import argparse
import time

import torch
import transformers
from measures_util import end_measure, log_measures, start_measure
from transformers import AutoConfig, AutoModelForCausalLM, AutoModelForSeqaSeqLM, AutoTokenizer

from accelerate.utils import compute_module_sizes


snake_case__						      =			{
    'gpt-j-6b': {'is_causal': True, 'model': 'sgugger/sharded-gpt-j-6B', 'tokenizer': 'EleutherAI/gpt-j-6B'},
    'gpt-neox': {'is_causal': True, 'model': 'EleutherAI/gpt-neox-20b'},
    'opt': {'is_causal': True, 'model': 'facebook/opt-30b'},
    'T0pp': {'is_causal': False, 'model': 'bigscience/T0pp', 'model_revision': 'sharded'},
}

snake_case__						      =			[
    'Hello, my name is',
    'Are unicorns real? Unicorns are',
    'For the first time in several years,',
    'My name is Julien and I am',
    'The goal of life is',
    'Whenever I\'m sad, I like to',
]
def    __magic_name__(	)    ->     List[Any]:



      '''simple docstring'''

      _lowerCamelCase           =					argparse.ArgumentParser(description='''Run and time generations on a big model using Accelerate.'''		)
      parser.add_argument('''model_name'''			,    type=lowercase__			,    default=lowercase__			,    help='''The name of the model to try.'''		)
      parser.add_argument(
          '''--tokenizer_name'''			,    type=lowercase__			,    default=lowercase__			,    help='''The name of the tokenizer (if different from the model.'''		)
      parser.add_argument('''--is_causal'''			,    type=lowercase__			,    default=lowercase__			,    help='''Whether or not the model is causal.'''		)
      parser.add_argument(
          '''--model_revision'''			,    type=lowercase__			,    default=lowercase__			,    help='''The revision to use for the model checkpoint.'''		)
      parser.add_argument('''--torch_dtype'''			,    type=lowercase__			,    default=lowercase__			,    help='''The dtype for the model.'''		)
      parser.add_argument('''--disk_offload'''			,    action='''store_true'''		)

      _lowerCamelCase           =					parser.parse_args()

      # Sanitize args
      if args.model_name in DEFAULT_MODELS:
            _lowerCamelCase           =					DEFAULT_MODELS[args.model_name]
            _lowerCamelCase           =					defaults['''model''']
            if args.tokenizer_name is None:
                  _lowerCamelCase           =					defaults.get('''tokenizer'''			,    args.model_name		)
            if args.is_causal is None:
                  _lowerCamelCase           =					defaults['''is_causal''']
            if args.model_revision is None:
                  _lowerCamelCase           =					defaults.get('''model_revision'''			,    '''main'''		)

      if args.is_causal is None:
            raise ValueError('''Could not infer the default for `--is_causal`, pass either True or False for it.'''		)
      if args.tokenizer_name is None:
            _lowerCamelCase           =					args.model_name
      if args.model_revision is None:
            _lowerCamelCase           =					'''main'''

      return args
def    __magic_name__(	)    ->     int:



      '''simple docstring'''

      transformers.utils.logging.set_verbosity_error()
      _lowerCamelCase           =					parse_args()

      if args.torch_dtype is None:
            _lowerCamelCase           =					AutoConfig.from_pretrained(args.model_name		)
            _lowerCamelCase           =					getattr(lowercase__			,    '''torch_dtype'''			,    torch.floataa		)
      else:
            _lowerCamelCase           =					getattr(lowercase__			,    args.torch_dtype		)
      _lowerCamelCase           =					AutoModelForCausalLM if args.is_causal else AutoModelForSeqaSeqLM
      _lowerCamelCase           =					{
          '''torch_dtype''': torch_dtype,
          '''revision''': args.model_revision,
      }
      if args.disk_offload:
            _lowerCamelCase           =					'''tmp_offload'''
            _lowerCamelCase           =					True

