

import unittest

import numpy as np
import requests

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

      from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
      __a    :int      =			False

if is_vision_available():
      from PIL import Image

      from transformers import PixaStructImageProcessor

class   _a					(			unittest.TestCase				):




      """simple docstring"""

      def __init__(   self							:				int						,			UpperCAmelCase							:				Optional[int]						,			UpperCAmelCase							:				List[Any]=7						,			UpperCAmelCase							:				Dict=3						,			UpperCAmelCase							:				str=18						,			UpperCAmelCase							:				str=30						,			UpperCAmelCase							:				Dict=400						,			UpperCAmelCase							:				Dict=None						,			UpperCAmelCase							:				Tuple=True						,			UpperCAmelCase							:				Union[str, Any]=True						,			UpperCAmelCase							:				Union[str, Any]=None						,			):
            A_						  =				size if size is not None else {"height": 20, "width": 20}
            A_						  =				parent
            A_						  =				batch_size
            A_						  =				num_channels
            A_						  =				image_size
            A_						  =				min_resolution
            A_						  =				max_resolution
            A_						  =				size
            A_						  =				do_normalize
            A_						  =				do_convert_rgb
            A_						  =				[512, 1024, 2048, 4096]
            A_						  =				patch_size if patch_size is not None else {"height": 16, "width": 16}





      def 		__A	(   self							:				List[Any]	):
            return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}







      def 		__A	(   self							:				Dict	):
            A_						  =				"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
            A_						  =				Image.open(requests.get(UpperCAmelCase						,			stream=UpperCAmelCase	).raw	).convert("RGB"	)
            return raw_image




@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11     ,				reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'     ,				)
@require_torch
@require_vision
class   _a					(			snake_case_     ,				unittest.TestCase				):




      """simple docstring"""

      _lowerCamelCase       :			Optional[Any]      		=       PixaStructImageProcessor if is_vision_available() else None





      def 		__A	(   self							:				Dict	):
            A_						  =				PixaStructImageProcessingTester(self	)





      @property
      def 		__A	(   self							:				Any	):
            return self.image_processor_tester.prepare_image_processor_dict()





      def 		__A	(   self							:				Any	):
            A_						  =				self.image_processing_class(**self.image_processor_dict	)
            self.assertTrue(hasattr(UpperCAmelCase						,			"do_normalize"	)	)
            self.assertTrue(hasattr(UpperCAmelCase						,			"do_convert_rgb"	)	)





      def 		__A	(   self							:				str	):
            A_						  =				self.image_processor_tester.prepare_dummy_image()

            A_						  =				self.image_processing_class(**self.image_processor_dict	)
            A_						  =				2048

            A_						  =				image_processor(UpperCAmelCase						,			return_tensors="pt"						,			max_patches=UpperCAmelCase	)
            self.assertTrue(torch.allclose(inputs.flattened_patches.mean()						,			torch.tensor(0.0_606	)						,			atol=1E-3						,			rtol=1E-3	)	)





      def 		__A	(   self							:				List[Any]	):
            A_						  =				self.image_processing_class(**self.image_processor_dict	)
            # create random PIL images
            A_						  =				prepare_image_inputs(self.image_processor_tester						,			equal_resolution=UpperCAmelCase	)
            for image in image_inputs:
                  self.assertIsInstance(UpperCAmelCase						,			Image.Image	)

            # Test not batched input
            A_						  =				(
                (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
                * self.image_processor_tester.num_channels
            ) + 2

            for max_patch in self.image_processor_tester.max_patches:
                  # Test not batched input
                  A_						  =				image_processor(
                      image_inputs[0]						,			return_tensors="pt"						,			max_patches=UpperCAmelCase	).flattened_patches
                  self.assertEqual(
                      encoded_images.shape						,			(1, max_patch, expected_hidden_dim)						,			)

                  # Test batched
                  A_						  =				image_processor(
                      UpperCAmelCase						,			return_tensors="pt"						,			max_patches=UpperCAmelCase	).flattened_patches
                  self.assertEqual(
                      encoded_images.shape						,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)						,			)





      def 		__A	(   self							:				List[Any]	):
            A_						  =				self.image_processing_class(**self.image_processor_dict	)
            # create random PIL images
            A_						  =				prepare_image_inputs(self.image_processor_tester						,			equal_resolution=UpperCAmelCase	)
            for image in image_inputs:
                  self.assertIsInstance(UpperCAmelCase						,			Image.Image	)

            # Test not batched input
            A_						  =				(
                (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
                * self.image_processor_tester.num_channels
            ) + 2

