

import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import MODEL_MAPPING, GLPNConfig, GLPNForDepthEstimation, GLPNModel
    from transformers.models.glpn.modeling_glpn import GLPN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import GLPNImageProcessor

class 			lowerCamelCase  (					lowerCAmelCase_      ):


  '''simple docstring'''







  def    _a			(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       Tuple						=						self.config_class(**self.inputs_dict  )
        self.parent.assertTrue(hasattr(__a       ,					"""hidden_sizes"""  )  )
        self.parent.assertTrue(hasattr(__a       ,					"""num_attention_heads"""  )  )
        self.parent.assertTrue(hasattr(__a       ,					"""num_encoder_blocks"""  )  )




class 			lowerCamelCase  :


  '''simple docstring'''







  def __init__(self       ,					_lowerCamelCase       ,					_lowerCamelCase=13       ,					_lowerCamelCase=64       ,					_lowerCamelCase=3       ,					_lowerCamelCase=4       ,					_lowerCamelCase=[2, 2, 2, 2]       ,					_lowerCamelCase=[8, 4, 2, 1]       ,					_lowerCamelCase=[16, 32, 64, 128]       ,					_lowerCamelCase=[1, 4, 8, 16]       ,					_lowerCamelCase=[1, 2, 4, 8]       ,					_lowerCamelCase=True       ,					_lowerCamelCase=True       ,					_lowerCamelCase="gelu"       ,					_lowerCamelCase=0.1       ,					_lowerCamelCase=0.1       ,					_lowerCamelCase=0.02       ,					_lowerCamelCase=16       ,					_lowerCamelCase=3       ,					_lowerCamelCase=None       ,					):


        """simple docstring"""

        UpperCAmelCase__ :       Union[str, Any]						=						parent
        UpperCAmelCase__ :       List[Any]						=						batch_size
        UpperCAmelCase__ :       List[str]						=						image_size
        UpperCAmelCase__ :       Any						=						num_channels
        UpperCAmelCase__ :       str						=						num_encoder_blocks
        UpperCAmelCase__ :       Optional[int]						=						sr_ratios
        UpperCAmelCase__ :       Any						=						depths
        UpperCAmelCase__ :       Dict						=						hidden_sizes
        UpperCAmelCase__ :       List[Any]						=						downsampling_rates
        UpperCAmelCase__ :       Optional[Any]						=						num_attention_heads
        UpperCAmelCase__ :       Union[str, Any]						=						is_training
        UpperCAmelCase__ :       List[str]						=						use_labels
        UpperCAmelCase__ :       Union[str, Any]						=						hidden_act
        UpperCAmelCase__ :       Optional[Any]						=						hidden_dropout_prob
        UpperCAmelCase__ :       Optional[Any]						=						attention_probs_dropout_prob
        UpperCAmelCase__ :       List[Any]						=						initializer_range
        UpperCAmelCase__ :       Any						=						decoder_hidden_size
        UpperCAmelCase__ :       Dict						=						num_labels
        UpperCAmelCase__ :       Union[str, Any]						=						scope



  def    _a			(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       Optional[Any]						=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

        UpperCAmelCase__ :       int						=						self.get_config()
        return config, pixel_values, labels



  def    _a			(self  ):


        """simple docstring"""

        return GLPNConfig(
            image_size=self.image_size       ,					num_channels=self.num_channels       ,					num_encoder_blocks=self.num_encoder_blocks       ,					depths=self.depths       ,					hidden_sizes=self.hidden_sizes       ,					num_attention_heads=self.num_attention_heads       ,					hidden_act=self.hidden_act       ,					hidden_dropout_prob=self.hidden_dropout_prob       ,					attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,					initializer_range=self.initializer_range       ,					decoder_hidden_size=self.decoder_hidden_size       ,					)



  def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  ):


        """simple docstring"""

        UpperCAmelCase__ :       Optional[int]						=						GLPNModel(config=__a  )
        model.to(__a  )
        model.eval()
        UpperCAmelCase__ :       Tuple						=						model(__a  )
        UpperCAmelCase__ :       Optional[int]						=						self.image_size // (self.downsampling_rates[-1] * 2)
        self.parent.assertEqual(
            result.last_hidden_state.shape       ,					(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)  )



  def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  ):


        """simple docstring"""

        UpperCAmelCase__ :       Optional[int]						=						self.num_labels
        UpperCAmelCase__ :       List[Any]						=						GLPNForDepthEstimation(__a  )
        model.to(__a  )
        model.eval()
        UpperCAmelCase__ :       List[str]						=						model(__a  )
        self.parent.assertEqual(result.predicted_depth.shape       ,					(self.batch_size, self.image_size, self.image_size)  )
        UpperCAmelCase__ :       List[Any]						=						model(__a       ,					labels=__a  )
        self.parent.assertEqual(result.predicted_depth.shape       ,					(self.batch_size, self.image_size, self.image_size)  )




  def    _a			(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       Optional[Any]						=						self.prepare_config_and_inputs()
        UpperCAmelCase__ :       Tuple						=						config_and_inputs
        UpperCAmelCase__ :       List[str]						=						{'pixel_values': pixel_values}
        return config, inputs_dict




