



from jiwer import compute_measures

import datasets


lowercase_													=      '''\
@inproceedings{inproceedings,
    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
    year = {2004},
    month = {01},
    pages = {},
    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}
}
'''

lowercase_													=      '''\
Word error rate (WER) is a common metric of the performance of an automatic speech recognition system.

The general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.

This problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.

Word error rate can then be computed as:

WER = (S + D + I) / N = (S + D + I) / (S + D + C)

where

S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct words,
N is the number of words in the reference (N=S+D+C).

This value indicates the average number of errors per reference word. The lower the value, the better the
performance of the ASR system with a WER of 0 being a perfect score.
'''

lowercase_													=      '''
Compute WER score of transcribed segments against references.

Args:
    references: List of references for each speech input.
    predictions: List of transcriptions to score.
    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.

Returns:
    (float): the word error rate

Examples:

    >>> predictions = ["this is the prediction", "there is an other sample"]
    >>> references = ["this is the reference", "there is another one"]
    >>> wer = datasets.load_metric("wer")
    >>> wer_score = wer.compute(predictions=predictions, references=references)
    >>> print(wer_score)
    0.5
'''






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,     _KWARGS_DESCRIPTION					)
class     __a			(  datasets.Metric					):



	def 		UpperCamelCase				(    self				:     str)->				List[Any]:
						return datasets.MetricInfo(
						    description=_DESCRIPTION					,						citation=_CITATION					,						inputs_description=_KWARGS_DESCRIPTION					,						features=datasets.Features(
						        {
						            """predictions""": datasets.Value("""string"""					,						id="""sequence"""),
						            """references""": datasets.Value("""string"""					,						id="""sequence"""),
						        })					,						codebase_urls=["""https://github.com/jitsi/jiwer/"""]					,						reference_urls=[
						        """https://en.wikipedia.org/wiki/Word_error_rate""",
						    ]					,						)



	def 		UpperCamelCase				(    self				:     str					,						snake_case_				:     List[Any]=None					,						snake_case_				:     List[Any]=None					,						snake_case_				:     Optional[int]=False)->				Tuple:
						if concatenate_texts:
											return compute_measures(snake_case_					,						snake_case_)["wer"]
						else:
											__lowerCAmelCase						 =0
											__lowerCAmelCase						 =0
											for prediction, reference in zip(snake_case_					,						snake_case_):
																__lowerCAmelCase						 =compute_measures(snake_case_					,						snake_case_)
																incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
																total += measures["substitutions"] + measures["deletions"] + measures["hits"]
											return incorrect / total









import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class     __a			(  SCREAMING_SNAKE_CASE						,     unittest.TestCase					):
	SCREAMING_SNAKE_CASE  	=							UnCLIPImageVariationPipeline
	SCREAMING_SNAKE_CASE  	=							IMAGE_VARIATION_PARAMS - {"height", "width", "guidance_scale"}
	SCREAMING_SNAKE_CASE  	=							IMAGE_VARIATION_BATCH_PARAMS

	SCREAMING_SNAKE_CASE  	=							[
	    "generator",
	    "return_dict",
	    "decoder_num_inference_steps",
	    "super_res_num_inference_steps",
	]
	SCREAMING_SNAKE_CASE  	=							False



	@property
	def 		UpperCamelCase				(    self				:     int)->				Union[str, Any]:
						return 32



	@property
	def 		UpperCamelCase				(    self				:     List[Any])->				List[str]:
						return 32



	@property
	def 		UpperCamelCase				(    self				:     Dict)->				Dict:
						return self.time_input_dim



	@property
	def 		UpperCamelCase				(    self				:     Dict)->				Optional[int]:
						return self.time_input_dim * 4



	@property
	def 		UpperCamelCase				(    self				:     str)->				str:
						return 1_00



	@property
	def 		UpperCamelCase				(    self				:     Optional[Any])->				Union[str, Any]:
						__lowerCAmelCase						 =CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""")
						return tokenizer



