
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import MobileNetVaImageProcessor

class 				_a			(unittest.TestCase):





       """simple docstring"""




       def __init__(							self    ,							A__    ,							A__=7    ,							A__=3    ,							A__=18    ,							A__=30    ,							A__=4_00    ,							A__=True    ,							A__=None    ,							A__=True    ,							A__=None    ,							)      ->     Optional[Any]:
          _SCREAMING_SNAKE_CASE       	=			size if size is not None else {"""shortest_edge""": 20}
          _SCREAMING_SNAKE_CASE       	=			crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
          _SCREAMING_SNAKE_CASE       	=			parent
          _SCREAMING_SNAKE_CASE       	=			batch_size
          _SCREAMING_SNAKE_CASE       	=			num_channels
          _SCREAMING_SNAKE_CASE       	=			image_size
          _SCREAMING_SNAKE_CASE       	=			min_resolution
          _SCREAMING_SNAKE_CASE       	=			max_resolution
          _SCREAMING_SNAKE_CASE       	=			do_resize
          _SCREAMING_SNAKE_CASE       	=			size
          _SCREAMING_SNAKE_CASE       	=			do_center_crop
          _SCREAMING_SNAKE_CASE       	=			crop_size






       def     UpperCamelCase			(							self )      ->     Union[str, Any]:
          return {
              "do_resize": self.do_resize,
              "size": self.size,
              "do_center_crop": self.do_center_crop,
              "crop_size": self.crop_size,
          }






@require_torch
@require_vision
class 				_a			(_lowerCamelCase					,   unittest.TestCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE =					MobileNetVaImageProcessor if is_vision_available() else None






       def     UpperCamelCase			(							self )      ->     Optional[int]:
          _SCREAMING_SNAKE_CASE       	=			MobileNetVaImageProcessingTester(self )






       @property
       def     UpperCamelCase			(							self )      ->     List[str]:
          return self.image_processor_tester.prepare_image_processor_dict()






       def     UpperCamelCase			(							self )      ->     str:
          _SCREAMING_SNAKE_CASE       	=			self.image_processing_class(**self.image_processor_dict )
          self.assertTrue(hasattr(A__    ,							"""do_resize""" ) )
          self.assertTrue(hasattr(A__    ,							"""size""" ) )
          self.assertTrue(hasattr(A__    ,							"""do_center_crop""" ) )
          self.assertTrue(hasattr(A__    ,							"""center_crop""" ) )






       def     UpperCamelCase			(							self )      ->     Optional[Any]:
          _SCREAMING_SNAKE_CASE       	=			self.image_processing_class.from_dict(self.image_processor_dict )
          self.assertEqual(image_processor.size    ,							{"""shortest_edge""": 20} )
          self.assertEqual(image_processor.crop_size    ,							{"""height""": 18, """width""": 18} )

          _SCREAMING_SNAKE_CASE       	=			self.image_processing_class.from_dict(self.image_processor_dict    ,							size=42    ,							crop_size=84 )
          self.assertEqual(image_processor.size    ,							{"""shortest_edge""": 42} )
          self.assertEqual(image_processor.crop_size    ,							{"""height""": 84, """width""": 84} )






       def     UpperCamelCase			(							self )      ->     Optional[int]:
          pass






       def     UpperCamelCase			(							self )      ->     Dict:
          # Initialize image_processing
          _SCREAMING_SNAKE_CASE       	=			self.image_processing_class(**self.image_processor_dict )
          # create random PIL images
          _SCREAMING_SNAKE_CASE       	=			prepare_image_inputs(self.image_processor_tester    ,							equal_resolution=A__ )
          for image in image_inputs:
             self.assertIsInstance(A__    ,							Image.Image )

          # Test not batched input
          _SCREAMING_SNAKE_CASE       	=			image_processing(image_inputs[0]    ,							return_tensors="""pt""" ).pixel_values
          self.assertEqual(
              encoded_images.shape    ,							(
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["""height"""],
                  self.image_processor_tester.crop_size["""width"""],
              )    ,							)

