



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 OwlViTImageProcessor, OwlViTProcessor






@require_vision
class SCREAMING_SNAKE_CASE__					(		unittest.TestCase   ):




    '''simple docstring'''






    def       _lowerCAmelCase (	self		):
        A			:	str										=       tempfile.mkdtemp()

        # fmt: off
        A			:	List[Any]										=       ["""""", """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
        A			:	Optional[int]										=       dict(zip(lowerCamelCase__,		range(len(lowerCamelCase__		)		)		)		)
        A			:	Optional[Any]										=       ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
        A			:	Union[str, Any]										=       {"""unk_token""": """<unk>"""}

        A			:	List[Any]										=       os.path.join(self.tmpdirname,		VOCAB_FILES_NAMES["""vocab_file"""]		)
        A			:	int										=       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(lowerCamelCase__		) + """\n"""		)
        with open(self.merges_file,		"""w""",		encoding="""utf-8"""		) as fp:
            fp.write("""\n""".join(lowerCamelCase__		)		)

        A			:	int										=       {
            """do_resize""": True,
            """size""": 20,
            """do_center_crop""": True,
            """crop_size""": 18,
            """do_normalize""": True,
            """image_mean""": [0.4814_5466, 0.457_8275, 0.4082_1073],
            """image_std""": [0.2686_2954, 0.2613_0258, 0.2757_7711],
        }
        A			:	List[Any]										=       os.path.join(self.tmpdirname,		lowerCamelCase__		)
        with open(self.image_processor_file,		"""w""",		encoding="""utf-8"""		) as fp:
            json.dump(lowerCamelCase__,		lowerCamelCase__		)






    def       _lowerCAmelCase (	self,		**lowerCamelCase__		):
        return CLIPTokenizer.from_pretrained(self.tmpdirname,		pad_token="""!""",		**lowerCamelCase__		)






    def       _lowerCAmelCase (	self,		**lowerCamelCase__		):
        return CLIPTokenizerFast.from_pretrained(self.tmpdirname,		pad_token="""!""",		**lowerCamelCase__		)






    def       _lowerCAmelCase (	self,		**lowerCamelCase__		):
        return OwlViTImageProcessor.from_pretrained(self.tmpdirname,		**lowerCamelCase__		)






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






    def       _lowerCAmelCase (	self		):
        A			:	str										=       [np.random.randint(255,		size=(3, 30, 400),		dtype=np.uinta		)]

        A			:	Optional[int]										=       [Image.fromarray(np.moveaxis(lowerCamelCase__,		0,		-1		)		) for x in image_inputs]

        return image_inputs






    def       _lowerCAmelCase (	self		):
        A			:	Optional[Any]										=       self.get_tokenizer()
        A			:	Optional[Any]										=       self.get_rust_tokenizer()
        A			:	Optional[int]										=       self.get_image_processor()

        A			:	List[str]										=       OwlViTProcessor(tokenizer=lowerCamelCase__,		image_processor=lowerCamelCase__		)
        processor_slow.save_pretrained(self.tmpdirname		)
        A			:	Optional[int]										=       OwlViTProcessor.from_pretrained(self.tmpdirname,		use_fast=lowerCamelCase__		)

        A			:	Tuple										=       OwlViTProcessor(tokenizer=lowerCamelCase__,		image_processor=lowerCamelCase__		)
        processor_fast.save_pretrained(self.tmpdirname		)
        A			:	str										=       OwlViTProcessor.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,		lowerCamelCase__		)
        self.assertIsInstance(processor_fast.tokenizer,		lowerCamelCase__		)

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






    def       _lowerCAmelCase (	self		):
        A			:	List[str]										=       OwlViTProcessor(tokenizer=self.get_tokenizer(),		image_processor=self.get_image_processor()		)
        processor.save_pretrained(self.tmpdirname		)

        A			:	Optional[int]										=       self.get_tokenizer(bos_token="""(BOS)""",		eos_token="""(EOS)"""		)
        A			:	Tuple										=       self.get_image_processor(do_normalize=lowerCamelCase__		)