@require_torch
class 			lowerCamelCase  (					lowerCAmelCase_				,				lowerCAmelCase_				,				unittest.TestCase      ):


  '''simple docstring'''







  SCREAMING_SNAKE_CASE							=     (GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
  SCREAMING_SNAKE_CASE							=     (
      {'depth-estimation': GLPNForDepthEstimation, 'feature-extraction': GLPNModel} if is_torch_available() else {}
  )

  SCREAMING_SNAKE_CASE							=     False
  SCREAMING_SNAKE_CASE							=     False
  SCREAMING_SNAKE_CASE							=     False



  def    _a			(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       Optional[Any]						=						GLPNModelTester(self  )
        UpperCAmelCase__ :       Tuple						=						GLPNConfigTester(self       ,					config_class=__a  )



  def    _a			(self  ):


        """simple docstring"""

        self.config_tester.run_common_tests()



  def    _a			(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       Optional[int]						=						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__a  )



  def    _a			(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       Tuple						=						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_depth_estimation(*__a  )



  @unittest.skip("""GLPN does not use inputs_embeds"""  )
  def    _a			(self  ):


        """simple docstring"""

        pass



  @unittest.skip("""GLPN does not have get_input_embeddings method and get_output_embeddings methods"""  )
  def    _a			(self  ):


        """simple docstring"""

        pass



  def    _a			(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       str						=						self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              UpperCAmelCase__ :       Optional[int]						=						model_class(__a  )
              UpperCAmelCase__ :       Dict						=						inspect.signature(model.forward  )
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              UpperCAmelCase__ :       int						=						[*signature.parameters.keys()]

              UpperCAmelCase__ :       Tuple						=						['pixel_values']
              self.assertListEqual(arg_names[:1]       ,					__a  )



  def    _a			(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       List[Any]						=						self.model_tester.prepare_config_and_inputs_for_common()
        UpperCAmelCase__ :       Dict						=						True

        for model_class in self.all_model_classes:
              UpperCAmelCase__ :       Dict						=						True
              UpperCAmelCase__ :       Tuple						=						False
              UpperCAmelCase__ :       Tuple						=						True
              UpperCAmelCase__ :       Dict						=						model_class(__a  )
              model.to(__a  )
              model.eval()
              with torch.no_grad():
                    UpperCAmelCase__ :       str						=						model(**self._prepare_for_class(__a       ,					__a  )  )
              UpperCAmelCase__ :       Dict						=						outputs.attentions

              UpperCAmelCase__ :       Union[str, Any]						=						sum(self.model_tester.depths  )
              self.assertEqual(len(__a  )       ,					__a  )

              # check that output_attentions also work using config
              del inputs_dict["output_attentions"]
              UpperCAmelCase__ :       List[Any]						=						True
              UpperCAmelCase__ :       str						=						model_class(__a  )
              model.to(__a  )
              model.eval()
              with torch.no_grad():
                    UpperCAmelCase__ :       Dict						=						model(**self._prepare_for_class(__a       ,					__a  )  )
              UpperCAmelCase__ :       Any						=						outputs.attentions

              self.assertEqual(len(__a  )       ,					__a  )

              # verify the first attentions (first block, first layer)
              UpperCAmelCase__ :       Optional[Any]						=						(self.model_tester.image_size // 4) ** 2
              UpperCAmelCase__ :       int						=						(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
              self.assertListEqual(
                  list(attentions[0].shape[-3:]  )       ,					[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]       ,					)

              # verify the last attentions (last block, last layer)
              UpperCAmelCase__ :       int						=						(self.model_tester.image_size // 32) ** 2
              UpperCAmelCase__ :       List[str]						=						(self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
              self.assertListEqual(
                  list(attentions[-1].shape[-3:]  )       ,					[self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]       ,					)
              UpperCAmelCase__ :       Dict						=						len(__a  )

              # Check attention is always last and order is fine
              UpperCAmelCase__ :       Tuple						=						True
              UpperCAmelCase__ :       Dict						=						True
              UpperCAmelCase__ :       int						=						model_class(__a  )
              model.to(__a  )
              model.eval()
              with torch.no_grad():
                    UpperCAmelCase__ :       int						=						model(**self._prepare_for_class(__a       ,					__a  )  )

              self.assertEqual(out_len + 1       ,					len(__a  )  )

