

import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
 import torch


if is_vision_available():
 from PIL import Image

 from transformers import MgpstrProcessor, ViTImageProcessor







@require_torch
@require_vision
class   __lowercase      (unittest.TestCase ):
 _UpperCamelCase			  =  ViTImageProcessor if is_vision_available() else None
 @property
 def 	UpperCamelCase__  (							self					)					->str:



       '''simple docstring'''





       return self.image_processor_tester.prepare_image_processor_dict()
 def 	UpperCamelCase__  (							self					)					->str:



       '''simple docstring'''





       __lowerCAmelCase					:						Tuple					=					(3, 32, 128)
       __lowerCAmelCase					:						List[str]					=					tempfile.mkdtemp()

       # fmt: off
       __lowerCAmelCase					:						List[str]					=					['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
       # fmt: on
       __lowerCAmelCase					:						Optional[int]					=					dict(zip(A_      ,							range(len(A_					)					)					)					)

       __lowerCAmelCase					:						Tuple					=					os.path.join(self.tmpdirname      ,							VOCAB_FILES_NAMES['''vocab_file''']					)
       with open(self.vocab_file      ,							'''w'''      ,							encoding='''utf-8'''					) as fp:
             fp.write(json.dumps(A_					) + '''\n'''					)

       __lowerCAmelCase					:						Union[str, Any]					=					{
           '''do_normalize''': False,
           '''do_resize''': True,
           '''image_processor_type''': '''ViTImageProcessor''',
           '''resample''': 3,
           '''size''': {'''height''': 32, '''width''': 128},
       }
       __lowerCAmelCase					:						Optional[Any]					=					os.path.join(self.tmpdirname      ,							A_					)
       with open(self.image_processor_file      ,							'''w'''      ,							encoding='''utf-8'''					) as fp:
             json.dump(A_      ,							A_					)
 def 	UpperCamelCase__  (							self      ,							**A_					)					->Tuple:



       '''simple docstring'''





       return MgpstrTokenizer.from_pretrained(self.tmpdirname      ,							**A_					)
 def 	UpperCamelCase__  (							self      ,							**A_					)					->Tuple:



       '''simple docstring'''





       return ViTImageProcessor.from_pretrained(self.tmpdirname      ,							**A_					)
 def 	UpperCamelCase__  (							self					)					->Optional[int]:



       '''simple docstring'''





       shutil.rmtree(self.tmpdirname					)
 def 	UpperCamelCase__  (							self					)					->Optional[Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						Tuple					=					np.random.randint(255      ,							size=(3, 30, 400)      ,							dtype=np.uinta					)

       __lowerCAmelCase					:						str					=					Image.fromarray(np.moveaxis(A_      ,							0      ,							-1					)					)

       return image_input
 def 	UpperCamelCase__  (							self					)					->Any:



       '''simple docstring'''





       __lowerCAmelCase					:						Dict					=					self.get_tokenizer()
       __lowerCAmelCase					:						List[Any]					=					self.get_image_processor()

       __lowerCAmelCase					:						List[Any]					=					MgpstrProcessor(tokenizer=A_      ,							image_processor=A_					)
       processor.save_pretrained(self.tmpdirname					)
       __lowerCAmelCase					:						Union[str, Any]					=					MgpstrProcessor.from_pretrained(self.tmpdirname      ,							use_fast=A_					)

       self.assertEqual(processor.char_tokenizer.get_vocab()      ,							tokenizer.get_vocab()					)
       self.assertIsInstance(processor.char_tokenizer      ,							A_					)

       self.assertEqual(processor.image_processor.to_json_string()      ,							image_processor.to_json_string()					)
       self.assertIsInstance(processor.image_processor      ,							A_					)
 def 	UpperCamelCase__  (							self					)					->Optional[int]:



       '''simple docstring'''





       __lowerCAmelCase					:						Any					=					self.get_tokenizer()
       __lowerCAmelCase					:						Union[str, Any]					=					self.get_image_processor()

       __lowerCAmelCase					:						List[Any]					=					MgpstrProcessor(tokenizer=A_      ,							image_processor=A_					)
       processor.save_pretrained(self.tmpdirname					)

       __lowerCAmelCase					:						List[Any]					=					self.get_tokenizer(bos_token='''(BOS)'''      ,							eos_token='''(EOS)'''					)
       __lowerCAmelCase					:						int					=					self.get_image_processor(do_normalize=A_      ,							padding_value=1.0					)

