
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


_lowercase    =		random.Random()


def A				(__lowerCamelCase				:Union[str, Any]			,     __lowerCamelCase				:int=1.0			,     __lowerCamelCase				:List[str]=None			,     __lowerCamelCase				:List[str]=None		):
						if rng is None:
												_lowerCAmelCase  						=			global_rng

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

						return values




class   UpperCAmelCase_					(		unittest.TestCase			):






						'''simple docstring'''






						def __init__( self   ,       _lowercase   ,       _lowercase=7   ,       _lowercase=400   ,       _lowercase=2_000   ,       _lowercase=1   ,       _lowercase=0.0   ,       _lowercase=16_000   ,       _lowercase=True   ,       _lowercase=True   ,       ):

												"""simple docstring"""



												_lowerCAmelCase  						=			parent
												_lowerCAmelCase  						=			batch_size
												_lowerCAmelCase  						=			min_seq_length
												_lowerCAmelCase  						=			max_seq_length
												_lowerCAmelCase  						=			(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
												_lowerCAmelCase  						=			feature_size
												_lowerCAmelCase  						=			padding_value
												_lowerCAmelCase  						=			sampling_rate
												_lowerCAmelCase  						=			return_attention_mask
												_lowerCAmelCase  						=			do_normalize




						def  _lowercase     ( 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  _lowercase     ( self   ,       _lowercase=False   ,       _lowercase=False				):

												"""simple docstring"""



												def _flatten(_lowercase				):
																		return list(itertools.chain(*_a				)				)

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

												return speech_inputs






class   UpperCAmelCase_					(		UpperCamelCase_	,							unittest.TestCase			):






						'''simple docstring'''






						_lowercase		:				Union[str, Any]									=       WavaVecaFeatureExtractor




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			WavaVecaFeatureExtractionTester(self				)




						def  _lowercase     ( self   ,       _lowercase				):

												"""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  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
												# create three inputs of length 800, 1000, and 1200
												_lowerCAmelCase  						=			[floats_list((1, x)				)[0] for x in range(800   ,       1_400   ,       200				)]
												_lowerCAmelCase  						=			[np.asarray(_a				) for speech_input in speech_inputs]

												# Test not batched input
												_lowerCAmelCase  						=			feat_extract(speech_inputs[0]   ,       return_tensors="""np"""				).input_values
												_lowerCAmelCase  						=			feat_extract(np_speech_inputs[0]   ,       return_tensors="""np"""				).input_values
												self.assertTrue(np.allclose(_a   ,       _a   ,       atol=1e-3				)				)

												# Test batched
												_lowerCAmelCase  						=			feat_extract(_a   ,       return_tensors="""np"""				).input_values
												_lowerCAmelCase  						=			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  						=			[floats_list((1, x)				)[0] for x in (800, 800, 800)]
												_lowerCAmelCase  						=			np.asarray(_a				)
												_lowerCAmelCase  						=			feat_extract(_a   ,       return_tensors="""np"""				).input_values
												_lowerCAmelCase  						=			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  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
												_lowerCAmelCase  						=			[floats_list((1, x)				)[0] for x in range(800   ,       1_400   ,       200				)]

												_lowerCAmelCase  						=			["""longest""", """max_length""", """do_not_pad"""]
												_lowerCAmelCase  						=			[None, 1_600, None]
												for max_length, padding in zip(_a   ,       _a				):
																		_lowerCAmelCase  						=			feat_extract(_a   ,       padding=_a   ,       max_length=_a   ,       return_tensors="""np"""				)
																		_lowerCAmelCase  						=			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][:1_000]				)
																		self.assertTrue(input_values[0][1_000:].sum() < 1e-6				)
																		self._check_zero_mean_unit_variance(input_values[2][:1_200]				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
												_lowerCAmelCase  						=			range(800   ,       1_400   ,       200				)
												_lowerCAmelCase  						=			[floats_list((1, x)				)[0] for x in lengths]