          # Test batched
          _SCREAMING_SNAKE_CASE       	=			image_processing(A__    ,							return_tensors="""pt""" ).pixel_values
          self.assertEqual(
              encoded_images.shape    ,							(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["""height"""],
                  self.image_processor_tester.crop_size["""width"""],
              )    ,							)






       def     UpperCamelCase			(							self )      ->     Union[str, Any]:
          # Initialize image_processing
          _SCREAMING_SNAKE_CASE       	=			self.image_processing_class(**self.image_processor_dict )
          # create random numpy tensors
          _SCREAMING_SNAKE_CASE       	=			prepare_image_inputs(self.image_processor_tester    ,							equal_resolution=A__    ,							numpify=A__ )
          for image in image_inputs:
             self.assertIsInstance(A__    ,							np.ndarray )

          # Test not batched input
          _SCREAMING_SNAKE_CASE       	=			image_processing(image_inputs[0]    ,							return_tensors="""pt""" ).pixel_values
          self.assertEqual(
              encoded_images.shape    ,							(
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["""height"""],
                  self.image_processor_tester.crop_size["""width"""],
              )    ,							)

          # Test batched
          _SCREAMING_SNAKE_CASE       	=			image_processing(A__    ,							return_tensors="""pt""" ).pixel_values
          self.assertEqual(
              encoded_images.shape    ,							(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["""height"""],
                  self.image_processor_tester.crop_size["""width"""],
              )    ,							)






       def     UpperCamelCase			(							self )      ->     Tuple:
          # Initialize image_processing
          _SCREAMING_SNAKE_CASE       	=			self.image_processing_class(**self.image_processor_dict )
          # create random PyTorch tensors
          _SCREAMING_SNAKE_CASE       	=			prepare_image_inputs(self.image_processor_tester    ,							equal_resolution=A__    ,							torchify=A__ )
          for image in image_inputs:
             self.assertIsInstance(A__    ,							torch.Tensor )

          # Test not batched input
          _SCREAMING_SNAKE_CASE       	=			image_processing(image_inputs[0]    ,							return_tensors="""pt""" ).pixel_values
          self.assertEqual(
              encoded_images.shape    ,							(
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["""height"""],
                  self.image_processor_tester.crop_size["""width"""],
              )    ,							)

          # Test batched
          _SCREAMING_SNAKE_CASE       	=			image_processing(A__    ,							return_tensors="""pt""" ).pixel_values
          self.assertEqual(
              encoded_images.shape    ,							(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["""height"""],
                  self.image_processor_tester.crop_size["""width"""],
              )    ,							)








'''simple docstring'''



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


UpperCamelCase__ :						Tuple    							=					{
    "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"},
}

UpperCamelCase__ :						int    							=					[
    "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 				lowerCAmelCase_		(			)       ->       str:




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

   _SCREAMING_SNAKE_CASE       	=			parser.parse_args()

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

   return args
def 				lowerCAmelCase_		(			)       ->       Optional[int]:




   """simple docstring"""
   transformers.utils.logging.set_verbosity_error()
   _SCREAMING_SNAKE_CASE       	=			parse_args()

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

   _SCREAMING_SNAKE_CASE       	=			start_measure()
   _SCREAMING_SNAKE_CASE       	=			model_cls.from_pretrained(args.model_name      , device_map="""auto"""      , **SCREAMING_SNAKE_CASE_  )
   _SCREAMING_SNAKE_CASE       	=			end_measure(SCREAMING_SNAKE_CASE_  )
   log_measures(SCREAMING_SNAKE_CASE_      , """Model loading"""  )

   _SCREAMING_SNAKE_CASE       	=			compute_module_sizes(SCREAMING_SNAKE_CASE_  )
   _SCREAMING_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]
   _SCREAMING_SNAKE_CASE       	=			"""\n""".join([F"- {device}: {size // 2**20}MiB" for device, size in device_size.items()]  )
   print(F"\nTheoretical use:\n{message}"  )

   _SCREAMING_SNAKE_CASE       	=			AutoTokenizer.from_pretrained(args.tokenizer_name  )

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

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

   _SCREAMING_SNAKE_CASE       	=			end_measure(SCREAMING_SNAKE_CASE_  )
   log_measures(SCREAMING_SNAKE_CASE_      , """Model generation"""  )

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


if __name__ == "__main__":
     main()