        A			:	Optional[Any]										=       OwlViTProcessor.from_pretrained(
            self.tmpdirname,		bos_token="""(BOS)""",		eos_token="""(EOS)""",		do_normalize=lowerCamelCase__		)

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

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






    def       _lowerCAmelCase (	self		):
        A			:	List[Any]										=       self.get_image_processor()
        A			:	str										=       self.get_tokenizer()

        A			:	List[str]										=       OwlViTProcessor(tokenizer=lowerCamelCase__,		image_processor=lowerCamelCase__		)

        A			:	Optional[Any]										=       self.prepare_image_inputs()

        A			:	Optional[Any]										=       image_processor(lowerCamelCase__,		return_tensors="""np"""		)
        A			:	Any										=       processor(images=lowerCamelCase__,		return_tensors="""np"""		)

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






    def       _lowerCAmelCase (	self		):
        A			:	int										=       self.get_image_processor()
        A			:	Optional[Any]										=       self.get_tokenizer()

        A			:	Optional[int]										=       OwlViTProcessor(tokenizer=lowerCamelCase__,		image_processor=lowerCamelCase__		)

        A			:	Any										=       """lower newer"""

        A			:	Union[str, Any]										=       processor(text=lowerCamelCase__,		return_tensors="""np"""		)

        A			:	str										=       tokenizer(lowerCamelCase__,		return_tensors="""np"""		)

        for key in encoded_tok.keys():
            self.assertListEqual(encoded_tok[key][0].tolist(),		encoded_processor[key][0].tolist()		)






    def       _lowerCAmelCase (	self		):
        A			:	Tuple										=       self.get_image_processor()
        A			:	int										=       self.get_tokenizer()

        A			:	str										=       OwlViTProcessor(tokenizer=lowerCamelCase__,		image_processor=lowerCamelCase__		)

        A			:	List[str]										=       """lower newer"""
        A			:	Any										=       self.prepare_image_inputs()

        A			:	Tuple										=       processor(text=lowerCamelCase__,		images=lowerCamelCase__		)

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

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






    def       _lowerCAmelCase (	self		):
        A			:	str										=       """google/owlvit-base-patch32"""
        A			:	Dict										=       OwlViTProcessor.from_pretrained(lowerCamelCase__		)

        A			:	str										=       ["""cat""", """nasa badge"""]
        A			:	Optional[int]										=       processor(text=lowerCamelCase__		)

        A			:	Any										=       16
        self.assertListEqual(list(inputs.keys()		),		["""input_ids""", """attention_mask"""]		)
        self.assertEqual(inputs["""input_ids"""].shape,		(2, seq_length)		)

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






    def       _lowerCAmelCase (	self		):
        A			:	Tuple										=       """google/owlvit-base-patch32"""
        A			:	Any										=       OwlViTProcessor.from_pretrained(lowerCamelCase__		)

        A			:	int										=       [["""cat""", """nasa badge"""], ["""person"""]]
        A			:	List[Any]										=       processor(text=lowerCamelCase__		)

        A			:	Dict										=       16
        A			:	List[str]										=       len(lowerCamelCase__		)
        A			:	List[str]										=       max([len(lowerCamelCase__		) for texts in input_texts]		)

        self.assertListEqual(list(inputs.keys()		),		["""input_ids""", """attention_mask"""]		)
        self.assertEqual(inputs["""input_ids"""].shape,		(batch_size * num_max_text_queries, seq_length)		)

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






    def       _lowerCAmelCase (	self		):
        A			:	Dict										=       """google/owlvit-base-patch32"""
        A			:	int										=       OwlViTProcessor.from_pretrained(lowerCamelCase__		)

        A			:	str										=       ["""cat""", """nasa badge"""]
        A			:	Optional[Any]										=       processor(text=lowerCamelCase__		)

