
import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
	import torch

if is_datasets_available():
	from datasets import load_dataset

UpperCAmelCase	      = random.Random()






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE=1.0 ,							__SCREAMING_SNAKE_CASE=None ,							__SCREAMING_SNAKE_CASE=None							):
	if rng is None:
		lowercase       =    global_rng

	lowercase       =    []
	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		,    snake_case		,    snake_case=7		,    snake_case=400		,    snake_case=2000		,    snake_case=2048		,    snake_case=128		,    snake_case=1		,    snake_case=512		,    snake_case=30		,    snake_case=4_4100		,    ):
		lowercase       =    parent
		lowercase       =    batch_size
		lowercase       =    min_seq_length
		lowercase       =    max_seq_length
		lowercase       =    (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
		lowercase       =    spectrogram_length
		lowercase       =    feature_size
		lowercase       =    num_audio_channels
		lowercase       =    hop_length
		lowercase       =    chunk_length
		lowercase       =    sampling_rate





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		return {
		    "spectrogram_length": self.spectrogram_length,
		    "feature_size": self.feature_size,
		    "num_audio_channels": self.num_audio_channels,
		    "hop_length": self.hop_length,
		    "chunk_length": self.chunk_length,
		    "sampling_rate": self.sampling_rate,
		}





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case=False		,    snake_case=False       ):
		def _flatten(snake_case       ):
			return list(itertools.chain(*snake_case       )       )

		if equal_length:
			lowercase       =    [floats_list((self.max_seq_length, self.feature_size)       ) for _ in range(self.batch_size       )]
		else:
			# make sure that inputs increase in size
			lowercase       =    [
			    floats_list((x, self.feature_size)       )
			    for x in range(self.min_seq_length		,    self.max_seq_length		,    self.seq_length_diff       )
			]
		if numpify:
			lowercase       =    [np.asarray(snake_case       ) for x in speech_inputs]
		return speech_inputs







@require_torch
@require_torchaudio
class        A_   (			__lowerCamelCase      ,	unittest.TestCase   ):




	'''simple docstring'''


	_UpperCamelCase     :     Union[str, Any]							 =       TvltFeatureExtractor





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    TvltFeatureExtractionTester(self       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.feature_extraction_class(**self.feat_extract_dict       )
		self.assertTrue(hasattr(snake_case		,    'spectrogram_length'       )       )
		self.assertTrue(hasattr(snake_case		,    'feature_size'       )       )
		self.assertTrue(hasattr(snake_case		,    'num_audio_channels'       )       )
		self.assertTrue(hasattr(snake_case		,    'hop_length'       )       )
		self.assertTrue(hasattr(snake_case		,    'chunk_length'       )       )
		self.assertTrue(hasattr(snake_case		,    'sampling_rate'       )       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.feature_extraction_class(**self.feat_extract_dict       )

		with tempfile.TemporaryDirectory() as tmpdirname:
			lowercase       =    feat_extract_first.save_pretrained(snake_case       )[0]
			check_json_file_has_correct_format(snake_case       )
			lowercase       =    self.feature_extraction_class.from_pretrained(snake_case       )

		lowercase       =    feat_extract_first.to_dict()
		lowercase       =    feat_extract_second.to_dict()
		lowercase       =    dict_first.pop('mel_filters'       )
		lowercase       =    dict_second.pop('mel_filters'       )
		self.assertTrue(np.allclose(snake_case		,    snake_case       )       )
		self.assertEqual(snake_case		,    snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.feature_extraction_class(**self.feat_extract_dict       )

		with tempfile.TemporaryDirectory() as tmpdirname:
			lowercase       =    os.path.join(snake_case		,    'feat_extract.json'       )
			feat_extract_first.to_json_file(snake_case       )
			lowercase       =    self.feature_extraction_class.from_json_file(snake_case       )

		lowercase       =    feat_extract_first.to_dict()
		lowercase       =    feat_extract_second.to_dict()
		lowercase       =    dict_first.pop('mel_filters'       )
		lowercase       =    dict_second.pop('mel_filters'       )
		self.assertTrue(np.allclose(snake_case		,    snake_case       )       )
		self.assertEqual(snake_case		,    snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		# Initialize feature_extractor
		lowercase       =    self.feature_extraction_class(**self.feat_extract_dict       )

