





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


_lowerCamelCase      							=   random.Random()




def  __UpperCAmelCase(   lowercase_			,     lowercase_=1.0			,     lowercase_=None			,     lowercase_=None   ):
       if rng is None:
              _lowerCamelCase				:  Dict						=		global_rng

       _lowerCamelCase				:  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   ,				a__   ,				a__=7   ,				a__=400   ,				a__=2000   ,				a__=1   ,				a__=0.0   ,				a__=1_6000   ,				a__=True   ,				a__=True   ,				):



             """simple docstring"""





             _lowerCamelCase				:  List[str]						=		parent
             _lowerCamelCase				:  int						=		batch_size
             _lowerCamelCase				:  Optional[Any]						=		min_seq_length
             _lowerCamelCase				:  str						=		max_seq_length
             _lowerCamelCase				:  Union[str, Any]						=		(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
             _lowerCamelCase				:  Any						=		feature_size
             _lowerCamelCase				:  Any						=		padding_value
             _lowerCamelCase				:  Union[str, Any]						=		sampling_rate
             _lowerCamelCase				:  Any						=		return_attention_mask
             _lowerCamelCase				:  Optional[Any]						=		do_normalize


      def 					__snake_case      (		self):



             """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 					__snake_case      (		self   ,				a__=False   ,				a__=False):



             """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				:  Union[str, Any]						=		[
                        _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				:  Any						=		[np.asarray(a__) for x in speech_inputs]

             return speech_inputs







class       __A							(   lowerCamelCase__			,unittest.TestCase	):




      """simple docstring"""







      UpperCAmelCase__							=   WavaVecaFeatureExtractor


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Dict						=		WavaVecaFeatureExtractionTester(self)


      def 					__snake_case      (		self   ,				a__):



             """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 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Tuple						=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
             # create three inputs of length 800, 1000, and 1200
             _lowerCamelCase				:  Tuple						=		[floats_list((1, x))[0] for x in range(800   ,				1400   ,				200)]
             _lowerCamelCase				:  int						=		[np.asarray(a__) for speech_input in speech_inputs]

             # Test not batched input
             _lowerCamelCase				:  int						=		feat_extract(speech_inputs[0]   ,				return_tensors='''np''').input_values
             _lowerCamelCase				:  Optional[int]						=		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				:  Optional[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))

             # Test 2-D numpy arrays are batched.
             _lowerCamelCase				:  Optional[int]						=		[floats_list((1, x))[0] for x in (800, 800, 800)]
             _lowerCamelCase				:  Any						=		np.asarray(a__)
             _lowerCamelCase				:  Tuple						=		feat_extract(a__   ,				return_tensors='''np''').input_values
             _lowerCamelCase				:  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 					__snake_case      (		self):



             """simple docstring"""





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

             _lowerCamelCase				:  Dict						=		['''longest''', '''max_length''', '''do_not_pad''']
             _lowerCamelCase				:  Optional[int]						=		[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 					__snake_case      (		self):



             """simple docstring"""





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

             _lowerCamelCase				:  List[Any]						=		['''longest''', '''max_length''', '''do_not_pad''']
             _lowerCamelCase				:  List[Any]						=		[None, 1600, None]

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


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[Any]						=		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				:  Any						=		feat_extract(
                 a__   ,				truncation=a__   ,				max_length=1000   ,				padding='''max_length'''   ,				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])
             self._check_zero_mean_unit_variance(input_values[2])


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Tuple						=		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				:  str						=		feat_extract(
                 a__   ,				truncation=a__   ,				max_length=1000   ,				padding='''longest'''   ,				return_tensors='''np''')
             _lowerCamelCase				:  Dict						=		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				:  Optional[int]						=		[floats_list((1, x))[0] for x in range(800   ,				1400   ,				200)]
             _lowerCamelCase				:  int						=		feat_extract(
                 a__   ,				truncation=a__   ,				max_length=2000   ,				padding='''longest'''   ,				return_tensors='''np''')
             _lowerCamelCase				:  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):



             """simple docstring"""





             import torch

             _lowerCamelCase				:  Tuple						=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
             _lowerCamelCase				:  Optional[Any]						=		np.random.rand(100).astype(np.floataa)
             _lowerCamelCase				:  List[str]						=		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 					__snake_case      (		self):



             """simple docstring"""





             for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
                    _lowerCamelCase				:  List[Any]						=		WavaVecaConfig.from_pretrained(a__)
                    _lowerCamelCase				:  str						=		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 __future__ import annotations

import unittest

from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel







@require_tf
class       __A							:




      """simple docstring"""







