


"""simple docstring"""







import inspect
import unittest

from transformers import YolosConfig
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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch
		from torch import nn

		from transformers import YolosForObjectDetection, YolosModel
		from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor




class 			UpperCamelCase_     :

	"""simple docstring"""





	def __init__(			self      :    Any      ,      UpperCAmelCase__      :    Dict      ,      UpperCAmelCase__      :    List[str]=1_3      ,      UpperCAmelCase__      :    List[str]=[3_0, 3_0]      ,      UpperCAmelCase__      :    Any=2      ,      UpperCAmelCase__      :    Optional[int]=3      ,      UpperCAmelCase__      :    str=True      ,      UpperCAmelCase__      :    Tuple=True      ,      UpperCAmelCase__      :    str=3_2      ,      UpperCAmelCase__      :    Dict=5      ,      UpperCAmelCase__      :    Any=4      ,      UpperCAmelCase__      :    Union[str, Any]=3_7      ,      UpperCAmelCase__      :    str="gelu"      ,      UpperCAmelCase__      :    int=0.1      ,      UpperCAmelCase__      :    List[str]=0.1      ,      UpperCAmelCase__      :    Any=1_0      ,      UpperCAmelCase__      :    Optional[Any]=0.02      ,      UpperCAmelCase__      :    Any=3      ,      UpperCAmelCase__      :    int=None      ,      UpperCAmelCase__      :    Optional[Any]=8      ,      UpperCAmelCase__      :    str=1_0      ,      )	->     Optional[Any]:
						__SCREAMING_SNAKE_CASE						       =    parent
						__SCREAMING_SNAKE_CASE						       =    batch_size
						__SCREAMING_SNAKE_CASE						       =    image_size
						__SCREAMING_SNAKE_CASE						       =    patch_size
						__SCREAMING_SNAKE_CASE						       =    num_channels
						__SCREAMING_SNAKE_CASE						       =    is_training
						__SCREAMING_SNAKE_CASE						       =    use_labels
						__SCREAMING_SNAKE_CASE						       =    hidden_size
						__SCREAMING_SNAKE_CASE						       =    num_hidden_layers
						__SCREAMING_SNAKE_CASE						       =    num_attention_heads
						__SCREAMING_SNAKE_CASE						       =    intermediate_size
						__SCREAMING_SNAKE_CASE						       =    hidden_act
						__SCREAMING_SNAKE_CASE						       =    hidden_dropout_prob
						__SCREAMING_SNAKE_CASE						       =    attention_probs_dropout_prob
						__SCREAMING_SNAKE_CASE						       =    type_sequence_label_size
						__SCREAMING_SNAKE_CASE						       =    initializer_range
						__SCREAMING_SNAKE_CASE						       =    num_labels
						__SCREAMING_SNAKE_CASE						       =    scope
						__SCREAMING_SNAKE_CASE						       =    n_targets
						__SCREAMING_SNAKE_CASE						       =    num_detection_tokens
						# we set the expected sequence length (which is used in several tests)
						# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
						__SCREAMING_SNAKE_CASE						       =    (image_size[1] // patch_size) * (image_size[0] // patch_size)
						__SCREAMING_SNAKE_CASE						       =    num_patches + 1 + self.num_detection_tokens




	def   UpperCAmelCase_				(			self      :    Any       )	->     Union[str, Any]:
						__SCREAMING_SNAKE_CASE						       =    floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]       )

						__SCREAMING_SNAKE_CASE						       =    None
						if self.use_labels:
											# labels is a list of Dict (each Dict being the labels for a given example in the batch)
											__SCREAMING_SNAKE_CASE						       =    []
											for i in range(self.batch_size       ):
																__SCREAMING_SNAKE_CASE						       =    {}
																__SCREAMING_SNAKE_CASE						       =    torch.randint(
																    high=self.num_labels      ,      size=(self.n_targets,)      ,      device=UpperCAmelCase__       )
																__SCREAMING_SNAKE_CASE						       =    torch.rand(self.n_targets      ,      4      ,      device=UpperCAmelCase__       )
																labels.append(UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    self.get_config()

						return config, pixel_values, labels




	def   UpperCAmelCase_				(			self      :    Optional[Any]       )	->     Dict:
						return YolosConfig(
						    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      ,      is_decoder=UpperCAmelCase__      ,      initializer_range=self.initializer_range      ,      num_detection_tokens=self.num_detection_tokens      ,      num_labels=self.num_labels      ,      )