       __lowerCAmelCase					:						int					=					MgpstrProcessor.from_pretrained(
           self.tmpdirname      ,							bos_token='''(BOS)'''      ,							eos_token='''(EOS)'''      ,							do_normalize=A_      ,							padding_value=1.0					)

       self.assertEqual(processor.char_tokenizer.get_vocab()      ,							tokenizer_add_kwargs.get_vocab()					)
       self.assertIsInstance(processor.char_tokenizer      ,							A_					)

       self.assertEqual(processor.image_processor.to_json_string()      ,							image_processor_add_kwargs.to_json_string()					)
       self.assertIsInstance(processor.image_processor      ,							A_					)
 def 	UpperCamelCase__  (							self					)					->List[str]:



       '''simple docstring'''





       __lowerCAmelCase					:						Any					=					self.get_image_processor()
       __lowerCAmelCase					:						Optional[Any]					=					self.get_tokenizer()

       __lowerCAmelCase					:						int					=					MgpstrProcessor(tokenizer=A_      ,							image_processor=A_					)

       __lowerCAmelCase					:						Optional[int]					=					self.prepare_image_inputs()

       __lowerCAmelCase					:						Optional[Any]					=					image_processor(A_      ,							return_tensors='''np'''					)
       __lowerCAmelCase					:						Tuple					=					processor(images=A_      ,							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 	UpperCamelCase__  (							self					)					->str:



       '''simple docstring'''





       __lowerCAmelCase					:						str					=					self.get_image_processor()
       __lowerCAmelCase					:						Union[str, Any]					=					self.get_tokenizer()

       __lowerCAmelCase					:						Optional[Any]					=					MgpstrProcessor(tokenizer=A_      ,							image_processor=A_					)

       __lowerCAmelCase					:						Any					=					'''test'''

       __lowerCAmelCase					:						Dict					=					processor(text=A_					)

       __lowerCAmelCase					:						str					=					tokenizer(A_					)
       for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key]      ,							encoded_processor[key]					)
 def 	UpperCamelCase__  (							self					)					->Optional[int]:



       '''simple docstring'''





       __lowerCAmelCase					:						Dict					=					self.get_image_processor()
       __lowerCAmelCase					:						Any					=					self.get_tokenizer()

       __lowerCAmelCase					:						str					=					MgpstrProcessor(tokenizer=A_      ,							image_processor=A_					)

       __lowerCAmelCase					:						List[Any]					=					'''test'''
       __lowerCAmelCase					:						int					=					self.prepare_image_inputs()

       __lowerCAmelCase					:						int					=					processor(text=A_      ,							images=A_					)

       self.assertListEqual(list(inputs.keys()					)      ,							['''pixel_values''', '''labels''']					)

       # test if it raises when no input is passed
       with pytest.raises(A_					):
             processor()
 def 	UpperCamelCase__  (							self					)					->Union[str, Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						List[Any]					=					self.get_image_processor()
       __lowerCAmelCase					:						int					=					self.get_tokenizer()

       __lowerCAmelCase					:						Any					=					MgpstrProcessor(tokenizer=A_      ,							image_processor=A_					)

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

       __lowerCAmelCase					:						Optional[int]					=					processor.char_decode(A_					)
       __lowerCAmelCase					:						Tuple					=					tokenizer.batch_decode(A_					)
       __lowerCAmelCase					:						Any					=					[seq.replace(''' '''      ,							''''''					) for seq in decoded_tok]

       self.assertListEqual(A_      ,							A_					)
 def 	UpperCamelCase__  (							self					)					->Any:



       '''simple docstring'''





       __lowerCAmelCase					:						str					=					self.get_image_processor()
       __lowerCAmelCase					:						Any					=					self.get_tokenizer()

       __lowerCAmelCase					:						int					=					MgpstrProcessor(tokenizer=A_      ,							image_processor=A_					)

       __lowerCAmelCase					:						Union[str, Any]					=					None
       __lowerCAmelCase					:						Optional[Any]					=					self.prepare_image_inputs()

       __lowerCAmelCase					:						List[Any]					=					processor(text=A_      ,							images=A_					)

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





 def 	UpperCamelCase__  (							self					)					->List[str]:



       '''simple docstring'''





       __lowerCAmelCase					:						List[str]					=					self.get_image_processor()
       __lowerCAmelCase					:						List[str]					=					self.get_tokenizer()

