
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


_lowerCamelCase     					=    {
    '''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'''},
}

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

			_snake_case						=    parser.parse_args()

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

			return args






def  a_	( )				->						List[str]:
			transformers.utils.logging.set_verbosity_error()
			_snake_case						=    parse_args()

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

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

			_snake_case						=    compute_module_sizes(snake_case__				)
			_snake_case						=    {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]
			_snake_case						=    '\n'.join([f'''- {device}: {size // 2**20}MiB''' for device, size in device_size.items()]				)
			print(f'''\nTheoretical use:\n{message}'''				)

			_snake_case						=    AutoTokenizer.from_pretrained(args.tokenizer_name				)

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

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

			_snake_case						=    end_measure(snake_case__				)
			log_measures(snake_case__ ,      'Model generation'				)

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


if __name__ == "__main__":
			main()

# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


_lowerCamelCase :       int     					=    {
    '''configuration_tvlt''': ['''TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TvltConfig'''],
    '''feature_extraction_tvlt''': ['''TvltFeatureExtractor'''],
    '''processing_tvlt''': ['''TvltProcessor'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_lowerCamelCase :       str     					=    [
			    '''TVLT_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TvltModel''',
			    '''TvltForPreTraining''',
			    '''TvltForAudioVisualClassification''',
			    '''TvltPreTrainedModel''',
			]

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_lowerCamelCase :       str     					=    ['''TvltImageProcessor''']


if TYPE_CHECKING:
			from .configuration_tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig
			from .processing_tvlt import TvltProcessor
			from .feature_extraction_tvlt import TvltFeatureExtractor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tvlt import (
						    TVLT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TvltForAudioVisualClassification,
						    TvltForPreTraining,
						    TvltModel,
						    TvltPreTrainedModel,
						)

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .image_processing_tvlt import TvltImageProcessor


else:
			import sys

			_lowerCamelCase :       Dict     					=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)