      UpperCAmelCase__							=   BlenderbotSmallConfig
      UpperCAmelCase__							=   {}
      UpperCAmelCase__							=   """gelu"""


      def __init__(		self   ,				a__   ,				a__=13   ,				a__=7   ,				a__=True   ,				a__=False   ,				a__=99   ,				a__=32   ,				a__=2   ,				a__=4   ,				a__=37   ,				a__=0.1   ,				a__=0.1   ,				a__=20   ,				a__=2   ,				a__=1   ,				a__=0   ,				):



             """simple docstring"""





             _lowerCamelCase				:  str						=		parent
             _lowerCamelCase				:  Any						=		batch_size
             _lowerCamelCase				:  str						=		seq_length
             _lowerCamelCase				:  Optional[int]						=		is_training
             _lowerCamelCase				:  int						=		use_labels
             _lowerCamelCase				:  List[str]						=		vocab_size
             _lowerCamelCase				:  List[Any]						=		hidden_size
             _lowerCamelCase				:  Tuple						=		num_hidden_layers
             _lowerCamelCase				:  List[Any]						=		num_attention_heads
             _lowerCamelCase				:  int						=		intermediate_size

             _lowerCamelCase				:  int						=		hidden_dropout_prob
             _lowerCamelCase				:  Tuple						=		attention_probs_dropout_prob
             _lowerCamelCase				:  Dict						=		max_position_embeddings
             _lowerCamelCase				:  str						=		eos_token_id
             _lowerCamelCase				:  Any						=		pad_token_id
             _lowerCamelCase				:  str						=		bos_token_id


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[str]						=		ids_tensor([self.batch_size, self.seq_length - 1]   ,				self.vocab_size)
             _lowerCamelCase				:  Any						=		tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size)   ,				1)
             _lowerCamelCase				:  Optional[Any]						=		tf.concat([input_ids, eos_tensor]   ,				axis=1)

             _lowerCamelCase				:  List[str]						=		ids_tensor([self.batch_size, self.seq_length]   ,				self.vocab_size)

             _lowerCamelCase				:  Dict						=		self.config_cls(
                 vocab_size=self.vocab_size   ,				d_model=self.hidden_size   ,				encoder_layers=self.num_hidden_layers   ,				decoder_layers=self.num_hidden_layers   ,				encoder_attention_heads=self.num_attention_heads   ,				decoder_attention_heads=self.num_attention_heads   ,				encoder_ffn_dim=self.intermediate_size   ,				decoder_ffn_dim=self.intermediate_size   ,				dropout=self.hidden_dropout_prob   ,				attention_dropout=self.attention_probs_dropout_prob   ,				max_position_embeddings=self.max_position_embeddings   ,				eos_token_ids=[2]   ,				bos_token_id=self.bos_token_id   ,				pad_token_id=self.pad_token_id   ,				decoder_start_token_id=self.pad_token_id   ,				**self.config_updates   ,				)
             _lowerCamelCase				:  Optional[int]						=		prepare_blenderbot_small_inputs_dict(a__   ,				a__   ,				a__)
             return config, inputs_dict



      def 					__snake_case      (		self   ,				a__   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  Optional[int]						=		TFBlenderbotSmallModel(config=a__).get_decoder()
             _lowerCamelCase				:  Dict						=		inputs_dict['''input_ids''']

             _lowerCamelCase				:  int						=		input_ids[:1, :]
             _lowerCamelCase				:  Union[str, Any]						=		inputs_dict['''attention_mask'''][:1, :]
             _lowerCamelCase				:  List[str]						=		inputs_dict['''head_mask''']
             _lowerCamelCase				:  Optional[int]						=		1

             # first forward pass
             _lowerCamelCase				:  Union[str, Any]						=		model(a__   ,				attention_mask=a__   ,				head_mask=a__   ,				use_cache=a__)

             _lowerCamelCase,	_lowerCamelCase				:  str						=		outputs.to_tuple()

             # create hypothetical next token and extent to next_input_ids
             _lowerCamelCase				:  Any						=		ids_tensor((self.batch_size, 3)   ,				config.vocab_size)
             _lowerCamelCase				:  Optional[Any]						=		tf.cast(ids_tensor((self.batch_size, 3)   ,				2)   ,				tf.inta)

             # append to next input_ids and
             _lowerCamelCase				:  Any						=		tf.concat([input_ids, next_tokens]   ,				axis=-1)
             _lowerCamelCase				:  Optional[Any]						=		tf.concat([attention_mask, next_attn_mask]   ,				axis=-1)