       __lowerCAmelCase					:						Any					=					MgpstrProcessor(tokenizer=A_      ,							image_processor=A_					)

       __lowerCAmelCase					:						List[Any]					=					torch.randn(1      ,							27      ,							38					)
       __lowerCAmelCase					:						Optional[int]					=					torch.randn(1      ,							27      ,							5_0257					)
       __lowerCAmelCase					:						Optional[Any]					=					torch.randn(1      ,							27      ,							3_0522					)

       __lowerCAmelCase					:						List[str]					=					processor.batch_decode([char_input, bpe_input, wp_input]					)

       self.assertListEqual(list(results.keys()					)      ,							['''generated_text''', '''scores''', '''char_preds''', '''bpe_preds''', '''wp_preds''']					)





import itertools
import random
import unittest

import numpy as np

from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


_UpperCamelCase 			=		random.Random()







def 		_lowercase (     lowercase__   ,	lowercase__=1.0   ,	lowercase__=None   ,	lowercase__=None						):
      if rng is None:
            __lowerCAmelCase					:						Optional[Any]					=					global_rng

      __lowerCAmelCase					:						Tuple					=					[]
      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      ,							A_      ,							A_=7      ,							A_=400      ,							A_=2000      ,							A_=1      ,							A_=0.0      ,							A_=1_6000      ,							A_=True      ,							A_=True      ,							)					->List[Any]:



       '''simple docstring'''





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



       '''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 	UpperCamelCase__  (							self      ,							A_=False      ,							A_=False					)					->Union[str, Any]:



       '''simple docstring'''





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

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

       return speech_inputs







class   __lowercase      (_UpperCAmelCase		,   unittest.TestCase ):
 _UpperCamelCase			  =  WavaVecaFeatureExtractor
 def 	UpperCamelCase__  (							self					)					->Dict:



       '''simple docstring'''





       __lowerCAmelCase					:						List[Any]					=					WavaVecaFeatureExtractionTester(self					)
 def 	UpperCamelCase__  (							self      ,							A_					)					->Optional[Any]:



       '''simple docstring'''





       self.assertTrue(np.all(np.mean(A_      ,							axis=0					) < 1e-3					)					)
       self.assertTrue(np.all(np.abs(np.var(A_      ,							axis=0					) - 1					) < 1e-3					)					)
 def 	UpperCamelCase__  (							self					)					->Tuple:



       '''simple docstring'''





       __lowerCAmelCase					:						List[str]					=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
       # create three inputs of length 800, 1000, and 1200
       __lowerCAmelCase					:						Optional[Any]					=					[floats_list((1, x)					)[0] for x in range(800      ,							1400      ,							200					)]
       __lowerCAmelCase					:						Any					=					[np.asarray(A_					) for speech_input in speech_inputs]

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

       # Test batched
       __lowerCAmelCase					:						Dict					=					feat_extract(A_      ,							return_tensors='''np'''					).input_values
       __lowerCAmelCase					:						Dict					=					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					)					)

       # Test 2-D numpy arrays are batched.
       __lowerCAmelCase					:						int					=					[floats_list((1, x)					)[0] for x in (800, 800, 800)]
       __lowerCAmelCase					:						List[Any]					=					np.asarray(A_					)
       __lowerCAmelCase					:						Any					=					feat_extract(A_      ,							return_tensors='''np'''					).input_values
       __lowerCAmelCase					:						Union[str, Any]					=					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					)					)
 def 	UpperCamelCase__  (							self					)					->Union[str, Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						Optional[int]					=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
       __lowerCAmelCase					:						str					=					[floats_list((1, x)					)[0] for x in range(800      ,							1400      ,							200					)]

       __lowerCAmelCase					:						str					=					['''longest''', '''max_length''', '''do_not_pad''']
       __lowerCAmelCase					:						str					=					[None, 1600, None]
       for max_length, padding in zip(A_      ,							A_					):
             __lowerCAmelCase					:						Optional[int]					=					feat_extract(A_      ,							padding=A_      ,							max_length=A_      ,							return_tensors='''np'''					)
             __lowerCAmelCase					:						Optional[Any]					=					processed.input_values

             self._check_zero_mean_unit_variance(input_values[0][:800]					)
             self.assertTrue(input_values[0][800:].sum() < 1e-6					)
             self._check_zero_mean_unit_variance(input_values[1][:1000]					)
             self.assertTrue(input_values[0][1000:].sum() < 1e-6					)
             self._check_zero_mean_unit_variance(input_values[2][:1200]					)
 def 	UpperCamelCase__  (							self					)					->Dict:



