Dict="gelu"					,      lowercase_			:					str=0.1					,      lowercase_			:					Optional[int]=0.1					,      lowercase_			:					List[Any]=10					,      lowercase_			:					Union[str, Any]=0.02					,      lowercase_			:					Optional[int]=None					,      )						->     Optional[int]:
      UpperCAmelCase	:     Tuple						 =			parent
      UpperCAmelCase	:     List[Any]						 =			batch_size
      UpperCAmelCase	:     Dict						 =			image_size
      UpperCAmelCase	:     Any						 =			patch_size
      UpperCAmelCase	:     List[Any]						 =			num_channels
      UpperCAmelCase	:     List[Any]						 =			is_training
      UpperCAmelCase	:     Tuple						 =			use_labels
      UpperCAmelCase	:     List[Any]						 =			hidden_size
      UpperCAmelCase	:     Tuple						 =			num_hidden_layers
      UpperCAmelCase	:     Optional[int]						 =			num_attention_heads
      UpperCAmelCase	:     int						 =			intermediate_size
      UpperCAmelCase	:     Optional[Any]						 =			hidden_act
      UpperCAmelCase	:     str						 =			hidden_dropout_prob
      UpperCAmelCase	:     Any						 =			attention_probs_dropout_prob
      UpperCAmelCase	:     Union[str, Any]						 =			type_sequence_label_size
      UpperCAmelCase	:     Optional[Any]						 =			initializer_range
      UpperCAmelCase	:     Dict						 =			scope

      # in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
      UpperCAmelCase	:     List[str]						 =			(image_size // patch_size) ** 2
      UpperCAmelCase	:     int						 =			num_patches + 1


     def        UpperCAmelCase_	(		self			:					List[str]	)						->     Any:
      UpperCAmelCase	:     Tuple						 =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

      UpperCAmelCase	:     str						 =			None
      if self.use_labels:
       UpperCAmelCase	:     Optional[Any]						 =			ids_tensor([self.batch_size]					,      self.type_sequence_label_size	)

      UpperCAmelCase	:     int						 =			self.get_config()

      return config, pixel_values, labels


     def        UpperCAmelCase_	(		self			:					Dict	)						->     List[Any]:
      return ViTMSNConfig(
          image_size=self.image_size					,      patch_size=self.patch_size					,      num_channels=self.num_channels					,      hidden_size=self.hidden_size					,      num_hidden_layers=self.num_hidden_layers					,      num_attention_heads=self.num_attention_heads					,      intermediate_size=self.intermediate_size					,      hidden_act=self.hidden_act					,      hidden_dropout_prob=self.hidden_dropout_prob					,      attention_probs_dropout_prob=self.attention_probs_dropout_prob					,      initializer_range=self.initializer_range					,      )


     def        UpperCAmelCase_	(		self			:					str					,      lowercase_			:					List[Any]					,      lowercase_			:					List[str]					,      lowercase_			:					Optional[Any]	)						->     int:
      UpperCAmelCase	:     str						 =			ViTMSNModel(config=lowercase_	)
      model.to(lowercase_	)
      model.eval()
      UpperCAmelCase	:     Union[str, Any]						 =			model(lowercase_	)
      self.parent.assertEqual(result.last_hidden_state.shape					,      (self.batch_size, self.seq_length, self.hidden_size)	)


     def        UpperCAmelCase_	(		self			:					List[str]					,      lowercase_			:					Dict					,      lowercase_			:					Dict					,      lowercase_			:					Any	)						->     Optional[Any]:
      UpperCAmelCase	:     Tuple						 =			self.type_sequence_label_size
      UpperCAmelCase	:     int						 =			ViTMSNForImageClassification(lowercase_	)
      model.to(lowercase_	)
      model.eval()
      UpperCAmelCase	:     Any						 =			model(lowercase_					,      labels=lowercase_	)
      print('Pixel and labels shape: {pixel_values.shape}, {labels.shape}'	)
      print('Labels: {labels}'	)
      self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.type_sequence_label_size)	)

      # test greyscale images
      UpperCAmelCase	:     Optional[Any]						 =			1
      UpperCAmelCase	:     Optional[int]						 =			ViTMSNForImageClassification(lowercase_	)
      model.to(lowercase_	)
      model.eval()

