



import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
    import faiss



@require_faiss
class     lowerCAmelCase_		(  SCREAMING_SNAKE_CASE__							):
       def  snake_case_	(      self	)					->		Optional[int]:
          UpperCamelCase   :  Any						     =     tempfile.mkdtemp()
          UpperCamelCase   :  Optional[Any]						     =     8

          # DPR tok
          UpperCamelCase   :  Dict						     =     [
              '[UNK]',
              '[CLS]',
              '[SEP]',
              '[PAD]',
              '[MASK]',
              'want',
              '##want',
              '##ed',
              'wa',
              'un',
              'runn',
              '##ing',
              ',',
              'low',
              'lowest',
          ]
          UpperCamelCase   :  List[Any]						     =     os.path.join(self.tmpdirname,      'dpr_tokenizer'	)
          os.makedirs(SCREAMING_SNAKE_CASE_,      exist_ok=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Union[str, Any]						     =     os.path.join(SCREAMING_SNAKE_CASE_,      DPR_VOCAB_FILES_NAMES['vocab_file']	)
          with open(self.vocab_file,      'w',      encoding='utf-8'	) as vocab_writer:
             vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]	)	)

          # BART tok
          UpperCamelCase   :  Optional[Any]						     =     [
              'l',
              'o',
              'w',
              'e',
              'r',
              's',
              't',
              'i',
              'd',
              'n',
              '\u0120',
              '\u0120l',
              '\u0120n',
              '\u0120lo',
              '\u0120low',
              'er',
              '\u0120lowest',
              '\u0120newer',
              '\u0120wider',
              '<unk>',
          ]
          UpperCamelCase   :  Optional[Any]						     =     dict(zip(SCREAMING_SNAKE_CASE_,      range(len(SCREAMING_SNAKE_CASE_	)	)	)	)
          UpperCamelCase   :  Optional[Any]						     =     ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
          UpperCamelCase   :  int						     =     {'unk_token': '<unk>'}

          UpperCamelCase   :  Union[str, Any]						     =     os.path.join(self.tmpdirname,      'bart_tokenizer'	)
          os.makedirs(SCREAMING_SNAKE_CASE_,      exist_ok=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  int						     =     os.path.join(SCREAMING_SNAKE_CASE_,      BART_VOCAB_FILES_NAMES['vocab_file']	)
          UpperCamelCase   :  str						     =     os.path.join(SCREAMING_SNAKE_CASE_,      BART_VOCAB_FILES_NAMES['merges_file']	)
          with open(self.vocab_file,      'w',      encoding='utf-8'	) as fp:
             fp.write(json.dumps(SCREAMING_SNAKE_CASE_	) + '\n'	)
          with open(self.merges_file,      'w',      encoding='utf-8'	) as fp:
             fp.write('\n'.join(SCREAMING_SNAKE_CASE_	)	)
       def  snake_case_	(      self	)					->		DPRQuestionEncoderTokenizer:
          return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname,      'dpr_tokenizer'	)	)
       def  snake_case_	(      self	)					->		DPRContextEncoderTokenizer:
          return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname,      'dpr_tokenizer'	)	)
       def  snake_case_	(      self	)					->		BartTokenizer:
          return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname,      'bart_tokenizer'	)	)
       def  snake_case_	(      self	)					->		Union[str, Any]:
          shutil.rmtree(self.tmpdirname	)
       def  snake_case_	(      self	)					->		Optional[int]:
          UpperCamelCase   :  Dict						     =     Dataset.from_dict(
              {
                  'id': ['0', '1'],
                  'text': ['foo', 'bar'],
                  'title': ['Foo', 'Bar'],
                  'embeddings': [np.ones(self.retrieval_vector_size	), 2 * np.ones(self.retrieval_vector_size	)],
              }	)
          dataset.add_faiss_index('embeddings',      string_factory='Flat',      metric_type=faiss.METRIC_INNER_PRODUCT	)
          return dataset
       def  snake_case_	(      self	)					->		Tuple:
          UpperCamelCase   :  str						     =     self.get_dummy_dataset()
          UpperCamelCase   :  Tuple						     =     RagConfig(
              retrieval_vector_size=self.retrieval_vector_size,      question_encoder=DPRConfig().to_dict(),      generator=BartConfig().to_dict(),      )
          with patch('transformers.models.rag.retrieval_rag.load_dataset'	) as mock_load_dataset:
             UpperCamelCase   :  Optional[Any]						     =     dataset
             UpperCamelCase   :  Dict						     =     RagRetriever(
                 SCREAMING_SNAKE_CASE_,      question_encoder_tokenizer=self.get_dpr_tokenizer(),      generator_tokenizer=self.get_bart_tokenizer(),      )
          return retriever
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		List[str]:
          UpperCamelCase   :  List[Any]						     =     self.get_dummy_dataset()
          UpperCamelCase   :  Any						     =     RagConfig(
              retrieval_vector_size=self.retrieval_vector_size,      question_encoder=DPRConfig().to_dict(),      generator=BartConfig().to_dict(),      index_name='custom',      )
          if from_disk:
             UpperCamelCase   :  Optional[Any]						     =     os.path.join(self.tmpdirname,      'dataset'	)
             UpperCamelCase   :  str						     =     os.path.join(self.tmpdirname,      'index.faiss'	)
             dataset.get_index('embeddings'	).save(os.path.join(self.tmpdirname,      'index.faiss'	)	)
             dataset.drop_index('embeddings'	)
             dataset.save_to_disk(os.path.join(self.tmpdirname,      'dataset'	)	)
             del dataset
             UpperCamelCase   :  Optional[Any]						     =     RagRetriever(
                 SCREAMING_SNAKE_CASE_,      question_encoder_tokenizer=self.get_dpr_tokenizer(),      generator_tokenizer=self.get_bart_tokenizer(),      )
          else:
             UpperCamelCase   :  Tuple						     =     RagRetriever(
                 SCREAMING_SNAKE_CASE_,      question_encoder_tokenizer=self.get_dpr_tokenizer(),      generator_tokenizer=self.get_bart_tokenizer(),      index=CustomHFIndex(config.retrieval_vector_size,      SCREAMING_SNAKE_CASE_	),      )
          return retriever
       def  snake_case_	(      self	)					->		str:
          UpperCamelCase   :  int						     =     Dataset.from_dict(
              {
                  'id': ['0', '1'],
                  'text': ['foo', 'bar'],
                  'title': ['Foo', 'Bar'],
                  'embeddings': [np.ones(self.retrieval_vector_size + 1	), 2 * np.ones(self.retrieval_vector_size + 1	)],
              }	)
          dataset.add_faiss_index('embeddings',      string_factory='Flat',      metric_type=faiss.METRIC_INNER_PRODUCT	)

