
"""simple docstring"""





import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch
				from torch import nn

				from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
				from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image






class        _UpperCAmelCase:



		def __init__(   self							,						__a							,						__a=13							,						__a=64							,						__a=2							,						__a=3							,						__a=True							,						__a=True							,						__a=32							,						__a=5							,						__a=4							,						__a=37							,						__a="gelu"							,						__a=0.1							,						__a=0.1							,						__a=10							,						__a=0.02							,						__a=[1, 16, 4, 4]							,						__a=None							,						)			->			Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    parent
							_UpperCamelCase									=    batch_size
							_UpperCamelCase									=    image_size
							_UpperCamelCase									=    patch_size
							_UpperCamelCase									=    num_channels
							_UpperCamelCase									=    is_training
							_UpperCamelCase									=    use_labels
							_UpperCamelCase									=    hidden_size
							_UpperCamelCase									=    num_hidden_layers
							_UpperCamelCase									=    num_attention_heads
							_UpperCamelCase									=    intermediate_size
							_UpperCamelCase									=    hidden_act
							_UpperCamelCase									=    hidden_dropout_prob
							_UpperCamelCase									=    attention_probs_dropout_prob
							_UpperCamelCase									=    type_sequence_label_size
							_UpperCamelCase									=    initializer_range
							_UpperCamelCase									=    scope
							_UpperCamelCase									=    backbone_featmap_shape

							# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
							# the number of patches is based on the feature map of the backbone, which by default uses an output stride
							# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
							_UpperCamelCase									=    (self.image_size // 32) ** 2
							_UpperCamelCase									=    num_patches + 1



		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							_UpperCamelCase									=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

							_UpperCamelCase									=    None
							if self.use_labels:
												_UpperCamelCase									=    ids_tensor([self.batch_size]							,						self.type_sequence_label_size)

							_UpperCamelCase									=    self.get_config()

							return config, pixel_values, labels



		def 		UpperCAmelCase				(   self)			->			Optional[int]:






							'''simple docstring'''
							_UpperCamelCase									=    {
							    '''global_padding''': '''same''',
							    '''layer_type''': '''bottleneck''',
							    '''depths''': [3, 4, 9],
							    '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
							    '''embedding_dynamic_padding''': True,
							    '''hidden_sizes''': [4, 8, 16, 32],
							    '''num_groups''': 2,
							}

							return ViTHybridConfig(
							    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=__a							,						initializer_range=self.initializer_range							,						backbone_featmap_shape=self.backbone_featmap_shape							,						backbone_config=__a							,						)



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a)			->			Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    ViTHybridModel(config=__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase									=    model(__a)
							self.parent.assertEqual(result.last_hidden_state.shape							,						(self.batch_size, self.seq_length, self.hidden_size))



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a)			->			Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase									=    self.type_sequence_label_size
							_UpperCamelCase									=    ViTHybridForImageClassification(__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase									=    model(__a							,						labels=__a)
							self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.type_sequence_label_size))




		def 		UpperCAmelCase				(   self)			->			List[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    self.prepare_config_and_inputs()
							_UpperCamelCase					,    _UpperCamelCase					,    _UpperCamelCase									=    config_and_inputs
							_UpperCamelCase									=    {'''pixel_values''': pixel_values}
							return config, inputs_dict






@require_torch
class        _UpperCAmelCase(       lowerCamelCase ,	lowerCamelCase ,	unittest.TestCase      ):
		lowercase__         = (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
		lowercase__         = (
		    {'feature-extraction': ViTHybridModel, 'image-classification': ViTHybridForImageClassification}
		    if is_torch_available()
		    else {}
		)
		lowercase__         = False
		lowercase__         = False
		lowercase__         = False



		def 		UpperCAmelCase				(   self)			->			Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    ViTHybridModelTester(self)
							_UpperCamelCase									=    ConfigTester(self							,						config_class=__a							,						has_text_modality=__a							,						hidden_size=37)



		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							self.config_tester.run_common_tests()



		@unittest.skip(reason='''ViT does not use inputs_embeds''')
		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							pass



		def 		UpperCAmelCase				(   self)			->			Optional[int]:






							'''simple docstring'''
							_UpperCamelCase					,    _UpperCamelCase									=    self.model_tester.prepare_config_and_inputs_for_common()

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



		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase					,    _UpperCamelCase									=    self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
												_UpperCamelCase									=    model_class(__a)
												_UpperCamelCase									=    inspect.signature(model.forward)
												# signature.parameters is an OrderedDict => so arg_names order is deterministic
												_UpperCamelCase									=    [*signature.parameters.keys()]

												_UpperCamelCase									=    ['''pixel_values''']
												self.assertListEqual(arg_names[:1]							,						__a)



