




from __future__ import annotations

import inspect
import unittest

from transformers import MobileViTConfig
from transformers.file_utils import is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import numpy as np
					import tensorflow as tf

					from transformers import TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation, TFMobileViTModel
					from transformers.models.mobilevit.modeling_tf_mobilevit import TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import MobileViTImageProcessor
class      a__       (   snake_case__ ):






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			self.config_class(**self.inputs_dict     )
											self.parent.assertTrue(hasattr(_UpperCAmelCase						,     "hidden_sizes"     )     )
											self.parent.assertTrue(hasattr(_UpperCAmelCase						,     "neck_hidden_sizes"     )     )
											self.parent.assertTrue(hasattr(_UpperCAmelCase						,     "num_attention_heads"     )     )






class      a__       :






						def __init__(  self						,     _A						,     _A=1_3						,     _A=3_2						,     _A=2						,     _A=3						,     _A=6_4_0						,     _A=4						,     _A="silu"						,     _A=3						,     _A=3_2						,     _A=0.1						,     _A=0.1						,     _A=0.1						,     _A=0.02						,     _A=True						,     _A=True						,     _A=1_0						,     _A=None						,     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			parent
											__lowerCAmelCase							  =			batch_size
											__lowerCAmelCase							  =			image_size
											__lowerCAmelCase							  =			patch_size
											__lowerCAmelCase							  =			num_channels
											__lowerCAmelCase							  =			last_hidden_size
											__lowerCAmelCase							  =			num_attention_heads
											__lowerCAmelCase							  =			hidden_act
											__lowerCAmelCase							  =			conv_kernel_size
											__lowerCAmelCase							  =			output_stride
											__lowerCAmelCase							  =			hidden_dropout_prob
											__lowerCAmelCase							  =			attention_probs_dropout_prob
											__lowerCAmelCase							  =			classifier_dropout_prob
											__lowerCAmelCase							  =			use_labels
											__lowerCAmelCase							  =			is_training
											__lowerCAmelCase							  =			num_labels
											__lowerCAmelCase							  =			initializer_range
											__lowerCAmelCase							  =			scope






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

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

											__lowerCAmelCase							  =			self.get_config()

											return config, pixel_values, labels, pixel_labels






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											return MobileViTConfig(
											    image_size=self.image_size						,     patch_size=self.patch_size						,     num_channels=self.num_channels						,     num_attention_heads=self.num_attention_heads						,     hidden_act=self.hidden_act						,     conv_kernel_size=self.conv_kernel_size						,     output_stride=self.output_stride						,     hidden_dropout_prob=self.hidden_dropout_prob						,     attention_probs_dropout_prob=self.attention_probs_dropout_prob						,     classifier_dropout_prob=self.classifier_dropout_prob						,     initializer_range=self.initializer_range						,     )






						def 							__SCREAMING_SNAKE_CASE(  self						,     _A						,     _A						,     _A						,     _A     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			TFMobileViTModel(config=_UpperCAmelCase     )
											__lowerCAmelCase							  =			model(_UpperCAmelCase						,     training=_UpperCAmelCase     )
											__lowerCAmelCase							  =			self.image_size // self.output_stride
											self.parent.assertEqual(
											    result.last_hidden_state.shape						,     (self.batch_size, self.last_hidden_size, expected_height, expected_width)     )






						def 							__SCREAMING_SNAKE_CASE(  self						,     _A						,     _A						,     _A						,     _A     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			self.num_labels
											__lowerCAmelCase							  =			TFMobileViTForImageClassification(_UpperCAmelCase     )
											__lowerCAmelCase							  =			model(_UpperCAmelCase						,     labels=_UpperCAmelCase						,     training=_UpperCAmelCase     )
											self.parent.assertEqual(result.logits.shape						,     (self.batch_size, self.num_labels)     )






						def 							__SCREAMING_SNAKE_CASE(  self						,     _A						,     _A						,     _A						,     _A     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			self.num_labels
											__lowerCAmelCase							  =			TFMobileViTForSemanticSegmentation(_UpperCAmelCase     )
											__lowerCAmelCase							  =			self.image_size // self.output_stride

