import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


_UpperCAmelCase						=		random.Random()

if is_torch_available():
	import torch




def 				UpperCamelCase						(       __lowercase						: Dict						,__lowercase						: str=1.0						,__lowercase						: Tuple=None						,__lowercase						: List[Any]=None	):
		'''simple docstring'''




		if rng is None:
				A_		:				List[str]     =      global_rng

		A_		:				Union[str, Any]     =      []
		for batch_idx in range(shape[0]	):
				values.append([]	)
				for _ in range(shape[1]	):
						values[-1].append(rng.random() * scale	)

		return values





class UpperCAmelCase						(					unittest.TestCase  ):


				'''simple docstring'''






				def __init__(						self		,	lowercase		,	lowercase=7		,	lowercase=4_0_0		,	lowercase=2_0_0_0		,	lowercase=1		,	lowercase=0.0		,	lowercase=1_6_0_0_0		,	lowercase=True		,	lowercase=True		,	):



						"""simple docstring"""





						A_		:				List[str]     =      parent
						A_		:				str     =      batch_size
						A_		:				Dict     =      min_seq_length
						A_		:				List[Any]     =      max_seq_length
						A_		:				Tuple     =      (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
						A_		:				Dict     =      feature_size
						A_		:				Any     =      padding_value
						A_		:				List[str]     =      sampling_rate
						A_		:				Tuple     =      return_attention_mask
						A_		:				Union[str, Any]     =      do_normalize
				def   lowerCAmelCase_   (						self					):



						"""simple docstring"""





						return {
						    "feature_size": self.feature_size,
						    "padding_value": self.padding_value,
						    "sampling_rate": self.sampling_rate,
						    "return_attention_mask": self.return_attention_mask,
						    "do_normalize": self.do_normalize,
						}







				def   lowerCAmelCase_   (						self		,	lowercase=False		,	lowercase=False					):



						"""simple docstring"""





						def _flatten(lowercase					):
								return list(itertools.chain(*__a					)					)

						if equal_length:
								A_		:				Dict     =      floats_list((self.batch_size, self.max_seq_length)					)
						else:
								# make sure that inputs increase in size
								A_		:				int     =      [
								    _flatten(floats_list((x, self.feature_size)					)					)
								    for x in range(self.min_seq_length		,	self.max_seq_length		,	self.seq_length_diff					)
								]

						if numpify:
								A_		:				Any     =      [np.asarray(__a					) for x in speech_inputs]

						return speech_inputs





@require_torch
@require_torchaudio
class UpperCAmelCase						(					lowerCAmelCase__					,       unittest.TestCase  ):


				'''simple docstring'''






				lowerCamelCase_    =					ASTFeatureExtractor
				def   lowerCAmelCase_   (						self					):



						"""simple docstring"""





						A_		:				int     =      ASTFeatureExtractionTester(self					)
				def   lowerCAmelCase_   (						self					):



						"""simple docstring"""





						A_		:				List[Any]     =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
						# create three inputs of length 800, 1000, and 1200
						A_		:				Tuple     =      [floats_list((1, x)					)[0] for x in range(8_0_0		,	1_4_0_0		,	2_0_0					)]
						A_		:				Union[str, Any]     =      [np.asarray(__a					) for speech_input in speech_inputs]

						# Test not batched input
						A_		:				str     =      feat_extract(speech_inputs[0]		,	return_tensors='np'					).input_values
						A_		:				Any     =      feat_extract(np_speech_inputs[0]		,	return_tensors='np'					).input_values
						self.assertTrue(np.allclose(__a		,	__a		,	atol=1E-3					)					)

						# Test batched
						A_		:				Any     =      feat_extract(__a		,	padding=__a		,	return_tensors='np'					).input_values
						A_		:				List[Any]     =      feat_extract(__a		,	padding=__a		,	return_tensors='np'					).input_values
						for enc_seq_a, enc_seq_a in zip(__a		,	__a					):
								self.assertTrue(np.allclose(__a		,	__a		,	atol=1E-3					)					)

