
import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
			from PIL import Image

			from transformers import CLIPSegProcessor, ViTImageProcessor





@require_vision
class 			_lowerCAmelCase      (			unittest.TestCase ):




				def   __a		(      self  )       ->   str:
							lowerCAmelCase_      			=			tempfile.mkdtemp()

							# fmt: off
							lowerCAmelCase_      			=			["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
							# fmt: on
							lowerCAmelCase_      			=			dict(zip(_UpperCamelCase       ,       range(len(_UpperCamelCase  )  )  )  )
							lowerCAmelCase_      			=			["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
							lowerCAmelCase_      			=			{"unk_token": "<unk>"}

							lowerCAmelCase_      			=			os.path.join(self.tmpdirname       ,       VOCAB_FILES_NAMES["vocab_file"]  )
							lowerCAmelCase_      			=			os.path.join(self.tmpdirname       ,       VOCAB_FILES_NAMES["merges_file"]  )
							with open(self.vocab_file       ,       "w"       ,       encoding="utf-8"  ) as fp:
										fp.write(json.dumps(_UpperCamelCase  ) + "\n"  )
							with open(self.merges_file       ,       "w"       ,       encoding="utf-8"  ) as fp:
										fp.write("\n".join(_UpperCamelCase  )  )

							lowerCAmelCase_      			=			{
							    "do_resize": True,
							    "size": 20,
							    "do_center_crop": True,
							    "crop_size": 18,
							    "do_normalize": True,
							    "image_mean": [0.48145466, 0.4578275, 0.40821073],
							    "image_std": [0.26862954, 0.26130258, 0.27577711],
							}
							lowerCAmelCase_      			=			os.path.join(self.tmpdirname       ,       _UpperCamelCase  )
							with open(self.image_processor_file       ,       "w"       ,       encoding="utf-8"  ) as fp:
										json.dump(_UpperCamelCase       ,       _UpperCamelCase  )




				def   __a		(      self       ,       **_UpperCamelCase  )       ->   int:
							return CLIPTokenizer.from_pretrained(self.tmpdirname       ,       **_UpperCamelCase  )




				def   __a		(      self       ,       **_UpperCamelCase  )       ->   int:
							return CLIPTokenizerFast.from_pretrained(self.tmpdirname       ,       **_UpperCamelCase  )




				def   __a		(      self       ,       **_UpperCamelCase  )       ->   int:
							return ViTImageProcessor.from_pretrained(self.tmpdirname       ,       **_UpperCamelCase  )




				def   __a		(      self  )       ->   int:
							shutil.rmtree(self.tmpdirname  )




				def   __a		(      self  )       ->   Optional[Any]:
							lowerCAmelCase_      			=			[np.random.randint(255       ,       size=(3, 30, 400)       ,       dtype=np.uinta  )]

							lowerCAmelCase_      			=			[Image.fromarray(np.moveaxis(_UpperCamelCase       ,       0       ,       -1  )  ) for x in image_inputs]

							return image_inputs




				def   __a		(      self  )       ->   str:
							lowerCAmelCase_      			=			self.get_tokenizer()
							lowerCAmelCase_      			=			self.get_rust_tokenizer()
							lowerCAmelCase_      			=			self.get_image_processor()

							lowerCAmelCase_      			=			CLIPSegProcessor(tokenizer=_UpperCamelCase       ,       image_processor=_UpperCamelCase  )
							processor_slow.save_pretrained(self.tmpdirname  )
							lowerCAmelCase_      			=			CLIPSegProcessor.from_pretrained(self.tmpdirname       ,       use_fast=_UpperCamelCase  )

							lowerCAmelCase_      			=			CLIPSegProcessor(tokenizer=_UpperCamelCase       ,       image_processor=_UpperCamelCase  )
							processor_fast.save_pretrained(self.tmpdirname  )
							lowerCAmelCase_      			=			CLIPSegProcessor.from_pretrained(self.tmpdirname  )

