


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


_snake_case  :	Optional[int]			      =       random.Random()







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase=1.0							,			__lowerCamelCase=None							,			__lowerCamelCase=None			):
							if rng is None:
														__snake_case	:    Optional[Any]											=     global_rng

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

							return values

class        a	(unittest.TestCase   ):




							"""simple docstring"""


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

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

														if equal_length:
																					__snake_case	:    str											=     floats_list((self.batch_size, self.max_seq_length)       )
														else:
																					# make sure that inputs increase in size
																					__snake_case	:    Optional[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:
																					__snake_case	:    int											=     [np.asarray(lowerCamelCase       ) for x in speech_inputs]

														return speech_inputs

class        a	(_lowerCAmelCase  ,       unittest.TestCase   ):




							"""simple docstring"""


							__UpperCAmelCase					:							Dict  						=						WavaVecaFeatureExtractor

							def 					__snake_case      (							self :						Union[str, Any]       )			->					Any:
														__snake_case	:    Union[str, Any]											=     WavaVecaFeatureExtractionTester(self       )

							def 					__snake_case      (							self :						Any					,			lowerCamelCase :						Optional[int]       )			->					Optional[Any]:
														self.assertTrue(np.all(np.mean(lowerCamelCase					,			axis=0       ) < 1E-3       )       )
														self.assertTrue(np.all(np.abs(np.var(lowerCamelCase					,			axis=0       ) - 1       ) < 1E-3       )       )

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

														# Test not batched input
														__snake_case	:    Dict											=     feat_extract(speech_inputs[0]					,			return_tensors="np"       ).input_values
														__snake_case	:    Optional[int]											=     feat_extract(np_speech_inputs[0]					,			return_tensors="np"       ).input_values
														self.assertTrue(np.allclose(lowerCamelCase					,			lowerCamelCase					,			atol=1E-3       )       )

														# Test batched
														__snake_case	:    str											=     feat_extract(lowerCamelCase					,			return_tensors="np"       ).input_values
														__snake_case	:    List[str]											=     feat_extract(lowerCamelCase					,			return_tensors="np"       ).input_values
														for enc_seq_a, enc_seq_a in zip(lowerCamelCase					,			lowerCamelCase       ):
																					self.assertTrue(np.allclose(lowerCamelCase					,			lowerCamelCase					,			atol=1E-3       )       )

														# Test 2-D numpy arrays are batched.
														__snake_case	:    Union[str, Any]											=     [floats_list((1, x)       )[0] for x in (800, 800, 800)]
														__snake_case	:    Dict											=     np.asarray(lowerCamelCase       )
														__snake_case	:    int											=     feat_extract(lowerCamelCase					,			return_tensors="np"       ).input_values
														__snake_case	:    Optional[int]											=     feat_extract(lowerCamelCase					,			return_tensors="np"       ).input_values
														for enc_seq_a, enc_seq_a in zip(lowerCamelCase					,			lowerCamelCase       ):
																					self.assertTrue(np.allclose(lowerCamelCase					,			lowerCamelCase					,			atol=1E-3       )       )

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

														__snake_case	:    Optional[int]											=     ["longest", "max_length", "do_not_pad"]
														__snake_case	:    str											=     [None, 1600, None]
														for max_length, padding in zip(lowerCamelCase					,			lowerCamelCase       ):
																					__snake_case	:    int											=     feat_extract(lowerCamelCase					,			padding=lowerCamelCase					,			max_length=lowerCamelCase					,			return_tensors="np"       )
																					__snake_case	:    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 					__snake_case      (							self :						int       )			->					List[str]:
														__snake_case	:    Any											=     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
														__snake_case	:    List[str]											=     range(800					,			1400					,			200       )
														__snake_case	:    List[str]											=     [floats_list((1, x)       )[0] for x in lengths]

														__snake_case	:    List[str]											=     ["longest", "max_length", "do_not_pad"]
														__snake_case	:    Tuple											=     [None, 1600, None]

														for max_length, padding in zip(lowerCamelCase					,			lowerCamelCase       ):
																					__snake_case	:    List[Any]											=     feat_extract(lowerCamelCase					,			max_length=lowerCamelCase					,			padding=lowerCamelCase       )
																					__snake_case	:    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][:1200]       )

							def 					__snake_case      (							self :						Optional[int]       )			->					Any:
														__snake_case	:    Any											=     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
														__snake_case	:    Optional[Any]											=     [floats_list((1, x)       )[0] for x in range(800					,			1400					,			200       )]
														__snake_case	:    str											=     feat_extract(
														    lowerCamelCase					,			truncation=lowerCamelCase					,			max_length=1000					,			padding="max_length"					,			return_tensors="np"       )
														__snake_case	:    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 					__snake_case      (							self :						Optional[Any]       )			->					List[str]:
														__snake_case	:    Optional[Any]											=     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
														__snake_case	:    str											=     [floats_list((1, x)       )[0] for x in range(800					,			1400					,			200       )]
														__snake_case	:    Optional[Any]											=     feat_extract(
														    lowerCamelCase					,			truncation=lowerCamelCase					,			max_length=1000					,			padding="longest"					,			return_tensors="np"       )
														__snake_case	:    Optional[int]											=     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)       )