						# Test 2-D numpy arrays are batched.
						A_		:				List[str]     =      [floats_list((1, x)					)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
						A_		:				Tuple     =      np.asarray(__a					)
						A_		:				Any     =      feat_extract(__a		,	return_tensors='np'					).input_values
						A_		:				Tuple     =      feat_extract(__a		,	return_tensors='np'					).input_values
						for enc_seq_a, enc_seq_a in zip(__a		,	__a					):
								self.assertTrue(np.allclose(__a		,	__a		,	atol=1E-3					)					)
				@require_torch
				def   lowerCAmelCase_   (						self					):



						"""simple docstring"""





						import torch

						A_		:				int     =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
						A_		:				Any     =      np.random.rand(1_0_0					).astype(np.floataa					)
						A_		:				Tuple     =      np_speech_inputs.tolist()

						for inputs in [py_speech_inputs, np_speech_inputs]:
								A_		:				Union[str, Any]     =      feature_extractor.pad([{'input_values': inputs}]		,	return_tensors='np'					)
								self.assertTrue(np_processed.input_values.dtype == np.floataa					)
								A_		:				Dict     =      feature_extractor.pad([{'input_values': inputs}]		,	return_tensors='pt'					)
								self.assertTrue(pt_processed.input_values.dtype == torch.floataa					)
				def   lowerCAmelCase_   (						self		,	lowercase					):



						"""simple docstring"""





						from datasets import load_dataset

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

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







				@require_torch
				def   lowerCAmelCase_   (						self					):



						"""simple docstring"""





						A_		:				Optional[int]     =      torch.tensor(
						    [-0.9894, -1.2776, -0.9066, -1.2776, -0.9349, -1.2609, -1.0386, -1.2776,
						     -1.1561, -1.2776, -1.2052, -1.2723, -1.2190, -1.2132, -1.2776, -1.1133,
						     -1.1953, -1.1343, -1.1584, -1.2203, -1.1770, -1.2474, -1.2381, -1.1936,
						     -0.9270, -0.8317, -0.8049, -0.7706, -0.7565, -0.7869]					)
						# fmt: on

						A_		:				Optional[int]     =      self._load_datasamples(1					)
						A_		:				str     =      ASTFeatureExtractor()
						A_		:				Any     =      feature_extractor(__a		,	return_tensors='pt'					).input_values
						self.assertEquals(input_values.shape		,	(1, 1_0_2_4, 1_2_8)					)
						self.assertTrue(torch.allclose(input_values[0, 0, :3_0]		,	__a		,	atol=1E-4					)					)










"""simple docstring"""


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
	from PIL import Image

	from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor
@require_vision
class   _UpperCamelCase  (   unittest.TestCase		):
							'''simple docstring'''







							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			tempfile.mkdtemp()

										__lowerCAmelCase				    =			[
										    "[UNK]",
										    "[CLS]",
										    "[SEP]",
										    "[PAD]",
										    "[MASK]",
										    "的",
										    "价",
										    "格",
										    "是",
										    "15",
										    "便",
										    "alex",
										    "##andra",
										    "，",
										    "。",
										    "-",
										    "t",
										    "shirt",
										]
										__lowerCAmelCase				    =			os.path.join(self.tmpdirname    ,   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]	)	)

										__lowerCAmelCase				    =			{
										    "do_resize": True,
										    "size": {"height": 2_24, "width": 2_24},
										    "do_center_crop": True,
										    "crop_size": {"height": 18, "width": 18},
										    "do_normalize": True,
										    "image_mean": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
										    "image_std": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
										    "do_convert_rgb": True,
										}
										__lowerCAmelCase				    =			os.path.join(self.tmpdirname    ,   __a	)
										with open(self.image_processor_file    ,   "w"    ,   encoding="utf-8"	) as fp:
													json.dump(__a    ,   __a	)

							def 			snake_case				(     self    ,   **__a	):
										return BertTokenizer.from_pretrained(self.tmpdirname    ,   **__a	)

							def 			snake_case				(     self    ,   **__a	):
										return BertTokenizerFast.from_pretrained(self.tmpdirname    ,   **__a	)

							def 			snake_case				(     self    ,   **__a	):
										return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname    ,   **__a	)