											__lowerCAmelCase							  =			model(_UpperCAmelCase						,     training=_UpperCAmelCase     )
											self.parent.assertEqual(
											    result.logits.shape						,     (self.batch_size, self.num_labels, expected_height, expected_width)     )

											__lowerCAmelCase							  =			model(_UpperCAmelCase						,     labels=_UpperCAmelCase						,     training=_UpperCAmelCase     )
											self.parent.assertEqual(
											    result.logits.shape						,     (self.batch_size, self.num_labels, expected_height, expected_width)     )






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



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






@require_tf
class      a__       (   snake_case__					,							snake_case__					,							unittest.TestCase ):
						_a				:       Optional[Any]   =       (
						    (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
						    if is_tf_available()
						    else ()
						)
						_a				:       int   =       (
						    {"""feature-extraction""": TFMobileViTModel, """image-classification""": TFMobileViTForImageClassification}
						    if is_tf_available()
						    else {}
						)

						_a				:       Any   =       False
						_a				:       List[str]   =       False
						_a				:       Tuple   =       False
						_a				:       Optional[int]   =       False
						_a				:       Tuple   =       False






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			TFMobileViTModelTester(self     )
											__lowerCAmelCase							  =			TFMobileViTConfigTester(self						,     config_class=_UpperCAmelCase						,     has_text_modality=_UpperCAmelCase     )






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											self.config_tester.run_common_tests()






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




											"""simple docstring"""



											pass






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




											"""simple docstring"""



											pass






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




											"""simple docstring"""



											pass






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			self.model_tester.prepare_config_and_inputs_for_common()

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

																__lowerCAmelCase							  =			['''pixel_values''']
																self.assertListEqual(arg_names[:1]						,     _UpperCAmelCase     )






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*_UpperCAmelCase     )






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											def check_hidden_states_output(_A						,     _A						,     _A     ):
																__lowerCAmelCase							  =			model_class(_UpperCAmelCase     )

																__lowerCAmelCase							  =			model(**self._prepare_for_class(_UpperCAmelCase						,     _UpperCAmelCase     )     )

																__lowerCAmelCase							  =			outputs.hidden_states

																__lowerCAmelCase							  =			5
																self.assertEqual(len(_UpperCAmelCase     )						,     _UpperCAmelCase     )

																# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
																# with the width and height being successively divided by 2.
																__lowerCAmelCase							  =			2
																for i in range(len(_UpperCAmelCase     )     ):
																					self.assertListEqual(
																					    list(hidden_states[i].shape[-2:]     )						,     [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]						,     )
																					divisor *= 2

																self.assertEqual(self.model_tester.output_stride						,     divisor // 2     )

											__lowerCAmelCase							  =			self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																__lowerCAmelCase							  =			True
																check_hidden_states_output(_UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase     )

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

																check_hidden_states_output(_UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase     )






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase     )






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_semantic_segmentation(*_UpperCAmelCase     )






						@unittest.skipIf(
						    not is_tf_available() or len(tf.config.list_physical_devices("GPU"     )     ) == 0						,     reason="TF does not support backprop for grouped convolutions on CPU."						,     )
						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											super().test_dataset_conversion()






						def 							__SCREAMING_SNAKE_CASE(  self						,     _A						,     _A						,     _A=2E-1						,     _A=2E-1     ):




											"""simple docstring"""



											self.assertTrue(np.allclose(_UpperCAmelCase						,     _UpperCAmelCase						,     atol=_UpperCAmelCase						,     rtol=_UpperCAmelCase     )     )