												_lowerCAmelCase  						=			["""longest""", """max_length""", """do_not_pad"""]
												_lowerCAmelCase  						=			[None, 1_600, None]

												for max_length, padding in zip(_a   ,       _a				):
																		_lowerCAmelCase  						=			feat_extract(_a   ,       max_length=_a   ,       padding=_a				)
																		_lowerCAmelCase  						=			processed.input_values

																		self._check_zero_mean_unit_variance(input_values[0][:800]				)
																		self._check_zero_mean_unit_variance(input_values[1][:1_000]				)
																		self._check_zero_mean_unit_variance(input_values[2][:1_200]				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
												_lowerCAmelCase  						=			[floats_list((1, x)				)[0] for x in range(800   ,       1_400   ,       200				)]
												_lowerCAmelCase  						=			feat_extract(
												    _a   ,       truncation=_a   ,       max_length=1_000   ,       padding="""max_length"""   ,       return_tensors="""np"""				)
												_lowerCAmelCase  						=			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  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
												_lowerCAmelCase  						=			[floats_list((1, x)				)[0] for x in range(800   ,       1_400   ,       200				)]
												_lowerCAmelCase  						=			feat_extract(
												    _a   ,       truncation=_a   ,       max_length=1_000   ,       padding="""longest"""   ,       return_tensors="""np"""				)
												_lowerCAmelCase  						=			processed.input_values

												self._check_zero_mean_unit_variance(input_values[0, :800]				)
												self._check_zero_mean_unit_variance(input_values[1, :1_000]				)
												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, 1_000)				)

												_lowerCAmelCase  						=			[floats_list((1, x)				)[0] for x in range(800   ,       1_400   ,       200				)]
												_lowerCAmelCase  						=			feat_extract(
												    _a   ,       truncation=_a   ,       max_length=2_000   ,       padding="""longest"""   ,       return_tensors="""np"""				)
												_lowerCAmelCase  						=			processed.input_values

												self._check_zero_mean_unit_variance(input_values[0, :800]				)
												self._check_zero_mean_unit_variance(input_values[1, :1_000]				)
												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, 1_200)				)




						@require_torch
						def  _lowercase     ( self				):

												"""simple docstring"""



												import torch

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

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




						@slow
						@require_torch
						def  _lowercase     ( self				):

												"""simple docstring"""



												for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
																		_lowerCAmelCase  						=			WavaVecaConfig.from_pretrained(_a				)
																		_lowerCAmelCase  						=			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"""				)


'''simple docstring'''



from typing import Callable, List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModel, CLIPTokenizer

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


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




class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						@register_to_config
						def __init__( self   ,       _lowercase   ,       _lowercase = None   ,       _lowercase = None				):

												"""simple docstring"""



												super().__init__()

												_lowerCAmelCase  						=			learnable

												if self.learnable:
																		assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
																		assert length is not None, "learnable=True requires `length` to be set"

																		_lowerCAmelCase  						=			torch.zeros(_lowercase   ,       _lowercase				)
												else:
																		_lowerCAmelCase  						=			None

												_lowerCAmelCase  						=			torch.nn.Parameter(_lowercase				)






class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						_lowercase		:				VQModel
						_lowercase		:				CLIPTextModel
						_lowercase		:				CLIPTokenizer
						_lowercase		:				TransformeraDModel
						_lowercase		:				LearnedClassifierFreeSamplingEmbeddings
						_lowercase		:				VQDiffusionScheduler




						def __init__( self   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       ):

												"""simple docstring"""



												super().__init__()

												self.register_modules(
												    vqvae=_lowercase   ,       transformer=_lowercase   ,       text_encoder=_lowercase   ,       tokenizer=_lowercase   ,       scheduler=_lowercase   ,       learned_classifier_free_sampling_embeddings=_lowercase   ,       )




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			len(_lowercase				) if isinstance(_lowercase   ,       _lowercase				) else 1

