



'''simple docstring'''





import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow



@require_torch
class     _UpperCAmelCase			(       unittest.TestCase     ):




     def 		snake_case_		(				self):
         A__		 =					'''ylacombe/bark-small'''
         A__		 =					tempfile.mkdtemp()
         A__		 =					'''en_speaker_1'''
         A__		 =					'''This is a test string'''
         A__		 =					'''speaker_embeddings_path.json'''
         A__		 =					'''speaker_embeddings'''




     def 		snake_case_		(				self ,						**a__):
         return AutoTokenizer.from_pretrained(self.checkpoint ,						**a__)




     def 		snake_case_		(				self):
         shutil.rmtree(self.tmpdirname)




     def 		snake_case_		(				self):
         A__		 =					self.get_tokenizer()

         A__		 =					BarkProcessor(tokenizer=a__)

         processor.save_pretrained(self.tmpdirname)
         A__		 =					BarkProcessor.from_pretrained(self.tmpdirname)

         self.assertEqual(processor.tokenizer.get_vocab() ,						tokenizer.get_vocab())




     @slow
     def 		snake_case_		(				self):
         A__		 =					BarkProcessor.from_pretrained(
             pretrained_processor_name_or_path=self.checkpoint ,						speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,						)
         processor.save_pretrained(
             self.tmpdirname ,						speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,						speaker_embeddings_directory=self.speaker_embeddings_directory ,						)

         A__		 =					self.get_tokenizer(bos_token='''(BOS)''' ,						eos_token='''(EOS)''')

         A__		 =					BarkProcessor.from_pretrained(
             self.tmpdirname ,						self.speaker_embeddings_dict_path ,						bos_token='''(BOS)''' ,						eos_token='''(EOS)''' ,						)

         self.assertEqual(processor.tokenizer.get_vocab() ,						tokenizer_add_kwargs.get_vocab())




     def 		snake_case_		(				self):
         A__		 =					BarkProcessor.from_pretrained(
             pretrained_processor_name_or_path=self.checkpoint ,						speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,						)

         A__		 =					3_5
         A__		 =					2
         A__		 =					8

         A__		 =					{
             '''semantic_prompt''': np.ones(a__),
             '''coarse_prompt''': np.ones((nb_codebooks_coarse, seq_len)),
             '''fine_prompt''': np.ones((nb_codebooks_total, seq_len)),
         }

         # test providing already loaded voice_preset
         A__		 =					processor(text=self.input_string ,						voice_preset=a__)

         A__		 =					inputs['''history_prompt''']
         for key in voice_preset:
             self.assertListEqual(voice_preset[key].tolist() ,						processed_voice_preset.get(a__ ,						np.array([])).tolist())

         # test loading voice preset from npz file
         A__		 =					os.path.join(self.tmpdirname ,						'''file.npz''')
         np.savez(a__ ,						**a__)
         A__		 =					processor(text=self.input_string ,						voice_preset=a__)
         A__		 =					inputs['''history_prompt''']

         for key in voice_preset:
             self.assertListEqual(voice_preset[key].tolist() ,						processed_voice_preset.get(a__ ,						np.array([])).tolist())

         # test loading voice preset from the hub
         A__		 =					processor(text=self.input_string ,						voice_preset=self.voice_preset)




     def 		snake_case_		(				self):
         A__		 =					self.get_tokenizer()

         A__		 =					BarkProcessor(tokenizer=a__)

         A__		 =					processor(text=self.input_string)

         A__		 =					tokenizer(
             self.input_string ,						padding='''max_length''' ,						max_length=2_5_6 ,						add_special_tokens=a__ ,						return_attention_mask=a__ ,						return_token_type_ids=a__ ,						)

         for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key] ,						encoded_processor[key].squeeze().tolist())





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     _UpperCAmelCase			:




        @staticmethod
        def 		snake_case_		(				*a__ ,						**a__):
            pass



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Image	)->  str:
    A__		 =					hashlib.mda(image.tobytes()	)
    return m.hexdigest()



@is_pipeline_test
@require_vision
@require_timm
@require_torch
class     _UpperCAmelCase			(       unittest.TestCase     ):
     UpperCamelCase__		    =				MODEL_FOR_DEPTH_ESTIMATION_MAPPING




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__):
         A__		 =					DepthEstimationPipeline(model=a__ ,						image_processor=a__)
         return depth_estimator, [
             "./tests/fixtures/tests_samples/COCO/000000039769.png",
             "./tests/fixtures/tests_samples/COCO/000000039769.png",
         ]




     def 		snake_case_		(				self ,						a__ ,						a__):
         A__		 =					depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
         self.assertEqual({'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)} ,						a__)
         import datasets

         A__		 =					datasets.load_dataset('''hf-internal-testing/fixtures_image_utils''' ,						'''image''' ,						split='''test''')
         A__		 =					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)},
             ] ,						a__ ,						)




     @require_tf
     @unittest.skip('''Depth estimation is not implemented in TF''')
     def 		snake_case_		(				self):
         pass




     @slow
     @require_torch
     def 		snake_case_		(				self):
         A__		 =					'''Intel/dpt-large'''
         A__		 =					pipeline('''depth-estimation''' ,						model=a__)
         A__		 =					depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg''')
         A__		 =					hashimage(outputs['''depth'''])

         # This seems flaky.
         # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
         self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item()) ,						2_9.3_0_4)
         self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item()) ,						2.6_6_2)




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