		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__a)



		def 		UpperCAmelCase				(   self)			->			Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*__a)



		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							_UpperCamelCase					,    _UpperCamelCase									=    self.model_tester.prepare_config_and_inputs_for_common()

							_UpperCamelCase									=    _config_zero_init(__a)
							for model_class in self.all_model_classes:
												_UpperCamelCase									=    model_class(config=__a)
												# Skip the check for the backbone
												for name, module in model.named_modules():
																	if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
																						_UpperCamelCase									=    [F'''{name}.{key}''' for key in module.state_dict().keys()]
																						break

												for name, param in model.named_parameters():
																	if param.requires_grad:
																						if name in backbone_params:
																											continue
																						self.assertIn(
																						    ((param.data.mean() * 1e9).round() / 1e9).item()							,						[0.0, 1.0]							,						msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''							,						)




		@slow
		def 		UpperCAmelCase				(   self)			->			Optional[Any]:






							'''simple docstring'''
							for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												_UpperCamelCase									=    ViTHybridModel.from_pretrained(__a)
												self.assertIsNotNone(__a)





def  lowerCamelCase__						(     )				->						Tuple:


					"""simple docstring"""





					_UpperCamelCase									=    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
					return image






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



		@cached_property
		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							return (
							    ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0])
							    if is_vision_available()
							    else None
							)



		@slow
		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(
							    __a)

							_UpperCamelCase									=    self.default_image_processor
							_UpperCamelCase									=    prepare_img()
							_UpperCamelCase									=    image_processor(images=__a							,						return_tensors='''pt''').to(__a)

							# forward pass
							with torch.no_grad():
												_UpperCamelCase									=    model(**__a)

							# verify the logits
							_UpperCamelCase									=    torch.Size((1, 10_00))
							self.assertEqual(outputs.logits.shape							,						__a)

							_UpperCamelCase									=    torch.tensor([-1.9090, -0.4993, -0.2389]).to(__a)

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




		@slow
		@require_accelerate
		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							_UpperCamelCase									=    ViTHybridImageProcessor.from_pretrained('''google/vit-hybrid-base-bit-384''')
							_UpperCamelCase									=    ViTHybridForImageClassification.from_pretrained('''google/vit-hybrid-base-bit-384'''							,						device_map='''auto''')

							_UpperCamelCase									=    prepare_img()

							_UpperCamelCase									=    image_processor(images=__a							,						return_tensors='''pt''')
							_UpperCamelCase									=    model(**__a)
							_UpperCamelCase									=    outputs.logits
							# model predicts one of the 1000 ImageNet classes
							_UpperCamelCase									=    logits.argmax(-1).item()

							self.assertTrue(model.config.idalabel[predicted_class_idx]							,						'''tabby, tabby cat''')













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      UpperCAmelCase_    (    _UpperCAmelCase							:str  ,						_UpperCAmelCase							:Optional[Any]				)	->				Optional[int]:
     '''simple docstring'''


     if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
          # expert layer
          A_          =       flax_key_tuple[:-1] + ('''weight''',)
          A_          =       torch.permute(_UpperCAmelCase  ,						(0, 2, 1)				)
     elif flax_key_tuple[-1] == "kernel" and ".".join(_UpperCAmelCase				):
          # linear layer
          A_          =       flax_key_tuple[:-1] + ('''weight''',)
          A_          =       flax_tensor.T
     elif flax_key_tuple[-1] in ["scale", "embedding"]:
          A_          =       flax_key_tuple[:-1] + ('''weight''',)

     return flax_key_tuple, flax_tensor

def      UpperCAmelCase_    (    _UpperCAmelCase							:Optional[int]  ,						_UpperCAmelCase							:str  ,						_UpperCAmelCase							:Any				)	->				Dict:
     '''simple docstring'''


     if "metadata" in layer:
          A_          =       layer.split('''metadata'''				)
          A_          =       ''''''.join(split_layer[0]				)[:-1]
          A_          =       [tuple(('''metadata''' + split_layer[1]).split('''/'''				)				)]
     elif "kvstore" in layer:
          A_          =       layer.split('''kvstore'''				)
          A_          =       ''''''.join(split_layer[0]				)[:-1]
          A_          =       [tuple(('''kvstore''' + split_layer[1]).split('''/'''				)				)]

     else:
          A_          =       layer.split('''/'''				)
          A_          =       '''/'''.join(split_layer[:-1]				)
          A_          =       (split_layer[-1],)

     if "kvstore/path" in layer:
          A_          =       f'{switch_checkpoint_path}/{checkpoint_info[layer]}'
     elif "kvstore/driver" in layer:
          A_          =       '''file'''
     else:
          A_          =       checkpoint_info[layer]

     return curr_real_layer_name, split_layer, content

def      UpperCAmelCase_    (    _UpperCAmelCase							:int  ,						_UpperCAmelCase							:Union[str, Any]				)	->				Any:
     '''simple docstring'''