						@unittest.skipIf(
						    not is_tf_available() or len(tf.config.list_physical_devices("GPU"     )     ) == 0						,     reason="TF does not support backprop for grouped convolutions on CPU."						,     )
						@slow
						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																# Since `TFMobileViTModel` cannot operate with the default `fit()` method.
																if model_class.__name__ != "TFMobileViTModel":
																					__lowerCAmelCase							  =			model_class(_UpperCAmelCase     )
																					if getattr(_UpperCAmelCase						,     "hf_compute_loss"						,     _UpperCAmelCase     ):
																										super().test_keras_fit()






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			self.model_tester.prepare_config_and_inputs_for_common()
											for model_class in self.all_model_classes:
																# set an ignore index to correctly test the masked loss used in
																# `TFMobileViTForSemanticSegmentation`.
																if model_class.__name__ != "TFMobileViTForSemanticSegmentation":
																					__lowerCAmelCase							  =			5

																__lowerCAmelCase							  =			model_class(_UpperCAmelCase     )
																if getattr(_UpperCAmelCase						,     "hf_compute_loss"						,     _UpperCAmelCase     ):
																					# The number of elements in the loss should be the same as the number of elements in the label
																					__lowerCAmelCase							  =			self._prepare_for_class(inputs_dict.copy()						,     _UpperCAmelCase						,     return_labels=_UpperCAmelCase     )
																					__lowerCAmelCase							  =			prepared_for_class[
																					    sorted(prepared_for_class.keys() - inputs_dict.keys()						,     reverse=_UpperCAmelCase     )[0]
																					]
																					__lowerCAmelCase							  =			added_label.shape.as_list()[:1]

																					# Test that model correctly compute the loss with kwargs
																					__lowerCAmelCase							  =			self._prepare_for_class(inputs_dict.copy()						,     _UpperCAmelCase						,     return_labels=_UpperCAmelCase     )
																					__lowerCAmelCase							  =			{'''input_ids''', '''pixel_values''', '''input_features'''}
																					__lowerCAmelCase							  =			possible_input_names.intersection(set(_UpperCAmelCase     )     ).pop()
																					__lowerCAmelCase							  =			prepared_for_class.pop(_UpperCAmelCase     )

																					__lowerCAmelCase							  =			model(_UpperCAmelCase						,     **_UpperCAmelCase     )[0]
																					self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]     )

																					# Test that model correctly compute the loss when we mask some positions
																					__lowerCAmelCase							  =			self._prepare_for_class(inputs_dict.copy()						,     _UpperCAmelCase						,     return_labels=_UpperCAmelCase     )
																					__lowerCAmelCase							  =			{'''input_ids''', '''pixel_values''', '''input_features'''}
																					__lowerCAmelCase							  =			possible_input_names.intersection(set(_UpperCAmelCase     )     ).pop()
																					__lowerCAmelCase							  =			prepared_for_class.pop(_UpperCAmelCase     )
																					if "labels" in prepared_for_class:
																										__lowerCAmelCase							  =			prepared_for_class['''labels'''].numpy()
																										if len(labels.shape     ) > 1 and labels.shape[1] != 1:
																															# labels[0] = -100
																															__lowerCAmelCase							  =			tf.convert_to_tensor(_UpperCAmelCase     )
																															__lowerCAmelCase							  =			model(_UpperCAmelCase						,     **_UpperCAmelCase     )[0]
																															self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]     )
																															self.assertTrue(not np.any(np.isnan(loss.numpy()     )     )     )

                # Test that model correctly compute the loss with a dict
																					__lowerCAmelCase							  =			self._prepare_for_class(inputs_dict.copy()						,     _UpperCAmelCase						,     return_labels=_UpperCAmelCase     )
																					__lowerCAmelCase							  =			model(_UpperCAmelCase     )[0]
																					self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]     )

																					# Test that model correctly compute the loss with a tuple
																					__lowerCAmelCase							  =			self._prepare_for_class(inputs_dict.copy()						,     _UpperCAmelCase						,     return_labels=_UpperCAmelCase     )

