

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


__A      		=     random.Random()

if is_torch_available():
 import torch



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase=1.0			,	_UpperCamelCase=None			,	_UpperCamelCase=None					)							->     Optional[int]:



  """simple docstring"""






  if rng is None:
    _snake_case						=							global_rng

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

  return values



class   lowercase_   (  unittest.TestCase					):



      def __init__(      self				:    Dict      , A__				:    List[Any]      , A__				:    Optional[int]=7      , A__				:    Optional[int]=400      , A__				:    Tuple=2000      , A__				:    str=1      , A__				:    str=0.0      , A__				:    Optional[Any]=16000      , A__				:    List[Any]=True      , A__				:    Optional[Any]=True      , )   ->					List[Any]:
        _snake_case						=							parent
        _snake_case						=							batch_size
        _snake_case						=							min_seq_length
        _snake_case						=							max_seq_length
        _snake_case						=							(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        _snake_case						=							feature_size
        _snake_case						=							padding_value
        _snake_case						=							sampling_rate
        _snake_case						=							return_attention_mask
        _snake_case						=							do_normalize



      def 						UpperCamelCase_		(      self				:    int		)   ->					Optional[int]:
        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 						UpperCamelCase_		(      self				:    List[Any]      , A__				:    Tuple=False      , A__				:    Tuple=False		)   ->					Union[str, Any]:
        def _flatten(A__				:    List[str]		):
          return list(itertools.chain(*A__		)		)

        if equal_length:
          _snake_case						=							floats_list((self.batch_size, self.max_seq_length)		)
        else:
          # make sure that inputs increase in size
          _snake_case						=							[
              _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:
          _snake_case						=							[np.asarray(A__		) for x in speech_inputs]

        return speech_inputs



@require_torch
@require_torchaudio
class   lowercase_   (  __lowercase       ,							unittest.TestCase					):
      UpperCamelCase_      :		List[Any]										=    ASTFeatureExtractor



      def 						UpperCamelCase_		(      self				:    Optional[Any]		)   ->					List[Any]:
        _snake_case						=							ASTFeatureExtractionTester(self		)



      def 						UpperCamelCase_		(      self				:    Dict		)   ->					Optional[Any]:
        # Tests that all call wrap to encode_plus and batch_encode_plus
        _snake_case						=							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
        # create three inputs of length 800, 1000, and 1200
        _snake_case						=							[floats_list((1, x)		)[0] for x in range(800      , 1400      , 200		)]
        _snake_case						=							[np.asarray(A__		) for speech_input in speech_inputs]

        # Test not batched input
        _snake_case						=							feat_extract(speech_inputs[0]      , return_tensors='''np'''		).input_values
        _snake_case						=							feat_extract(np_speech_inputs[0]      , return_tensors='''np'''		).input_values
        self.assertTrue(np.allclose(A__      , A__      , atol=1e-3		)		)

        # Test batched
        _snake_case						=							feat_extract(A__      , padding=A__      , return_tensors='''np'''		).input_values
        _snake_case						=							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.
        _snake_case						=							[floats_list((1, x)		)[0] for x in (800, 800, 800)]
        _snake_case						=							np.asarray(A__		)
        _snake_case						=							feat_extract(A__      , return_tensors='''np'''		).input_values
        _snake_case						=							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 						UpperCamelCase_		(      self				:    Dict		)   ->					str:
        import torch

        _snake_case						=							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
        _snake_case						=							np.random.rand(100		).astype(np.floataa		)
        _snake_case						=							np_speech_inputs.tolist()

        for inputs in [py_speech_inputs, np_speech_inputs]:
          _snake_case						=							feature_extractor.pad([{'''input_values''': inputs}]      , return_tensors='''np'''		)
          self.assertTrue(np_processed.input_values.dtype == np.floataa		)
          _snake_case						=							feature_extractor.pad([{'''input_values''': inputs}]      , return_tensors='''pt'''		)
          self.assertTrue(pt_processed.input_values.dtype == torch.floataa		)



      def 						UpperCamelCase_		(      self				:    List[Any]      , A__				:    Optional[Any]		)   ->					Tuple:
        from datasets import load_dataset

