





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_		:







  def __init__(		self			,     __lowercase			,     __lowercase=3			,     __lowercase=3_2			,     __lowercase=3			,     __lowercase=1_0			,     __lowercase=[1_0, 2_0, 3_0, 4_0]			,     __lowercase=[1, 1, 2, 1]			,     __lowercase=True			,     __lowercase=True			,     __lowercase="relu"			,     __lowercase=3			,     __lowercase=None			,     )				->   Dict:
    lowerCamelCase   : Any								=parent
    lowerCamelCase   : int								=batch_size
    lowerCamelCase   : Union[str, Any]								=image_size
    lowerCamelCase   : List[str]								=num_channels
    lowerCamelCase   : List[str]								=embeddings_size
    lowerCamelCase   : Optional[int]								=hidden_sizes
    lowerCamelCase   : Any								=depths
    lowerCamelCase   : Optional[int]								=is_training
    lowerCamelCase   : int								=use_labels
    lowerCamelCase   : Optional[int]								=hidden_act
    lowerCamelCase   : str								=num_labels
    lowerCamelCase   : Dict								=scope
    lowerCamelCase   : Dict								=len(__lowercase   )







  def 			__lowercase   (		self   )				->   Any:
    lowerCamelCase   : List[str]								=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

    lowerCamelCase   : Tuple								=self.get_config()

    return config, pixel_values, labels







  def 			__lowercase   (		self   )				->   int:
    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 			__lowercase   (		self			,     __lowercase			,     __lowercase			,     __lowercase   )				->   List[str]:
    lowerCamelCase   : Union[str, Any]								=RegNetModel(config=__lowercase   )
    model.to(__lowercase   )
    model.eval()
    lowerCamelCase   : 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 			__lowercase   (		self			,     __lowercase			,     __lowercase			,     __lowercase   )				->   List[str]:
    lowerCamelCase   : List[Any]								=self.num_labels
    lowerCamelCase   : int								=RegNetForImageClassification(__lowercase   )
    model.to(__lowercase   )
    model.eval()
    lowerCamelCase   : Optional[Any]								=model(__lowercase			,     labels=__lowercase   )
    self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.num_labels)   )







  def 			__lowercase   (		self   )				->   int:
    lowerCamelCase   : Union[str, Any]								=self.prepare_config_and_inputs()
    lowerCamelCase   : Any								=config_and_inputs
    lowerCamelCase   : Dict								={'''pixel_values''': pixel_values}
    return config, inputs_dict

@require_torch
class        snake_case_		(			_A						,					_A						,					unittest.TestCase):
  lowerCamelCase    :Optional[int]						=     (RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
  lowerCamelCase    :int						=     (
      {"feature-extraction": RegNetModel, "image-classification": RegNetForImageClassification}
      if is_torch_available()
      else {}
  )

  lowerCamelCase    :Tuple						=     False
  lowerCamelCase    :List[Any]						=     False
  lowerCamelCase    :Optional[int]						=     False
  lowerCamelCase    :Tuple						=     False







  def 			__lowercase   (		self   )				->   Optional[int]:
    lowerCamelCase   : Union[str, Any]								=RegNetModelTester(self   )
    lowerCamelCase   : int								=ConfigTester(self			,     config_class=__lowercase			,     has_text_modality=__lowercase   )







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







  @unittest.skip(reason='''RegNet does not use inputs_embeds'''   )
  def 			__lowercase   (		self   )				->   List[Any]:
    pass







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







  def 			__lowercase   (		self   )				->   List[str]:
    lowerCamelCase   : Union[str, Any]								=self.model_tester.prepare_config_and_inputs_for_common()

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

      lowerCamelCase   : List[Any]								=['''pixel_values''']
      self.assertListEqual(arg_names[:1]			,     __lowercase   )







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







  def 			__lowercase   (		self   )				->   Any:
    lowerCamelCase   : Dict								=self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      lowerCamelCase   : int								=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 			__lowercase   (		self   )				->   Dict:
    def check_hidden_states_output(__lowercase			,     __lowercase			,     __lowercase   ):
      lowerCamelCase   : Optional[Any]								=model_class(__lowercase   )
      model.to(__lowercase   )
      model.eval()

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

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

      lowerCamelCase   : Tuple								=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]			,     )

    lowerCamelCase   : Union[str, Any]								=self.model_tester.prepare_config_and_inputs_for_common()
    lowerCamelCase   : Optional[Any]								=['''basic''', '''bottleneck''']
    for model_class in self.all_model_classes:
      for layer_type in layers_type:
        lowerCamelCase   : Union[str, Any]								=layer_type
        lowerCamelCase   : int								=True
        check_hidden_states_output(__lowercase			,     __lowercase			,     __lowercase   )

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

        check_hidden_states_output(__lowercase			,     __lowercase			,     __lowercase   )







  def 			__lowercase   (		self   )				->   List[Any]:
    lowerCamelCase   : List[Any]								=self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*__lowercase   )







  @slow
  def 			__lowercase   (		self   )				->   Any:
    for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      lowerCamelCase   : Optional[int]								=RegNetModel.from_pretrained(__lowercase   )
      self.assertIsNotNone(__lowercase   )


def 					A__	(       )   ->     Optional[Any]:
  lowerCamelCase   : Union[str, Any]								=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
  return image