																					# Get keys that were added with the _prepare_for_class function
																					__lowerCAmelCase							  =			prepared_for_class.keys() - inputs_dict.keys()
																					__lowerCAmelCase							  =			inspect.signature(model.call     ).parameters
																					__lowerCAmelCase							  =			list(signature.keys()     )

																					# Create a dictionary holding the location of the tensors in the tuple
																					__lowerCAmelCase							  =			{0: input_name}
																					for label_key in label_keys:
																										__lowerCAmelCase							  =			signature_names.index(_UpperCAmelCase     )
																										__lowerCAmelCase							  =			label_key
																					__lowerCAmelCase							  =			sorted(tuple_index_mapping.items()     )
																					# Initialize a list with their default values, update the values and convert to a tuple
																					__lowerCAmelCase							  =			[]

																					for name in signature_names:
																										if name != "kwargs":
																															list_input.append(signature[name].default     )

																					for index, value in sorted_tuple_index_mapping:
																										__lowerCAmelCase							  =			prepared_for_class[value]

																					__lowerCAmelCase							  =			tuple(_UpperCAmelCase     )

																					# Send to model
																					__lowerCAmelCase							  =			model(tuple_input[:-1]     )[0]

																					self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]     )






						@slow
						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__lowerCAmelCase							  =			TFMobileViTModel.from_pretrained(_UpperCAmelCase     )
																self.assertIsNotNone(_UpperCAmelCase     )







def       _a (		):
					__lowerCAmelCase							  =			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
					return image


@require_tf
class      a__       (   unittest.TestCase ):






						@slow
						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			TFMobileViTForImageClassification.from_pretrained("apple/mobilevit-xx-small"     )

											__lowerCAmelCase							  =			MobileViTImageProcessor.from_pretrained("apple/mobilevit-xx-small"     )
											__lowerCAmelCase							  =			prepare_img()
											__lowerCAmelCase							  =			image_processor(images=_UpperCAmelCase						,     return_tensors="tf"     )

											# forward pass
											__lowerCAmelCase							  =			model(**_UpperCAmelCase						,     training=_UpperCAmelCase     )

											# verify the logits
											__lowerCAmelCase							  =			tf.TensorShape((1, 1_0_0_0)     )
											self.assertEqual(outputs.logits.shape						,     _UpperCAmelCase     )

											__lowerCAmelCase							  =			tf.constant([-1.93_64, -1.23_27, -0.46_53]     )

											tf.debugging.assert_near(outputs.logits[0, :3]						,     _UpperCAmelCase						,     atol=1E-4						,     rtol=1E-0_4     )






						@slow
						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			TFMobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small"     )

											__lowerCAmelCase							  =			MobileViTImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-xx-small"     )

											__lowerCAmelCase							  =			prepare_img()
											__lowerCAmelCase							  =			image_processor(images=_UpperCAmelCase						,     return_tensors="tf"     )

											# forward pass
											__lowerCAmelCase							  =			model(inputs.pixel_values						,     training=_UpperCAmelCase     )
											__lowerCAmelCase							  =			outputs.logits

											# verify the logits
											__lowerCAmelCase							  =			tf.TensorShape((1, 2_1, 3_2, 3_2)     )
											self.assertEqual(logits.shape						,     _UpperCAmelCase     )

											__lowerCAmelCase							  =			tf.constant(
											    [
											        [[6.97_13, 6.97_86, 7.24_22], [7.28_93, 7.28_25, 7.44_46], [7.65_80, 7.87_97, 7.94_20]],
											        [[-10.68_69, -10.32_50, -10.34_71], [-10.42_28, -9.98_68, -9.71_32], [-11.04_05, -11.02_21, -10.73_18]],
											        [[-3.30_89, -2.85_39, -2.67_40], [-3.27_06, -2.56_21, -2.51_08], [-3.25_34, -2.66_15, -2.66_51]],
											    ]     )

											tf.debugging.assert_near(logits[0, :3, :3, :3]						,     _UpperCAmelCase						,     rtol=1E-4						,     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)
