


"""simple docstring"""







import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
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 transformers import (
		    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
		    MODEL_MAPPING,
		    LevitForImageClassification,
		    LevitForImageClassificationWithTeacher,
		    LevitModel,
		)
		from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import LevitImageProcessor




class 			UpperCamelCase_     (			UpperCamelCase):

	"""simple docstring"""





	def   UpperCAmelCase_				(			self      :    List[Any]       )	->     Tuple:
						__SCREAMING_SNAKE_CASE						       =    self.config_class(**self.inputs_dict       )
						self.parent.assertTrue(hasattr(UpperCAmelCase__      ,      "hidden_sizes"       )       )
						self.parent.assertTrue(hasattr(UpperCAmelCase__      ,      "num_attention_heads"       )       )




class 			UpperCamelCase_     :

	"""simple docstring"""





	def __init__(			self      :    Dict      ,      UpperCAmelCase__      :    Any      ,      UpperCAmelCase__      :    Dict=1_3      ,      UpperCAmelCase__      :    Dict=6_4      ,      UpperCAmelCase__      :    Any=3      ,      UpperCAmelCase__      :    Tuple=3      ,      UpperCAmelCase__      :    str=2      ,      UpperCAmelCase__      :    Optional[Any]=1      ,      UpperCAmelCase__      :    int=1_6      ,      UpperCAmelCase__      :    Any=[1_2_8, 2_5_6, 3_8_4]      ,      UpperCAmelCase__      :    Any=[4, 6, 8]      ,      UpperCAmelCase__      :    Dict=[2, 3, 4]      ,      UpperCAmelCase__      :    int=[1_6, 1_6, 1_6]      ,      UpperCAmelCase__      :    List[str]=0      ,      UpperCAmelCase__      :    Tuple=[2, 2, 2]      ,      UpperCAmelCase__      :    Optional[int]=[2, 2, 2]      ,      UpperCAmelCase__      :    Any=0.02      ,      UpperCAmelCase__      :    Dict=True      ,      UpperCAmelCase__      :    Tuple=True      ,      UpperCAmelCase__      :    Union[str, Any]=2      ,      )	->     Optional[int]:
						__SCREAMING_SNAKE_CASE						       =    parent
						__SCREAMING_SNAKE_CASE						       =    batch_size
						__SCREAMING_SNAKE_CASE						       =    image_size
						__SCREAMING_SNAKE_CASE						       =    num_channels
						__SCREAMING_SNAKE_CASE						       =    kernel_size
						__SCREAMING_SNAKE_CASE						       =    stride
						__SCREAMING_SNAKE_CASE						       =    padding
						__SCREAMING_SNAKE_CASE						       =    hidden_sizes
						__SCREAMING_SNAKE_CASE						       =    num_attention_heads
						__SCREAMING_SNAKE_CASE						       =    depths
						__SCREAMING_SNAKE_CASE						       =    key_dim
						__SCREAMING_SNAKE_CASE						       =    drop_path_rate
						__SCREAMING_SNAKE_CASE						       =    patch_size
						__SCREAMING_SNAKE_CASE						       =    attention_ratio
						__SCREAMING_SNAKE_CASE						       =    mlp_ratio
						__SCREAMING_SNAKE_CASE						       =    initializer_range
						__SCREAMING_SNAKE_CASE						       =    [
						    ["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
						    ["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
						]
						__SCREAMING_SNAKE_CASE						       =    is_training
						__SCREAMING_SNAKE_CASE						       =    use_labels
						__SCREAMING_SNAKE_CASE						       =    num_labels
						__SCREAMING_SNAKE_CASE						       =    initializer_range




	def   UpperCAmelCase_				(			self      :    str       )	->     Union[str, Any]:
						__SCREAMING_SNAKE_CASE						       =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

						__SCREAMING_SNAKE_CASE						       =    None
						if self.use_labels:
											__SCREAMING_SNAKE_CASE						       =    ids_tensor([self.batch_size]      ,      self.num_labels       )