      UpperCAmelCase	:     Optional[Any]						 =			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
      UpperCAmelCase	:     Any						 =			model(lowercase_	)
      self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.type_sequence_label_size)	)




     def        UpperCAmelCase_	(		self			:					List[str]	)						->     List[Any]:
      UpperCAmelCase	:     Tuple						 =			self.prepare_config_and_inputs()
      UpperCAmelCase ,			UpperCAmelCase ,			UpperCAmelCase	:     Optional[int]						 =			config_and_inputs
      UpperCAmelCase	:     Union[str, Any]						 =			{'pixel_values': pixel_values}
      return config, inputs_dict




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






     '''simple docstring'''



     UpperCAmelCase_						:     List[str]  		=  (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
     UpperCAmelCase_						:     int  		=  (
         {"""feature-extraction""": ViTMSNModel, """image-classification""": ViTMSNForImageClassification}
         if is_torch_available()
         else {}
     )

     UpperCAmelCase_						:     List[Any]  		=  False
     UpperCAmelCase_						:     Tuple  		=  False
     UpperCAmelCase_						:     Union[str, Any]  		=  False
     UpperCAmelCase_						:     Optional[int]  		=  False


     def        UpperCAmelCase_	(		self			:					Union[str, Any]	)						->     str:
      UpperCAmelCase	:     Tuple						 =			ViTMSNModelTester(self	)
      UpperCAmelCase	:     List[Any]						 =			ConfigTester(self					,      config_class=lowercase_					,      has_text_modality=lowercase_					,      hidden_size=37	)


     def        UpperCAmelCase_	(		self			:					Optional[int]	)						->     Dict:
      self.config_tester.run_common_tests()


     @unittest.skip(reason='ViTMSN does not use inputs_embeds'	)
     def        UpperCAmelCase_	(		self			:					Dict	)						->     Union[str, Any]:
      pass


     def        UpperCAmelCase_	(		self			:					Any	)						->     Any:
      UpperCAmelCase ,			UpperCAmelCase	:     Tuple						 =			self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       UpperCAmelCase	:     str						 =			model_class(lowercase_	)
       self.assertIsInstance(model.get_input_embeddings()					,      (nn.Module)	)
       UpperCAmelCase	:     Any						 =			model.get_output_embeddings()
       self.assertTrue(x is None or isinstance(lowercase_					,      nn.Linear	)	)


     def        UpperCAmelCase_	(		self			:					int	)						->     List[Any]:
      UpperCAmelCase ,			UpperCAmelCase	:     int						 =			self.model_tester.prepare_config_and_inputs_for_common()

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

       UpperCAmelCase	:     int						 =			['pixel_values']
       self.assertListEqual(arg_names[:1]					,      lowercase_	)


     def        UpperCAmelCase_	(		self			:					Union[str, Any]	)						->     Tuple:
      UpperCAmelCase	:     str						 =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowercase_	)


     def        UpperCAmelCase_	(		self			:					Union[str, Any]	)						->     Any:
      UpperCAmelCase	:     Union[str, Any]						 =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*lowercase_	)




     @slow
     def        UpperCAmelCase_	(		self			:					str	)						->     Any:
      for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       UpperCAmelCase	:     Union[str, Any]						 =			ViTMSNModel.from_pretrained(lowercase_	)
       self.assertIsNotNone(lowercase_	)




def 				UpperCamelCase(					):
 UpperCAmelCase	:     Optional[Any]						 =			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
 return image




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






     '''simple docstring'''



     @cached_property
     def        UpperCAmelCase_	(		self			:					List[str]	)						->     Union[str, Any]:
      return ViTImageProcessor.from_pretrained('facebook/vit-msn-small'	) if is_vision_available() else None




     @slow
     def        UpperCAmelCase_	(		self			:					str	)						->     List[Any]:
      torch.manual_seed(2	)
      UpperCAmelCase	:     str						 =			ViTMSNForImageClassification.from_pretrained('facebook/vit-msn-small'	).to(lowercase_	)

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

      # forward pass
      with torch.no_grad():
       UpperCAmelCase	:     Any						 =			model(**lowercase_	)

      # verify the logits
      UpperCAmelCase	:     Any						 =			torch.Size((1, 1_000)	)
      self.assertEqual(outputs.logits.shape					,      lowercase_	)