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

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







      @require_torch
      def 						UpperCamelCase_		(      self				:    List[str]		)   ->					Dict:
        # fmt: off
        _snake_case						=							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

        _snake_case						=							self._load_datasamples(1		)
        _snake_case						=							ASTFeatureExtractor()
        _snake_case						=							feature_extractor(A__      , return_tensors='''pt'''		).input_values
        self.assertEquals(input_values.shape      , (1, 1024, 128)		)
        self.assertTrue(torch.allclose(input_values[0, 0, :30]      , A__      , atol=1e-4		)		)




import json
import os
import unittest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class   lowercase_   (  __lowercase       ,							unittest.TestCase					):
      UpperCamelCase_      :		Union[str, Any]										=    CLIPTokenizer
      UpperCamelCase_      :		Optional[int]										=    CLIPTokenizerFast
      UpperCamelCase_      :		Dict										=    True
      UpperCamelCase_      :		Union[str, Any]										=    {}
      UpperCamelCase_      :		Optional[Any]										=    False



      def 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					Dict:
        super().setUp()

        # fmt: off
        _snake_case						=							['''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
        _snake_case						=							dict(zip(A__      , range(len(A__		)		)		)		)
        _snake_case						=							['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''']
        _snake_case						=							{'''unk_token''': '''<unk>'''}

        _snake_case						=							os.path.join(self.tmpdirname      , VOCAB_FILES_NAMES['''vocab_file''']		)
        _snake_case						=							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(A__		) + '''\n'''		)
        with open(self.merges_file      , '''w'''      , encoding='''utf-8'''		) as fp:
          fp.write('''\n'''.join(A__		)		)



      def 						UpperCamelCase_		(      self				:    List[Any]      , **A__				:    int		)   ->					Union[str, Any]:
        kwargs.update(self.special_tokens_map		)
        return CLIPTokenizer.from_pretrained(self.tmpdirname      , **A__		)



      def 						UpperCamelCase_		(      self				:    Any      , **A__				:    Tuple		)   ->					Optional[Any]:
        kwargs.update(self.special_tokens_map		)
        return CLIPTokenizerFast.from_pretrained(self.tmpdirname      , **A__		)



      def 						UpperCamelCase_		(      self				:    Optional[Any]      , A__				:    str		)   ->					str:
        _snake_case						=							'''lower newer'''
        _snake_case						=							'''lower newer'''
        return input_text, output_text



      def 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					Optional[int]:
        _snake_case						=							CLIPTokenizer(self.vocab_file      , self.merges_file      , **self.special_tokens_map		)
        _snake_case						=							'''lower newer'''
        _snake_case						=							['''lo''', '''w''', '''er</w>''', '''n''', '''e''', '''w''', '''er</w>''']
        _snake_case						=							tokenizer.tokenize(A__		)
        self.assertListEqual(A__      , A__		)

        _snake_case						=							tokens + [tokenizer.unk_token]
        _snake_case						=							[10, 2, 16, 9, 3, 2, 16, 20]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(A__		)      , A__		)



      @require_ftfy
      def 						UpperCamelCase_		(      self				:    Any		)   ->					Optional[int]:
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
          with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""		):
            _snake_case						=							self.tokenizer_class.from_pretrained(A__      , **A__		)
            _snake_case						=							self.rust_tokenizer_class.from_pretrained(A__      , **A__		)

            _snake_case						=							'''A\n\'ll 11p223RF☆ho!!to?\'d\'d\'\'d of a cat to-$\'\'d.'''
            _snake_case						=							tokenizer_s.tokenize(A__		)
            _snake_case						=							tokenizer_r.tokenize(A__		)

            self.assertListEqual(A__      , A__		)

