



import inspect
import unittest

from transformers import BitConfig
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_backbone_common import BackboneTesterMixin
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 BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
						from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image


class 				_a						:





							'''simple docstring'''

							def __init__(							self ,					__UpperCAmelCase ,					__UpperCAmelCase=3 ,					__UpperCAmelCase=32 ,					__UpperCAmelCase=3 ,					__UpperCAmelCase=10 ,					__UpperCAmelCase=[8, 16, 32, 64] ,					__UpperCAmelCase=[1, 1, 2, 1] ,					__UpperCAmelCase=True ,					__UpperCAmelCase=True ,					__UpperCAmelCase="relu" ,					__UpperCAmelCase=3 ,					__UpperCAmelCase=None ,					__UpperCAmelCase=["stage2", "stage3", "stage4"] ,					__UpperCAmelCase=[2, 3, 4] ,					__UpperCAmelCase=1 ,					):







											"""simple docstring"""



											a__	:							Optional[int]      						= parent
											a__	:							Union[str, Any]      						= batch_size
											a__	:							Union[str, Any]      						= image_size
											a__	:							List[str]      						= num_channels
											a__	:							Optional[int]      						= embeddings_size
											a__	:							int      						= hidden_sizes
											a__	:							List[Any]      						= depths
											a__	:							int      						= is_training
											a__	:							Union[str, Any]      						= use_labels
											a__	:							List[str]      						= hidden_act
											a__	:							int      						= num_labels
											a__	:							Union[str, Any]      						= scope
											a__	:							int      						= len(__UpperCAmelCase				)
											a__	:							List[Any]      						= out_features
											a__	:							Union[str, Any]      						= out_indices
											a__	:							Optional[Any]      						= num_groups



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Tuple      						= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

											a__	:							Union[str, Any]      						= None
											if self.use_labels:
															a__	:							str      						= ids_tensor([self.batch_size] ,					self.num_labels				)

											a__	:							Dict      						= self.get_config()

											return config, pixel_values, labels



							def 		_A	(							self				):







											"""simple docstring"""



											return BitConfig(
											    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 ,					out_features=self.out_features ,					out_indices=self.out_indices ,					num_groups=self.num_groups ,					)



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Any      						= BitModel(config=__UpperCAmelCase				)
											model.to(__UpperCAmelCase				)
											model.eval()
											a__	:							Union[str, Any]      						= model(__UpperCAmelCase				)
											self.parent.assertEqual(
											    result.last_hidden_state.shape ,					(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) ,					)



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Union[str, Any]      						= self.num_labels
											a__	:							Optional[int]      						= BitForImageClassification(__UpperCAmelCase				)
											model.to(__UpperCAmelCase				)
											model.eval()
											a__	:							List[str]      						= model(__UpperCAmelCase ,					labels=__UpperCAmelCase				)
											self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.num_labels)				)



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							List[str]      						= BitBackbone(config=__UpperCAmelCase				)
											model.to(__UpperCAmelCase				)
											model.eval()
											a__	:							Optional[int]      						= model(__UpperCAmelCase				)

											# verify feature maps
											self.parent.assertEqual(len(result.feature_maps				) ,					len(config.out_features				)				)
											self.parent.assertListEqual(list(result.feature_maps[0].shape				) ,					[self.batch_size, self.hidden_sizes[1], 4, 4]				)

											# verify channels
											self.parent.assertEqual(len(model.channels				) ,					len(config.out_features				)				)
											self.parent.assertListEqual(model.channels ,					config.hidden_sizes[1:]				)

											# verify backbone works with out_features=None
											a__	:							Optional[Any]      						= None
											a__	:							str      						= BitBackbone(config=__UpperCAmelCase				)
											model.to(__UpperCAmelCase				)
											model.eval()
											a__	:							Any      						= model(__UpperCAmelCase				)

											# verify feature maps
											self.parent.assertEqual(len(result.feature_maps				) ,					1				)
											self.parent.assertListEqual(list(result.feature_maps[0].shape				) ,					[self.batch_size, self.hidden_sizes[-1], 1, 1]				)

											# verify channels
											self.parent.assertEqual(len(model.channels				) ,					1				)
											self.parent.assertListEqual(model.channels ,					[config.hidden_sizes[-1]]				)




							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							str      						= self.prepare_config_and_inputs()
											a__						,   a__						,   a__	:							str      						= config_and_inputs
											a__	:							Union[str, Any]      						= {"pixel_values": pixel_values}
											return config, inputs_dict