          UpperCamelCase   :  Dict						     =     os.path.join(self.tmpdirname,      'hf_bert_base.hnswSQ8_correct_phi_128.c_index'	)
          dataset.save_faiss_index('embeddings',      index_file_name + '.index.dpr'	)
          pickle.dump(dataset['id'],      open(index_file_name + '.index_meta.dpr',      'wb'	)	)

          UpperCamelCase   :  List[str]						     =     os.path.join(self.tmpdirname,      'psgs_w100.tsv.pkl'	)
          UpperCamelCase   :  List[Any]						     =     {sample['id']: [sample['text'], sample['title']] for sample in dataset}
          pickle.dump(SCREAMING_SNAKE_CASE_,      open(SCREAMING_SNAKE_CASE_,      'wb'	)	)

          UpperCamelCase   :  str						     =     RagConfig(
              retrieval_vector_size=self.retrieval_vector_size,      question_encoder=DPRConfig().to_dict(),      generator=BartConfig().to_dict(),      index_name='legacy',      index_path=self.tmpdirname,      )
          UpperCamelCase   :  List[Any]						     =     RagRetriever(
              SCREAMING_SNAKE_CASE_,      question_encoder_tokenizer=self.get_dpr_tokenizer(),      generator_tokenizer=self.get_bart_tokenizer()	)
          return retriever
       def  snake_case_	(      self	)					->		Union[str, Any]:
          UpperCamelCase   :  str						     =     1
          UpperCamelCase   :  List[Any]						     =     self.get_dummy_canonical_hf_index_retriever()
          UpperCamelCase   :  Optional[Any]						     =     np.array(
              [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)],      dtype=np.floataa	)
          UpperCamelCase   :  Tuple						     =     retriever.retrieve(SCREAMING_SNAKE_CASE_,      n_docs=SCREAMING_SNAKE_CASE_	)
          self.assertEqual(retrieved_doc_embeds.shape,      (2, n_docs, self.retrieval_vector_size)	)
          self.assertEqual(len(SCREAMING_SNAKE_CASE_	),      2	)
          self.assertEqual(sorted(doc_dicts[0]	),      ['embeddings', 'id', 'text', 'title']	)
          self.assertEqual(len(doc_dicts[0]['id']	),      SCREAMING_SNAKE_CASE_	)
          self.assertEqual(doc_dicts[0]['id'][0],      '1'	)  # max inner product is reached with second doc
          self.assertEqual(doc_dicts[1]['id'][0],      '0'	)  # max inner product is reached with first doc
          self.assertListEqual(doc_ids.tolist(),      [[1], [0]]	)
       def  snake_case_	(      self	)					->		int:
          UpperCamelCase   :  Union[str, Any]						     =     self.get_dummy_canonical_hf_index_retriever()
          with tempfile.TemporaryDirectory() as tmp_dirname:
             with patch('transformers.models.rag.retrieval_rag.load_dataset'	) as mock_load_dataset:
                UpperCamelCase   :  str						     =     self.get_dummy_dataset()
                retriever.save_pretrained(SCREAMING_SNAKE_CASE_	)
                UpperCamelCase   :  Optional[Any]						     =     RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE_	)
                self.assertIsInstance(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
                UpperCamelCase   :  int						     =     np.array(
                    [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)],      dtype=np.floataa	)
                UpperCamelCase   :  int						     =     retriever.retrieve(SCREAMING_SNAKE_CASE_,      n_docs=1	)
                self.assertTrue(out is not None	)
       def  snake_case_	(      self	)					->		int:
          UpperCamelCase   :  Tuple						     =     1
          UpperCamelCase   :  Union[str, Any]						     =     self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Union[str, Any]						     =     np.array(
              [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)],      dtype=np.floataa	)
          UpperCamelCase   :  int						     =     retriever.retrieve(SCREAMING_SNAKE_CASE_,      n_docs=SCREAMING_SNAKE_CASE_	)
          self.assertEqual(retrieved_doc_embeds.shape,      (2, n_docs, self.retrieval_vector_size)	)
          self.assertEqual(len(SCREAMING_SNAKE_CASE_	),      2	)
          self.assertEqual(sorted(doc_dicts[0]	),      ['embeddings', 'id', 'text', 'title']	)
          self.assertEqual(len(doc_dicts[0]['id']	),      SCREAMING_SNAKE_CASE_	)
          self.assertEqual(doc_dicts[0]['id'][0],      '1'	)  # max inner product is reached with second doc
          self.assertEqual(doc_dicts[1]['id'][0],      '0'	)  # max inner product is reached with first doc
          self.assertListEqual(doc_ids.tolist(),      [[1], [0]]	)
       def  snake_case_	(      self	)					->		List[Any]:
          UpperCamelCase   :  Any						     =     self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE_	)
