





'''simple docstring'''






import inspect
import unittest

from transformers import ResNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
     import jax
     import jax.numpy as jnp

     from transformers.models.resnet.modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel

if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor


class 	_lowercase    (				unittest.TestCase					):


   def __init__(			self     , _UpperCAmelCase     , _UpperCAmelCase=3     , _UpperCAmelCase=32     , _UpperCAmelCase=3     , _UpperCAmelCase=10     , _UpperCAmelCase=[10, 20, 30, 40]     , _UpperCAmelCase=[1, 1, 2, 1]     , _UpperCAmelCase=True     , _UpperCAmelCase=True     , _UpperCAmelCase="relu"     , _UpperCAmelCase=3     , _UpperCAmelCase=None     , ):
       A							:							Optional[int]				=    parent
       A							:							List[str]				=    batch_size
       A							:							Optional[int]				=    image_size
       A							:							Optional[Any]				=    num_channels
       A							:							Union[str, Any]				=    embeddings_size
       A							:							Dict				=    hidden_sizes
       A							:							int				=    depths
       A							:							Optional[int]				=    is_training
       A							:							Tuple				=    use_labels
       A							:							Any				=    hidden_act
       A							:							Optional[Any]				=    num_labels
       A							:							str				=    scope
       A							:							int				=    len(_UpperCAmelCase       )


   def        snake_case					(			self       ):
       A							:							Any				=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

       A							:							Tuple				=    self.get_config()

       return config, pixel_values


   def        snake_case					(			self       ):
       return ResNetConfig(
           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     , image_size=self.image_size     , )


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase       ):
       A							:							str				=    FlaxResNetModel(config=_UpperCAmelCase       )
       A							:							str				=    model(_UpperCAmelCase       )

       # Output shape (b, c, h, w)
       self.parent.assertEqual(
           result.last_hidden_state.shape     , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)     , )


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase       ):
       A							:							Optional[Any]				=    self.num_labels
       A							:							List[Any]				=    FlaxResNetForImageClassification(config=_UpperCAmelCase       )
       A							:							List[str]				=    model(_UpperCAmelCase       )
       self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.num_labels)       )


   def        snake_case					(			self       ):
       A							:							Union[str, Any]				=    self.prepare_config_and_inputs()
       A,						A							:							int				=    config_and_inputs
       A							:							Optional[Any]				=    {'''pixel_values''': pixel_values}
       return config, inputs_dict







@require_flax
class 	_lowercase    (				a		,       unittest.TestCase					):
   _UpperCamelCase									= (FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

   _UpperCamelCase									= False
   _UpperCamelCase									= False
   _UpperCamelCase									= False


   def        snake_case					(			self       ):
       A							:							Optional[Any]				=    FlaxResNetModelTester(self       )
       A							:							Union[str, Any]				=    ConfigTester(self     , config_class=_UpperCAmelCase     , has_text_modality=_UpperCAmelCase       )


   def        snake_case					(			self       ):
       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        snake_case					(			self       ):
       return


   def        snake_case					(			self       ):
       A							:							str				=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_UpperCAmelCase       )


   def        snake_case					(			self       ):
       A							:							Dict				=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase       )


   @unittest.skip(reason='''ResNet does not use inputs_embeds'''       )
   def        snake_case					(			self       ):
       pass


   @unittest.skip(reason='''ResNet does not support input and output embeddings'''       )
   def        snake_case					(			self       ):
       pass


   def        snake_case					(			self       ):
       A,						A							:							Tuple				=    self.model_tester.prepare_config_and_inputs_for_common()

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

           A							:							List[str]				=    ['''pixel_values''']
           self.assertListEqual(arg_names[:1]     , _UpperCAmelCase       )


   def        snake_case					(			self       ):
       def check_hidden_states_output(_UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase       ):
           A							:							Union[str, Any]				=    model_class(_UpperCAmelCase       )

           A							:							Optional[Any]				=    model(**self._prepare_for_class(_UpperCAmelCase     , _UpperCAmelCase       )       )