@require_torch
class 				_a						(       SCREAMING_SNAKE_CASE       ,  SCREAMING_SNAKE_CASE       ,  unittest.TestCase			):





							'''simple docstring'''

							A				:Dict =       (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
							A				:List[Any] =       (
							    {"feature-extraction": BitModel, "image-classification": BitForImageClassification}
							    if is_torch_available()
							    else {}
							)

							A				:List[str] =       False
							A				:List[str] =       False
							A				:Optional[int] =       False
							A				:Dict =       False
							A				:Dict =       False



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Optional[Any]      						= BitModelTester(self				)
											a__	:							Union[str, Any]      						= ConfigTester(self ,					config_class=__UpperCAmelCase ,					has_text_modality=__UpperCAmelCase				)



							def 		_A	(							self				):







											"""simple docstring"""



											self.create_and_test_config_common_properties()
											self.config_tester.create_and_test_config_to_json_string()
											self.config_tester.create_and_test_config_to_json_file()
											self.config_tester.create_and_test_config_from_and_save_pretrained()
											self.config_tester.create_and_test_config_with_num_labels()
											self.config_tester.check_config_can_be_init_without_params()
											self.config_tester.check_config_arguments_init()



							def 		_A	(							self				):







											"""simple docstring"""



											return



							@unittest.skip(reason="Bit does not output attentions"				)
							def 		_A	(							self				):







											"""simple docstring"""



											pass



							@unittest.skip(reason="Bit does not use inputs_embeds"				)
							def 		_A	(							self				):







											"""simple docstring"""



											pass



							@unittest.skip(reason="Bit does not support input and output embeddings"				)
							def 		_A	(							self				):







											"""simple docstring"""



											pass



							def 		_A	(							self				):







											"""simple docstring"""



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

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

															a__	:							Union[str, Any]      						= ["pixel_values"]
															self.assertListEqual(arg_names[:1] ,					__UpperCAmelCase				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Optional[Any]      						= self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__UpperCAmelCase				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							List[str]      						= self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_backbone(*__UpperCAmelCase				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__						,   a__	:							str      						= self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															a__	:							List[str]      						= model_class(config=__UpperCAmelCase				)
															for name, module in model.named_modules():
																			if isinstance(__UpperCAmelCase ,					(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 		_A	(							self				):







											"""simple docstring"""



											def check_hidden_states_output(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				):
															a__	:							Optional[int]      						= model_class(__UpperCAmelCase				)
															model.to(__UpperCAmelCase				)
															model.eval()

															with torch.no_grad():
																			a__	:							Union[str, Any]      						= model(**self._prepare_for_class(__UpperCAmelCase ,					__UpperCAmelCase				)				)

															a__	:							str      						= outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

															a__	:							Optional[Any]      						= self.model_tester.num_stages
															self.assertEqual(len(__UpperCAmelCase				) ,					expected_num_stages + 1				)

															# Bit'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 // 4, self.model_tester.image_size // 4] ,					)

											a__						,   a__	:							Union[str, Any]      						= self.model_tester.prepare_config_and_inputs_for_common()
											a__	:							Optional[Any]      						= ["preactivation", "bottleneck"]
											for model_class in self.all_model_classes:
															for layer_type in layers_type:
																			a__	:							List[Any]      						= layer_type
																			a__	:							str      						= True
																			check_hidden_states_output(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				)

																			# check that output_hidden_states also work using config
																			del inputs_dict["output_hidden_states"]
																			a__	:							Optional[int]      						= True

																			check_hidden_states_output(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				)



							@unittest.skip(reason="Bit does not use feedforward chunking"				)
							def 		_A	(							self				):







											"""simple docstring"""



											pass



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Optional[Any]      						= self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase				)




							@slow
							def 		_A	(							self				):







											"""simple docstring"""



											for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															a__	:							List[Any]      						= BitModel.from_pretrained(__UpperCAmelCase				)
															self.assertIsNotNone(__UpperCAmelCase				)






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


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





							'''simple docstring'''

							@cached_property
							def 		_A	(							self				):







											"""simple docstring"""



											return (
											    BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]				) if is_vision_available() else None
											)




							@slow
							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Optional[Any]      						= BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]				).to(__UpperCAmelCase				)

											a__	:							Tuple      						= self.default_image_processor
											a__	:							List[Any]      						= prepare_img()
											a__	:							Dict      						= image_processor(images=__UpperCAmelCase ,					return_tensors="pt"				).to(__UpperCAmelCase				)

											# forward pass
											with torch.no_grad():
															a__	:							Optional[int]      						= model(**__UpperCAmelCase				)