														__snake_case	:    List[Any]											=     [floats_list((1, x)       )[0] for x in range(800					,			1400					,			200       )]
														__snake_case	:    int											=     feat_extract(
														    lowerCamelCase					,			truncation=lowerCamelCase					,			max_length=2000					,			padding="longest"					,			return_tensors="np"       )
														__snake_case	:    List[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 longest
														self.assertTrue(input_values.shape == (3, 1200)       )

							@require_torch
							def 					__snake_case      (							self :						int       )			->					Union[str, Any]:
														import torch

														__snake_case	:    str											=     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
														__snake_case	:    Any											=     np.random.rand(100       ).astype(np.floataa       )
														__snake_case	:    Any											=     np_speech_inputs.tolist()

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






							@slow
							@require_torch
							def 					__snake_case      (							self :						Optional[Any]       )			->					str:
														# 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:
																					__snake_case	:    int											=     WavaVecaConfig.from_pretrained(lowerCamelCase       )
																					__snake_case	:    Union[str, Any]											=     WavaVecaFeatureExtractor.from_pretrained(lowerCamelCase       )

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





import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
_snake_case  :	Dict			      =       {"LayoutLMv2Config", "LayoutLMv3Config"}

@is_pipeline_test
class        a	(unittest.TestCase   ):




							"""simple docstring"""


							__UpperCAmelCase					:							Union[str, Any]  						=						MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
							__UpperCAmelCase					:							Optional[int]  						=						TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

							if model_mapping is not None:
														__UpperCAmelCase					:							Optional[int]  						=						{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
							if tf_model_mapping is not None:
														__UpperCAmelCase					:							Any  						=						{
														    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
														}

							@require_torch
							def 					__snake_case      (							self :						str       )			->					str:
														__snake_case	:    Dict											=     pipeline(
														    task="text-classification"					,			model="hf-internal-testing/tiny-random-distilbert"					,			framework="pt"       )

														__snake_case	:    Optional[Any]											=     text_classifier("This is great !"       )
														self.assertEqual(nested_simplify(lowerCamelCase       )					,			[{"label": "LABEL_0", "score": 0.5_04}]       )

														__snake_case	:    Any											=     text_classifier("This is great !"					,			top_k=2       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase       )					,			[{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}]       )

														__snake_case	:    int											=     text_classifier(["This is great !", "This is bad"]					,			top_k=2       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase       )					,			[
														        [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
														        [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
														    ]					,			)

														__snake_case	:    List[str]											=     text_classifier("This is great !"					,			top_k=1       )
														self.assertEqual(nested_simplify(lowerCamelCase       )					,			[{"label": "LABEL_0", "score": 0.5_04}]       )

														# Legacy behavior
														__snake_case	:    Dict											=     text_classifier("This is great !"					,			return_all_scores=lowerCamelCase       )
														self.assertEqual(nested_simplify(lowerCamelCase       )					,			[{"label": "LABEL_0", "score": 0.5_04}]       )

														__snake_case	:    Any											=     text_classifier("This is great !"					,			return_all_scores=lowerCamelCase       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase       )					,			[[{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}]]       )

														__snake_case	:    Tuple											=     text_classifier(["This is great !", "Something else"]					,			return_all_scores=lowerCamelCase       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase       )					,			[
														        [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
														        [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
														    ]					,			)

														__snake_case	:    Tuple											=     text_classifier(["This is great !", "Something else"]					,			return_all_scores=lowerCamelCase       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase       )					,			[
														        {"label": "LABEL_0", "score": 0.5_04},
														        {"label": "LABEL_0", "score": 0.5_04},
														    ]					,			)

							@require_torch
							def 					__snake_case      (							self :						Optional[int]       )			->					List[Any]:
														import torch

														__snake_case	:    Dict											=     pipeline(
														    task="text-classification"					,			model="hf-internal-testing/tiny-random-distilbert"					,			framework="pt"					,			device=torch.device("cpu"       )					,			)

														__snake_case	:    Optional[int]											=     text_classifier("This is great !"       )
														self.assertEqual(nested_simplify(lowerCamelCase       )					,			[{"label": "LABEL_0", "score": 0.5_04}]       )

							@require_tf
							def 					__snake_case      (							self :						Any       )			->					Tuple:
														__snake_case	:    List[Any]											=     pipeline(
														    task="text-classification"					,			model="hf-internal-testing/tiny-random-distilbert"					,			framework="tf"       )

														__snake_case	:    str											=     text_classifier("This is great !"       )
														self.assertEqual(nested_simplify(lowerCamelCase       )					,			[{"label": "LABEL_0", "score": 0.5_04}]       )