            # Test that the tokenization is identical on an example containing a character (Latin Small Letter A
            # with Tilde) encoded in 2 different ways
            _snake_case						=							'''xa\u0303y''' + ''' ''' + '''x\xe3y'''
            _snake_case						=							tokenizer_s.tokenize(A__		)
            _snake_case						=							tokenizer_r.tokenize(A__		)

            self.assertListEqual(A__      , A__		)

            # Test that the tokenization is identical on unicode of space type
            _snake_case						=							[
                '''\u0009''',  # (horizontal tab, '\t')
                '''\u000B''',  # (vertical tab)
                '''\u000C''',  # (form feed)
                '''\u0020''',  # (space, ' ')
                '''\u200E''',  # (left-to-right mark):w
                '''\u200F''',  # (right-to-left mark)
            ]
            for unicode_seq in spaces_unicodes:
              _snake_case						=							tokenizer_s.tokenize(A__		)
              _snake_case						=							tokenizer_r.tokenize(A__		)

              self.assertListEqual(A__      , A__		)

            # Test that the tokenization is identical on unicode of line break type
            _snake_case						=							[
                '''\u000A''',  # (line feed, '\n')
                '''\r\n''',  # (carriage return and line feed, '\r\n')
                '''\u000D''',  # (carriage return, '\r')
                '''\r''',  # (carriage return, '\r')
                '''\u000D''',  # (carriage return, '\r')
                '''\u2028''',  # (line separator)
                '''\u2029''',  # (paragraph separator)
                # "\u0085", # (next line)
            ]

            # The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
            # it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
            # space (and thus into an empty list).

            for unicode_seq in line_break_unicodes:
              _snake_case						=							tokenizer_s.tokenize(A__		)
              _snake_case						=							tokenizer_r.tokenize(A__		)

              self.assertListEqual(A__      , A__		)



      def 						UpperCamelCase_		(      self				:    List[Any]		)   ->					Optional[Any]:
        # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space`
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
          with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""		):
            _snake_case						=							'''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
            _snake_case						=							f"""{text_of_1_token} {text_of_1_token}"""

            _snake_case						=							self.rust_tokenizer_class.from_pretrained(
                A__      , use_fast=A__      , )
            _snake_case						=							tokenizer_r(A__      , return_offsets_mapping=A__      , add_special_tokens=A__		)
            self.assertEqual(encoding.offset_mapping[0]      , (0, len(A__		))		)
            self.assertEqual(
                encoding.offset_mapping[1]      , (len(A__		) + 1, len(A__		) + 1 + len(A__		))      , )

            _snake_case						=							f""" {text}"""

            _snake_case						=							self.rust_tokenizer_class.from_pretrained(
                A__      , use_fast=A__      , )
            _snake_case						=							tokenizer_r(A__      , return_offsets_mapping=A__      , add_special_tokens=A__		)
            self.assertEqual(encoding.offset_mapping[0]      , (1, 1 + len(A__		))		)
            self.assertEqual(
                encoding.offset_mapping[1]      , (1 + len(A__		) + 1, 1 + len(A__		) + 1 + len(A__		))      , )



      def 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					Optional[Any]:
        # Test related to the breaking change introduced in transformers v4.17.0
        # We need to check that an error in raised when the user try to load a previous version of the tokenizer.
        with self.assertRaises(A__		) as context:
          self.rust_tokenizer_class.from_pretrained('''robot-test/old-clip-tokenizer'''		)

        self.assertTrue(
            context.exception.args[0].startswith(
                '''The `backend_tokenizer` provided does not match the expected format.'''		)		)



      @require_ftfy
      def 						UpperCamelCase_		(      self				:    Dict		)   ->					Union[str, Any]:
        super().test_tokenization_python_rust_equals()







      def 						UpperCamelCase_		(      self				:    str		)   ->					Optional[int]:
        # CLIP always lower cases letters
        pass