												# get prompt text embeddings
												_lowerCAmelCase  						=			self.tokenizer(
												    _lowercase   ,       padding="""max_length"""   ,       max_length=self.tokenizer.model_max_length   ,       return_tensors="""pt"""   ,       )
												_lowerCAmelCase  						=			text_inputs.input_ids

												if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																		_lowerCAmelCase  						=			self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]				)
																		logger.warning(
																		    """The following part of your input was truncated because CLIP can only handle sequences up to"""
																		    F' {self.tokenizer.model_max_length} tokens: {removed_text}'				)
																		_lowerCAmelCase  						=			text_input_ids[:, : self.tokenizer.model_max_length]
												_lowerCAmelCase  						=			self.text_encoder(text_input_ids.to(self.device				)				)[0]

												# NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
												# While CLIP does normalize the pooled output of the text transformer when combining
												# the image and text embeddings, CLIP does not directly normalize the last hidden state.
												#
												# CLIP normalizing the pooled output.
												# https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
												_lowerCAmelCase  						=			prompt_embeds / prompt_embeds.norm(dim=-1   ,       keepdim=_lowercase				)

												# duplicate text embeddings for each generation per prompt
												_lowerCAmelCase  						=			prompt_embeds.repeat_interleave(_lowercase   ,       dim=0				)

												if do_classifier_free_guidance:
																		if self.learned_classifier_free_sampling_embeddings.learnable:
																								_lowerCAmelCase  						=			self.learned_classifier_free_sampling_embeddings.embeddings
																								_lowerCAmelCase  						=			negative_prompt_embeds.unsqueeze(0				).repeat(_lowercase   ,       1   ,       1				)
																		else:
																								_lowerCAmelCase  						=			[""""""] * batch_size

																								_lowerCAmelCase  						=			text_input_ids.shape[-1]
																								_lowerCAmelCase  						=			self.tokenizer(
																								    _lowercase   ,       padding="""max_length"""   ,       max_length=_lowercase   ,       truncation=_lowercase   ,       return_tensors="""pt"""   ,       )
																								_lowerCAmelCase  						=			self.text_encoder(uncond_input.input_ids.to(self.device				)				)[0]
																								# See comment for normalizing text embeddings
																								_lowerCAmelCase  						=			negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1   ,       keepdim=_lowercase				)

																		# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
																		_lowerCAmelCase  						=			negative_prompt_embeds.shape[1]
																		_lowerCAmelCase  						=			negative_prompt_embeds.repeat(1   ,       _lowercase   ,       1				)
																		_lowerCAmelCase  						=			negative_prompt_embeds.view(batch_size * num_images_per_prompt   ,       _lowercase   ,       -1				)

																		# For classifier free guidance, we need to do two forward passes.
																		# Here we concatenate the unconditional and text embeddings into a single batch
																		# to avoid doing two forward passes
																		_lowerCAmelCase  						=			torch.cat([negative_prompt_embeds, prompt_embeds]				)

												return prompt_embeds




						@torch.no_grad()
						def __call__( self   ,       _lowercase   ,       _lowercase = 100   ,       _lowercase = 5.0   ,       _lowercase = 1.0   ,       _lowercase = 1   ,       _lowercase = None   ,       _lowercase = None   ,       _lowercase = "pil"   ,       _lowercase = True   ,       _lowercase = None   ,       _lowercase = 1   ,       ):

												"""simple docstring"""



												if isinstance(_lowercase   ,       _lowercase				):
																		_lowerCAmelCase  						=			1
												elif isinstance(_lowercase   ,       _lowercase				):
																		_lowerCAmelCase  						=			len(_lowercase				)
												else:
																		raise ValueError(F'`prompt` has to be of type `str` or `list` but is {type(_lowercase				)}'				)

												_lowerCAmelCase  						=			batch_size * num_images_per_prompt

												_lowerCAmelCase  						=			guidance_scale > 1.0

												_lowerCAmelCase  						=			self._encode_prompt(_lowercase   ,       _lowercase   ,       _lowercase				)

												if (callback_steps is None) or (
												    callback_steps is not None and (not isinstance(_lowercase   ,       _lowercase				) or callback_steps <= 0)
												):
																		raise ValueError(
																		    F'`callback_steps` has to be a positive integer but is {callback_steps} of type'
																		    F' {type(_lowercase				)}.'				)