      _lowerCamelCase           =					start_measure()
      _lowerCamelCase           =					model_cls.from_pretrained(args.model_name			,    device_map='''auto'''			,    **lowercase__		)
      _lowerCamelCase           =					end_measure(lowercase__		)
      log_measures(lowercase__			,    '''Model loading'''		)

      _lowerCamelCase           =					compute_module_sizes(lowercase__		)
      _lowerCamelCase           =					{v: 0 for v in model.hf_device_map.values()}
      for module, device in model.hf_device_map.items():
            device_size[device] += module_sizes[module]
      _lowerCamelCase           =					'''\n'''.join([F'- {device}: {size // 2**20}MiB' for device, size in device_size.items()]		)
      print(F'\nTheoretical use:\n{message}'		)

      _lowerCamelCase           =					AutoTokenizer.from_pretrained(args.tokenizer_name		)

      _lowerCamelCase           =					start_measure()
      _lowerCamelCase           =					[]
      _lowerCamelCase           =					[]
      _lowerCamelCase           =					[]
      for prompt in PROMPTS:
            _lowerCamelCase           =					tokenizer(lowercase__			,    return_tensors='''pt'''		).to(0		)
            _lowerCamelCase           =					inputs['''input_ids'''][0].tolist()
            _lowerCamelCase           =					time.time()
            _lowerCamelCase           =					model.generate(inputs['''input_ids''']		)
            _lowerCamelCase           =					time.time()
            _lowerCamelCase           =					outputs[0].tolist()
            _lowerCamelCase           =					len(lowercase__		) if outputs[: len(lowercase__		)] != tokens else len(lowercase__		) - len(lowercase__		)
            _lowerCamelCase           =					after_generate - before_generate

            _lowerCamelCase           =					tokenizer.decode(lowercase__			,    skip_special_tokens=lowercase__		)
            texts_outs.append(lowercase__		)
            generation_times.append(lowercase__		)
            gen_tokens.append(lowercase__		)
            print(F'Prompt: {prompt}\nGeneration {text_out}\nIn {generation_time:.2f}s for {num_gen_tokens} tokens\n'		)

      _lowerCamelCase           =					end_measure(lowercase__		)
      log_measures(lowercase__			,    '''Model generation'''		)

      _lowerCamelCase           =					[gen / tok for gen, tok in zip(lowercase__			,    lowercase__		)]
      _lowerCamelCase           =					sum(lowercase__		) / len(lowercase__		)
      print(F'Average time of generation per token: {avg_gen:.2f}s'		)
      print(F'First generation (avg time per token): {generation_times_per_token[0]:.2f}s'		)
      _lowerCamelCase           =					sum(generation_times_per_token[1:]		) / (len(lowercase__		) - 1)
      print(F'Average time of generation per token (excluding the first): {avg_gen:.2f}s'		)


if __name__ == "__main__":
     main()


"""simple docstring"""




import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, slow, torch_device

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin





class      __UpperCamelCase					(  _A	,						_A	,						unittest.TestCase		):
		SCREAMING_SNAKE_CASE														=       UNetaDModel
		SCREAMING_SNAKE_CASE														=       "sample"


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						Tuple):
							A     =   4
							A     =   1_4
							A     =   1_6

							A     =   floats_tensor((batch_size, num_features, seq_len)).to(__SCREAMING_SNAKE_CASE)
							A     =   torch.tensor([1_0] * batch_size).to(__SCREAMING_SNAKE_CASE)

							return {"sample": noise, "timestep": time_step}


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[int]):
							return (4, 1_4, 1_6)


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						int):
							return (4, 1_4, 1_6)


		def    SCREAMING_SNAKE_CASE__  (self					:						List[Any]):
							pass


		def    SCREAMING_SNAKE_CASE__  (self					:						str):
							pass


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						Tuple):
							super().test_determinism()


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						Any):
							super().test_outputs_equivalence()