            A_						  =				True

            for max_patch in self.image_processor_tester.max_patches:
                  # Test not batched input
                  with self.assertRaises(UpperCAmelCase	):
                        A_						  =				image_processor(
                            image_inputs[0]						,			return_tensors="pt"						,			max_patches=UpperCAmelCase	).flattened_patches

                  A_						  =				"Hello"

                  A_						  =				image_processor(
                      image_inputs[0]						,			return_tensors="pt"						,			max_patches=UpperCAmelCase						,			header_text=UpperCAmelCase	).flattened_patches
                  self.assertEqual(
                      encoded_images.shape						,			(1, max_patch, expected_hidden_dim)						,			)

                  # Test batched
                  A_						  =				image_processor(
                      UpperCAmelCase						,			return_tensors="pt"						,			max_patches=UpperCAmelCase						,			header_text=UpperCAmelCase	).flattened_patches
                  self.assertEqual(
                      encoded_images.shape						,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)						,			)





      def 		__A	(   self							:				List[Any]	):
            A_						  =				self.image_processing_class(**self.image_processor_dict	)
            # create random numpy tensors
            A_						  =				prepare_image_inputs(self.image_processor_tester						,			equal_resolution=UpperCAmelCase						,			numpify=UpperCAmelCase	)
            for image in image_inputs:
                  self.assertIsInstance(UpperCAmelCase						,			np.ndarray	)

            A_						  =				(
                (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
                * self.image_processor_tester.num_channels
            ) + 2

            for max_patch in self.image_processor_tester.max_patches:
                  # Test not batched input
                  A_						  =				image_processor(
                      image_inputs[0]						,			return_tensors="pt"						,			max_patches=UpperCAmelCase	).flattened_patches
                  self.assertEqual(
                      encoded_images.shape						,			(1, max_patch, expected_hidden_dim)						,			)

                  # Test batched
                  A_						  =				image_processor(
                      UpperCAmelCase						,			return_tensors="pt"						,			max_patches=UpperCAmelCase	).flattened_patches
                  self.assertEqual(
                      encoded_images.shape						,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)						,			)







      def 		__A	(   self							:				Dict	):
            A_						  =				self.image_processing_class(**self.image_processor_dict	)
            # create random PyTorch tensors
            A_						  =				prepare_image_inputs(self.image_processor_tester						,			equal_resolution=UpperCAmelCase						,			torchify=UpperCAmelCase	)
            for image in image_inputs:
                  self.assertIsInstance(UpperCAmelCase						,			torch.Tensor	)

            # Test not batched input
            A_						  =				(
                (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
                * self.image_processor_tester.num_channels
            ) + 2

            for max_patch in self.image_processor_tester.max_patches:
                  # Test not batched input
                  A_						  =				image_processor(
                      image_inputs[0]						,			return_tensors="pt"						,			max_patches=UpperCAmelCase	).flattened_patches
                  self.assertEqual(
                      encoded_images.shape						,			(1, max_patch, expected_hidden_dim)						,			)

                  # Test batched
                  A_						  =				image_processor(
                      UpperCAmelCase						,			return_tensors="pt"						,			max_patches=UpperCAmelCase	).flattened_patches
                  self.assertEqual(
                      encoded_images.shape						,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)						,			)




@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11     ,				reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'     ,				)
@require_torch
@require_vision
class   _a					(			snake_case_     ,				unittest.TestCase				):




      """simple docstring"""

      _lowerCamelCase       :			Optional[Any]      		=       PixaStructImageProcessor if is_vision_available() else None





      def 		__A	(   self							:				Optional[Any]	):
            A_						  =				PixaStructImageProcessingTester(self						,			num_channels=4	)
            A_						  =				3





      @property
      def 		__A	(   self							:				Union[str, Any]	):
            return self.image_processor_tester.prepare_image_processor_dict()





      def 		__A	(   self							:				int	):
            A_						  =				self.image_processing_class(**self.image_processor_dict	)
            self.assertTrue(hasattr(UpperCAmelCase						,			"do_normalize"	)	)
            self.assertTrue(hasattr(UpperCAmelCase						,			"do_convert_rgb"	)	)







      def 		__A	(   self							:				Dict	):
            A_						  =				self.image_processing_class(**self.image_processor_dict	)
            # create random PIL images
            A_						  =				prepare_image_inputs(self.image_processor_tester						,			equal_resolution=UpperCAmelCase	)
            for image in image_inputs:
                  self.assertIsInstance(UpperCAmelCase						,			Image.Image	)