           A							:							Optional[int]				=    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

           A							:							int				=    self.model_tester.num_stages
           self.assertEqual(len(_UpperCAmelCase       )     , expected_num_stages + 1       )


   @unittest.skip(reason='''ResNet does not use feedforward chunking'''       )
   def        snake_case					(			self       ):
       pass


   def        snake_case					(			self       ):
       A,						A							:							int				=    self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__       ):
               A							:							int				=    self._prepare_for_class(_UpperCAmelCase     , _UpperCAmelCase       )
               A							:							str				=    model_class(_UpperCAmelCase       )

               @jax.jit
               def model_jitted(_UpperCAmelCase     , **_UpperCAmelCase       ):
                   return model(pixel_values=_UpperCAmelCase     , **_UpperCAmelCase       )

               with self.subTest('''JIT Enabled'''       ):
                   A							:							Dict				=    model_jitted(**_UpperCAmelCase       ).to_tuple()

               with self.subTest('''JIT Disabled'''       ):
                   with jax.disable_jit():
                       A							:							List[Any]				=    model_jitted(**_UpperCAmelCase       ).to_tuple()

               self.assertEqual(len(_UpperCAmelCase       )     , len(_UpperCAmelCase       )       )
               for jitted_output, output in zip(_UpperCAmelCase     , _UpperCAmelCase       ):
                   self.assertEqual(jitted_output.shape     , output.shape       )







def 				_lowerCamelCase(	)		->	Optional[int]:
    A							:							str				=    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
    return image





@require_flax
class 	_lowercase    (				unittest.TestCase					):


   @cached_property
   def        snake_case					(			self       ):
       return AutoImageProcessor.from_pretrained('''microsoft/resnet-50'''       ) if is_vision_available() else None


   @slow
   def        snake_case					(			self       ):
       A							:							Optional[Any]				=    FlaxResNetForImageClassification.from_pretrained('''microsoft/resnet-50'''       )

       A							:							Tuple				=    self.default_image_processor
       A							:							List[Any]				=    prepare_img()
       A							:							Tuple				=    image_processor(images=_UpperCAmelCase     , return_tensors='''np'''       )

       A							:							List[str]				=    model(**_UpperCAmelCase       )

       # verify the logits
       A							:							Any				=    (1, 1_000)
       self.assertEqual(outputs.logits.shape     , _UpperCAmelCase       )

       A							:							Tuple				=    jnp.array([-11.1069, -9.7877, -8.3777]       )

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







'''simple docstring'''






import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig
def 				_lowerCamelCase(	UpperCamelCase__     :			List[str]     ,							UpperCamelCase__     :			Optional[Any]     ,							UpperCamelCase__     :			List[str]=0		)		->	Optional[int]:
    # Format the message.
    if name is None:
        A							:							Dict				=    None
    else:
        A							:							Optional[Any]				=    '''.''' * max(0     ,							spaces - 2		) + '''# {:''' + str(50 - spaces		) + '''s}'''
        A							:							Tuple				=    fmt.format(UpperCamelCase__		)

    # Print and recurse (if needed).
    if isinstance(UpperCamelCase__     ,							UpperCamelCase__		):
        if msg is not None:
            print(UpperCamelCase__		)
        for k in val.keys():
            recursive_print(UpperCamelCase__     ,							val[k]     ,							spaces + 2		)
    elif isinstance(UpperCamelCase__     ,							torch.Tensor		):
        print(UpperCamelCase__     ,							''':'''     ,							val.size()		)
    else:
        print(UpperCamelCase__     ,							''':'''     ,							UpperCamelCase__		)
def 				_lowerCamelCase(	UpperCamelCase__     :			Dict     ,							UpperCamelCase__     :			List[Any]     ,							UpperCamelCase__     :			Dict     ,							UpperCamelCase__     :			List[str]		)		->	Dict:
    # Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
    # for compatibility with later versions of NVIDIA Megatron-LM.
    # The inverse operation is performed inside Megatron-LM to read checkpoints:
    # https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
    # If param is the weight tensor of the self-attention block, the returned tensor
    # will have to be transposed one more time to be read by HuggingFace GPT2.
    A							:							int				=    param.size()
    # other versions store [num_heads * num_splits * hidden_size, :]
    A							:							List[str]				=    (num_heads, num_splits, hidden_size) + input_shape[1:]
    A							:							Optional[int]				=    param.view(*UpperCamelCase__		)
    A							:							Optional[Any]				=    param.transpose(0     ,							1		).contiguous()
    A							:							Dict				=    param.view(*UpperCamelCase__		)
    return param
def 				_lowerCamelCase(	UpperCamelCase__     :			Any     ,							UpperCamelCase__     :			Tuple		)		->	Optional[int]:
    A							:							int				=    config.n_positions
    A							:							Dict				=    config.n_layer
    A							:							List[str]				=    config.vocab_size
    A							:							Optional[Any]				=    config.n_head
    A							:							Union[str, Any]				=    config.n_embd // config.n_head