							self.assertEqual(processor_slow.tokenizer.get_vocab()       ,       tokenizer_slow.get_vocab()  )
							self.assertEqual(processor_fast.tokenizer.get_vocab()       ,       tokenizer_fast.get_vocab()  )
							self.assertEqual(tokenizer_slow.get_vocab()       ,       tokenizer_fast.get_vocab()  )
							self.assertIsInstance(processor_slow.tokenizer       ,       _UpperCamelCase  )
							self.assertIsInstance(processor_fast.tokenizer       ,       _UpperCamelCase  )

							self.assertEqual(processor_slow.image_processor.to_json_string()       ,       image_processor.to_json_string()  )
							self.assertEqual(processor_fast.image_processor.to_json_string()       ,       image_processor.to_json_string()  )
							self.assertIsInstance(processor_slow.image_processor       ,       _UpperCamelCase  )
							self.assertIsInstance(processor_fast.image_processor       ,       _UpperCamelCase  )




				def   __a		(      self  )       ->   List[Any]:
							lowerCAmelCase_      			=			CLIPSegProcessor(tokenizer=self.get_tokenizer()       ,       image_processor=self.get_image_processor()  )
							processor.save_pretrained(self.tmpdirname  )

							lowerCAmelCase_      			=			self.get_tokenizer(bos_token="(BOS)"       ,       eos_token="(EOS)"  )
							lowerCAmelCase_      			=			self.get_image_processor(do_normalize=_UpperCamelCase       ,       padding_value=1.0  )

							lowerCAmelCase_      			=			CLIPSegProcessor.from_pretrained(
							    self.tmpdirname       ,       bos_token="(BOS)"       ,       eos_token="(EOS)"       ,       do_normalize=_UpperCamelCase       ,       padding_value=1.0  )

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

							self.assertEqual(processor.image_processor.to_json_string()       ,       image_processor_add_kwargs.to_json_string()  )
							self.assertIsInstance(processor.image_processor       ,       _UpperCamelCase  )




				def   __a		(      self  )       ->   Tuple:
							lowerCAmelCase_      			=			self.get_image_processor()
							lowerCAmelCase_      			=			self.get_tokenizer()

							lowerCAmelCase_      			=			CLIPSegProcessor(tokenizer=_UpperCamelCase       ,       image_processor=_UpperCamelCase  )

							lowerCAmelCase_      			=			self.prepare_image_inputs()

							lowerCAmelCase_      			=			image_processor(_UpperCamelCase       ,       return_tensors="np"  )
							lowerCAmelCase_      			=			processor(images=_UpperCamelCase       ,       return_tensors="np"  )

							for key in input_feat_extract.keys():
										self.assertAlmostEqual(input_feat_extract[key].sum()       ,       input_processor[key].sum()       ,       delta=1e-2  )




				def   __a		(      self  )       ->   Any:
							lowerCAmelCase_      			=			self.get_image_processor()
							lowerCAmelCase_      			=			self.get_tokenizer()

							lowerCAmelCase_      			=			CLIPSegProcessor(tokenizer=_UpperCamelCase       ,       image_processor=_UpperCamelCase  )

							lowerCAmelCase_      			=			"lower newer"

							lowerCAmelCase_      			=			processor(text=_UpperCamelCase  )

							lowerCAmelCase_      			=			tokenizer(_UpperCamelCase  )

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




				def   __a		(      self  )       ->   Union[str, Any]:
							lowerCAmelCase_      			=			self.get_image_processor()
							lowerCAmelCase_      			=			self.get_tokenizer()

							lowerCAmelCase_      			=			CLIPSegProcessor(tokenizer=_UpperCamelCase       ,       image_processor=_UpperCamelCase  )

							lowerCAmelCase_      			=			"lower newer"
							lowerCAmelCase_      			=			self.prepare_image_inputs()

							lowerCAmelCase_      			=			processor(text=_UpperCamelCase       ,       images=_UpperCamelCase  )

							self.assertListEqual(list(inputs.keys()  )       ,       ["input_ids", "attention_mask", "pixel_values"]  )