            # Test not batched input
            A_						  =				(
                (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
                * (self.image_processor_tester.num_channels - 1)
            ) + 2

            for max_patch in self.image_processor_tester.max_patches:
                  # Test not batched input
                  A_						  =				image_processor(
                      image_inputs[0]						,			return_tensors="pt"						,			max_patches=UpperCAmelCase	).flattened_patches
                  self.assertEqual(
                      encoded_images.shape						,			(1, max_patch, expected_hidden_dim)						,			)

                  # Test batched
                  A_						  =				image_processor(
                      UpperCAmelCase						,			return_tensors="pt"						,			max_patches=UpperCAmelCase	).flattened_patches
                  self.assertEqual(
                      encoded_images.shape						,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)						,			)


"""simple docstring"""

import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
    import torch

if is_datasets_available():
    from datasets import load_dataset

_UpperCamelCase		     =    random.Random()



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__=1.0					,					lowercase__=None					,					lowercase__=None   )			->						Optional[Any]:
 if rng is None:
  lowerCAmelCase__   :				Union[str, Any] 			=				global_rng

 lowerCAmelCase__   :				Dict 			=				[]
 for batch_idx in range(shape[0]   ):
  values.append([]   )
  for _ in range(shape[1]   ):
   values[-1].append(rng.random() * scale   )

 return values






class 			__a					(						unittest.TestCase       ):





       """simple docstring"""


       def __init__(   self			,					snake_case			,					snake_case=7			,					snake_case=400			,					snake_case=2_000			,					snake_case=2_048			,					snake_case=128			,					snake_case=1			,					snake_case=512			,					snake_case=30			,					snake_case=44_100			,					):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[int] 			=				parent
        lowerCAmelCase__   :				Optional[int] 			=				batch_size
        lowerCAmelCase__   :				Optional[Any] 			=				min_seq_length
        lowerCAmelCase__   :				Optional[int] 			=				max_seq_length
        lowerCAmelCase__   :				Dict 			=				(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        lowerCAmelCase__   :				Dict 			=				spectrogram_length
        lowerCAmelCase__   :				Any 			=				feature_size
        lowerCAmelCase__   :				int 			=				num_audio_channels
        lowerCAmelCase__   :				Optional[int] 			=				hop_length
        lowerCAmelCase__   :				List[str] 			=				chunk_length
        lowerCAmelCase__   :				Optional[Any] 			=				sampling_rate






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        return {
            "spectrogram_length": self.spectrogram_length,
            "feature_size": self.feature_size,
            "num_audio_channels": self.num_audio_channels,
            "hop_length": self.hop_length,
            "chunk_length": self.chunk_length,
            "sampling_rate": self.sampling_rate,
        }






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case=False			,					snake_case=False   ):





        """simple docstring"""

        def _flatten(snake_case   ):
         return list(itertools.chain(*snake_case   )   )

        if equal_length:
         lowerCAmelCase__   :				Dict 			=				[floats_list((self.max_seq_length, self.feature_size)   ) for _ in range(self.batch_size   )]
        else:
         # make sure that inputs increase in size
         lowerCAmelCase__   :				Tuple 			=				[
             floats_list((x, self.feature_size)   )
             for x in range(self.min_seq_length			,					self.max_seq_length			,					self.seq_length_diff   )
         ]
        if numpify:
         lowerCAmelCase__   :				Dict 			=				[np.asarray(snake_case   ) for x in speech_inputs]
        return speech_inputs






@require_torch
@require_torchaudio
class 			__a					(						__magic_name__       ,	unittest.TestCase       ):





       """simple docstring"""


       __UpperCamelCase					:      Optional[Any]    =						TvltFeatureExtractor






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[Any] 			=				TvltFeatureExtractionTester(self   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[str] 			=				self.feature_extraction_class(**self.feat_extract_dict   )
        self.assertTrue(hasattr(snake_case			,					"spectrogram_length"   )   )
        self.assertTrue(hasattr(snake_case			,					"feature_size"   )   )
        self.assertTrue(hasattr(snake_case			,					"num_audio_channels"   )   )
        self.assertTrue(hasattr(snake_case			,					"hop_length"   )   )
        self.assertTrue(hasattr(snake_case			,					"chunk_length"   )   )
        self.assertTrue(hasattr(snake_case			,					"sampling_rate"   )   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				int 			=				self.feature_extraction_class(**self.feat_extract_dict   )