	@property
	def 		UpperCamelCase				(    self				:     List[str])->				List[Any]:
						torch.manual_seed(0)
						__lowerCAmelCase						 =CLIPTextConfig(
						    bos_token_id=0					,						eos_token_id=2					,						hidden_size=self.text_embedder_hidden_size					,						projection_dim=self.text_embedder_hidden_size					,						intermediate_size=37					,						layer_norm_eps=1e-05					,						num_attention_heads=4					,						num_hidden_layers=5					,						pad_token_id=1					,						vocab_size=10_00					,						)
						return CLIPTextModelWithProjection(snake_case_)



	@property
	def 		UpperCamelCase				(    self				:     List[str])->				Dict:
						torch.manual_seed(0)
						__lowerCAmelCase						 =CLIPVisionConfig(
						    hidden_size=self.text_embedder_hidden_size					,						projection_dim=self.text_embedder_hidden_size					,						num_hidden_layers=5					,						num_attention_heads=4					,						image_size=32					,						intermediate_size=37					,						patch_size=1					,						)
						return CLIPVisionModelWithProjection(snake_case_)



	@property
	def 		UpperCamelCase				(    self				:     str)->				Union[str, Any]:
						torch.manual_seed(0)

						__lowerCAmelCase						 ={
						    """clip_embeddings_dim""": self.text_embedder_hidden_size,
						    """time_embed_dim""": self.time_embed_dim,
						    """cross_attention_dim""": self.cross_attention_dim,
						}

						__lowerCAmelCase						 =UnCLIPTextProjModel(**snake_case_)
						return model



	@property
	def 		UpperCamelCase				(    self				:     Optional[int])->				List[str]:
						torch.manual_seed(0)

						__lowerCAmelCase						 ={
						    """sample_size""": 32,
						    # RGB in channels
						    """in_channels""": 3,
						    # Out channels is double in channels because predicts mean and variance
						    """out_channels""": 6,
						    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
						    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
						    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
						    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
						    """layers_per_block""": 1,
						    """cross_attention_dim""": self.cross_attention_dim,
						    """attention_head_dim""": 4,
						    """resnet_time_scale_shift""": """scale_shift""",
						    """class_embed_type""": """identity""",
						}

						__lowerCAmelCase						 =UNetaDConditionModel(**snake_case_)
						return model



	@property
	def 		UpperCamelCase				(    self				:     int)->				str:
						return {
						    "sample_size": 64,
						    "layers_per_block": 1,
						    "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
						    "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
						    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
						    "in_channels": 6,
						    "out_channels": 3,
						}



	@property
	def 		UpperCamelCase				(    self				:     Optional[int])->				List[Any]:
						torch.manual_seed(0)

						__lowerCAmelCase						 =UNetaDModel(**self.dummy_super_res_kwargs)
						return model



	@property
	def 		UpperCamelCase				(    self				:     Tuple)->				int:
						# seeded differently to get different unet than `self.dummy_super_res_first`
						torch.manual_seed(1)

						__lowerCAmelCase						 =UNetaDModel(**self.dummy_super_res_kwargs)
						return model



	def 		UpperCamelCase				(    self				:     List[Any])->				Tuple:
						__lowerCAmelCase						 =self.dummy_decoder
						__lowerCAmelCase						 =self.dummy_text_proj
						__lowerCAmelCase						 =self.dummy_text_encoder
						__lowerCAmelCase						 =self.dummy_tokenizer
						__lowerCAmelCase						 =self.dummy_super_res_first
						__lowerCAmelCase						 =self.dummy_super_res_last

						__lowerCAmelCase						 =UnCLIPScheduler(
						    variance_type="""learned_range"""					,						prediction_type="""epsilon"""					,						num_train_timesteps=10_00					,						)

						__lowerCAmelCase						 =UnCLIPScheduler(
						    variance_type="""fixed_small_log"""					,						prediction_type="""epsilon"""					,						num_train_timesteps=10_00					,						)

