



"""simple docstring"""






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			:			List[str]										=						random.Random()






def 					snake_case__       (		__lowerCamelCase    :   Any							,		__lowerCamelCase    :   Dict=1.0							,		__lowerCamelCase    :   str=None							,		__lowerCamelCase    :   Dict=None     ):
			"""simple docstring"""
			if rng is None:
						lowerCamelCase__		:		str		     =global_rng

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

			return values

class 	__SCREAMING_SNAKE_CASE     (				unittest.TestCase       ):


					'''simple docstring'''





					def __init__(	self							:  str,  lowerCamelCase							:  Dict,  lowerCamelCase							:  Any=7,  lowerCamelCase							:  Optional[int]=400,  lowerCamelCase							:  Tuple=2000,  lowerCamelCase							:  Dict=1,  lowerCamelCase							:  Dict=0.0,  lowerCamelCase							:  List[str]=1_6000,  lowerCamelCase							:  int=True,  lowerCamelCase							:  str=True,  )-> List[str]:
								lowerCamelCase__		:		Dict		     =parent
								lowerCamelCase__		:		str		     =batch_size
								lowerCamelCase__		:		List[str]		     =min_seq_length
								lowerCamelCase__		:		Optional[Any]		     =max_seq_length
								lowerCamelCase__		:		Union[str, Any]		     =(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
								lowerCamelCase__		:		Optional[int]		     =feature_size
								lowerCamelCase__		:		Optional[Any]		     =padding_value
								lowerCamelCase__		:		List[Any]		     =sampling_rate
								lowerCamelCase__		:		List[str]		     =return_attention_mask
								lowerCamelCase__		:		Any		     =do_normalize



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

								if equal_length:
											lowerCamelCase__		:		int		     =floats_list((self.batch_size, self.max_seq_length)					)
								else:
											# make sure that inputs increase in size
											lowerCamelCase__		:		Dict		     =[
											    _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(lowerCamelCase					) for x in speech_inputs]

								return speech_inputs





class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_		,       unittest.TestCase       ):


					'''simple docstring'''





					_a             =							WavaVecaFeatureExtractor



					def 		snake_case	(	self							:  Optional[int]					)-> int:
								lowerCamelCase__		:		Union[str, Any]		     =WavaVecaFeatureExtractionTester(self					)



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

								# Test not batched input
								lowerCamelCase__		:		Union[str, Any]		     =feat_extract(speech_inputs[0],  return_tensors='''np'''					).input_values
								lowerCamelCase__		:		List[Any]		     =feat_extract(np_speech_inputs[0],  return_tensors='''np'''					).input_values
								self.assertTrue(np.allclose(lowerCamelCase,  lowerCamelCase,  atol=1E-3					)					)

								# Test batched
								lowerCamelCase__		:		List[Any]		     =feat_extract(lowerCamelCase,  return_tensors='''np'''					).input_values
								lowerCamelCase__		:		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					)					)

								# Test 2-D numpy arrays are batched.
								lowerCamelCase__		:		Optional[Any]		     =[floats_list((1, x)					)[0] for x in (800, 800, 800)]
								lowerCamelCase__		:		Optional[Any]		     =np.asarray(lowerCamelCase					)
								lowerCamelCase__		:		Union[str, Any]		     =feat_extract(lowerCamelCase,  return_tensors='''np'''					).input_values
								lowerCamelCase__		:		Union[str, Any]		     =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							:  Optional[int]					)-> Tuple:
								lowerCamelCase__		:		Union[str, Any]		     =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
								lowerCamelCase__		:		List[Any]		     =[floats_list((1, x)					)[0] for x in range(800,  1400,  200					)]