        with tempfile.TemporaryDirectory() as tmpdirname:
         lowerCAmelCase__   :				Union[str, Any] 			=				feat_extract_first.save_pretrained(snake_case   )[0]
         check_json_file_has_correct_format(snake_case   )
         lowerCAmelCase__   :				Tuple 			=				self.feature_extraction_class.from_pretrained(snake_case   )

        lowerCAmelCase__   :				Union[str, Any] 			=				feat_extract_first.to_dict()
        lowerCAmelCase__   :				int 			=				feat_extract_second.to_dict()
        lowerCAmelCase__   :				Union[str, Any] 			=				dict_first.pop("mel_filters"   )
        lowerCAmelCase__   :				List[Any] 			=				dict_second.pop("mel_filters"   )
        self.assertTrue(np.allclose(snake_case			,					snake_case   )   )
        self.assertEqual(snake_case			,					snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Any 			=				self.feature_extraction_class(**self.feat_extract_dict   )

        with tempfile.TemporaryDirectory() as tmpdirname:
         lowerCAmelCase__   :				List[Any] 			=				os.path.join(snake_case			,					"feat_extract.json"   )
         feat_extract_first.to_json_file(snake_case   )
         lowerCAmelCase__   :				List[Any] 			=				self.feature_extraction_class.from_json_file(snake_case   )

        lowerCAmelCase__   :				Optional[Any] 			=				feat_extract_first.to_dict()
        lowerCAmelCase__   :				Dict 			=				feat_extract_second.to_dict()
        lowerCAmelCase__   :				Tuple 			=				dict_first.pop("mel_filters"   )
        lowerCAmelCase__   :				Dict 			=				dict_second.pop("mel_filters"   )
        self.assertTrue(np.allclose(snake_case			,					snake_case   )   )
        self.assertEqual(snake_case			,					snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[int] 			=				self.feature_extraction_class(**self.feat_extract_dict   )

        # create three inputs of length 800, 1000, and 1200
        lowerCAmelCase__   :				Union[str, Any] 			=				[floats_list((1, x)   )[0] for x in range(800			,					1_400			,					200   )]
        lowerCAmelCase__   :				Optional[int] 			=				[np.asarray(snake_case   ) for speech_input in speech_inputs]

        # Test not batched input
        lowerCAmelCase__   :				Tuple 			=				feature_extractor(np_speech_inputs[0]			,					return_tensors="np"			,					sampling_rate=44_100   ).audio_values

        self.assertTrue(encoded_audios.ndim == 4   )
        self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size   )
        self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length   )
        self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels   )

        # Test batched
        lowerCAmelCase__   :				Optional[int] 			=				feature_extractor(snake_case			,					return_tensors="np"			,					sampling_rate=44_100   ).audio_values

        self.assertTrue(encoded_audios.ndim == 4   )
        self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size   )
        self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length   )
        self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels   )

        # Test audio masking
        lowerCAmelCase__   :				List[str] 			=				feature_extractor(
            snake_case			,					return_tensors="np"			,					sampling_rate=44_100			,					mask_audio=snake_case   ).audio_values

        self.assertTrue(encoded_audios.ndim == 4   )
        self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size   )
        self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length   )
        self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels   )

        # Test 2-D numpy arrays are batched.
        lowerCAmelCase__   :				List[str] 			=				[floats_list((1, x)   )[0] for x in (800, 800, 800)]
        lowerCAmelCase__   :				Tuple 			=				np.asarray(snake_case   )
        lowerCAmelCase__   :				List[Any] 			=				feature_extractor(snake_case			,					return_tensors="np"			,					sampling_rate=44_100   ).audio_values
        self.assertTrue(encoded_audios.ndim == 4   )
        self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size   )
        self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length   )
        self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels   )






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				str 			=				load_dataset("hf-internal-testing/librispeech_asr_dummy"			,					"clean"			,					split="validation"   )
        # automatic decoding with librispeech
        lowerCAmelCase__   :				int 			=				ds.sort("id"   ).select(range(snake_case   )   )[:num_samples]["audio"]

        return [x["array"] for x in speech_samples]






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				str 			=				self._load_datasamples(1   )
        lowerCAmelCase__   :				Optional[Any] 			=				TvltFeatureExtractor()
        lowerCAmelCase__   :				Dict 			=				feature_extractor(snake_case			,					return_tensors="pt"   ).audio_values

        self.assertEquals(audio_values.shape			,					(1, 1, 192, 128)   )

        lowerCAmelCase__   :				Dict 			=				torch.tensor([[-0.3_032, -0.2_708], [-0.4_434, -0.4_007]]   )
        self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]			,					snake_case			,					atol=1e-4   )   )