        A			:	int										=       16
        A			:	Optional[Any]										=       inputs["""input_ids"""]
        A			:	Optional[int]										=       [
            [4_9406, 2368, 4_9407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
            [4_9406, 6841, 1_1301, 4_9407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
        ]

        self.assertListEqual(list(inputs.keys()		),		["""input_ids""", """attention_mask"""]		)
        self.assertEqual(inputs["""input_ids"""].shape,		(2, seq_length)		)
        self.assertListEqual(list(input_ids[0]		),		predicted_ids[0]		)
        self.assertListEqual(list(input_ids[1]		),		predicted_ids[1]		)






    def       _lowerCAmelCase (	self		):
        A			:	Tuple										=       self.get_image_processor()
        A			:	Dict										=       self.get_tokenizer()

        A			:	Optional[Any]										=       OwlViTProcessor(tokenizer=lowerCamelCase__,		image_processor=lowerCamelCase__		)

        A			:	Any										=       self.prepare_image_inputs()
        A			:	Optional[Any]										=       self.prepare_image_inputs()

        A			:	List[str]										=       processor(images=lowerCamelCase__,		query_images=lowerCamelCase__		)

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

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






    def       _lowerCAmelCase (	self		):
        A			:	Any										=       self.get_image_processor()
        A			:	Optional[Any]										=       self.get_tokenizer()

        A			:	List[str]										=       OwlViTProcessor(tokenizer=lowerCamelCase__,		image_processor=lowerCamelCase__		)

        A			:	Any										=       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        A			:	Optional[Any]										=       processor.batch_decode(lowerCamelCase__		)
        A			:	Union[str, Any]										=       tokenizer.batch_decode(lowerCamelCase__		)

        self.assertListEqual(lowerCamelCase__,		lowerCamelCase__		)







from jiwer import compute_measures

import datasets


SCREAMING_SNAKE_CASE_:str					=						"""\
@inproceedings{inproceedings,
    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
    year = {2004},
    month = {01},
    pages = {},
    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}
}
"""

SCREAMING_SNAKE_CASE_:Union[str, Any]					=						"""\
Word error rate (WER) is a common metric of the performance of an automatic speech recognition system.

The general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.

This problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.

Word error rate can then be computed as:

WER = (S + D + I) / N = (S + D + I) / (S + D + C)

where

S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct words,
N is the number of words in the reference (N=S+D+C).

This value indicates the average number of errors per reference word. The lower the value, the better the
performance of the ASR system with a WER of 0 being a perfect score.
"""

SCREAMING_SNAKE_CASE_:List[Any]					=						"""
Compute WER score of transcribed segments against references.

Args:
    references: List of references for each speech input.
    predictions: List of transcriptions to score.
    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.

Returns:
    (float): the word error rate

Examples:

    >>> predictions = [\"this is the prediction\", \"there is an other sample\"]
    >>> references = [\"this is the reference\", \"there is another one\"]
    >>> wer = datasets.load_metric(\"wer\")
    >>> wer_score = wer.compute(predictions=predictions, references=references)
    >>> print(wer_score)
    0.5
"""






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,						_KWARGS_DESCRIPTION   )
class SCREAMING_SNAKE_CASE__					(		datasets.Metric   ):




    '''simple docstring'''






    def       _lowerCAmelCase (	self		):
        return datasets.MetricInfo(
            description=_DESCRIPTION,		citation=_CITATION,		inputs_description=_KWARGS_DESCRIPTION,		features=datasets.Features(
                {
                    """predictions""": datasets.Value("""string""",		id="""sequence"""		),
                    """references""": datasets.Value("""string""",		id="""sequence"""		),
                }		),		codebase_urls=["""https://github.com/jitsi/jiwer/"""],		reference_urls=[
                """https://en.wikipedia.org/wiki/Word_error_rate""",
            ],		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__=None,		lowerCamelCase__=None,		lowerCamelCase__=False		):
        if concatenate_texts:
            return compute_measures(lowerCamelCase__,		lowerCamelCase__		)["wer"]
        else:
            A			:	str										=       0
            A			:	int										=       0
            for prediction, reference in zip(lowerCamelCase__,		lowerCamelCase__		):
                A			:	Tuple										=       compute_measures(lowerCamelCase__,		lowerCamelCase__		)
                incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
                total += measures["substitutions"] + measures["deletions"] + measures["hits"]
            return incorrect / total