						__SCREAMING_SNAKE_CASE						       =    self.get_config()
						return config, pixel_values, labels




	def   UpperCAmelCase_				(			self      :    str       )	->     List[Any]:
						return LevitConfig(
						    image_size=self.image_size      ,      num_channels=self.num_channels      ,      kernel_size=self.kernel_size      ,      stride=self.stride      ,      padding=self.padding      ,      patch_size=self.patch_size      ,      hidden_sizes=self.hidden_sizes      ,      num_attention_heads=self.num_attention_heads      ,      depths=self.depths      ,      key_dim=self.key_dim      ,      drop_path_rate=self.drop_path_rate      ,      mlp_ratio=self.mlp_ratio      ,      attention_ratio=self.attention_ratio      ,      initializer_range=self.initializer_range      ,      down_ops=self.down_ops      ,      )




	def   UpperCAmelCase_				(			self      :    str      ,      UpperCAmelCase__      :    str      ,      UpperCAmelCase__      :    Union[str, Any]      ,      UpperCAmelCase__      :    Any       )	->     List[Any]:
						__SCREAMING_SNAKE_CASE						       =    LevitModel(config=UpperCAmelCase__       )
						model.to(UpperCAmelCase__       )
						model.eval()
						__SCREAMING_SNAKE_CASE						       =    model(UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    (self.image_size, self.image_size)
						__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    image_size[0], image_size[1]
						for _ in range(4       ):
											__SCREAMING_SNAKE_CASE						       =    floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1       )
											__SCREAMING_SNAKE_CASE						       =    floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1       )
						self.parent.assertEqual(
						    result.last_hidden_state.shape      ,      (self.batch_size, ceil(height / 4       ) * ceil(width / 4       ), self.hidden_sizes[-1])      ,      )




	def   UpperCAmelCase_				(			self      :    Tuple      ,      UpperCAmelCase__      :    int      ,      UpperCAmelCase__      :    List[str]      ,      UpperCAmelCase__      :    List[str]       )	->     List[Any]:
						__SCREAMING_SNAKE_CASE						       =    self.num_labels
						__SCREAMING_SNAKE_CASE						       =    LevitForImageClassification(UpperCAmelCase__       )
						model.to(UpperCAmelCase__       )
						model.eval()
						__SCREAMING_SNAKE_CASE						       =    model(UpperCAmelCase__      ,      labels=UpperCAmelCase__       )
						self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.num_labels)       )




	def   UpperCAmelCase_				(			self      :    int       )	->     str:
						__SCREAMING_SNAKE_CASE						       =    self.prepare_config_and_inputs()
						__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    config_and_inputs
						__SCREAMING_SNAKE_CASE						       =    {"pixel_values": pixel_values}
						return config, inputs_dict




@require_torch
class 			UpperCamelCase_     (			UpperCamelCase      ,  UpperCamelCase      ,  unittest.TestCase):

	"""simple docstring"""





	snake_case__    :  List[str]    =      (
	    (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
	    if is_torch_available()
	    else ()
	)
	snake_case__    :  Optional[int]    =      (
	    {
	        "feature-extraction": LevitModel,
	        "image-classification": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
	    }
	    if is_torch_available()
	    else {}
	)

	snake_case__    :  Dict    =      False
	snake_case__    :  str    =      False
	snake_case__    :  List[Any]    =      False
	snake_case__    :  Optional[Any]    =      False
	snake_case__    :  Optional[int]    =      False




	def   UpperCAmelCase_				(			self      :    Dict       )	->     str:
						__SCREAMING_SNAKE_CASE						       =    LevitModelTester(self       )
						__SCREAMING_SNAKE_CASE						       =    ConfigTester(self      ,      config_class=UpperCAmelCase__      ,      has_text_modality=UpperCAmelCase__      ,      hidden_size=3_7       )




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




	@unittest.skip(reason="Levit does not use inputs_embeds"       )
	def   UpperCAmelCase_				(			self      :    List[str]       )	->     Dict:
						pass




	@unittest.skip(reason="Levit does not support input and output embeddings"       )
	def   UpperCAmelCase_				(			self      :    Optional[int]       )	->     int:
						pass




	@unittest.skip(reason="Levit does not output attentions"       )
	def   UpperCAmelCase_				(			self      :    Dict       )	->     List[str]:
						pass




	def   UpperCAmelCase_				(			self      :    str       )	->     List[str]:
						__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(UpperCAmelCase__       )
											__SCREAMING_SNAKE_CASE						       =    inspect.signature(model.forward       )
											# 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]      ,      UpperCAmelCase__       )




	def   UpperCAmelCase_				(			self      :    Optional[Any]       )	->     Union[str, Any]:
						def check_hidden_states_output(UpperCAmelCase__      :    Any      ,      UpperCAmelCase__      :    Any      ,      UpperCAmelCase__      :    int       ):
											__SCREAMING_SNAKE_CASE						       =    model_class(UpperCAmelCase__       )
											model.to(UpperCAmelCase__       )
											model.eval()

											with torch.no_grad():
																__SCREAMING_SNAKE_CASE						       =    model(**self._prepare_for_class(UpperCAmelCase__      ,      UpperCAmelCase__       )       )