							@slow
							@require_torch
							def 					__snake_case      (							self :						int       )			->					int:
														__snake_case	:    Dict											=     pipeline("text-classification"       )

														__snake_case	:    Dict											=     text_classifier("This is great !"       )
														self.assertEqual(nested_simplify(lowerCamelCase       )					,			[{"label": "POSITIVE", "score": 1.0}]       )
														__snake_case	:    int											=     text_classifier("This is bad !"       )
														self.assertEqual(nested_simplify(lowerCamelCase       )					,			[{"label": "NEGATIVE", "score": 1.0}]       )
														__snake_case	:    str											=     text_classifier("Birds are a type of animal"       )
														self.assertEqual(nested_simplify(lowerCamelCase       )					,			[{"label": "POSITIVE", "score": 0.9_88}]       )

							@slow
							@require_tf
							def 					__snake_case      (							self :						List[Any]       )			->					str:
														__snake_case	:    Optional[Any]											=     pipeline("text-classification"					,			framework="tf"       )

														__snake_case	:    Any											=     text_classifier("This is great !"       )
														self.assertEqual(nested_simplify(lowerCamelCase       )					,			[{"label": "POSITIVE", "score": 1.0}]       )
														__snake_case	:    Optional[int]											=     text_classifier("This is bad !"       )
														self.assertEqual(nested_simplify(lowerCamelCase       )					,			[{"label": "NEGATIVE", "score": 1.0}]       )
														__snake_case	:    Any											=     text_classifier("Birds are a type of animal"       )
														self.assertEqual(nested_simplify(lowerCamelCase       )					,			[{"label": "POSITIVE", "score": 0.9_88}]       )

							def 					__snake_case      (							self :						str					,			lowerCamelCase :						Tuple					,			lowerCamelCase :						Any					,			lowerCamelCase :						Union[str, Any]       )			->					List[Any]:
														__snake_case	:    Union[str, Any]											=     TextClassificationPipeline(model=lowerCamelCase					,			tokenizer=lowerCamelCase       )
														return text_classifier, ["HuggingFace is in", "This is another test"]






							def 					__snake_case      (							self :						List[str]					,			lowerCamelCase :						Optional[Any]					,			lowerCamelCase :						Tuple       )			->					str:
														__snake_case	:    Tuple											=     text_classifier.model
														# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
														__snake_case	:    Any											=     "HuggingFace is in"
														__snake_case	:    Tuple											=     text_classifier(lowerCamelCase       )

														self.assertEqual(nested_simplify(lowerCamelCase       )					,			[{"label": ANY(lowerCamelCase       ), "score": ANY(lowerCamelCase       )}]       )
														self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()       )

														__snake_case	:    Union[str, Any]											=     ["HuggingFace is in ", "Paris is in France"]
														__snake_case	:    Union[str, Any]											=     text_classifier(lowerCamelCase       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase       )					,			[{"label": ANY(lowerCamelCase       ), "score": ANY(lowerCamelCase       )}, {"label": ANY(lowerCamelCase       ), "score": ANY(lowerCamelCase       )}]					,			)
														self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()       )
														self.assertTrue(outputs[1]["label"] in model.config.idalabel.values()       )

														# Forcing to get all results with `top_k=None`
														# This is NOT the legacy format
														__snake_case	:    str											=     text_classifier(lowerCamelCase					,			top_k=lowerCamelCase       )
														__snake_case	:    int											=     len(model.config.idalabel.values()       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase       )					,			[[{"label": ANY(lowerCamelCase       ), "score": ANY(lowerCamelCase       )}] * N, [{"label": ANY(lowerCamelCase       ), "score": ANY(lowerCamelCase       )}] * N]					,			)

														__snake_case	:    Optional[int]											=     {"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
														__snake_case	:    Optional[Any]											=     text_classifier(lowerCamelCase       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase       )					,			{"label": ANY(lowerCamelCase       ), "score": ANY(lowerCamelCase       )}					,			)
														self.assertTrue(outputs["label"] in model.config.idalabel.values()       )

														# This might be used a text pair, but tokenizer + pipe interaction
														# makes it hard to understand that it's not using the pair properly
														# https://github.com/huggingface/transformers/issues/17305
														# We disabled this usage instead as it was outputting wrong outputs.
														__snake_case	:    Any											=     [["HuggingFace is in ", "Paris is in France"]]
														with self.assertRaises(lowerCamelCase       ):
																					text_classifier(lowerCamelCase       )

														# This used to be valid for doing text pairs
														# We're keeping it working because of backward compatibility
														__snake_case	:    List[Any]											=     text_classifier([[["HuggingFace is in ", "Paris is in France"]]]       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase       )					,			[{"label": ANY(lowerCamelCase       ), "score": ANY(lowerCamelCase       )}]					,			)
														self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()       )