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						Any):
							super().test_from_save_pretrained()


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						Any):
							super().test_from_save_pretrained_variant()


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						Any):
							super().test_model_from_pretrained()


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						List[Any]):
							super().test_output()


		def    SCREAMING_SNAKE_CASE__  (self					:						Union[str, Any]):
							A     =   {
							    "block_out_channels": (3_2, 6_4, 1_2_8, 2_5_6),
							    "in_channels": 1_4,
							    "out_channels": 1_4,
							    "time_embedding_type": "positional",
							    "use_timestep_embedding": True,
							    "flip_sin_to_cos": False,
							    "freq_shift": 1.0,
							    "out_block_type": "OutConv1DBlock",
							    "mid_block_type": "MidResTemporalBlock1D",
							    "down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
							    "up_block_types": ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D"),
							    "act_fn": "mish",
							}
							A     =   self.dummy_input
							return init_dict, inputs_dict


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[Any]):
							A     ,       A     =   UNetaDModel.from_pretrained(
							    "bglick13/hopper-medium-v2-value-function-hor32"	,	output_loading_info=__SCREAMING_SNAKE_CASE	,	subfolder="unet")
							self.assertIsNotNone(__SCREAMING_SNAKE_CASE)
							self.assertEqual(len(loading_info["missing_keys"])	,	0)

							model.to(__SCREAMING_SNAKE_CASE)
							A     =   model(**self.dummy_input)

							assert image is not None, "Make sure output is not None"


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[Any]):
							A     =   UNetaDModel.from_pretrained("bglick13/hopper-medium-v2-value-function-hor32"	,	subfolder="unet")
							torch.manual_seed(0)
							if torch.cuda.is_available():
												torch.cuda.manual_seed_all(0)

							A     =   model.config.in_channels
							A     =   1_6
							A     =   torch.randn((1, seq_len, num_features)).permute(
							    0	,	2	,	1)  # match original, we can update values and remove
							A     =   torch.full((num_features,)	,	0)

							with torch.no_grad():
												A     =   model(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE).sample.permute(0	,	2	,	1)

							A     =   output[0, -3:, -3:].flatten()
							# fmt: off
							A     =   torch.tensor([-2.1_3_7_1_7_2, 1.1_4_2_6_0_1_6, 0.3_6_8_8_6_8_7, -0.7_6_6_9_2_2, 0.7_3_0_3_1_4_6, 0.1_1_0_3_8_8_6_4, -0.4_7_6_0_6_3_3, 0.1_3_2_7_0_1_7_2, 0.0_2_5_9_1_3_4_8])
							# fmt: on
							self.assertTrue(torch.allclose(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE	,	rtol=1E-3))


		def    SCREAMING_SNAKE_CASE__  (self					:						Union[str, Any]):
							# Not implemented yet for this UNet
							pass


		@slow
		def    SCREAMING_SNAKE_CASE__  (self					:						Dict):
							A     =   "harmonai/maestro-150k"
							A     =   UNetaDModel.from_pretrained(__SCREAMING_SNAKE_CASE	,	subfolder="unet")
							model.to(__SCREAMING_SNAKE_CASE)

							A     =   6_5_5_3_6
							A     =   torch.sin(torch.arange(__SCREAMING_SNAKE_CASE)[None, None, :].repeat(1	,	2	,	1)).to(__SCREAMING_SNAKE_CASE)
							A     =   torch.tensor([1]).to(__SCREAMING_SNAKE_CASE)

							with torch.no_grad():
												A     =   model(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE).sample

							A     =   output.abs().sum()
							A     =   output.abs().max()

							assert (output_sum - 2_2_4.0_8_9_6).abs() < 0.5
							assert (output_max - 0.0_6_0_7).abs() < 4E-4





class      __UpperCamelCase					(  _A	,						_A	,						unittest.TestCase		):
		SCREAMING_SNAKE_CASE														=       UNetaDModel
		SCREAMING_SNAKE_CASE														=       "sample"