             _lowerCamelCase				:  List[str]						=		model(a__   ,				attention_mask=a__)[0]
             _lowerCamelCase				:  str						=		model(a__   ,				attention_mask=a__   ,				past_key_values=a__)[0]

             self.parent.assertEqual(next_tokens.shape[1]   ,				output_from_past.shape[1])

             # select random slice
             _lowerCamelCase				:  int						=		int(ids_tensor((1,)   ,				output_from_past.shape[-1]))
             _lowerCamelCase				:  Any						=		output_from_no_past[:, -3:, random_slice_idx]
             _lowerCamelCase				:  int						=		output_from_past[:, :, random_slice_idx]

             # test that outputs are equal for slice
             tf.debugging.assert_near(a__   ,				a__   ,				rtol=1e-3)




def  __UpperCAmelCase(   lowercase_			,     lowercase_			,     lowercase_			,     lowercase_=None			,     lowercase_=None			,     lowercase_=None			,     lowercase_=None			,     lowercase_=None			,     ):
       if attention_mask is None:
              _lowerCamelCase				:  List[Any]						=		tf.cast(tf.math.not_equal(lowercase_			,     config.pad_token_id   )			,     tf.inta   )
       if decoder_attention_mask is None:
              _lowerCamelCase				:  Union[str, Any]						=		tf.concat(
                  [
                      tf.ones(decoder_input_ids[:, :1].shape			,     dtype=tf.inta   ),
                      tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]			,     config.pad_token_id   )			,     tf.inta   ),
                  ]			,     axis=-1			,     )
       if head_mask is None:
              _lowerCamelCase				:  List[str]						=		tf.ones((config.encoder_layers, config.encoder_attention_heads)   )
       if decoder_head_mask is None:
              _lowerCamelCase				:  Optional[int]						=		tf.ones((config.decoder_layers, config.decoder_attention_heads)   )
       if cross_attn_head_mask is None:
              _lowerCamelCase				:  Union[str, Any]						=		tf.ones((config.decoder_layers, config.decoder_attention_heads)   )
       return {
           "input_ids": input_ids,
           "decoder_input_ids": decoder_input_ids,
           "attention_mask": attention_mask,
           "decoder_attention_mask": decoder_attention_mask,
           "head_mask": head_mask,
           "decoder_head_mask": decoder_head_mask,
           "cross_attn_head_mask": cross_attn_head_mask,
       }







@require_tf
class       __A							(   lowerCamelCase__			,lowerCamelCase__			,unittest.TestCase	):




      """simple docstring"""







      UpperCAmelCase__							=   (
          (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
      )
      UpperCAmelCase__							=   (TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
      UpperCAmelCase__							=   (
          {
              """conversational""": TFBlenderbotSmallForConditionalGeneration,
              """feature-extraction""": TFBlenderbotSmallModel,
              """summarization""": TFBlenderbotSmallForConditionalGeneration,
              """text2text-generation""": TFBlenderbotSmallForConditionalGeneration,
              """translation""": TFBlenderbotSmallForConditionalGeneration,
          }
          if is_tf_available()
          else {}
      )
      UpperCAmelCase__							=   True
      UpperCAmelCase__							=   False
      UpperCAmelCase__							=   False


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[str]						=		TFBlenderbotSmallModelTester(self)
             _lowerCamelCase				:  Optional[int]						=		ConfigTester(self   ,				config_class=a__)


      def 					__snake_case      (		self):



             """simple docstring"""





             self.config_tester.run_common_tests()



      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Any						=		self.model_tester.prepare_config_and_inputs_for_common()
             self.model_tester.check_decoder_model_past_large_inputs(*a__)







@require_tokenizers
@require_tf
class       __A							(   unittest.TestCase	):




      """simple docstring"""







      UpperCAmelCase__							=   [
          """Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like """
          """  i'm going to throw up.\nand why is that?"""
      ]
      UpperCAmelCase__							=   """facebook/blenderbot_small-90M"""


      @cached_property
      def 					__snake_case      (		self):



             """simple docstring"""





             return BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''')


      @cached_property
      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[Any]						=		TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
             return model



      @slow
      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Union[str, Any]						=		self.tokenizer(self.src_text   ,				return_tensors='''tf''')
             _lowerCamelCase				:  str						=		self.model.generate(
                 model_inputs.input_ids   ,				attention_mask=model_inputs.attention_mask   ,				num_beams=2   ,				use_cache=a__   ,				)
             _lowerCamelCase				:  Tuple						=		self.tokenizer.batch_decode(generated_ids.numpy()   ,				skip_special_tokens=a__)[0]
             assert generated_words in (
                 "i don't know. i just feel like i'm going to throw up. it's not fun.",
                 "i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
                 "i'm not sure. i just feel like i've been in a bad situation.",
             )