      UpperCAmelCase	:     Union[str, Any]						 =			torch.tensor([-0.0803, -0.4454, -0.2375]	).to(lowercase_	)

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







'''simple docstring'''

import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
lowercase__         =      {
    "/attention/": "/0/SelfAttention/",
    "/self_attention/": "/0/SelfAttention/",
    "/encoder_decoder_attention/": "/1/EncDecAttention/",
    "value": "v",
    "query": "q",
    "key": "k",
    "out": "o",
    "pre_self_attention_layer_norm": "0/layer_norm",
    "pre_cross_attention_layer_norm": "1/layer_norm",
    "pre_attention_layer_norm": "0/layer_norm",  # previously 1, but seems wrong
    "token_embedder": "shared",
    "encoder_norm": "final_layer_norm",
    "decoder_norm": "final_layer_norm",
    "relpos_bias/rel_embedding": "block/0/layer/0/SelfAttention/relative_attention_bias/weight",
    "router/router_weights/w/": "router/classifier/",
    "roer/roer_weights/w/": "router/classifier/",
    "logits_dense": "lm_head",
}




def 				UpperCamelCase(					UpperCAmelCase_     ):
 # 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
 # the original model
 UpperCAmelCase	:     List[Any]						 =			list(s_dict.keys()     )
 for key in keys:
  UpperCAmelCase	:     Union[str, Any]						 =			R'.*/layers_(\d+)'
  UpperCAmelCase	:     List[str]						 =			key
  if re.match(UpperCAmelCase_  ,	UpperCAmelCase_     ):
   UpperCAmelCase	:     Union[str, Any]						 =			re.sub(R'layers_(\d+)'  ,	R'block/\1/layer'  ,	UpperCAmelCase_     )

  UpperCAmelCase	:     str						 =			R'(encoder|decoder)\/'

  if re.match(UpperCAmelCase_  ,	UpperCAmelCase_     ):
   UpperCAmelCase	:     Tuple						 =			re.match(UpperCAmelCase_  ,	UpperCAmelCase_     ).groups()
   if groups[0] == "encoder":
    UpperCAmelCase	:     Union[str, Any]						 =			re.sub(R'/mlp/'  ,	R'/1/mlp/'  ,	UpperCAmelCase_     )
    UpperCAmelCase	:     List[Any]						 =			re.sub(R'/pre_mlp_layer_norm/'  ,	R'/1/layer_norm/'  ,	UpperCAmelCase_     )

   elif groups[0] == "decoder":
    UpperCAmelCase	:     Tuple						 =			re.sub(R'/mlp/'  ,	R'/2/mlp/'  ,	UpperCAmelCase_     )
    UpperCAmelCase	:     Union[str, Any]						 =			re.sub(R'/pre_mlp_layer_norm/'  ,	R'/2/layer_norm/'  ,	UpperCAmelCase_     )

        # 2. Convert other classic mappings
  for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
   if old_key in new_key:
    UpperCAmelCase	:     List[str]						 =			new_key.replace(UpperCAmelCase_  ,	UpperCAmelCase_     )

  print(F"""{key} -> {new_key}"""     )
  UpperCAmelCase	:     List[Any]						 =			s_dict.pop(UpperCAmelCase_     )

 if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
  UpperCAmelCase	:     Optional[int]						 =			s_dict[
      'encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'
  ].T
 if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
  UpperCAmelCase	:     Optional[int]						 =			s_dict[
      'decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'
  ].T

 # 3. Take extra care of the EXPERTS layer
 for key in list(s_dict.keys()     ):
  if "expert" in key:
   UpperCAmelCase	:     List[str]						 =			s_dict[key].shape[0]
   UpperCAmelCase	:     List[Any]						 =			s_dict[key]
   for idx in range(UpperCAmelCase_     ):
    UpperCAmelCase	:     Union[str, Any]						 =			expert_weihts[idx]
    print(F"""{key} -> {key.replace("expert/"  ,	"nested fstring"     )}"""     )

   s_dict.pop(UpperCAmelCase_     )