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						Any):
							A     =   4
							A     =   1_4
							A     =   1_6

							A     =   floats_tensor((batch_size, num_features, seq_len)).to(__SCREAMING_SNAKE_CASE)
							A     =   torch.tensor([1_0] * batch_size).to(__SCREAMING_SNAKE_CASE)

							return {"sample": noise, "timestep": time_step}


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						int):
							return (4, 1_4, 1_6)


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						Tuple):
							return (4, 1_4, 1)


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[Any]):
							super().test_determinism()


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						str):
							super().test_outputs_equivalence()


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						Dict):
							super().test_from_save_pretrained()


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						Dict):
							super().test_from_save_pretrained_variant()


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						List[Any]):
							super().test_model_from_pretrained()


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						Any):
							# UNetRL is a value-function is different output shape
							A     ,       A     =   self.prepare_init_args_and_inputs_for_common()
							A     =   self.model_class(**__SCREAMING_SNAKE_CASE)
							model.to(__SCREAMING_SNAKE_CASE)
							model.eval()

							with torch.no_grad():
												A     =   model(**__SCREAMING_SNAKE_CASE)

												if isinstance(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE):
																	A     =   output.sample

							self.assertIsNotNone(__SCREAMING_SNAKE_CASE)
							A     =   torch.Size((inputs_dict["sample"].shape[0], 1))
							self.assertEqual(output.shape	,	__SCREAMING_SNAKE_CASE	,	"Input and output shapes do not match")


		def    SCREAMING_SNAKE_CASE__  (self					:						Union[str, Any]):
							pass


		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[int]):
							pass


		def    SCREAMING_SNAKE_CASE__  (self					:						Any):
							A     =   {
							    "in_channels": 1_4,
							    "out_channels": 1_4,
							    "down_block_types": ["DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"],
							    "up_block_types": [],
							    "out_block_type": "ValueFunction",
							    "mid_block_type": "ValueFunctionMidBlock1D",
							    "block_out_channels": [3_2, 6_4, 1_2_8, 2_5_6],
							    "layers_per_block": 1,
							    "downsample_each_block": True,
							    "use_timestep_embedding": True,
							    "freq_shift": 1.0,
							    "flip_sin_to_cos": False,
							    "time_embedding_type": "positional",
							    "act_fn": "mish",
							}
							A     =   self.dummy_input
							return init_dict, inputs_dict


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						int):
							A     ,       A     =   UNetaDModel.from_pretrained(
							    "bglick13/hopper-medium-v2-value-function-hor32"	,	output_loading_info=__SCREAMING_SNAKE_CASE	,	subfolder="value_function")
							self.assertIsNotNone(__SCREAMING_SNAKE_CASE)
							self.assertEqual(len(vf_loading_info["missing_keys"])	,	0)

							value_function.to(__SCREAMING_SNAKE_CASE)
							A     =   value_function(**self.dummy_input)

							assert image is not None, "Make sure output is not None"


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						Union[str, Any]):
							A     ,       A     =   UNetaDModel.from_pretrained(
							    "bglick13/hopper-medium-v2-value-function-hor32"	,	output_loading_info=__SCREAMING_SNAKE_CASE	,	subfolder="value_function")
							torch.manual_seed(0)
							if torch.cuda.is_available():
												torch.cuda.manual_seed_all(0)

							A     =   value_function.config.in_channels
							A     =   1_4
							A     =   torch.randn((1, seq_len, num_features)).permute(
							    0	,	2	,	1)  # match original, we can update values and remove
							A     =   torch.full((num_features,)	,	0)

							with torch.no_grad():
												A     =   value_function(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE).sample

							# fmt: off
							A     =   torch.tensor([1_6_5.2_5] * seq_len)
							# fmt: on
							self.assertTrue(torch.allclose(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE	,	rtol=1E-3))


		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[Any]):
							# Not implemented yet for this UNet
							pass