											__SCREAMING_SNAKE_CASE						       =    outputs.hidden_states

											__SCREAMING_SNAKE_CASE						       =    len(self.model_tester.depths       ) + 1
											self.assertEqual(len(UpperCAmelCase__       )      ,      UpperCAmelCase__       )

											__SCREAMING_SNAKE_CASE						       =    (self.model_tester.image_size, self.model_tester.image_size)
											__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    image_size[0], image_size[1]
											for _ in range(4       ):
																__SCREAMING_SNAKE_CASE						       =    floor(
																    (
																        (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
																        / self.model_tester.stride
																    )
																    + 1       )
																__SCREAMING_SNAKE_CASE						       =    floor(
																    (
																        (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
																        / self.model_tester.stride
																    )
																    + 1       )
											# verify the first hidden states (first block)
											self.assertListEqual(
											    list(hidden_states[0].shape[-2:]       )      ,      [
											        height * width,
											        self.model_tester.hidden_sizes[0],
											    ]      ,      )

						__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(UpperCAmelCase__      ,      UpperCAmelCase__      ,      UpperCAmelCase__       )

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

											check_hidden_states_output(UpperCAmelCase__      ,      UpperCAmelCase__      ,      UpperCAmelCase__       )




	@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."       )
	def   UpperCAmelCase_				(			self      :    Optional[Any]       )	->     Optional[Any]:
						pass




	def   UpperCAmelCase_				(			self      :    str      ,      UpperCAmelCase__      :    Tuple      ,      UpperCAmelCase__      :    Any      ,      UpperCAmelCase__      :    str=False       )	->     List[str]:
						__SCREAMING_SNAKE_CASE						       =    super()._prepare_for_class(UpperCAmelCase__      ,      UpperCAmelCase__      ,      return_labels=UpperCAmelCase__       )

						if return_labels:
											if model_class.__name__ == "LevitForImageClassificationWithTeacher":
																del inputs_dict["labels"]

						return inputs_dict




	def   UpperCAmelCase_				(			self      :    str       )	->     Dict:
						__SCREAMING_SNAKE_CASE						       =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*UpperCAmelCase__       )




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




	def   UpperCAmelCase_				(			self      :    int       )	->     List[str]:
						if not self.model_tester.is_training:
											return

						__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    self.model_tester.prepare_config_and_inputs_for_common()
						__SCREAMING_SNAKE_CASE						       =    True

						for model_class in self.all_model_classes:
											# LevitForImageClassificationWithTeacher supports inference-only
											if (
											    model_class in get_values(UpperCAmelCase__       )
											    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
											):
																continue
											__SCREAMING_SNAKE_CASE						       =    model_class(UpperCAmelCase__       )
											model.to(UpperCAmelCase__       )
											model.train()
											__SCREAMING_SNAKE_CASE						       =    self._prepare_for_class(UpperCAmelCase__      ,      UpperCAmelCase__      ,      return_labels=UpperCAmelCase__       )
											__SCREAMING_SNAKE_CASE						       =    model(**UpperCAmelCase__       ).loss
											loss.backward()




	def   UpperCAmelCase_				(			self      :    Optional[Any]       )	->     Optional[int]:
						__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    self.model_tester.prepare_config_and_inputs_for_common()
						if not self.model_tester.is_training:
											return

						__SCREAMING_SNAKE_CASE						       =    False
						__SCREAMING_SNAKE_CASE						       =    True

						for model_class in self.all_model_classes:
											if model_class in get_values(UpperCAmelCase__       ) or not model_class.supports_gradient_checkpointing:
																continue
											# LevitForImageClassificationWithTeacher supports inference-only
											if model_class.__name__ == "LevitForImageClassificationWithTeacher":
																continue
											__SCREAMING_SNAKE_CASE						       =    model_class(UpperCAmelCase__       )
											model.gradient_checkpointing_enable()
											model.to(UpperCAmelCase__       )
											model.train()
											__SCREAMING_SNAKE_CASE						       =    self._prepare_for_class(UpperCAmelCase__      ,      UpperCAmelCase__      ,      return_labels=UpperCAmelCase__       )
											__SCREAMING_SNAKE_CASE						       =    model(**UpperCAmelCase__       ).loss
											loss.backward()




	def   UpperCAmelCase_				(			self      :    Any       )	->     int:
						__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    self.model_tester.prepare_config_and_inputs_for_common()