 return s_dict


lowercase__         =      {
    "NUM_ENCODER_LAYERS": "num_layers",
    "NUM_DECODER_LAYERS": "num_decoder_layers",
    "NUM_HEADS": "num_heads",
    "HEAD_DIM": "d_kv",
    "EMBED_DIM": "d_model",
    "MLP_DIM": "d_ff",
    "NUM_SELECTED_EXPERTS": "num_selected_experts",
    "NUM_ENCODER_SPARSE_LAYERS": "num_sparse_encoder_layers",
    "NUM_DECODER_SPARSE_LAYERS": "num_sparse_decoder_layers",
    "dense.MlpBlock.activations": "feed_forward_proj",
}




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 # Convert a google style config to the hugging face fromat
 import regex as re

 with open(UpperCAmelCase_  ,	'r'     ) as f:
  UpperCAmelCase	:     Union[str, Any]						 =			f.read()

 UpperCAmelCase	:     Union[str, Any]						 =			re.findall(R'(.*) = ([0-9.]*)'  ,	UpperCAmelCase_     )
 UpperCAmelCase	:     str						 =			{}
 for param, value in regex_match:
  if param in GIN_TO_CONFIG_MAPPING and value != "":
   UpperCAmelCase	:     Dict						 =			float(UpperCAmelCase_     ) if '.' in value else int(UpperCAmelCase_     )

 UpperCAmelCase	:     str						 =			re.findall(R'(.*activations) = \(\'(.*)\',\)'  ,	UpperCAmelCase_     )[0]
 UpperCAmelCase	:     Union[str, Any]						 =			str(activation[1]     )

 UpperCAmelCase	:     Optional[int]						 =			num_experts
 UpperCAmelCase	:     List[str]						 =			SwitchTransformersConfig(**UpperCAmelCase_     )
 return config




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_=None  ,	UpperCAmelCase_="./"  ,	UpperCAmelCase_=8     ):
 # Initialise PyTorch model

 print(F"""Loading flax weights from : {flax_checkpoint_path}"""     )
 UpperCAmelCase	:     List[Any]						 =			checkpoints.load_tax_checkpoint(UpperCAmelCase_     )

 if gin_file is not None:
  UpperCAmelCase	:     List[Any]						 =			convert_gin_to_config(UpperCAmelCase_  ,	UpperCAmelCase_     )
 else:
  UpperCAmelCase	:     str						 =			SwitchTransformersConfig.from_pretrained(UpperCAmelCase_     )

 UpperCAmelCase	:     str						 =			SwitchTransformersForConditionalGeneration(UpperCAmelCase_     )

 UpperCAmelCase	:     str						 =			flax_params['target']
 UpperCAmelCase	:     Union[str, Any]						 =			flatten_dict(UpperCAmelCase_  ,	sep='/'     )
 UpperCAmelCase	:     Tuple						 =			rename_keys(UpperCAmelCase_     )
 UpperCAmelCase	:     Optional[int]						 =			unflatten_dict(UpperCAmelCase_  ,	sep='/'     )

 # Load the flax params in the PT model
 load_flax_weights_in_pytorch_model(UpperCAmelCase_  ,	UpperCAmelCase_     )

 print(F"""Save PyTorch model to {pytorch_dump_path}"""     )
 pt_model.save_pretrained(UpperCAmelCase_     )


if __name__ == "__main__":
 lowercase__         =      argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--switch_t5x_checkpoint_path",
     default=None,
     type=str,
     required=True,
     help=(
         "The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the"
         " model architecture. If not provided, a `gin_file` has to be provided."
     ),
 )
 parser.add_argument(
     "--gin_file",
     default=None,
     type=str,
     required=False,
     help="Path to the gin config file. If not provided, a `config_file` has to be passed   ",
 )
 parser.add_argument(
     "--config_name", default=None, type=str, required=False, help="Config name of SwitchTransformers model."
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output pytorch model."
 )
 parser.add_argument("--num_experts", default=8, type=int, required=False, help="Number of experts")
 lowercase__         =      parser.parse_args()
 convert_flax_checkpoint_to_pytorch(
     args.switch_tax_checkpoint_path,
     args.config_name,
     args.gin_file,
     args.pytorch_dump_folder_path,
     args.num_experts,
 )