						__lowerCAmelCase						 =CLIPImageProcessor(crop_size=32					,						size=32)

						__lowerCAmelCase						 =self.dummy_image_encoder

						return {
						    "decoder": decoder,
						    "text_encoder": text_encoder,
						    "tokenizer": tokenizer,
						    "text_proj": text_proj,
						    "feature_extractor": feature_extractor,
						    "image_encoder": image_encoder,
						    "super_res_first": super_res_first,
						    "super_res_last": super_res_last,
						    "decoder_scheduler": decoder_scheduler,
						    "super_res_scheduler": super_res_scheduler,
						}



	def 		UpperCamelCase				(    self				:     Optional[int]					,						snake_case_				:     Optional[int]					,						snake_case_				:     Optional[int]=0					,						snake_case_				:     List[str]=True)->				List[str]:
						__lowerCAmelCase						 =floats_tensor((1, 3, 32, 32)					,						rng=random.Random(snake_case_)).to(snake_case_)
						if str(snake_case_).startswith("""mps"""):
											__lowerCAmelCase						 =torch.manual_seed(snake_case_)
						else:
											__lowerCAmelCase						 =torch.Generator(device=snake_case_).manual_seed(snake_case_)

						if pil_image:
											__lowerCAmelCase						 =input_image * 0.5 + 0.5
											__lowerCAmelCase						 =input_image.clamp(0					,						1)
											__lowerCAmelCase						 =input_image.cpu().permute(0					,						2					,						3					,						1).float().numpy()
											__lowerCAmelCase						 =DiffusionPipeline.numpy_to_pil(snake_case_)[0]

						return {
						    "image": input_image,
						    "generator": generator,
						    "decoder_num_inference_steps": 2,
						    "super_res_num_inference_steps": 2,
						    "output_type": "np",
						}



	def 		UpperCamelCase				(    self				:     Optional[Any])->				str:
						__lowerCAmelCase						 ="""cpu"""

						__lowerCAmelCase						 =self.get_dummy_components()

						__lowerCAmelCase						 =self.pipeline_class(**snake_case_)
						__lowerCAmelCase						 =pipe.to(snake_case_)

						pipe.set_progress_bar_config(disable=snake_case_)

						__lowerCAmelCase						 =self.get_dummy_inputs(snake_case_					,						pil_image=snake_case_)

						__lowerCAmelCase						 =pipe(**snake_case_)
						__lowerCAmelCase						 =output.images

						__lowerCAmelCase						 =self.get_dummy_inputs(snake_case_					,						pil_image=snake_case_)

						__lowerCAmelCase						 =pipe(
						    **snake_case_					,						return_dict=snake_case_					,						)[0]

						__lowerCAmelCase						 =image[0, -3:, -3:, -1]
						__lowerCAmelCase						 =image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)

						__lowerCAmelCase						 =np.array(
						    [
						        0.9_9_9_7,
						        0.0_0_0_2,
						        0.9_9_9_7,
						        0.9_9_9_7,
						        0.9_9_6_9,
						        0.0_0_2_3,
						        0.9_9_9_7,
						        0.9_9_6_9,
						        0.9_9_7_0,
						    ])

						assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
						assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2



	def 		UpperCamelCase				(    self				:     Optional[Any])->				List[Any]:
						__lowerCAmelCase						 ="""cpu"""

						__lowerCAmelCase						 =self.get_dummy_components()

						__lowerCAmelCase						 =self.pipeline_class(**snake_case_)
						__lowerCAmelCase						 =pipe.to(snake_case_)

						pipe.set_progress_bar_config(disable=snake_case_)

						__lowerCAmelCase						 =self.get_dummy_inputs(snake_case_					,						pil_image=snake_case_)

						__lowerCAmelCase						 =pipe(**snake_case_)
						__lowerCAmelCase						 =output.images

						__lowerCAmelCase						 =self.get_dummy_inputs(snake_case_					,						pil_image=snake_case_)

						__lowerCAmelCase						 =pipe(
						    **snake_case_					,						return_dict=snake_case_					,						)[0]

						__lowerCAmelCase						 =image[0, -3:, -3:, -1]
						__lowerCAmelCase						 =image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)

						__lowerCAmelCase						 =np.array([0.9_9_9_7, 0.0_0_0_3, 0.9_9_9_7, 0.9_9_9_7, 0.9_9_7_0, 0.0_0_2_4, 0.9_9_9_7, 0.9_9_7_1, 0.9_9_7_1])

						assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
						assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2



	def 		UpperCamelCase				(    self				:     int)->				Optional[Any]:
						__lowerCAmelCase						 ="""cpu"""

						__lowerCAmelCase						 =self.get_dummy_components()

						__lowerCAmelCase						 =self.pipeline_class(**snake_case_)
						__lowerCAmelCase						 =pipe.to(snake_case_)

						pipe.set_progress_bar_config(disable=snake_case_)