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







  @cached_property
  def 			__lowercase   (		self   )				->   Tuple:
    return (
        AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )
        if is_vision_available()
        else None
    )







  @slow
  def 			__lowercase   (		self   )				->   List[Any]:
    lowerCamelCase   : Optional[Any]								=RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]   ).to(__lowercase   )

    lowerCamelCase   : List[Any]								=self.default_image_processor
    lowerCamelCase   : List[Any]								=prepare_img()
    lowerCamelCase   : List[str]								=image_processor(images=__lowercase			,     return_tensors='''pt'''   ).to(__lowercase   )

    # forward pass
    with torch.no_grad():
      lowerCamelCase   : Union[str, Any]								=model(**__lowercase   )

    # verify the logits
    lowerCamelCase   : List[str]								=torch.Size((1, 1_0_0_0)   )
    self.assertEqual(outputs.logits.shape			,     __lowercase   )

    lowerCamelCase   : List[Any]								=torch.tensor([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6]   ).to(__lowercase   )

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








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
snake_case_							=			{
    '''/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 					A__	(       SCREAMING_SNAKE_CASE_		)   ->     int:
		# 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
		# the original model
		lowerCamelCase   : List[Any]								=list(s_dict.keys()		)
		for key in keys:
				lowerCamelCase   : Dict								=R'''.*/layers_(\d+)'''
				lowerCamelCase   : Optional[int]								=key
				if re.match(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		):
						lowerCamelCase   : Optional[Any]								=re.sub(R'''layers_(\d+)'''							,       R'''block/\1/layer'''							,       SCREAMING_SNAKE_CASE_		)

				lowerCamelCase   : Dict								=R'''(encoder|decoder)\/'''

				if re.match(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		):
						lowerCamelCase   : Dict								=re.match(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		).groups()
						if groups[0] == "encoder":
								lowerCamelCase   : Dict								=re.sub(R'''/mlp/'''							,       R'''/1/mlp/'''							,       SCREAMING_SNAKE_CASE_		)
								lowerCamelCase   : Optional[int]								=re.sub(R'''/pre_mlp_layer_norm/'''							,       R'''/1/layer_norm/'''							,       SCREAMING_SNAKE_CASE_		)

						elif groups[0] == "decoder":
								lowerCamelCase   : List[str]								=re.sub(R'''/mlp/'''							,       R'''/2/mlp/'''							,       SCREAMING_SNAKE_CASE_		)
								lowerCamelCase   : str								=re.sub(R'''/pre_mlp_layer_norm/'''							,       R'''/2/layer_norm/'''							,       SCREAMING_SNAKE_CASE_		)

        # 2. Convert other classic mappings
				for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
						if old_key in new_key:
								lowerCamelCase   : Dict								=new_key.replace(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)

				print(F"{key} -> {new_key}"		)
				lowerCamelCase   : Optional[Any]								=s_dict.pop(SCREAMING_SNAKE_CASE_		)

		if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
				lowerCamelCase   : Dict								=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:
				lowerCamelCase   : Union[str, Any]								=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:
						lowerCamelCase   : List[Any]								=s_dict[key].shape[0]
						lowerCamelCase   : int								=s_dict[key]
						for idx in range(SCREAMING_SNAKE_CASE_		):
								lowerCamelCase   : Tuple								=expert_weihts[idx]
								print(F"{key} -> {key.replace('expert/'							,       'nested fstring'		)}"		)

						s_dict.pop(SCREAMING_SNAKE_CASE_		)

		return s_dict


snake_case_							=			{
    '''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 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)   ->     Union[str, Any]:
		# Convert a google style config to the hugging face fromat
		import regex as re

		with open(SCREAMING_SNAKE_CASE_							,       '''r'''		) as f:
				lowerCamelCase   : str								=f.read()

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

		lowerCamelCase   : int								=re.findall(R'''(.*activations) = \(\'(.*)\',\)'''							,       SCREAMING_SNAKE_CASE_		)[0]
		lowerCamelCase   : Any								=str(activation[1]		)

		lowerCamelCase   : Tuple								=num_experts
		lowerCamelCase   : Any								=SwitchTransformersConfig(**SCREAMING_SNAKE_CASE_		)
		return config


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_=None							,       SCREAMING_SNAKE_CASE_="./"							,       SCREAMING_SNAKE_CASE_=8		)   ->     Dict:
		# Initialise PyTorch model

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

		if gin_file is not None:
				lowerCamelCase   : Dict								=convert_gin_to_config(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)
		else:
				lowerCamelCase   : Optional[int]								=SwitchTransformersConfig.from_pretrained(SCREAMING_SNAKE_CASE_		)

		lowerCamelCase   : Any								=SwitchTransformersForConditionalGeneration(SCREAMING_SNAKE_CASE_		)

		lowerCamelCase   : int								=flax_params['''target''']
		lowerCamelCase   : Optional[int]								=flatten_dict(SCREAMING_SNAKE_CASE_							,       sep='''/'''		)
		lowerCamelCase   : str								=rename_keys(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase   : List[str]								=unflatten_dict(SCREAMING_SNAKE_CASE_							,       sep='''/'''		)

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

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


if __name__ == "__main__":
				snake_case_							=			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''')
				snake_case_							=			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,
				)