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

								lowerCamelCase__		:		Optional[int]		     =['''longest''', '''max_length''', '''do_not_pad''']
								lowerCamelCase__		:		List[Any]		     =[None, 1600, None]

								for max_length, padding in zip(lowerCamelCase,  lowerCamelCase					):
											lowerCamelCase__		:		Union[str, Any]		     =feat_extract(lowerCamelCase,  max_length=lowerCamelCase,  padding=lowerCamelCase					)
											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][:1200]					)



					def 		snake_case	(	self							:  str					)-> Tuple:
								lowerCamelCase__		:		List[Any]		     =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
								lowerCamelCase__		:		Union[str, Any]		     =[floats_list((1, x)					)[0] for x in range(800,  1400,  200					)]
								lowerCamelCase__		:		str		     =feat_extract(
								    lowerCamelCase,  truncation=lowerCamelCase,  max_length=1000,  padding='''max_length''',  return_tensors='''np'''					)
								lowerCamelCase__		:		str		     =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							:  List[Any]					)-> List[str]:
								lowerCamelCase__		:		Tuple		     =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
								lowerCamelCase__		:		Tuple		     =[floats_list((1, x)					)[0] for x in range(800,  1400,  200					)]
								lowerCamelCase__		:		Any		     =feat_extract(
								    lowerCamelCase,  truncation=lowerCamelCase,  max_length=1000,  padding='''longest''',  return_tensors='''np'''					)
								lowerCamelCase__		:		Union[str, 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 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__		:		Any		     =feat_extract(
								    lowerCamelCase,  truncation=lowerCamelCase,  max_length=2000,  padding='''longest''',  return_tensors='''np'''					)
								lowerCamelCase__		:		List[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]					)

								# make sure that if max_length > longest -> then pad to longest
								self.assertTrue(input_values.shape == (3, 1200)					)



					@require_torch
					def 		snake_case	(	self							:  Dict					)-> str:
								import torch

								lowerCamelCase__		:		List[Any]		     =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
								lowerCamelCase__		:		Optional[int]		     =np.random.rand(100					).astype(np.floataa					)
								lowerCamelCase__		:		Any		     =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__		:		Tuple		     =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							:  int					)-> List[Any]:
								# 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:
											lowerCamelCase__		:		Optional[int]		     =WavaVecaConfig.from_pretrained(lowerCamelCase					)
											lowerCamelCase__		:		List[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'''					)









"""simple docstring"""






import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
		import jax
		import jax.numpy as jnp
		from flax.jax_utils import replicate
		from flax.training.common_utils import shard

@slow
@require_flax
class 	__SCREAMING_SNAKE_CASE     (				unittest.TestCase       ):


					'''simple docstring'''





					def 		snake_case	(	self							:  Union[str, Any]					)-> Dict:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()



					def 		snake_case	(	self							:  str					)-> Any:
								lowerCamelCase__     ,       lowerCamelCase__		:		Optional[Any]		     =FlaxControlNetModel.from_pretrained(
								    '''lllyasviel/sd-controlnet-canny''',  from_pt=lowerCamelCase,  dtype=jnp.bfloataa					)
								lowerCamelCase__     ,       lowerCamelCase__		:		Optional[int]		     =FlaxStableDiffusionControlNetPipeline.from_pretrained(
								    '''runwayml/stable-diffusion-v1-5''',  controlnet=lowerCamelCase,  from_pt=lowerCamelCase,  dtype=jnp.bfloataa					)
								lowerCamelCase__		:		Optional[int]		     =controlnet_params

								lowerCamelCase__		:		Dict		     ='''bird'''
								lowerCamelCase__		:		List[str]		     =jax.device_count()
								lowerCamelCase__		:		Optional[Any]		     =pipe.prepare_text_inputs([prompts] * num_samples					)

								lowerCamelCase__		:		Dict		     =load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'''					)
								lowerCamelCase__		:		List[Any]		     =pipe.prepare_image_inputs([canny_image] * num_samples					)

								lowerCamelCase__		:		Optional[int]		     =jax.random.PRNGKey(0					)
								lowerCamelCase__		:		Dict		     =jax.random.split(lowerCamelCase,  jax.device_count()					)