    A							:							Optional[int]				=    sd_megatron['''model.language_model.embedding.word_embeddings.weight'''][:vocab_size, :]
    A							:							Any				=    {
        '''transformer.wte.weight''': word_embeddings,
        '''transformer.wpe.weight''': sd_megatron['''model.language_model.embedding.position_embeddings.weight'''],
        '''transformer.ln_f.weight''': sd_megatron['''model.language_model.encoder.final_layernorm.weight'''],
        '''transformer.ln_f.bias''': sd_megatron['''model.language_model.encoder.final_layernorm.bias'''],
    }

    A							:							Optional[int]				=    '''model.language_model.encoder.layers.'''
    for i in range(UpperCamelCase__		):
        A							:							Dict				=    torch.tril(torch.ones((n_positions, n_positions)     ,							dtype=torch.bool		)		)
        A							:							Any				=    causal_mask.view(1     ,							1     ,							UpperCamelCase__     ,							UpperCamelCase__		)
        A							:							List[Any]				=    causal_mask
        A							:							List[str]				=    torch.tensor(-1e4     ,							dtype=torch.bfloataa		)

        A							:							Optional[int]				=    sd_megatron[F'''{pf}{i}.input_layernorm.weight''']
        A							:							Optional[int]				=    sd_megatron[F'''{pf}{i}.input_layernorm.bias''']

        A							:							Any				=    sd_megatron[F'''{pf}{i}.self_attention.query_key_value.weight''']
        A							:							List[Any]				=    fix_query_key_value_ordering(UpperCamelCase__     ,							3     ,							UpperCamelCase__     ,							UpperCamelCase__		)
        A							:							List[Any]				=    vala.transpose(0     ,							1		).contiguous()

        A							:							List[Any]				=    sd_megatron[F'''{pf}{i}.self_attention.query_key_value.bias''']
        A							:							Any				=    fix_query_key_value_ordering(UpperCamelCase__     ,							3     ,							UpperCamelCase__     ,							UpperCamelCase__		)
        A							:							List[Any]				=    vala

        A							:							Tuple				=    sd_megatron[F'''{pf}{i}.self_attention.dense.weight'''].transpose(
            0     ,							1		)
        A							:							Optional[int]				=    sd_megatron[F'''{pf}{i}.self_attention.dense.bias''']
        A							:							Any				=    sd_megatron[F'''{pf}{i}.post_attention_layernorm.weight''']
        A							:							Any				=    sd_megatron[F'''{pf}{i}.post_attention_layernorm.bias''']
        A							:							str				=    sd_megatron[F'''{pf}{i}.mlp.dense_h_to_4h.weight'''].transpose(0     ,							1		)
        A							:							List[Any]				=    sd_megatron[F'''{pf}{i}.mlp.dense_h_to_4h.bias''']
        A							:							Tuple				=    sd_megatron[F'''{pf}{i}.mlp.dense_4h_to_h.weight'''].transpose(
            0     ,							1		)
        A							:							Union[str, Any]				=    sd_megatron[F'''{pf}{i}.mlp.dense_4h_to_h.bias''']