												# get the initial completely masked latents unless the user supplied it

												_lowerCAmelCase  						=			(batch_size, self.transformer.num_latent_pixels)
												if latents is None:
																		_lowerCAmelCase  						=			self.transformer.num_vector_embeds - 1
																		_lowerCAmelCase  						=			torch.full(_lowercase   ,       _lowercase				).to(self.device				)
												else:
																		if latents.shape != latents_shape:
																								raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {latents_shape}'				)
																		if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
																								raise ValueError(
																								    """Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,"""
																								    F' {self.transformer.num_vector_embeds - 1} (inclusive).'				)
																		_lowerCAmelCase  						=			latents.to(self.device				)

												# set timesteps
												self.scheduler.set_timesteps(_lowercase   ,       device=self.device				)

												_lowerCAmelCase  						=			self.scheduler.timesteps.to(self.device				)

												_lowerCAmelCase  						=			latents

												for i, t in enumerate(self.progress_bar(_lowercase				)				):
																		# expand the sample if we are doing classifier free guidance
																		_lowerCAmelCase  						=			torch.cat([sample] * 2				) if do_classifier_free_guidance else sample

																		# predict the un-noised image
																		# model_output == `log_p_x_0`
																		_lowerCAmelCase  						=			self.transformer(_lowercase   ,       encoder_hidden_states=_lowercase   ,       timestep=_lowercase				).sample

																		if do_classifier_free_guidance:
																								_lowerCAmelCase						,   _lowerCAmelCase  						=			model_output.chunk(2				)
																								_lowerCAmelCase  						=			model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
																								model_output -= torch.logsumexp(_lowercase   ,       dim=1   ,       keepdim=_lowercase				)

																		_lowerCAmelCase  						=			self.truncate(_lowercase   ,       _lowercase				)

																		# remove `log(0)`'s (`-inf`s)
																		_lowerCAmelCase  						=			model_output.clamp(-70				)

																		# compute the previous noisy sample x_t -> x_t-1
																		_lowerCAmelCase  						=			self.scheduler.step(_lowercase   ,       timestep=_lowercase   ,       sample=_lowercase   ,       generator=_lowercase				).prev_sample

																		# call the callback, if provided
																		if callback is not None and i % callback_steps == 0:
																								callback(_lowercase   ,       _lowercase   ,       _lowercase				)

												_lowerCAmelCase  						=			self.vqvae.config.vq_embed_dim
												_lowerCAmelCase  						=			(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
												_lowerCAmelCase  						=			self.vqvae.quantize.get_codebook_entry(_lowercase   ,       shape=_lowercase				)
												_lowerCAmelCase  						=			self.vqvae.decode(_lowercase   ,       force_not_quantize=_lowercase				).sample

												_lowerCAmelCase  						=			(image / 2 + 0.5).clamp(0   ,       1				)
												_lowerCAmelCase  						=			image.cpu().permute(0   ,       2   ,       3   ,       1				).numpy()

												if output_type == "pil":
																		_lowerCAmelCase  						=			self.numpy_to_pil(_lowercase				)

												if not return_dict:
																		return (image,)

												return ImagePipelineOutput(images=_lowercase				)




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase						,   _lowerCAmelCase  						=			torch.sort(_lowercase   ,       1   ,       descending=_lowercase				)
												_lowerCAmelCase  						=			torch.exp(_lowercase				)
												_lowerCAmelCase  						=			sorted_p_x_0.cumsum(dim=1				) < truncation_rate

												# Ensure that at least the largest probability is not zeroed out
												_lowerCAmelCase  						=			torch.full_like(keep_mask[:, 0:1, :]   ,       _lowercase				)
												_lowerCAmelCase  						=			torch.cat((all_true, keep_mask)   ,       dim=1				)
												_lowerCAmelCase  						=			keep_mask[:, :-1, :]

												_lowerCAmelCase  						=			keep_mask.gather(1   ,       indices.argsort(1				)				)

												_lowerCAmelCase  						=			log_p_x_0.clone()

												_lowerCAmelCase  						=			-torch.inf  # -inf = log(0)

												return rv