          with tempfile.TemporaryDirectory() as tmp_dirname:
             retriever.save_pretrained(SCREAMING_SNAKE_CASE_	)
             UpperCamelCase   :  Tuple						     =     RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE_	)
             self.assertIsInstance(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
             UpperCamelCase   :  Tuple						     =     np.array(
                 [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)],      dtype=np.floataa	)
             UpperCamelCase   :  List[Any]						     =     retriever.retrieve(SCREAMING_SNAKE_CASE_,      n_docs=1	)
             self.assertTrue(out is not None	)
       def  snake_case_	(      self	)					->		Union[str, Any]:
          UpperCamelCase   :  Dict						     =     1
          UpperCamelCase   :  Optional[int]						     =     self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  List[Any]						     =     np.array(
              [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)],      dtype=np.floataa	)
          UpperCamelCase   :  Tuple						     =     retriever.retrieve(SCREAMING_SNAKE_CASE_,      n_docs=SCREAMING_SNAKE_CASE_	)
          self.assertEqual(retrieved_doc_embeds.shape,      (2, n_docs, self.retrieval_vector_size)	)
          self.assertEqual(len(SCREAMING_SNAKE_CASE_	),      2	)
          self.assertEqual(sorted(doc_dicts[0]	),      ['embeddings', 'id', 'text', 'title']	)
          self.assertEqual(len(doc_dicts[0]['id']	),      SCREAMING_SNAKE_CASE_	)
          self.assertEqual(doc_dicts[0]['id'][0],      '1'	)  # max inner product is reached with second doc
          self.assertEqual(doc_dicts[1]['id'][0],      '0'	)  # max inner product is reached with first doc
          self.assertListEqual(doc_ids.tolist(),      [[1], [0]]	)
       def  snake_case_	(      self	)					->		Dict:
          UpperCamelCase   :  Optional[int]						     =     self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE_	)
          with tempfile.TemporaryDirectory() as tmp_dirname:
             retriever.save_pretrained(SCREAMING_SNAKE_CASE_	)
             UpperCamelCase   :  Optional[int]						     =     RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE_	)
             self.assertIsInstance(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
             UpperCamelCase   :  Dict						     =     np.array(
                 [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)],      dtype=np.floataa	)
             UpperCamelCase   :  int						     =     retriever.retrieve(SCREAMING_SNAKE_CASE_,      n_docs=1	)
             self.assertTrue(out is not None	)
       def  snake_case_	(      self	)					->		Dict:
          UpperCamelCase   :  str						     =     1
          UpperCamelCase   :  str						     =     self.get_dummy_legacy_index_retriever()
          UpperCamelCase   :  str						     =     np.array(
              [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)],      dtype=np.floataa	)
          UpperCamelCase   :  Dict						     =     retriever.retrieve(SCREAMING_SNAKE_CASE_,      n_docs=SCREAMING_SNAKE_CASE_	)
          self.assertEqual(retrieved_doc_embeds.shape,      (2, n_docs, self.retrieval_vector_size)	)
          self.assertEqual(len(SCREAMING_SNAKE_CASE_	),      2	)
          self.assertEqual(sorted(doc_dicts[0]	),      ['text', 'title']	)
          self.assertEqual(len(doc_dicts[0]['text']	),      SCREAMING_SNAKE_CASE_	)
          self.assertEqual(doc_dicts[0]['text'][0],      'bar'	)  # max inner product is reached with second doc
          self.assertEqual(doc_dicts[1]['text'][0],      'foo'	)  # max inner product is reached with first doc
          self.assertListEqual(doc_ids.tolist(),      [[1], [0]]	)
       def  snake_case_	(      self	)					->		Dict:
          UpperCamelCase   :  Optional[Any]						     =     self.get_dummy_legacy_index_retriever()
          with tempfile.TemporaryDirectory() as tmp_dirname:
             retriever.save_pretrained(SCREAMING_SNAKE_CASE_	)
             UpperCamelCase   :  List[str]						     =     RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE_	)
             self.assertIsInstance(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
             UpperCamelCase   :  int						     =     np.array(
                 [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)],      dtype=np.floataa	)
             UpperCamelCase   :  List[str]						     =     retriever.retrieve(SCREAMING_SNAKE_CASE_,      n_docs=1	)
             self.assertTrue(out is not None	)
       @require_torch
       @require_tokenizers
       @require_sentencepiece
       def  snake_case_	(      self	)					->		int:
          import torch

