

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__  :      Union[str, Any]			=    random.Random()


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		int ,   _SCREAMING_SNAKE_CASE					:		Optional[Any]=1.0 ,   _SCREAMING_SNAKE_CASE					:		Optional[int]=None ,   _SCREAMING_SNAKE_CASE					:		Optional[Any]=None       ):





				"""simple docstring"""


				if rng is None:
								SCREAMING_SNAKE_CASE_									=						global_rng

				SCREAMING_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       __snake_case  (    unittest.TestCase       ):

		def __init__(					self						,		_A						,		_A=7						,		_A=400						,		_A=2000						,		_A=1						,		_A=0.0						,		_A=16000						,		_A=True						,		_A=True						,		):
						SCREAMING_SNAKE_CASE_									=						parent
						SCREAMING_SNAKE_CASE_									=						batch_size
						SCREAMING_SNAKE_CASE_									=						min_seq_length
						SCREAMING_SNAKE_CASE_									=						max_seq_length
						SCREAMING_SNAKE_CASE_									=						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
						SCREAMING_SNAKE_CASE_									=						feature_size
						SCREAMING_SNAKE_CASE_									=						padding_value
						SCREAMING_SNAKE_CASE_									=						sampling_rate
						SCREAMING_SNAKE_CASE_									=						return_attention_mask
						SCREAMING_SNAKE_CASE_									=						do_normalize

		def 						lowerCAmelCase__     (					self):
						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						,		_A=False						,		_A=False):
						def _flatten(_A):
										return list(itertools.chain(*_A))

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

						return speech_inputs






class       __snake_case  (    lowerCAmelCase__     , unittest.TestCase       ):
		__lowerCAmelCase     :	str						 =  WavaVecaFeatureExtractor

		def 						lowerCAmelCase__     (					self):
						SCREAMING_SNAKE_CASE_									=						WavaVecaFeatureExtractionTester(self)

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

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

						# Test batched
						SCREAMING_SNAKE_CASE_									=						feat_extract(_A						,		return_tensors='np').input_values
						SCREAMING_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))

						# Test 2-D numpy arrays are batched.
						SCREAMING_SNAKE_CASE_									=						[floats_list((1, x))[0] for x in (800, 800, 800)]
						SCREAMING_SNAKE_CASE_									=						np.asarray(_A)
						SCREAMING_SNAKE_CASE_									=						feat_extract(_A						,		return_tensors='np').input_values
						SCREAMING_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))

		def 						lowerCAmelCase__     (					self):
						SCREAMING_SNAKE_CASE_									=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
						SCREAMING_SNAKE_CASE_									=						[floats_list((1, x))[0] for x in range(800						,		1400						,		200)]

						SCREAMING_SNAKE_CASE_									=						['longest', 'max_length', 'do_not_pad']
						SCREAMING_SNAKE_CASE_									=						[None, 1600, None]
						for max_length, padding in zip(_A						,		_A):
										SCREAMING_SNAKE_CASE_									=						feat_extract(_A						,		padding=_A						,		max_length=_A						,		return_tensors='np')
										SCREAMING_SNAKE_CASE_									=						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 						lowerCAmelCase__     (					self):
						SCREAMING_SNAKE_CASE_									=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
						SCREAMING_SNAKE_CASE_									=						range(800						,		1400						,		200)
						SCREAMING_SNAKE_CASE_									=						[floats_list((1, x))[0] for x in lengths]

						SCREAMING_SNAKE_CASE_									=						['longest', 'max_length', 'do_not_pad']
						SCREAMING_SNAKE_CASE_									=						[None, 1600, None]

						for max_length, padding in zip(_A						,		_A):
										SCREAMING_SNAKE_CASE_									=						feat_extract(_A						,		max_length=_A						,		padding=_A)
										SCREAMING_SNAKE_CASE_									=						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 						lowerCAmelCase__     (					self):
						SCREAMING_SNAKE_CASE_									=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
						SCREAMING_SNAKE_CASE_									=						[floats_list((1, x))[0] for x in range(800						,		1400						,		200)]
						SCREAMING_SNAKE_CASE_									=						feat_extract(
						    _A						,		truncation=_A						,		max_length=1000						,		padding='max_length'						,		return_tensors='np')
						SCREAMING_SNAKE_CASE_									=						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 						lowerCAmelCase__     (					self):
						SCREAMING_SNAKE_CASE_									=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
						SCREAMING_SNAKE_CASE_									=						[floats_list((1, x))[0] for x in range(800						,		1400						,		200)]
						SCREAMING_SNAKE_CASE_									=						feat_extract(
						    _A						,		truncation=_A						,		max_length=1000						,		padding='longest'						,		return_tensors='np')
						SCREAMING_SNAKE_CASE_									=						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))

						SCREAMING_SNAKE_CASE_									=						[floats_list((1, x))[0] for x in range(800						,		1400						,		200)]
						SCREAMING_SNAKE_CASE_									=						feat_extract(
						    _A						,		truncation=_A						,		max_length=2000						,		padding='longest'						,		return_tensors='np')
						SCREAMING_SNAKE_CASE_									=						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 						lowerCAmelCase__     (					self):
						import torch

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

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


		@slow
		@require_torch
		def 						lowerCAmelCase__     (					self):
						# this test makes sure that models that are using
						# group norm don't have their feature extractor return the
						# attention_mask
						for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
										SCREAMING_SNAKE_CASE_									=						WavaVecaConfig.from_pretrained(_A)
										SCREAMING_SNAKE_CASE_									=						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')





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


UpperCamelCase__  :      Tuple			=    {
    "configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP", "TrOCRConfig"],
    "processing_trocr": ["TrOCRProcessor"],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__  :      Tuple			=    [
           "TROCR_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TrOCRForCausalLM",
           "TrOCRPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
       from .processing_trocr import TrOCRProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
       import sys

       UpperCamelCase__  :      str			=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