       '''simple docstring'''





       __lowerCAmelCase					:						List[str]					=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
       __lowerCAmelCase					:						Optional[int]					=					range(800      ,							1400      ,							200					)
       __lowerCAmelCase					:						Union[str, Any]					=					[floats_list((1, x)					)[0] for x in lengths]

       __lowerCAmelCase					:						int					=					['''longest''', '''max_length''', '''do_not_pad''']
       __lowerCAmelCase					:						List[str]					=					[None, 1600, None]

       for max_length, padding in zip(A_      ,							A_					):
             __lowerCAmelCase					:						Union[str, Any]					=					feat_extract(A_      ,							max_length=A_      ,							padding=A_					)
             __lowerCAmelCase					:						Union[str, Any]					=					processed.input_values

             self._check_zero_mean_unit_variance(input_values[0][:800]					)
             self._check_zero_mean_unit_variance(input_values[1][:1000]					)
             self._check_zero_mean_unit_variance(input_values[2][:1200]					)
 def 	UpperCamelCase__  (							self					)					->Tuple:



       '''simple docstring'''





       __lowerCAmelCase					:						Dict					=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
       __lowerCAmelCase					:						Optional[Any]					=					[floats_list((1, x)					)[0] for x in range(800      ,							1400      ,							200					)]
       __lowerCAmelCase					:						List[str]					=					feat_extract(
           A_      ,							truncation=A_      ,							max_length=1000      ,							padding='''max_length'''      ,							return_tensors='''np'''					)
       __lowerCAmelCase					:						int					=					processed.input_values

       self._check_zero_mean_unit_variance(input_values[0, :800]					)
       self._check_zero_mean_unit_variance(input_values[1]					)
       self._check_zero_mean_unit_variance(input_values[2]					)
 def 	UpperCamelCase__  (							self					)					->List[str]:



       '''simple docstring'''





       __lowerCAmelCase					:						Dict					=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
       __lowerCAmelCase					:						int					=					[floats_list((1, x)					)[0] for x in range(800      ,							1400      ,							200					)]
       __lowerCAmelCase					:						int					=					feat_extract(
           A_      ,							truncation=A_      ,							max_length=1000      ,							padding='''longest'''      ,							return_tensors='''np'''					)
       __lowerCAmelCase					:						Optional[Any]					=					processed.input_values

       self._check_zero_mean_unit_variance(input_values[0, :800]					)
       self._check_zero_mean_unit_variance(input_values[1, :1000]					)
       self._check_zero_mean_unit_variance(input_values[2]					)

       # make sure that if max_length < longest -> then pad to max_length
       self.assertTrue(input_values.shape == (3, 1000)					)

       __lowerCAmelCase					:						Any					=					[floats_list((1, x)					)[0] for x in range(800      ,							1400      ,							200					)]
       __lowerCAmelCase					:						Optional[int]					=					feat_extract(
           A_      ,							truncation=A_      ,							max_length=2000      ,							padding='''longest'''      ,							return_tensors='''np'''					)
       __lowerCAmelCase					:						List[str]					=					processed.input_values

       self._check_zero_mean_unit_variance(input_values[0, :800]					)
       self._check_zero_mean_unit_variance(input_values[1, :1000]					)
       self._check_zero_mean_unit_variance(input_values[2]					)

       # make sure that if max_length > longest -> then pad to longest
       self.assertTrue(input_values.shape == (3, 1200)					)
 @require_torch
 def 	UpperCamelCase__  (							self					)					->Any:



       '''simple docstring'''





       import torch

       __lowerCAmelCase					:						Union[str, Any]					=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
       __lowerCAmelCase					:						Any					=					np.random.rand(100					).astype(np.floataa					)
       __lowerCAmelCase					:						List[Any]					=					np_speech_inputs.tolist()

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





 @slow
 @require_torch
 def 	UpperCamelCase__  (							self					)					->int:



       '''simple docstring'''





       for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
             __lowerCAmelCase					:						Any					=					WavaVecaConfig.from_pretrained(A_					)
             __lowerCAmelCase					:						Tuple					=					WavaVecaFeatureExtractor.from_pretrained(A_					)

             # only "layer" feature extraction norm should make use of
             # attention_mask
             self.assertEqual(feat_extract.return_attention_mask      ,							config.feat_extract_norm == '''layer'''					)