	def   UpperCAmelCase_				(			self      :    int      ,      UpperCAmelCase__      :    Tuple      ,      UpperCAmelCase__      :    List[Any]      ,      UpperCAmelCase__      :    Tuple       )	->     Optional[Any]:
						__SCREAMING_SNAKE_CASE						       =    YolosModel(config=UpperCAmelCase__       )
						model.to(UpperCAmelCase__       )
						model.eval()
						__SCREAMING_SNAKE_CASE						       =    model(UpperCAmelCase__       )
						self.parent.assertEqual(
						    result.last_hidden_state.shape      ,      (self.batch_size, self.expected_seq_len, self.hidden_size)       )




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

						__SCREAMING_SNAKE_CASE						       =    model(pixel_values=UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    model(UpperCAmelCase__       )

						self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.num_detection_tokens, self.num_labels + 1)       )
						self.parent.assertEqual(result.pred_boxes.shape      ,      (self.batch_size, self.num_detection_tokens, 4)       )

						__SCREAMING_SNAKE_CASE						       =    model(pixel_values=UpperCAmelCase__      ,      labels=UpperCAmelCase__       )

						self.parent.assertEqual(result.loss.shape      ,      ()       )
						self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.num_detection_tokens, self.num_labels + 1)       )
						self.parent.assertEqual(result.pred_boxes.shape      ,      (self.batch_size, self.num_detection_tokens, 4)       )




	def   UpperCAmelCase_				(			self      :    List[str]       )	->     Tuple:
						__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__    :  int    =      (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
	snake_case__    :  List[str]    =      (
	    {"feature-extraction": YolosModel, "object-detection": YolosForObjectDetection} if is_torch_available() else {}
	)

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




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

						if return_labels:
											if model_class.__name__ == "YolosForObjectDetection":
																__SCREAMING_SNAKE_CASE						       =    []
																for i in range(self.model_tester.batch_size       ):
																					__SCREAMING_SNAKE_CASE						       =    {}
																					__SCREAMING_SNAKE_CASE						       =    torch.ones(
																					    size=(self.model_tester.n_targets,)      ,      device=UpperCAmelCase__      ,      dtype=torch.long       )
																					__SCREAMING_SNAKE_CASE						       =    torch.ones(
																					    self.model_tester.n_targets      ,      4      ,      device=UpperCAmelCase__      ,      dtype=torch.float       )
																					labels.append(UpperCAmelCase__       )
																__SCREAMING_SNAKE_CASE						       =    labels

						return inputs_dict




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




	def   UpperCAmelCase_				(			self      :    str       )	->     List[Any]:
						self.config_tester.run_common_tests()




	def   UpperCAmelCase_				(			self      :    Tuple       )	->     Optional[Any]:
						# YOLOS does not use inputs_embeds
						pass




	def   UpperCAmelCase_				(			self      :    Optional[int]       )	->     List[Any]:
						__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__       )
											self.assertIsInstance(model.get_input_embeddings()      ,      (nn.Module)       )
											__SCREAMING_SNAKE_CASE						       =    model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(UpperCAmelCase__      ,      nn.Linear       )       )




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




	def   UpperCAmelCase_				(			self      :    List[Any]       )	->     Tuple:
						__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    self.model_tester.prepare_config_and_inputs_for_common()
						__SCREAMING_SNAKE_CASE						       =    True

						# in YOLOS, the seq_len is different
						__SCREAMING_SNAKE_CASE						       =    self.model_tester.expected_seq_len
						for model_class in self.all_model_classes:
											__SCREAMING_SNAKE_CASE						       =    True
											__SCREAMING_SNAKE_CASE						       =    False
											__SCREAMING_SNAKE_CASE						       =    True
											__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.attentions
											self.assertEqual(len(UpperCAmelCase__       )      ,      self.model_tester.num_hidden_layers       )

											# check that output_attentions also work using config
											del inputs_dict["output_attentions"]
											__SCREAMING_SNAKE_CASE						       =    True
											__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.attentions
											self.assertEqual(len(UpperCAmelCase__       )      ,      self.model_tester.num_hidden_layers       )

											self.assertListEqual(
											    list(attentions[0].shape[-3:]       )      ,      [self.model_tester.num_attention_heads, seq_len, seq_len]      ,      )
											__SCREAMING_SNAKE_CASE						       =    len(UpperCAmelCase__       )

