






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  snake_case__      (							lowerCAmelCase__   ):
 def     __magic_name__       (       self				)							->   Dict:
       __magic_name__    :     List[Any]     		=      self.config_class(**self.inputs_dict				)
       self.parent.assertTrue(hasattr(_lowerCamelCase    ,     """hidden_sizes"""				)				)
       self.parent.assertTrue(hasattr(_lowerCamelCase    ,     """num_attention_heads"""				)				)
       self.parent.assertTrue(hasattr(_lowerCamelCase    ,     """num_encoder_blocks"""				)				)





class  snake_case__      :
 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, 1_28]    ,     lowerCAmelCase__=[1, 4, 8, 16]    ,     lowerCAmelCase__=[1, 2, 4, 8]    ,     lowerCAmelCase__=True    ,     lowerCAmelCase__=True    ,     lowerCAmelCase__="gelu"    ,     lowerCAmelCase__=0.1    ,     lowerCAmelCase__=0.1    ,     lowerCAmelCase__=0.0_2    ,     lowerCAmelCase__=16    ,     lowerCAmelCase__=3    ,     lowerCAmelCase__=None    ,     )							->   Union[str, Any]:
       __magic_name__    :     Optional[Any]     		=      parent
       __magic_name__    :     int     		=      batch_size
       __magic_name__    :     str     		=      image_size
       __magic_name__    :     Tuple     		=      num_channels
       __magic_name__    :     Any     		=      num_encoder_blocks
       __magic_name__    :     List[Any]     		=      sr_ratios
       __magic_name__    :     Dict     		=      depths
       __magic_name__    :     Dict     		=      hidden_sizes
       __magic_name__    :     Dict     		=      downsampling_rates
       __magic_name__    :     Union[str, Any]     		=      num_attention_heads
       __magic_name__    :     Any     		=      is_training
       __magic_name__    :     Optional[Any]     		=      use_labels
       __magic_name__    :     int     		=      hidden_act
       __magic_name__    :     Optional[int]     		=      hidden_dropout_prob
       __magic_name__    :     Tuple     		=      attention_probs_dropout_prob
       __magic_name__    :     Tuple     		=      initializer_range
       __magic_name__    :     Optional[Any]     		=      decoder_hidden_size
       __magic_name__    :     Optional[int]     		=      num_labels
       __magic_name__    :     str     		=      scope
 def     __magic_name__       (       self				)							->   Dict:
       __magic_name__    :     str     		=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

       __magic_name__    :     Optional[int]     		=      None
       if self.use_labels:
             __magic_name__    :     str     		=      ids_tensor([self.batch_size, self.image_size, self.image_size]    ,     self.num_labels				)

       __magic_name__    :     Optional[int]     		=      self.get_config()
       return config, pixel_values, labels
 def     __magic_name__       (       self				)							->   Optional[int]:
       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     __magic_name__       (       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__				)							->   List[str]:
       __magic_name__    :     Optional[int]     		=      GLPNModel(config=_lowerCamelCase				)
       model.to(_lowerCamelCase				)
       model.eval()
       __magic_name__    :     Dict     		=      model(_lowerCamelCase				)
       __magic_name__    :     List[Any]     		=      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     __magic_name__       (       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__				)							->   Optional[Any]:
       __magic_name__    :     Optional[Any]     		=      self.num_labels
       __magic_name__    :     int     		=      GLPNForDepthEstimation(_lowerCamelCase				)
       model.to(_lowerCamelCase				)
       model.eval()
       __magic_name__    :     List[Any]     		=      model(_lowerCamelCase				)
       self.parent.assertEqual(result.predicted_depth.shape    ,     (self.batch_size, self.image_size, self.image_size)				)
       __magic_name__    :     Optional[Any]     		=      model(_lowerCamelCase    ,     labels=_lowerCamelCase				)
       self.parent.assertEqual(result.predicted_depth.shape    ,     (self.batch_size, self.image_size, self.image_size)				)







 def     __magic_name__       (       self				)							->   Any:
       __magic_name__    :     Union[str, Any]     		=      self.prepare_config_and_inputs()
       __magic_name__    :     Dict     		=      config_and_inputs
       __magic_name__    :     str     		=      {"""pixel_values""": pixel_values}
       return config, inputs_dict





