import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class __snake_case							(		__snake_case  ,						__snake_case  ,						unittest.TestCase						):
   lowerCAmelCase_								=				IFImgaImgPipeline
   lowerCAmelCase_								=				TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
   lowerCAmelCase_								=				TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
   lowerCAmelCase_								=				PipelineTesterMixin.required_optional_params - {"latents"}




   def       __a		(       self     :							int ):



     """simple docstring"""






     return self._get_dummy_components()




   def       __a		(       self     :							List[Any]			,      _lowercase     :							Optional[Any]			,      _lowercase     :							List[Any]=0 ):



     """simple docstring"""






     if str(__UpperCamelCase ).startswith("""mps""" ):
       SCREAMING_SNAKE_CASE__     				= torch.manual_seed(__UpperCamelCase )
     else:
       SCREAMING_SNAKE_CASE__     				= torch.Generator(device=__UpperCamelCase ).manual_seed(__UpperCamelCase )

     SCREAMING_SNAKE_CASE__     				= floats_tensor((1, 3, 32, 32)			,      rng=random.Random(__UpperCamelCase ) ).to(__UpperCamelCase )

     SCREAMING_SNAKE_CASE__     				= {
         """prompt""": """A painting of a squirrel eating a burger""",
         """image""": image,
         """generator""": generator,
         """num_inference_steps""": 2,
         """output_type""": """numpy""",
     }

     return inputs




   def       __a		(       self     :							Any ):



     """simple docstring"""






     self._test_save_load_optional_components()




   @unittest.skipIf(
       torch_device != """cuda""" or not is_xformers_available()			,      reason="""XFormers attention is only available with CUDA and `xformers` installed"""			,      )
   def       __a		(       self     :							List[Any] ):



     """simple docstring"""






     self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )




   @unittest.skipIf(torch_device != """cuda"""			,      reason="""float16 requires CUDA""" )
   def       __a		(       self     :							List[Any] ):



     """simple docstring"""






     super().test_save_load_floataa(expected_max_diff=1E-1 )




   @unittest.skipIf(torch_device != """cuda"""			,      reason="""float16 requires CUDA""" )
   def       __a		(       self     :							str ):



     """simple docstring"""






     super().test_floataa_inference(expected_max_diff=1E-1 )




   def       __a		(       self     :							Optional[Any] ):



     """simple docstring"""






     self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )




   def       __a		(       self     :							List[str] ):



     """simple docstring"""






     self._test_save_load_local()




   def       __a		(       self     :							int ):



     """simple docstring"""






     self._test_inference_batch_single_identical(
         expected_max_diff=1E-2			,      )


import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig





def        lowerCAmelCase       (				snake_case__   :      List[str]				,					snake_case__   :      List[str]				,					snake_case__   :      Optional[int]=0 )->			Union[str, Any]:
    # Format the message.
    if name is None:
        A_								=					None
    else:
        A_								=					"." * max(0				,					spaces - 2 ) + "# {:" + str(50 - spaces ) + "s}"
        A_								=					fmt.format(snake_case__ )

    # Print and recurse (if needed).
    if isinstance(snake_case__				,					snake_case__ ):
        if msg is not None:
            print(snake_case__ )
        for k in val.keys():
            recursive_print(snake_case__				,					val[k]				,					spaces + 2 )
    elif isinstance(snake_case__				,					torch.Tensor ):
        print(snake_case__				,					":"				,					val.size() )
    else:
        print(snake_case__				,					":"				,					snake_case__ )





def        lowerCAmelCase       (				snake_case__   :      Any				,					snake_case__   :      List[Any]				,					snake_case__   :      str				,					snake_case__   :      str )->			Union[str, Any]:
    # 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_								=					param.size()
    # other versions store [num_heads * num_splits * hidden_size, :]
    A_								=					(num_heads, num_splits, hidden_size) + input_shape[1:]
    A_								=					param.view(*snake_case__ )
    A_								=					param.transpose(0				,					1 ).contiguous()
    A_								=					param.view(*snake_case__ )
    return param





def        lowerCAmelCase       (				snake_case__   :      Dict				,					snake_case__   :      str )->			Optional[int]:
    A_								=					config.n_positions
    A_								=					config.n_layer
    A_								=					config.vocab_size
    A_								=					config.n_head
    A_								=					config.n_embd // config.n_head