          UpperCamelCase   :  int						     =     1
          UpperCamelCase   :  List[str]						     =     self.get_dummy_canonical_hf_index_retriever()
          UpperCamelCase   :  Union[str, Any]						     =     [[5, 7], [10, 11]]
          UpperCamelCase   :  int						     =     np.array(
              [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)],      dtype=np.floataa	)
          UpperCamelCase   :  Optional[Any]						     =     retriever(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      prefix=retriever.config.generator.prefix,      n_docs=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Dict						     =     (
              out['context_input_ids'],
              out['context_attention_mask'],
              out['retrieved_doc_embeds'],
          )
          self.assertEqual(retrieved_doc_embeds.shape,      (2, n_docs, self.retrieval_vector_size)	)
          self.assertIsInstance(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
          self.assertIsInstance(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
          self.assertIsInstance(SCREAMING_SNAKE_CASE_,      np.ndarray	)

          UpperCamelCase   :  Optional[Any]						     =     retriever(
              SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      prefix=retriever.config.generator.prefix,      n_docs=SCREAMING_SNAKE_CASE_,      return_tensors='pt',      )
          UpperCamelCase   :  Optional[Any]						     =     (  # noqa: F841
              out['context_input_ids'],
              out['context_attention_mask'],
              out['retrieved_doc_embeds'],
              out['doc_ids'],
          )
          self.assertEqual(retrieved_doc_embeds.shape,      (2, n_docs, self.retrieval_vector_size)	)
          self.assertIsInstance(SCREAMING_SNAKE_CASE_,      torch.Tensor	)
          self.assertIsInstance(SCREAMING_SNAKE_CASE_,      torch.Tensor	)
          self.assertIsInstance(SCREAMING_SNAKE_CASE_,      torch.Tensor	)