							# test if it raises when no input is passed
							with pytest.raises(_UpperCamelCase  ):
										processor()




				def   __a		(      self  )       ->   str:
							lowerCAmelCase_      			=			self.get_image_processor()
							lowerCAmelCase_      			=			self.get_tokenizer()

							lowerCAmelCase_      			=			CLIPSegProcessor(tokenizer=_UpperCamelCase       ,       image_processor=_UpperCamelCase  )

							lowerCAmelCase_      			=			self.prepare_image_inputs()
							lowerCAmelCase_      			=			self.prepare_image_inputs()

							lowerCAmelCase_      			=			processor(images=_UpperCamelCase       ,       visual_prompt=_UpperCamelCase  )

							self.assertListEqual(list(inputs.keys()  )       ,       ["pixel_values", "conditional_pixel_values"]  )

							# test if it raises when no input is passed
							with pytest.raises(_UpperCamelCase  ):
										processor()




				def   __a		(      self  )       ->   Union[str, Any]:
							lowerCAmelCase_      			=			self.get_image_processor()
							lowerCAmelCase_      			=			self.get_tokenizer()

							lowerCAmelCase_      			=			CLIPSegProcessor(tokenizer=_UpperCamelCase       ,       image_processor=_UpperCamelCase  )

							lowerCAmelCase_      			=			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							lowerCAmelCase_      			=			processor.batch_decode(_UpperCamelCase  )
							lowerCAmelCase_      			=			tokenizer.batch_decode(_UpperCamelCase  )

							self.assertListEqual(_UpperCamelCase       ,       _UpperCamelCase  )





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_A     =  {
    "configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Wav2Vec2Config"],
    "feature_extraction_wav2vec2": ["Wav2Vec2FeatureExtractor"],
    "processing_wav2vec2": ["Wav2Vec2Processor"],
    "tokenization_wav2vec2": ["Wav2Vec2CTCTokenizer", "Wav2Vec2Tokenizer"],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A     =  [
			    "WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "Wav2Vec2ForAudioFrameClassification",
			    "Wav2Vec2ForCTC",
			    "Wav2Vec2ForMaskedLM",
			    "Wav2Vec2ForPreTraining",
			    "Wav2Vec2ForSequenceClassification",
			    "Wav2Vec2ForXVector",
			    "Wav2Vec2Model",
			    "Wav2Vec2PreTrainedModel",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A     =  [
			    "TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFWav2Vec2ForCTC",
			    "TFWav2Vec2Model",
			    "TFWav2Vec2PreTrainedModel",
			    "TFWav2Vec2ForSequenceClassification",
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A     =  [
			    "FlaxWav2Vec2ForCTC",
			    "FlaxWav2Vec2ForPreTraining",
			    "FlaxWav2Vec2Model",
			    "FlaxWav2Vec2PreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
			from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
			from .processing_wavaveca import WavaVecaProcessor
			from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_wavaveca import (
						    WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    WavaVecaForAudioFrameClassification,
						    WavaVecaForCTC,
						    WavaVecaForMaskedLM,
						    WavaVecaForPreTraining,
						    WavaVecaForSequenceClassification,
						    WavaVecaForXVector,
						    WavaVecaModel,
						    WavaVecaPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_wavaveca import (
						    TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFWavaVecaForCTC,
						    TFWavaVecaForSequenceClassification,
						    TFWavaVecaModel,
						    TFWavaVecaPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_wavaveca import (
						    FlaxWavaVecaForCTC,
						    FlaxWavaVecaForPreTraining,
						    FlaxWavaVecaModel,
						    FlaxWavaVecaPreTrainedModel,
						)


else:
			import sys

			_A     =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