@require_torch
class  snake_case__      (							lowerCAmelCase__		,						lowerCAmelCase__		,						unittest.TestCase   ):
 lowercase__		:  int							   =					(GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
 lowercase__		:  Union[str, Any]							   =					(
     {'''depth-estimation''': GLPNForDepthEstimation, '''feature-extraction''': GLPNModel} if is_torch_available() else {}
 )

 lowercase__		:  Any							   =					False
 lowercase__		:  Any							   =					False
 lowercase__		:  Optional[Any]							   =					False
 def     __magic_name__       (       self				)							->   Tuple:
       __magic_name__    :     str     		=      GLPNModelTester(self				)
       __magic_name__    :     int     		=      GLPNConfigTester(self    ,     config_class=_lowerCamelCase				)
 def     __magic_name__       (       self				)							->   int:
       self.config_tester.run_common_tests()
 def     __magic_name__       (       self				)							->   Dict:
       __magic_name__    :     int     		=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_lowerCamelCase				)
 def     __magic_name__       (       self				)							->   Optional[Any]:
       __magic_name__    :     List[str]     		=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_depth_estimation(*_lowerCamelCase				)
 @unittest.skip("""GLPN does not use inputs_embeds"""				)
 def     __magic_name__       (       self				)							->   Optional[int]:
       pass
 @unittest.skip("""GLPN does not have get_input_embeddings method and get_output_embeddings methods"""				)
 def     __magic_name__       (       self				)							->   str:
       pass
 def     __magic_name__       (       self				)							->   List[Any]:
       __magic_name__    :     Union[str, Any]     		=      self.model_tester.prepare_config_and_inputs_for_common()

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

             __magic_name__    :     str     		=      ["""pixel_values"""]
             self.assertListEqual(arg_names[:1]    ,     _lowerCamelCase				)
 def     __magic_name__       (       self				)							->   Tuple:
       __magic_name__    :     Any     		=      self.model_tester.prepare_config_and_inputs_for_common()
       __magic_name__    :     int     		=      True

       for model_class in self.all_model_classes:
             __magic_name__    :     List[str]     		=      True
             __magic_name__    :     Optional[Any]     		=      False
             __magic_name__    :     Union[str, Any]     		=      True
             __magic_name__    :     List[str]     		=      model_class(_lowerCamelCase				)
             model.to(_lowerCamelCase				)
             model.eval()
             with torch.no_grad():
                   __magic_name__    :     Any     		=      model(**self._prepare_for_class(_lowerCamelCase    ,     _lowerCamelCase				)				)
             __magic_name__    :     Tuple     		=      outputs.attentions

             __magic_name__    :     Tuple     		=      sum(self.model_tester.depths				)
             self.assertEqual(len(_lowerCamelCase				)    ,     _lowerCamelCase				)

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

             self.assertEqual(len(_lowerCamelCase				)    ,     _lowerCamelCase				)

             # verify the first attentions (first block, first layer)
             __magic_name__    :     Optional[Any]     		=      (self.model_tester.image_size // 4) ** 2
             __magic_name__    :     Tuple     		=      (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)
             __magic_name__    :     List[str]     		=      (self.model_tester.image_size // 32) ** 2
             __magic_name__    :     Any     		=      (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]    ,     )
             __magic_name__    :     str     		=      len(_lowerCamelCase				)

             # Check attention is always last and order is fine
             __magic_name__    :     int     		=      True
             __magic_name__    :     Optional[int]     		=      True
             __magic_name__    :     Optional[Any]     		=      model_class(_lowerCamelCase				)
             model.to(_lowerCamelCase				)
             model.eval()
             with torch.no_grad():
                   __magic_name__    :     Tuple     		=      model(**self._prepare_for_class(_lowerCamelCase    ,     _lowerCamelCase				)				)

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

             __magic_name__    :     Tuple     		=      outputs.attentions

             self.assertEqual(len(_lowerCamelCase				)    ,     _lowerCamelCase				)
             # verify the first attentions (first block, first layer)
             __magic_name__    :     Any     		=      (self.model_tester.image_size // 4) ** 2
             __magic_name__    :     Optional[Any]     		=      (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     __magic_name__       (       self				)							->   Union[str, Any]:

       def check_hidden_states_output(lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__				):
             __magic_name__    :     Optional[Any]     		=      model_class(_lowerCamelCase				)
             model.to(_lowerCamelCase				)
             model.eval()

             with torch.no_grad():
                   __magic_name__    :     Dict     		=      model(**self._prepare_for_class(_lowerCamelCase    ,     _lowerCamelCase				)				)

             __magic_name__    :     Optional[Any]     		=      outputs.hidden_states

             __magic_name__    :     Optional[Any]     		=      self.model_tester.num_encoder_blocks
             self.assertEqual(len(_lowerCamelCase				)    ,     _lowerCamelCase				)