       @require_torch
       @require_tokenizers
       @require_sentencepiece
       def  snake_case_	(      self	)					->		Optional[Any]:
          UpperCamelCase   :  Any						     =     self.get_dpr_ctx_encoder_tokenizer()
          UpperCamelCase   :  int						     =     1
          UpperCamelCase   :  List[Any]						     =     self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE_	)
          retriever.set_ctx_encoder_tokenizer(SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  List[Any]						     =     [[5, 7], [10, 11]]
          UpperCamelCase   :  int						     =     np.array(
              [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)],      dtype=np.floataa	)
          UpperCamelCase   :  List[Any]						     =     retriever(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      prefix=retriever.config.generator.prefix,      n_docs=SCREAMING_SNAKE_CASE_	)

          self.assertEqual(
              len(SCREAMING_SNAKE_CASE_	),      6	)  # check whether the retriever output consist of 6 attributes including tokenized docs
          self.assertEqual(
              all(k in out for k in ('tokenized_doc_ids', 'tokenized_doc_attention_mask')	),      SCREAMING_SNAKE_CASE_	)  # check for doc token related keys in dictionary.




import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image
else:



    class     lowerCAmelCase_		:
           @staticmethod
           def  snake_case_	(      *SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)					->		Dict:
              pass
def 		UpperCamelCase     (       snake_case__	:   Image     )     ->					str:
   UpperCamelCase   :  Tuple						     =     hashlib.mda(image.tobytes()     )
   return m.hexdigest()



@is_pipeline_test
@require_vision
@require_timm
@require_torch
class     lowerCAmelCase_		(  unittest.TestCase							):
       UpperCAmelCase__    : str       					=  MODEL_FOR_DEPTH_ESTIMATION_MAPPING
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		Optional[Any]:
          UpperCamelCase   :  List[str]						     =     DepthEstimationPipeline(model=SCREAMING_SNAKE_CASE_,      image_processor=SCREAMING_SNAKE_CASE_	)
          return depth_estimator, [
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
          ]
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		Union[str, Any]:
          UpperCamelCase   :  Union[str, Any]						     =     depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
          self.assertEqual({'predicted_depth': ANY(torch.Tensor	), 'depth': ANY(Image.Image	)},      SCREAMING_SNAKE_CASE_	)
          import datasets

          UpperCamelCase   :  int						     =     datasets.load_dataset('hf-internal-testing/fixtures_image_utils',      'image',      split='test'	)
          UpperCamelCase   :  Tuple						     =     depth_estimator(
              [
                  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	),
                  'http://images.cocodataset.org/val2017/000000039769.jpg',
                  # RGBA
                  dataset[0]['file'],
                  # LA
                  dataset[1]['file'],
                  # L
                  dataset[2]['file'],
              ]	)
          self.assertEqual(
              [
                  {'predicted_depth': ANY(torch.Tensor	), 'depth': ANY(Image.Image	)},
                  {'predicted_depth': ANY(torch.Tensor	), 'depth': ANY(Image.Image	)},
                  {'predicted_depth': ANY(torch.Tensor	), 'depth': ANY(Image.Image	)},
                  {'predicted_depth': ANY(torch.Tensor	), 'depth': ANY(Image.Image	)},
                  {'predicted_depth': ANY(torch.Tensor	), 'depth': ANY(Image.Image	)},
              ],      SCREAMING_SNAKE_CASE_,      )
       @require_tf
       @unittest.skip('Depth estimation is not implemented in TF'	)
       def  snake_case_	(      self	)					->		Optional[int]:
          pass
       @slow
       @require_torch
       def  snake_case_	(      self	)					->		List[Any]:
          UpperCamelCase   :  Optional[Any]						     =     'Intel/dpt-large'
          UpperCamelCase   :  Tuple						     =     pipeline('depth-estimation',      model=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Any						     =     depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'	)
          UpperCamelCase   :  int						     =     hashimage(outputs['depth']	)

          # This seems flaky.
          # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
          self.assertEqual(nested_simplify(outputs['predicted_depth'].max().item()	),      29.3_04	)
          self.assertEqual(nested_simplify(outputs['predicted_depth'].min().item()	),      2.6_62	)






       @require_torch
       def  snake_case_	(      self	)					->		Optional[int]:
          # This is highly irregular to have no small tests.
          self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT'	)