		# create three inputs of length 800, 1000, and 1200
		lowercase       =    [floats_list((1, x)       )[0] for x in range(800		,    1400		,    200       )]
		lowercase       =    [np.asarray(snake_case       ) for speech_input in speech_inputs]

		# Test not batched input
		lowercase       =    feature_extractor(np_speech_inputs[0]		,    return_tensors='np'		,    sampling_rate=4_4100       ).audio_values

		self.assertTrue(encoded_audios.ndim == 4       )
		self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size       )
		self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length       )
		self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels       )

		# Test batched
		lowercase       =    feature_extractor(snake_case		,    return_tensors='np'		,    sampling_rate=4_4100       ).audio_values

		self.assertTrue(encoded_audios.ndim == 4       )
		self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size       )
		self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length       )
		self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels       )

		# Test audio masking
		lowercase       =    feature_extractor(
		    snake_case		,    return_tensors='np'		,    sampling_rate=4_4100		,    mask_audio=snake_case       ).audio_values

		self.assertTrue(encoded_audios.ndim == 4       )
		self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size       )
		self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length       )
		self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels       )

		# Test 2-D numpy arrays are batched.
		lowercase       =    [floats_list((1, x)       )[0] for x in (800, 800, 800)]
		lowercase       =    np.asarray(snake_case       )
		lowercase       =    feature_extractor(snake_case		,    return_tensors='np'		,    sampling_rate=4_4100       ).audio_values
		self.assertTrue(encoded_audios.ndim == 4       )
		self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size       )
		self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length       )
		self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels       )





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

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





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self._load_datasamples(1       )
		lowercase       =    TvltFeatureExtractor()
		lowercase       =    feature_extractor(snake_case		,    return_tensors='pt'       ).audio_values

		self.assertEquals(audio_values.shape		,    (1, 1, 192, 128)       )

		lowercase       =    torch.tensor([[-0.3_032, -0.2_708], [-0.4_434, -0.4_007]]       )
		self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]		,    snake_case		,    atol=1E-4       )       )



import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


UpperCAmelCase	      = logging.get_logger(__name__)  # pylint: disable=invalid-name







class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


	def __init__(       self		,    snake_case		,    snake_case=768       ):
		super().__init__(snake_case       )
		lowercase       =    proj_size

		lowercase       =    CLIPVisionModel(snake_case       )
		lowercase       =    PaintByExampleMapper(snake_case       )
		lowercase       =    nn.LayerNorm(config.hidden_size       )
		lowercase       =    nn.Linear(config.hidden_size		,    self.proj_size       )

		# uncondition for scaling
		lowercase       =    nn.Parameter(torch.randn((1, 1, self.proj_size)       )       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case=False       ):
		lowercase       =    self.model(pixel_values=snake_case       )
		lowercase       =    clip_output.pooler_output
		lowercase       =    self.mapper(latent_states[:, None]       )
		lowercase       =    self.final_layer_norm(snake_case       )
		lowercase       =    self.proj_out(snake_case       )
		if return_uncond_vector:
			return latent_states, self.uncond_vector

		return latent_states







class        A_   (			nn.Module   ):




	'''simple docstring'''


	def __init__(       self		,    snake_case       ):
		super().__init__()
		lowercase       =    (config.num_hidden_layers + 1) // 5
		lowercase       =    config.hidden_size
		lowercase       =    1
		lowercase       =    nn.ModuleList(
		    [
		        BasicTransformerBlock(snake_case		,    snake_case		,    snake_case		,    activation_fn='gelu'		,    attention_bias=snake_case       )
		        for _ in range(snake_case       )
		    ]       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case       ):
		for block in self.blocks:
			lowercase       =    block(snake_case       )

		return hidden_states