             # 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,
                 ]    ,     )

       __magic_name__    :     int     		=      self.model_tester.prepare_config_and_inputs_for_common()

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

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

             check_hidden_states_output(_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase				)
 def     __magic_name__       (       self				)							->   Tuple:

       if not self.model_tester.is_training:
             return

       __magic_name__    :     int     		=      self.model_tester.prepare_config_and_inputs_for_common()
       __magic_name__    :     List[Any]     		=      True

       for model_class in self.all_model_classes:
             if model_class in get_values(_lowerCamelCase				):
                   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":
                   __magic_name__    :     Any     		=      inputs_dict["""pixel_values"""].shape
                   __magic_name__    :     int     		=      torch.zeros(
                       [self.model_tester.batch_size, height, width]    ,     device=_lowerCamelCase				).long()
             __magic_name__    :     List[str]     		=      model_class(_lowerCamelCase				)
             model.to(_lowerCamelCase				)
             model.train()
             __magic_name__    :     List[str]     		=      self._prepare_for_class(_lowerCamelCase    ,     _lowerCamelCase    ,     return_labels=_lowerCamelCase				)
             __magic_name__    :     int     		=      model(**_lowerCamelCase				).loss
             loss.backward()







 @slow
 def     __magic_name__       (       self				)							->   List[str]:

       for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             __magic_name__    :     Dict     		=      GLPNModel.from_pretrained(_lowerCamelCase				)
             self.assertIsNotNone(_lowerCamelCase				)



def 					UpperCamelCase		( ):






      """simple docstring"""







      __magic_name__    :     Optional[Any]     		=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
      return image





@require_torch
@require_vision
@slow
class  snake_case__      (							unittest.TestCase   ):
 @slow
 def     __magic_name__       (       self				)							->   Tuple:
       __magic_name__    :     Optional[Any]     		=      GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)
       __magic_name__    :     List[str]     		=      GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]				).to(_lowerCamelCase				)

       __magic_name__    :     Optional[Any]     		=      prepare_img()
       __magic_name__    :     Tuple     		=      image_processor(images=_lowerCamelCase    ,     return_tensors="""pt"""				).to(_lowerCamelCase				)

       # forward pass
       with torch.no_grad():
             __magic_name__    :     int     		=      model(**_lowerCamelCase				)

       # verify the predicted depth
       __magic_name__    :     Tuple     		=      torch.Size([1, 4_80, 6_40]				)
       self.assertEqual(outputs.predicted_depth.shape    ,     _lowerCamelCase				)

       __magic_name__    :     List[str]     		=      torch.tensor(
           [[3.4_2_9_1, 2.7_8_6_5, 2.5_1_5_1], [3.2_8_4_1, 2.7_0_2_1, 2.3_5_0_2], [3.1_1_4_7, 2.4_6_2_5, 2.2_4_8_1]]				).to(_lowerCamelCase				)

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








import sacrebleu as scb
from packaging import version

import datasets


__magic_name__:  Optional[int]  	=			"\\n@inproceedings{post-2018-call,\n    title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n    author = \"Post, Matt\",\n    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n    month = oct,\n    year = \"2018\",\n    address = \"Belgium, Brussels\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W18-6319\",\n    pages = \"186--191\",\n}\n"

__magic_name__:  int  	=			"\\nSacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores.\nInspired by Rico Sennrich's `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text.\nIt also knows all the standard test sets and handles downloading, processing, and tokenization for you.\n\nSee the [README.md] file at https://github.com/mjpost/sacreBLEU for more information.\n"