											# verify the logits
											a__	:							Any      						= torch.Size((1, 1000)				)
											self.assertEqual(outputs.logits.shape ,					__UpperCAmelCase				)

											a__	:							str      						= torch.tensor([[-0.6_5_2_6, -0.5_2_6_3, -1.4_3_9_8]]				).to(__UpperCAmelCase				)

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





@require_torch
class 				_a						(       SCREAMING_SNAKE_CASE       ,  unittest.TestCase			):





							'''simple docstring'''

							A				:Optional[Any] =       (BitBackbone,) if is_torch_available() else ()
							A				:Any =       BitConfig

							A				:Tuple =       False



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							str      						= BitModelTester(self				)







import argparse
import json
import os

import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile

from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
    rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase )	->						Optional[Any]:
				if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
								# expert layer
								a__	:							List[str]      						= flax_key_tuple[:-1] + ("weight",)
								a__	:							Optional[int]      						= torch.permute(__UpperCamelCase	,				(0, 2, 1) )
				elif flax_key_tuple[-1] == "kernel" and ".".join(__UpperCamelCase ):
								# linear layer
								a__	:							Tuple      						= flax_key_tuple[:-1] + ("weight",)
								a__	:							Tuple      						= flax_tensor.T
				elif flax_key_tuple[-1] in ["scale", "embedding"]:
								a__	:							str      						= flax_key_tuple[:-1] + ("weight",)

				return flax_key_tuple, flax_tensor






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )	->						List[Any]:
				if "metadata" in layer:
								a__	:							Union[str, Any]      						= layer.split("metadata" )
								a__	:							Any      						= "".join(split_layer[0] )[:-1]
								a__	:							Optional[int]      						= [tuple(("metadata" + split_layer[1]).split("/" ) )]
				elif "kvstore" in layer:
								a__	:							Optional[int]      						= layer.split("kvstore" )
								a__	:							Union[str, Any]      						= "".join(split_layer[0] )[:-1]
								a__	:							Optional[Any]      						= [tuple(("kvstore" + split_layer[1]).split("/" ) )]

				else:
								a__	:							Any      						= layer.split("/" )
								a__	:							Union[str, Any]      						= "/".join(split_layer[:-1] )
								a__	:							Tuple      						= (split_layer[-1],)

				if "kvstore/path" in layer:
								a__	:							Tuple      						= F'{switch_checkpoint_path}/{checkpoint_info[layer]}'
				elif "kvstore/driver" in layer:
								a__	:							int      						= "file"
				else:
								a__	:							List[str]      						= checkpoint_info[layer]

				return curr_real_layer_name, split_layer, content






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase )	->						Optional[int]:
				a__	:							List[Any]      						= rename_keys(__UpperCamelCase )
				a__	:							Tuple      						= {}
				for k, v in current_block.items():
								a__	:							str      						= v
				a__	:							Optional[int]      						= new_current_block
				torch.save(__UpperCamelCase	,				__UpperCamelCase )






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase = WEIGHTS_NAME )	->						Any:
				a__	:							Optional[Any]      						= convert_file_size_to_int(__UpperCamelCase )
				a__	:							Tuple      						= []
				a__	:							Dict      						= {}
				a__	:							str      						= 0
				a__	:							Tuple      						= 0

				os.makedirs(__UpperCamelCase	,				exist_ok=__UpperCamelCase )
				with gfile.GFile(switch_checkpoint_path + "/checkpoint"	,				"rb" ) as fp:
								a__	:							Any      						= serialization.msgpack_restore(fp.read() )["optimizer"]["target"]
								a__	:							Tuple      						= flatten_dict(__UpperCamelCase	,				sep="/" )

				a__	:							Any      						= {}
				for layer in checkpoint_info.keys():
								a__						,   a__						,   a__	:							Optional[Any]      						= get_key_and_tensorstore_dict(
								    __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )
								if curr_real_layer_name in all_layers:
												a__	:							int      						= content
								else:
												a__	:							List[Any]      						= {split_layer[-1]: content}

				for key in all_layers.keys():
								# open tensorstore file
								a__	:							Tuple      						= ts.open(unflatten_dict(all_layers[key] ) ).result().read().result()
								a__	:							Tuple      						= torch.tensor(__UpperCamelCase )
								a__	:							str      						= raw_weights.numel() * dtype_byte_size(raw_weights.dtype )