											# Check attention is always last and order is fine
											__SCREAMING_SNAKE_CASE						       =    True
											__SCREAMING_SNAKE_CASE						       =    True
											__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						       =    1
											self.assertEqual(out_len + added_hidden_states      ,      len(UpperCAmelCase__       )       )

											__SCREAMING_SNAKE_CASE						       =    outputs.attentions

											self.assertEqual(len(UpperCAmelCase__       )      ,      self.model_tester.num_hidden_layers       )
											self.assertListEqual(
											    list(self_attentions[0].shape[-3:]       )      ,      [self.model_tester.num_attention_heads, seq_len, seq_len]      ,      )




	def   UpperCAmelCase_				(			self      :    Any       )	->     List[str]:
						def check_hidden_states_output(UpperCAmelCase__      :    Optional[int]      ,      UpperCAmelCase__      :    List[Any]      ,      UpperCAmelCase__      :    str       ):
											__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						       =    getattr(
											    self.model_tester      ,      "expected_num_hidden_layers"      ,      self.model_tester.num_hidden_layers + 1       )
											self.assertEqual(len(UpperCAmelCase__       )      ,      UpperCAmelCase__       )

											# YOLOS has a different seq_length
											__SCREAMING_SNAKE_CASE						       =    self.model_tester.expected_seq_len

											self.assertListEqual(
											    list(hidden_states[0].shape[-2:]       )      ,      [seq_length, self.model_tester.hidden_size]      ,      )

						__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__       )




	def   UpperCAmelCase_				(			self      :    Optional[Any]       )	->     Optional[int]:
						__SCREAMING_SNAKE_CASE						       =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_object_detection(*UpperCAmelCase__       )




	@slow
	def   UpperCAmelCase_				(			self      :    Any       )	->     Union[str, Any]:
						for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__SCREAMING_SNAKE_CASE						       =    YolosModel.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      :    List[str]       )	->     int:
						return AutoImageProcessor.from_pretrained("hustvl/yolos-small"       ) if is_vision_available() else None




	@slow
	def   UpperCAmelCase_				(			self      :    Tuple       )	->     Any:
						__SCREAMING_SNAKE_CASE						       =    YolosForObjectDetection.from_pretrained("hustvl/yolos-small"       ).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(inputs.pixel_values       )

						# verify outputs
						__SCREAMING_SNAKE_CASE						       =    torch.Size((1, 1_0_0, 9_2)       )
						self.assertEqual(outputs.logits.shape      ,      UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    torch.tensor(
						    [[-24.0_248, -10.3_024, -14.8_290], [-42.0_392, -16.8_200, -27.4_334], [-27.2_743, -11.8_154, -18.7_148]]      ,      device=UpperCAmelCase__      ,      )
						__SCREAMING_SNAKE_CASE						       =    torch.tensor(
						    [[0.2_559, 0.5_455, 0.4_706], [0.2_989, 0.7_279, 0.1_875], [0.7_732, 0.4_017, 0.4_462]]      ,      device=UpperCAmelCase__       )
						self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]      ,      UpperCAmelCase__      ,      atol=1E-4       )       )
						self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]      ,      UpperCAmelCase__      ,      atol=1E-4       )       )

						# verify postprocessing
						__SCREAMING_SNAKE_CASE						       =    image_processor.post_process_object_detection(
						    UpperCAmelCase__      ,      threshold=0.3      ,      target_sizes=[image.size[::-1]]       )[0]
						__SCREAMING_SNAKE_CASE						       =    torch.tensor([0.9_994, 0.9_790, 0.9_964, 0.9_972, 0.9_861]       ).to(UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    [7_5, 7_5, 1_7, 6_3, 1_7]
						__SCREAMING_SNAKE_CASE						       =    torch.tensor([335.0_609, 79.3_848, 375.4_216, 187.2_495]       ).to(UpperCAmelCase__       )

						self.assertEqual(len(results["scores"]       )      ,      5       )
						self.assertTrue(torch.allclose(results["scores"]      ,      UpperCAmelCase__      ,      atol=1E-4       )       )
						self.assertSequenceEqual(results["labels"].tolist()      ,      UpperCAmelCase__       )
						self.assertTrue(torch.allclose(results["boxes"][0, :]      ,      UpperCAmelCase__       )       )




"""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,
		)