__magic_name__:  List[Any]  	=			"\nProduces BLEU scores along with its sufficient statistics\nfrom a source against one or more references.\n\nArgs:\n    predictions (`list` of `str`): list of translations to score. Each translation should be tokenized into a list of tokens.\n    references (`list` of `list` of `str`): A list of lists of references. The contents of the first sub-list are the references for the first prediction, the contents of the second sub-list are for the second prediction, etc. Note that there must be the same number of references for each prediction (i.e. all sub-lists must be of the same length).\n    smooth_method (`str`): The smoothing method to use, defaults to `'exp'`. Possible values are:\n        - `'none'`: no smoothing\n        - `'floor'`: increment zero counts\n        - `'add-k'`: increment num/denom by k for n>1\n        - `'exp'`: exponential decay\n    smooth_value (`float`): The smoothing value. Only valid when `smooth_method='floor'` (in which case `smooth_value` defaults to `0.1`) or `smooth_method='add-k'` (in which case `smooth_value` defaults to `1`).\n    tokenize (`str`): Tokenization method to use for BLEU. If not provided, defaults to `'zh'` for Chinese, `'ja-mecab'` for Japanese and `'13a'` (mteval) otherwise. Possible values are:\n        - `'none'`: No tokenization.\n        - `'zh'`: Chinese tokenization.\n        - `'13a'`: mimics the `mteval-v13a` script from Moses.\n        - `'intl'`: International tokenization, mimics the `mteval-v14` script from Moses\n        - `'char'`: Language-agnostic character-level tokenization.\n        - `'ja-mecab'`: Japanese tokenization. Uses the [MeCab tokenizer](https://pypi.org/project/mecab-python3).\n    lowercase (`bool`): If `True`, lowercases the input, enabling case-insensitivity. Defaults to `False`.\n    force (`bool`): If `True`, insists that your tokenized input is actually detokenized. Defaults to `False`.\n    use_effective_order (`bool`): If `True`, stops including n-gram orders for which precision is 0. This should be `True`, if sentence-level BLEU will be computed. Defaults to `False`.\n\nReturns:\n    'score': BLEU score,\n    'counts': Counts,\n    'totals': Totals,\n    'precisions': Precisions,\n    'bp': Brevity penalty,\n    'sys_len': predictions length,\n    'ref_len': reference length,\n\nExamples:\n\n    Example 1:\n        >>> predictions = [\"hello there general kenobi\", \"foo bar foobar\"]\n        >>> references = [[\"hello there general kenobi\", \"hello there !\"], [\"foo bar foobar\", \"foo bar foobar\"]]\n        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")\n        >>> results = sacrebleu.compute(predictions=predictions, references=references)\n        >>> print(list(results.keys()))\n        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']\n        >>> print(round(results[\"score\"], 1))\n        100.0\n\n    Example 2:\n        >>> predictions = [\"hello there general kenobi\",\n        ...                 \"on our way to ankh morpork\"]\n        >>> references = [[\"hello there general kenobi\", \"hello there !\"],\n        ...                 [\"goodbye ankh morpork\", \"ankh morpork\"]]\n        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")\n        >>> results = sacrebleu.compute(predictions=predictions,\n        ...                             references=references)\n        >>> print(list(results.keys()))\n        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']\n        >>> print(round(results[\"score\"], 1))\n        39.8\n"





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,						_KWARGS_DESCRIPTION   )
class  snake_case__      (							datasets.Metric   ):
	def     __magic_name__       (       self				)							->   Optional[int]:
							if version.parse(scb.__version__				) < version.parse("""1.4.12"""				):
													raise ImportWarning(
													    """To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"""
													    """You can install it with `pip install \"sacrebleu>=1.4.12\"`."""				)
							return datasets.MetricInfo(
							    description=_DESCRIPTION    ,     citation=_CITATION    ,     homepage="""https://github.com/mjpost/sacreBLEU"""    ,     inputs_description=_KWARGS_DESCRIPTION    ,     features=datasets.Features(
							        {
							            """predictions""": datasets.Value("""string"""    ,     id="""sequence"""				),
							            """references""": datasets.Sequence(datasets.Value("""string"""    ,     id="""sequence"""				)    ,     id="""references"""				),
							        }				)    ,     codebase_urls=["""https://github.com/mjpost/sacreBLEU"""]    ,     reference_urls=[
							        """https://github.com/mjpost/sacreBLEU""",
							        """https://en.wikipedia.org/wiki/BLEU""",
							        """https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213""",
							    ]    ,     )







	def     __magic_name__       (       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__="exp"    ,     lowerCAmelCase__=None    ,     lowerCAmelCase__=False    ,     lowerCAmelCase__=False    ,     lowerCAmelCase__=None    ,     lowerCAmelCase__=False    ,     )							->   Optional[Any]:
							__magic_name__    :     Optional[int]     		=      len(references[0]				)
							if any(len(lowerCAmelCase__				) != references_per_prediction for refs in references				):
													raise ValueError("""Sacrebleu requires the same number of references for each prediction"""				)
							__magic_name__    :     Dict     		=      [[refs[i] for refs in references] for i in range(lowerCAmelCase__				)]
							__magic_name__    :     List[str]     		=      scb.corpus_bleu(
							    lowerCAmelCase__    ,     lowerCAmelCase__    ,     smooth_method=lowerCAmelCase__    ,     smooth_value=lowerCAmelCase__    ,     force=lowerCAmelCase__    ,     lowercase=lowerCAmelCase__    ,     use_effective_order=lowerCAmelCase__    ,     **({"""tokenize""": tokenize} if tokenize else {})    ,     )
							__magic_name__    :     List[str]     		=      {
							    """score""": output.score,
							    """counts""": output.counts,
							    """totals""": output.totals,
							    """precisions""": output.precisions,
							    """bp""": output.bp,
							    """sys_len""": output.sys_len,
							    """ref_len""": output.ref_len,
							}
							return output_dict