								lowerCamelCase__		:		Tuple		     =replicate(lowerCamelCase					)
								lowerCamelCase__		:		Tuple		     =shard(lowerCamelCase					)
								lowerCamelCase__		:		Optional[int]		     =shard(lowerCamelCase					)

								lowerCamelCase__		:		Tuple		     =pipe(
								    prompt_ids=lowerCamelCase,  image=lowerCamelCase,  params=lowerCamelCase,  prng_seed=lowerCamelCase,  num_inference_steps=50,  jit=lowerCamelCase,  ).images
								assert images.shape == (jax.device_count(), 1, 768, 512, 3)

								lowerCamelCase__		:		Dict		     =images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]					)
								lowerCamelCase__		:		Any		     =images[0, 253:256, 253:256, -1]

								lowerCamelCase__		:		Optional[Any]		     =jnp.asarray(jax.device_get(image_slice.flatten()					)					)
								lowerCamelCase__		:		Dict		     =jnp.array(
								    [0.167_969, 0.116_699, 0.081_543, 0.154_297, 0.132_812, 0.108_887, 0.169_922, 0.169_922, 0.205_078]					)
								print(F'''output_slice: {output_slice}'''					)
								assert jnp.abs(output_slice - expected_slice					).max() < 1E-2




					def 		snake_case	(	self							:  Optional[int]					)-> Optional[int]:
								lowerCamelCase__     ,       lowerCamelCase__		:		Dict		     =FlaxControlNetModel.from_pretrained(
								    '''lllyasviel/sd-controlnet-openpose''',  from_pt=lowerCamelCase,  dtype=jnp.bfloataa					)
								lowerCamelCase__     ,       lowerCamelCase__		:		List[Any]		     =FlaxStableDiffusionControlNetPipeline.from_pretrained(
								    '''runwayml/stable-diffusion-v1-5''',  controlnet=lowerCamelCase,  from_pt=lowerCamelCase,  dtype=jnp.bfloataa					)
								lowerCamelCase__		:		Optional[Any]		     =controlnet_params

								lowerCamelCase__		:		int		     ='''Chef in the kitchen'''
								lowerCamelCase__		:		Optional[Any]		     =jax.device_count()
								lowerCamelCase__		:		Any		     =pipe.prepare_text_inputs([prompts] * num_samples					)

								lowerCamelCase__		:		Any		     =load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png'''					)
								lowerCamelCase__		:		List[Any]		     =pipe.prepare_image_inputs([pose_image] * num_samples					)

								lowerCamelCase__		:		Tuple		     =jax.random.PRNGKey(0					)
								lowerCamelCase__		:		Optional[Any]		     =jax.random.split(lowerCamelCase,  jax.device_count()					)

								lowerCamelCase__		:		int		     =replicate(lowerCamelCase					)
								lowerCamelCase__		:		List[Any]		     =shard(lowerCamelCase					)
								lowerCamelCase__		:		int		     =shard(lowerCamelCase					)

								lowerCamelCase__		:		Tuple		     =pipe(
								    prompt_ids=lowerCamelCase,  image=lowerCamelCase,  params=lowerCamelCase,  prng_seed=lowerCamelCase,  num_inference_steps=50,  jit=lowerCamelCase,  ).images
								assert images.shape == (jax.device_count(), 1, 768, 512, 3)

								lowerCamelCase__		:		Dict		     =images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]					)
								lowerCamelCase__		:		List[str]		     =images[0, 253:256, 253:256, -1]

								lowerCamelCase__		:		int		     =jnp.asarray(jax.device_get(image_slice.flatten()					)					)
								lowerCamelCase__		:		Any		     =jnp.array(
								    [[0.271_484, 0.261_719, 0.275_391, 0.277_344, 0.279_297, 0.291_016, 0.294_922, 0.302_734, 0.302_734]]					)
								print(F'''output_slice: {output_slice}'''					)
								assert jnp.abs(output_slice - expected_slice					).max() < 1E-2