								# use the renaming pattern from the small conversion scripts
								a__						,   a__	:							int      						= rename_base_flax_keys(tuple(key.split("/" ) )	,				__UpperCamelCase )
								a__	:							Union[str, Any]      						= "/".join(__UpperCamelCase )

								# If this weight is going to tip up over the maximal size, we split.
								if current_block_size + weight_size > max_shard_size:
												a__	:							int      						= os.path.join(
												    __UpperCamelCase	,				weights_name.replace(".bin"	,				F'-{len(__UpperCamelCase )+1:05d}-of-???.bin' ) )
												rename_and_save_block(__UpperCamelCase	,				__UpperCamelCase )
												sharded_state_dicts.append(current_block.keys() )
												del current_block
												a__	:							int      						= {}
												a__	:							Optional[int]      						= 0

								a__	:							List[str]      						= raw_weights.to(getattr(__UpperCamelCase	,				__UpperCamelCase ) )
								current_block_size += weight_size
								total_size += weight_size

				# Add the last block
				a__	:							Optional[int]      						= os.path.join(__UpperCamelCase	,				weights_name.replace(".bin"	,				F'-{len(__UpperCamelCase )+1:05d}-of-???.bin' ) )
				rename_and_save_block(__UpperCamelCase	,				__UpperCamelCase )
				sharded_state_dicts.append(current_block.keys() )

				# If we only have one shard, we return it
				if len(__UpperCamelCase ) == 1:
								return {weights_name: sharded_state_dicts[0]}, None

				# Otherwise, let's build the index
				a__	:							int      						= {}
				a__	:							Tuple      						= {}
				for idx, shard in enumerate(__UpperCamelCase ):
								a__	:							Tuple      						= weights_name.replace(
								    ".bin"	,				F'-{idx+1:05d}-of-{len(__UpperCamelCase ):05d}.bin' )  # len(sharded_state_dicts):05d}
								a__	:							Optional[int]      						= os.path.join(__UpperCamelCase	,				weights_name.replace(".bin"	,				F'-{idx+1:05d}-of-???.bin' ) )
								os.rename(__UpperCamelCase	,				os.path.join(__UpperCamelCase	,				__UpperCamelCase ) )
								a__	:							List[str]      						= shard
								for key in shard:
												a__	:							Tuple      						= shard_file

    # Add the metadata
				a__	:							int      						= {"total_size": total_size}
				a__	:							List[str]      						= {"metadata": metadata, "weight_map": weight_map}

				with open(os.path.join(__UpperCamelCase	,				__UpperCamelCase )	,				"w"	,				encoding="utf-8" ) as f:
								a__	:							List[str]      						= json.dumps(__UpperCamelCase	,				indent=2	,				sort_keys=__UpperCamelCase ) + "\n"
								f.write(__UpperCamelCase )

				return metadata, index


if __name__ == "__main__":
						lowerCamelCase		       =      argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--switch_t5x_checkpoint_path""",
						    default="""/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600""",
						    type=str,
						    required=False,
						    help="""Path to a directory containing a folder per layer. Follows the original Google format.""",
						)
						parser.add_argument("""--max_shard_size""", default="""10GB""", required=False, help="""Max shard size""")
						parser.add_argument("""--dtype""", default="""bfloat16""", type=str, required=False, help="""dtype of the saved model""")
						parser.add_argument(
						    """--pytorch_dump_folder_path""",
						    default="""/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted""",
						    type=str,
						    required=False,
						    help="""Path to the output pytorch model.""",
						)
						lowerCamelCase		       =      parser.parse_args()
						shard_on_the_fly(
						    args.switch_tax_checkpoint_path,
						    args.pytorch_dump_folder_path,
						    args.max_shard_size,
						    args.dtype,
						)






def     SCREAMING_SNAKE_CASE(      )	->						int:
				from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

				a__	:							Any      						= SwitchTransformersConfig.from_pretrained("google/switch-base-8" )
				config.save_pretrained("/home/arthur_huggingface_co/transformers/switch_converted" )
				a__	:							List[str]      						= SwitchTransformersForConditionalGeneration.from_pretrained(
				    "/home/arthur_huggingface_co/transformers/switch_converted"	,				device_map="auto" )

				a__	:							str      						= TaTokenizer.from_pretrained("t5-small" )
				a__	:							Tuple      						= "A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."

				a__	:							Optional[int]      						= tokenizer(__UpperCamelCase	,				return_tensors="pt" ).input_ids
				a__	:							Optional[Any]      						= model.generate(__UpperCamelCase	,				decoder_start_token_id=0 )
				print(tokenizer.decode(out[0] ) )