    # For LM head, transformers' wants the matrix to weight embeddings.
    A							:							Optional[int]				=    word_embeddings

    return sd_hf
def 				_lowerCamelCase(	UpperCamelCase__     :			Union[str, Any]     ,							UpperCamelCase__     :			Optional[Any]		)		->	Union[str, Any]:
    A							:							List[str]				=    64_000
    A							:							str				=    config_megatron['''encoder_seq_length''']
    A							:							Union[str, Any]				=    config_megatron['''hidden_size''']
    A							:							Tuple				=    config_megatron['''num_layers''']
    A							:							List[str]				=    config_megatron['''num_attention_heads''']
    A							:							List[Any]				=    config_megatron['''ffn_hidden_size''']
    A							:							List[str]				=    '''gelu'''
    A							:							int				=    0.1
    A							:							Union[str, Any]				=    0.1
    A							:							Optional[int]				=    0.1
    A							:							Optional[int]				=    config_megatron['''layernorm_epsilon''']  # 1e-5
    A							:							Tuple				=    config_megatron['''init_method_std''']  # 0.02
    A							:							Any				=    config_megatron['''apply_query_key_layer_scaling''']  # True
    A							:							Tuple				=    True
    A							:							str				=    True

    # This identifies the 6.7B (7B) model which uses a different tokenizer
    if config_megatron["hidden_size"] == 4_096:
        A							:							Tuple				=    1  # <|endoftext|>
        A							:							Any				=    1  # <|endoftext|>
        A							:							List[str]				=    0  # <unk>
    else:
        A							:							Any				=    2  # <s>
        A							:							str				=    3  # <|endoftext|>
        A							:							Tuple				=    0  # <pad>

    return config_hf
def 				_lowerCamelCase(	UpperCamelCase__     :			List[Any]		)		->	Tuple:
    print(UpperCamelCase__		)

    A							:							Tuple				=    args.checkpoint_path
    A							:							Dict				=    args.save_path
    if isfile(UpperCamelCase__		):
        raise FileNotFoundError(F'''ERROR! could not find file {checkpoint_path}'''		)

    # Load the model.
    A							:							str				=    torch.load(UpperCamelCase__     ,							map_location='''cpu'''		)

    # Load the config.
    A							:							List[str]				=    checkpoint['''hyper_parameters''']['''cfg''']
    A							:							Tuple				=    GPTaConfig()
    A							:							int				=    copy_config(config_hf=UpperCamelCase__     ,							config_megatron=UpperCamelCase__		)
    A							:							Any				=    ['''GPT2LMHeadModel''']

    A							:							Optional[int]				=    checkpoint['''state_dict''']

    # Convert.
    print('''Converting'''		)
    A							:							Any				=    convert_megatron_checkpoint(UpperCamelCase__     ,							UpperCamelCase__		)

    # Print the structure of converted state dict.
    if args.print_checkpoint_structure:
        recursive_print(UpperCamelCase__     ,							UpperCamelCase__		)

    A							:							Union[str, Any]				=    '''GPTSw3Tokenizer'''

    # Store the config to file.
    print('''Saving config'''		)
    config_hf.save_pretrained(UpperCamelCase__		)

    # Store the state_dict to file.
    A							:							int				=    os.path.join(UpperCamelCase__     ,							'''pytorch_model.bin'''		)
    print(F'''Saving checkpoint to "{output_checkpoint_file}"'''		)
    torch.save(UpperCamelCase__     ,							UpperCamelCase__		)


if __name__ == "__main__":
     snake_case_     			=						argparse.ArgumentParser()
     parser.add_argument(
         """--checkpoint_path""",
         type=str,
         required=True,
         help="""e.g. megatron_gpt--val_loss=2.42-step=38000-consumed_samples=54720000""",
     )
     parser.add_argument("""--save_path""", type=str, required=True, help="""e.g. /home/user/gpt-sw3/hf""")
     parser.add_argument("""--print-checkpoint-structure""", action="""store_true""")
     snake_case_     			=						parser.parse_args()
     main(_args)