						__SCREAMING_SNAKE_CASE						       =    [
						    {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
						    {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
						    {"title": "regression", "num_labels": 1, "dtype": torch.float},
						]

						for model_class in self.all_model_classes:
											if (
											    model_class
											    not in [
											        *get_values(UpperCAmelCase__       ),
											    ]
											    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
											):
																continue

											for problem_type in problem_types:
																with self.subTest(msg=F"""Testing {model_class} with {problem_type['title']}"""       ):
																					__SCREAMING_SNAKE_CASE						       =    problem_type["title"]
																					__SCREAMING_SNAKE_CASE						       =    problem_type["num_labels"]

																					__SCREAMING_SNAKE_CASE						       =    model_class(UpperCAmelCase__       )
																					model.to(UpperCAmelCase__       )
																					model.train()

																					__SCREAMING_SNAKE_CASE						       =    self._prepare_for_class(UpperCAmelCase__      ,      UpperCAmelCase__      ,      return_labels=UpperCAmelCase__       )

																					if problem_type["num_labels"] > 1:
																										__SCREAMING_SNAKE_CASE						       =    inputs["labels"].unsqueeze(1       ).repeat(1      ,      problem_type["num_labels"]       )

																					__SCREAMING_SNAKE_CASE						       =    inputs["labels"].to(problem_type["dtype"]       )

																					# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
																					# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
																					# they have the same size." which is a symptom something in wrong for the regression problem.
																					# See https://github.com/huggingface/transformers/issues/11780
																					with warnings.catch_warnings(record=UpperCAmelCase__       ) as warning_list:
																										__SCREAMING_SNAKE_CASE						       =    model(**UpperCAmelCase__       ).loss
																					for w in warning_list:
																										if "Using a target size that is different to the input size" in str(w.message       ):
																															raise ValueError(
																															    F"""Something is going wrong in the regression problem: intercepted {w.message}"""       )

																					loss.backward()




	@slow
	def   UpperCAmelCase_				(			self      :    str       )	->     List[str]:
						for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__SCREAMING_SNAKE_CASE						       =    LevitModel.from_pretrained(UpperCAmelCase__       )
											self.assertIsNotNone(UpperCAmelCase__       )




def        UpperCAmelCase__	():




					'''simple docstring'''


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




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

	"""simple docstring"""





	@cached_property
	def   UpperCAmelCase_				(			self      :    Any       )	->     Dict:
						return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )




	@slow
	def   UpperCAmelCase_				(			self      :    int       )	->     List[Any]:
						__SCREAMING_SNAKE_CASE						       =    LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]       ).to(
						    UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    self.default_image_processor
						__SCREAMING_SNAKE_CASE						       =    prepare_img()
						__SCREAMING_SNAKE_CASE						       =    image_processor(images=UpperCAmelCase__      ,      return_tensors="pt"       ).to(UpperCAmelCase__       )

						# forward pass
						with torch.no_grad():
											__SCREAMING_SNAKE_CASE						       =    model(**UpperCAmelCase__       )

						# verify the logits
						__SCREAMING_SNAKE_CASE						       =    torch.Size((1, 1_0_0_0)       )
						self.assertEqual(outputs.logits.shape      ,      UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    torch.tensor([1.0_448, -0.3_745, -1.8_317]       ).to(UpperCAmelCase__       )

						self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,      UpperCAmelCase__      ,      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
a__    :							Optional[Any]            =					{
    '''/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__	(lowerCAmelCase_       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    list(s_dict.keys()       )
					for key in keys:
										__SCREAMING_SNAKE_CASE						       =    R".*/layers_(\d+)"
										__SCREAMING_SNAKE_CASE						       =    key
										if re.match(lowerCAmelCase_  ,						lowerCAmelCase_       ):
															__SCREAMING_SNAKE_CASE						       =    re.sub(R"layers_(\d+)"  ,						R"block/\1/layer"  ,						lowerCAmelCase_       )