    A_								=					sd_megatron["model.language_model.embedding.word_embeddings.weight"][:vocab_size, :]
    A_								=					{
        "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_								=					"model.language_model.encoder.layers."
    for i in range(snake_case__ ):
        A_								=					torch.tril(torch.ones((n_positions, n_positions)				,					dtype=torch.bool ) )
        A_								=					causal_mask.view(1				,					1				,					snake_case__				,					snake_case__ )
        A_								=					causal_mask
        A_								=					torch.tensor(-1e4				,					dtype=torch.bfloataa )

        A_								=					sd_megatron[f'{pf}{i}.input_layernorm.weight']
        A_								=					sd_megatron[f'{pf}{i}.input_layernorm.bias']

        A_								=					sd_megatron[f'{pf}{i}.self_attention.query_key_value.weight']
        A_								=					fix_query_key_value_ordering(snake_case__				,					3				,					snake_case__				,					snake_case__ )
        A_								=					vala.transpose(0				,					1 ).contiguous()

        A_								=					sd_megatron[f'{pf}{i}.self_attention.query_key_value.bias']
        A_								=					fix_query_key_value_ordering(snake_case__				,					3				,					snake_case__				,					snake_case__ )
        A_								=					vala

        A_								=					sd_megatron[f'{pf}{i}.self_attention.dense.weight'].transpose(
            0				,					1 )
        A_								=					sd_megatron[f'{pf}{i}.self_attention.dense.bias']
        A_								=					sd_megatron[f'{pf}{i}.post_attention_layernorm.weight']
        A_								=					sd_megatron[f'{pf}{i}.post_attention_layernorm.bias']
        A_								=					sd_megatron[f'{pf}{i}.mlp.dense_h_to_4h.weight'].transpose(0				,					1 )
        A_								=					sd_megatron[f'{pf}{i}.mlp.dense_h_to_4h.bias']
        A_								=					sd_megatron[f'{pf}{i}.mlp.dense_4h_to_h.weight'].transpose(
            0				,					1 )
        A_								=					sd_megatron[f'{pf}{i}.mlp.dense_4h_to_h.bias']

    # For LM head, transformers' wants the matrix to weight embeddings.
    A_								=					word_embeddings

    return sd_hf





def        lowerCAmelCase       (				snake_case__   :      Optional[int]				,					snake_case__   :      List[Any] )->			Dict:
    A_								=					64000
    A_								=					config_megatron["encoder_seq_length"]
    A_								=					config_megatron["hidden_size"]
    A_								=					config_megatron["num_layers"]
    A_								=					config_megatron["num_attention_heads"]
    A_								=					config_megatron["ffn_hidden_size"]
    A_								=					"gelu"
    A_								=					0.1
    A_								=					0.1
    A_								=					0.1
    A_								=					config_megatron["layernorm_epsilon"]  # 1e-5
    A_								=					config_megatron["init_method_std"]  # 0.02
    A_								=					config_megatron["apply_query_key_layer_scaling"]  # True
    A_								=					True
    A_								=					True

    # This identifies the 6.7B (7B) model which uses a different tokenizer
    if config_megatron["hidden_size"] == 4096:
        A_								=					1  # <|endoftext|>
        A_								=					1  # <|endoftext|>
        A_								=					0  # <unk>
    else:
        A_								=					2  # <s>
        A_								=					3  # <|endoftext|>
        A_								=					0  # <pad>

    return config_hf





def        lowerCAmelCase       (				snake_case__   :      Tuple )->			Dict:
    print(snake_case__ )

    A_								=					args.checkpoint_path
    A_								=					args.save_path
    if isfile(snake_case__ ):
        raise FileNotFoundError(f'ERROR! could not find file {checkpoint_path}' )

    # Load the model.
    A_								=					torch.load(snake_case__				,					map_location="cpu" )

    # Load the config.
    A_								=					checkpoint["hyper_parameters"]["cfg"]
    A_								=					GPTaConfig()
    A_								=					copy_config(config_hf=snake_case__				,					config_megatron=snake_case__ )
    A_								=					["GPT2LMHeadModel"]

    A_								=					checkpoint["state_dict"]

    # Convert.
    print("Converting" )
    A_								=					convert_megatron_checkpoint(snake_case__				,					snake_case__ )

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

    A_								=					"GPTSw3Tokenizer"

    # Store the config to file.
    print("Saving config" )
    config_hf.save_pretrained(snake_case__ )

    # Store the state_dict to file.
    A_								=					os.path.join(snake_case__				,					"pytorch_model.bin" )
    print(f'Saving checkpoint to "{output_checkpoint_file}"' )
    torch.save(snake_case__				,					snake_case__ )


if __name__ == "__main__":
     __magic_name__  :  Union[str, Any]     			=   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')
     __magic_name__  :  str     			=   parser.parse_args()
     main(_args)