              UpperCAmelCase__ :       Union[str, Any]						=						outputs.attentions

              self.assertEqual(len(__a  )       ,					__a  )
              # verify the first attentions (first block, first layer)
              UpperCAmelCase__ :       int						=						(self.model_tester.image_size // 4) ** 2
              UpperCAmelCase__ :       Tuple						=						(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
              self.assertListEqual(
                  list(self_attentions[0].shape[-3:]  )       ,					[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]       ,					)



  def    _a			(self  ):


        """simple docstring"""

        def check_hidden_states_output(_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  ):
              UpperCAmelCase__ :       Tuple						=						model_class(__a  )
              model.to(__a  )
              model.eval()

              with torch.no_grad():
                    UpperCAmelCase__ :       int						=						model(**self._prepare_for_class(__a       ,					__a  )  )

              UpperCAmelCase__ :       Dict						=						outputs.hidden_states

              UpperCAmelCase__ :       Any						=						self.model_tester.num_encoder_blocks
              self.assertEqual(len(__a  )       ,					__a  )

              # verify the first hidden states (first block)
              self.assertListEqual(
                  list(hidden_states[0].shape[-3:]  )       ,					[
                      self.model_tester.hidden_sizes[0],
                      self.model_tester.image_size // 4,
                      self.model_tester.image_size // 4,
                  ]       ,					)

        UpperCAmelCase__ :       Dict						=						self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              UpperCAmelCase__ :       Optional[int]						=						True
              check_hidden_states_output(__a       ,					__a       ,					__a  )

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              UpperCAmelCase__ :       Optional[Any]						=						True

              check_hidden_states_output(__a       ,					__a       ,					__a  )



  def    _a			(self  ):


        """simple docstring"""

        if not self.model_tester.is_training:
              return

        UpperCAmelCase__ :       Tuple						=						self.model_tester.prepare_config_and_inputs_for_common()
        UpperCAmelCase__ :       str						=						True

        for model_class in self.all_model_classes:
              if model_class in get_values(__a  ):
                    continue
              # TODO: remove the following 3 lines once we have a MODEL_FOR_DEPTH_ESTIMATION_MAPPING
              # this can then be incorporated into _prepare_for_class in test_modeling_common.py
              if model_class.__name__ == "GLPNForDepthEstimation":
                    UpperCAmelCase__ :       Any						=						inputs_dict['pixel_values'].shape
                    UpperCAmelCase__ :       Dict						=						torch.zeros(
                        [self.model_tester.batch_size, height, width]       ,					device=__a  ).long()
              UpperCAmelCase__ :       int						=						model_class(__a  )
              model.to(__a  )
              model.train()
              UpperCAmelCase__ :       Any						=						self._prepare_for_class(__a       ,					__a       ,					return_labels=__a  )
              UpperCAmelCase__ :       str						=						model(**__a  ).loss
              loss.backward()




  @slow
  def    _a			(self  ):


        """simple docstring"""

        for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCAmelCase__ :       str						=						GLPNModel.from_pretrained(__a  )
              self.assertIsNotNone(__a  )




def 				a__	(	)  ->   List[Any]:
      UpperCAmelCase__ :       str						=						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
      return image





@require_torch
@require_vision
@slow
class 			lowerCamelCase  (					unittest.TestCase      ):


  '''simple docstring'''







  @slow
  def    _a			(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       List[str]						=						GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]  )
        UpperCAmelCase__ :       Optional[int]						=						GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]  ).to(__a  )

        UpperCAmelCase__ :       Union[str, Any]						=						prepare_img()
        UpperCAmelCase__ :       List[str]						=						image_processor(images=__a       ,					return_tensors="""pt"""  ).to(__a  )

        # forward pass
        with torch.no_grad():
              UpperCAmelCase__ :       Optional[Any]						=						model(**__a  )

        # verify the predicted depth
        UpperCAmelCase__ :       List[str]						=						torch.Size([1, 480, 640]  )
        self.assertEqual(outputs.predicted_depth.shape       ,					__a  )

        UpperCAmelCase__ :       Union[str, Any]						=						torch.tensor(
            [[3.4_291, 2.7_865, 2.5_151], [3.2_841, 2.7_021, 2.3_502], [3.1_147, 2.4_625, 2.2_481]]  ).to(__a  )

        self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]       ,					__a       ,					atol=1e-4  )  )




"""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


_A						=     {
    """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"""},
}