										__SCREAMING_SNAKE_CASE						       =    R"(encoder|decoder)\/"

										if re.match(lowerCAmelCase_  ,						lowerCAmelCase_       ):
															__SCREAMING_SNAKE_CASE						       =    re.match(lowerCAmelCase_  ,						lowerCAmelCase_       ).groups()
															if groups[0] == "encoder":
																				__SCREAMING_SNAKE_CASE						       =    re.sub(R"/mlp/"  ,						R"/1/mlp/"  ,						lowerCAmelCase_       )
																				__SCREAMING_SNAKE_CASE						       =    re.sub(R"/pre_mlp_layer_norm/"  ,						R"/1/layer_norm/"  ,						lowerCAmelCase_       )

															elif groups[0] == "decoder":
																				__SCREAMING_SNAKE_CASE						       =    re.sub(R"/mlp/"  ,						R"/2/mlp/"  ,						lowerCAmelCase_       )
																				__SCREAMING_SNAKE_CASE						       =    re.sub(R"/pre_mlp_layer_norm/"  ,						R"/2/layer_norm/"  ,						lowerCAmelCase_       )

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

										print(f"""{key} -> {new_key}"""       )
										__SCREAMING_SNAKE_CASE						       =    s_dict.pop(lowerCAmelCase_       )

					if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
										__SCREAMING_SNAKE_CASE						       =    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:
										__SCREAMING_SNAKE_CASE						       =    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:
															__SCREAMING_SNAKE_CASE						       =    s_dict[key].shape[0]
															__SCREAMING_SNAKE_CASE						       =    s_dict[key]
															for idx in range(lowerCAmelCase_       ):
																				__SCREAMING_SNAKE_CASE						       =    expert_weihts[idx]
																				print(f"""{key} -> {key.replace('expert/'  ,						'nested fstring'       )}"""       )

															s_dict.pop(lowerCAmelCase_       )

					return s_dict


a__    :							List[Any]            =					{
    '''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__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					import regex as re

					with open(lowerCAmelCase_  ,						"r"       ) as f:
										__SCREAMING_SNAKE_CASE						       =    f.read()

					__SCREAMING_SNAKE_CASE						       =    re.findall(R"(.*) = ([0-9.]*)"  ,						lowerCAmelCase_       )
					__SCREAMING_SNAKE_CASE						       =    {}
					for param, value in regex_match:
										if param in GIN_TO_CONFIG_MAPPING and value != "":
															__SCREAMING_SNAKE_CASE						       =    float(lowerCAmelCase_       ) if "." in value else int(lowerCAmelCase_       )

					__SCREAMING_SNAKE_CASE						       =    re.findall(R"(.*activations) = \(\'(.*)\',\)"  ,						lowerCAmelCase_       )[0]
					__SCREAMING_SNAKE_CASE						       =    str(activation[1]       )

					__SCREAMING_SNAKE_CASE						       =    num_experts
					__SCREAMING_SNAKE_CASE						       =    SwitchTransformersConfig(**lowerCAmelCase_       )
					return config




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_="./"  ,						lowerCAmelCase_=8       ):




					'''simple docstring'''


					print(f"""Loading flax weights from : {flax_checkpoint_path}"""       )
					__SCREAMING_SNAKE_CASE						       =    checkpoints.load_tax_checkpoint(lowerCAmelCase_       )

					if gin_file is not None:
										__SCREAMING_SNAKE_CASE						       =    convert_gin_to_config(lowerCAmelCase_  ,						lowerCAmelCase_       )
					else:
										__SCREAMING_SNAKE_CASE						       =    SwitchTransformersConfig.from_pretrained(lowerCAmelCase_       )

					__SCREAMING_SNAKE_CASE						       =    SwitchTransformersForConditionalGeneration(lowerCAmelCase_       )

					__SCREAMING_SNAKE_CASE						       =    flax_params["target"]
					__SCREAMING_SNAKE_CASE						       =    flatten_dict(lowerCAmelCase_  ,						sep="/"       )
					__SCREAMING_SNAKE_CASE						       =    rename_keys(lowerCAmelCase_       )
					__SCREAMING_SNAKE_CASE						       =    unflatten_dict(lowerCAmelCase_  ,						sep="/"       )

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

					print(f"""Save PyTorch model to {pytorch_dump_path}"""       )
					pt_model.save_pretrained(lowerCAmelCase_       )


if __name__ == "__main__":
		a__    :							List[Any]            =					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''')
		a__    :							Tuple            =					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,
		)