     A_          =       rename_keys(_UpperCAmelCase				)
     A_          =       {}
     for k, v in current_block.items():
          A_          =       v
     A_          =       new_current_block
     torch.save(_UpperCAmelCase  ,						_UpperCAmelCase				)

def      UpperCAmelCase_    (    _UpperCAmelCase							:Union[str, Any]  ,						_UpperCAmelCase							:str  ,						_UpperCAmelCase							:List[Any]  ,						_UpperCAmelCase							:str  ,						_UpperCAmelCase							:str = WEIGHTS_NAME				)	->				Optional[int]:
     '''simple docstring'''


     A_          =       convert_file_size_to_int(_UpperCAmelCase				)
     A_          =       []
     A_          =       {}
     A_          =       0
     A_          =       0

     os.makedirs(_UpperCAmelCase  ,						exist_ok=_UpperCAmelCase				)
     with gfile.GFile(switch_checkpoint_path + '''/checkpoint'''  ,						'''rb'''				) as fp:
          A_          =       serialization.msgpack_restore(fp.read()				)['''optimizer''']['''target''']
          A_          =       flatten_dict(_UpperCAmelCase  ,						sep='''/'''				)

     A_          =       {}
     for layer in checkpoint_info.keys():
          A_   ,    A_   ,    A_          =       get_key_and_tensorstore_dict(
              _UpperCAmelCase  ,						_UpperCAmelCase  ,						_UpperCAmelCase				)
          if curr_real_layer_name in all_layers:
               A_          =       content
          else:
               A_          =       {split_layer[-1]: content}

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

          # use the renaming pattern from the small conversion scripts
          A_   ,    A_          =       rename_base_flax_keys(tuple(key.split('''/'''				)				)  ,						_UpperCAmelCase				)
          A_          =       '''/'''.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_          =       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_          =       {}
               A_          =       0

          A_          =       raw_weights.to(getattr(_UpperCAmelCase  ,						_UpperCAmelCase				)				)
          current_block_size += weight_size
          total_size += weight_size

     # Add the last block
     A_          =       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_          =       {}
     A_          =       {}
     for idx, shard in enumerate(_UpperCAmelCase				):
          A_          =       weights_name.replace(
              '''.bin'''  ,						f'-{idx+1:05d}-of-{len(_UpperCAmelCase				):05d}.bin'				)  # len(sharded_state_dicts):05d}
          A_          =       os.path.join(_UpperCAmelCase  ,						weights_name.replace('''.bin'''  ,						f'-{idx+1:05d}-of-???.bin'				)				)
          os.rename(_UpperCAmelCase  ,						os.path.join(_UpperCAmelCase  ,						_UpperCAmelCase				)				)
          A_          =       shard
          for key in shard:
               A_          =       shard_file

    # Add the metadata
     A_          =       {'''total_size''': total_size}
     A_          =       {'''metadata''': metadata, '''weight_map''': weight_map}

     with open(os.path.join(_UpperCAmelCase  ,						_UpperCAmelCase				)  ,						'''w'''  ,						encoding='''utf-8'''				) as f:
          A_          =       json.dumps(_UpperCAmelCase  ,						indent=2  ,						sort_keys=_UpperCAmelCase				) + '''\n'''
          f.write(_UpperCAmelCase				)

     return metadata, index


if __name__ == "__main__":
      a__      :     Dict         =		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.',
      )
      a__      :     Tuple         =		parser.parse_args()
      shard_on_the_fly(
          args.switch_tax_checkpoint_path,
          args.pytorch_dump_folder_path,
          args.max_shard_size,
          args.dtype,
      )


def      UpperCAmelCase_    (    )	->				Union[str, Any]:
     '''simple docstring'''


     from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

     A_          =       SwitchTransformersConfig.from_pretrained('''google/switch-base-8'''				)
     config.save_pretrained('''/home/arthur_huggingface_co/transformers/switch_converted'''				)
     A_          =       SwitchTransformersForConditionalGeneration.from_pretrained(
         '''/home/arthur_huggingface_co/transformers/switch_converted'''  ,						device_map='''auto'''				)

     A_          =       TaTokenizer.from_pretrained('''t5-small'''				)
     A_          =       '''A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.'''

     A_          =       tokenizer(_UpperCAmelCase  ,						return_tensors='''pt'''				).input_ids
     A_          =       model.generate(_UpperCAmelCase  ,						decoder_start_token_id=0				)
     print(tokenizer.decode(out[0]				)				)