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

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			[np.random.randint(2_55    ,   size=(3, 30, 4_00)    ,   dtype=np.uinta	)]

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

										return image_inputs

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.get_tokenizer()
										__lowerCAmelCase				    =			self.get_rust_tokenizer()
										__lowerCAmelCase				    =			self.get_image_processor()

										__lowerCAmelCase				    =			ChineseCLIPProcessor(tokenizer=__a    ,   image_processor=__a	)
										processor_slow.save_pretrained(self.tmpdirname	)
										__lowerCAmelCase				    =			ChineseCLIPProcessor.from_pretrained(self.tmpdirname    ,   use_fast=__a	)

										__lowerCAmelCase				    =			ChineseCLIPProcessor(tokenizer=__a    ,   image_processor=__a	)
										processor_fast.save_pretrained(self.tmpdirname	)
										__lowerCAmelCase				    =			ChineseCLIPProcessor.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    ,   __a	)
										self.assertIsInstance(processor_fast.tokenizer    ,   __a	)

										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    ,   __a	)
										self.assertIsInstance(processor_fast.image_processor    ,   __a	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			ChineseCLIPProcessor(tokenizer=self.get_tokenizer()    ,   image_processor=self.get_image_processor()	)
										processor.save_pretrained(self.tmpdirname	)

										__lowerCAmelCase				    =			self.get_tokenizer(cls_token="(CLS)"    ,   sep_token="(SEP)"	)
										__lowerCAmelCase				    =			self.get_image_processor(do_normalize=__a	)

										__lowerCAmelCase				    =			ChineseCLIPProcessor.from_pretrained(
										    self.tmpdirname    ,   cls_token="(CLS)"    ,   sep_token="(SEP)"    ,   do_normalize=__a	)

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

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

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.get_image_processor()
										__lowerCAmelCase				    =			self.get_tokenizer()

										__lowerCAmelCase				    =			ChineseCLIPProcessor(tokenizer=__a    ,   image_processor=__a	)

										__lowerCAmelCase				    =			self.prepare_image_inputs()

										__lowerCAmelCase				    =			image_processor(__a    ,   return_tensors="np"	)
										__lowerCAmelCase				    =			processor(images=__a    ,   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 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.get_image_processor()
										__lowerCAmelCase				    =			self.get_tokenizer()

										__lowerCAmelCase				    =			ChineseCLIPProcessor(tokenizer=__a    ,   image_processor=__a	)

										__lowerCAmelCase				    =			"Alexandra，T-shirt的价格是15便士。"

										__lowerCAmelCase				    =			processor(text=__a	)

										__lowerCAmelCase				    =			tokenizer(__a	)

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

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.get_image_processor()
										__lowerCAmelCase				    =			self.get_tokenizer()

										__lowerCAmelCase				    =			ChineseCLIPProcessor(tokenizer=__a    ,   image_processor=__a	)

										__lowerCAmelCase				    =			"Alexandra，T-shirt的价格是15便士。"
										__lowerCAmelCase				    =			self.prepare_image_inputs()

										__lowerCAmelCase				    =			processor(text=__a    ,   images=__a	)

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

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

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.get_image_processor()
										__lowerCAmelCase				    =			self.get_tokenizer()

										__lowerCAmelCase				    =			ChineseCLIPProcessor(tokenizer=__a    ,   image_processor=__a	)

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

										__lowerCAmelCase				    =			processor.batch_decode(__a	)
										__lowerCAmelCase				    =			tokenizer.batch_decode(__a	)

										self.assertListEqual(__a    ,   __a	)



							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.get_image_processor()
										__lowerCAmelCase				    =			self.get_tokenizer()

										__lowerCAmelCase				    =			ChineseCLIPProcessor(tokenizer=__a    ,   image_processor=__a	)

										__lowerCAmelCase				    =			"Alexandra，T-shirt的价格是15便士。"
										__lowerCAmelCase				    =			self.prepare_image_inputs()

										__lowerCAmelCase				    =			processor(text=__a    ,   images=__a	)

										self.assertListEqual(list(inputs.keys()	)    ,   processor.model_input_names	)