_A						=     [
    """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__	(	)  ->   Optional[Any]:
      UpperCAmelCase__ :       int						=						argparse.ArgumentParser(description="""Run and time generations on a big model using Accelerate."""						)
      parser.add_argument("""model_name""" ,  type=lowerCAmelCase ,  default=lowerCAmelCase ,  help="""The name of the model to try."""						)
      parser.add_argument(
          """--tokenizer_name""" ,  type=lowerCAmelCase ,  default=lowerCAmelCase ,  help="""The name of the tokenizer (if different from the model."""						)
      parser.add_argument("""--is_causal""" ,  type=lowerCAmelCase ,  default=lowerCAmelCase ,  help="""Whether or not the model is causal."""						)
      parser.add_argument(
          """--model_revision""" ,  type=lowerCAmelCase ,  default=lowerCAmelCase ,  help="""The revision to use for the model checkpoint."""						)
      parser.add_argument("""--torch_dtype""" ,  type=lowerCAmelCase ,  default=lowerCAmelCase ,  help="""The dtype for the model."""						)
      parser.add_argument("""--disk_offload""" ,  action="""store_true"""						)

      UpperCAmelCase__ :       Union[str, Any]						=						parser.parse_args()

      # Sanitize args
      if args.model_name in DEFAULT_MODELS:
            UpperCAmelCase__ :       Any						=						DEFAULT_MODELS[args.model_name]
            UpperCAmelCase__ :       Tuple						=						defaults["""model"""]
            if args.tokenizer_name is None:
                  UpperCAmelCase__ :       Optional[Any]						=						defaults.get("""tokenizer""" ,  args.model_name						)
            if args.is_causal is None:
                  UpperCAmelCase__ :       List[Any]						=						defaults["""is_causal"""]
            if args.model_revision is None:
                  UpperCAmelCase__ :       Optional[int]						=						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:
            UpperCAmelCase__ :       str						=						args.model_name
      if args.model_revision is None:
            UpperCAmelCase__ :       List[str]						=						"""main"""

      return args




def 				a__	(	)  ->   List[Any]:
      transformers.utils.logging.set_verbosity_error()
      UpperCAmelCase__ :       Optional[int]						=						parse_args()

      if args.torch_dtype is None:
            UpperCAmelCase__ :       List[str]						=						AutoConfig.from_pretrained(args.model_name						)
            UpperCAmelCase__ :       Optional[Any]						=						getattr(lowerCAmelCase ,  """torch_dtype""" ,  torch.floataa						)
      else:
            UpperCAmelCase__ :       Union[str, Any]						=						getattr(lowerCAmelCase ,  args.torch_dtype						)
      UpperCAmelCase__ :       List[Any]						=						AutoModelForCausalLM if args.is_causal else AutoModelForSeqaSeqLM
      UpperCAmelCase__ :       List[str]						=						{
          """torch_dtype""": torch_dtype,
          """revision""": args.model_revision,
      }
      if args.disk_offload:
            UpperCAmelCase__ :       List[str]						=						"""tmp_offload"""
            UpperCAmelCase__ :       int						=						True

      UpperCAmelCase__ :       Any						=						start_measure()
      UpperCAmelCase__ :       Optional[int]						=						model_cls.from_pretrained(args.model_name ,  device_map="""auto""" ,  **lowerCAmelCase						)
      UpperCAmelCase__ :       Any						=						end_measure(lowerCAmelCase						)
      log_measures(lowerCAmelCase ,  """Model loading"""						)

      UpperCAmelCase__ :       List[str]						=						compute_module_sizes(lowerCAmelCase						)
      UpperCAmelCase__ :       Dict						=						{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]
      UpperCAmelCase__ :       Optional[Any]						=						"""\n""".join([F"""- {device}: {size // 2**20}MiB""" for device, size in device_size.items()]						)
      print(F"""\nTheoretical use:\n{message}"""						)

      UpperCAmelCase__ :       Tuple						=						AutoTokenizer.from_pretrained(args.tokenizer_name						)

      UpperCAmelCase__ :       List[str]						=						start_measure()
      UpperCAmelCase__ :       List[Any]						=						[]
      UpperCAmelCase__ :       Tuple						=						[]
      UpperCAmelCase__ :       Optional[Any]						=						[]
      for prompt in PROMPTS:
            UpperCAmelCase__ :       Any						=						tokenizer(lowerCAmelCase ,  return_tensors="""pt"""						).to(0						)
            UpperCAmelCase__ :       List[str]						=						inputs["""input_ids"""][0].tolist()
            UpperCAmelCase__ :       Dict						=						time.time()
            UpperCAmelCase__ :       List[str]						=						model.generate(inputs["""input_ids"""]						)
            UpperCAmelCase__ :       str						=						time.time()
            UpperCAmelCase__ :       str						=						outputs[0].tolist()
            UpperCAmelCase__ :       Optional[Any]						=						len(lowerCAmelCase						) if outputs[: len(lowerCAmelCase						)] != tokens else len(lowerCAmelCase						) - len(lowerCAmelCase						)
            UpperCAmelCase__ :       Tuple						=						after_generate - before_generate

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

      UpperCAmelCase__ :       int						=						end_measure(lowerCAmelCase						)
      log_measures(lowerCAmelCase ,  """Model generation"""						)

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


if __name__ == "__main__":
    main()