						__lowerCAmelCase						 =self.get_dummy_inputs(snake_case_					,						pil_image=snake_case_)
						__lowerCAmelCase						 =[
						    pipeline_inputs["""image"""],
						    pipeline_inputs["""image"""],
						]

						__lowerCAmelCase						 =pipe(**snake_case_)
						__lowerCAmelCase						 =output.images

						__lowerCAmelCase						 =self.get_dummy_inputs(snake_case_					,						pil_image=snake_case_)
						__lowerCAmelCase						 =[
						    tuple_pipeline_inputs["""image"""],
						    tuple_pipeline_inputs["""image"""],
						]

						__lowerCAmelCase						 =pipe(
						    **snake_case_					,						return_dict=snake_case_					,						)[0]

						__lowerCAmelCase						 =image[0, -3:, -3:, -1]
						__lowerCAmelCase						 =image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (2, 64, 64, 3)

						__lowerCAmelCase						 =np.array(
						    [
						        0.9_9_9_7,
						        0.9_9_8_9,
						        0.0_0_0_8,
						        0.0_0_2_1,
						        0.9_9_6_0,
						        0.0_0_1_8,
						        0.0_0_1_4,
						        0.0_0_0_2,
						        0.9_9_3_3,
						    ])

						assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
						assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2



	def 		UpperCamelCase				(    self				:     Tuple)->				Optional[int]:
						__lowerCAmelCase						 =torch.device("""cpu""")






						class     __a			:
							SCREAMING_SNAKE_CASE  	=							1


						__lowerCAmelCase						 =self.get_dummy_components()

						__lowerCAmelCase						 =self.pipeline_class(**snake_case_)
						__lowerCAmelCase						 =pipe.to(snake_case_)

						pipe.set_progress_bar_config(disable=snake_case_)

						__lowerCAmelCase						 =torch.Generator(device=snake_case_).manual_seed(0)
						__lowerCAmelCase						 =pipe.decoder.dtype
						__lowerCAmelCase						 =1

						__lowerCAmelCase						 =(
						    batch_size,
						    pipe.decoder.config.in_channels,
						    pipe.decoder.config.sample_size,
						    pipe.decoder.config.sample_size,
						)
						__lowerCAmelCase						 =pipe.prepare_latents(
						    snake_case_					,						dtype=snake_case_					,						device=snake_case_					,						generator=snake_case_					,						latents=snake_case_					,						scheduler=DummyScheduler())

						__lowerCAmelCase						 =(
						    batch_size,
						    pipe.super_res_first.config.in_channels // 2,
						    pipe.super_res_first.config.sample_size,
						    pipe.super_res_first.config.sample_size,
						)
						__lowerCAmelCase						 =pipe.prepare_latents(
						    snake_case_					,						dtype=snake_case_					,						device=snake_case_					,						generator=snake_case_					,						latents=snake_case_					,						scheduler=DummyScheduler())

						__lowerCAmelCase						 =self.get_dummy_inputs(snake_case_					,						pil_image=snake_case_)

						__lowerCAmelCase						 =pipe(
						    **snake_case_					,						decoder_latents=snake_case_					,						super_res_latents=snake_case_).images

						__lowerCAmelCase						 =self.get_dummy_inputs(snake_case_					,						pil_image=snake_case_)
						# Don't pass image, instead pass embedding
						__lowerCAmelCase						 =pipeline_inputs.pop("""image""")
						__lowerCAmelCase						 =pipe.image_encoder(snake_case_).image_embeds

						__lowerCAmelCase						 =pipe(
						    **snake_case_					,						decoder_latents=snake_case_					,						super_res_latents=snake_case_					,						image_embeddings=snake_case_					,						).images

						# make sure passing text embeddings manually is identical
						assert np.abs(img_out_a - img_out_a).max() < 1e-4



	@skip_mps
	def 		UpperCamelCase				(    self				:     Optional[int])->				Optional[int]:
						__lowerCAmelCase						 =torch_device == """cpu"""

						# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
						__lowerCAmelCase						 =1e-2

						self._test_attention_slicing_forward_pass(
						    test_max_difference=snake_case_					,						expected_max_diff=snake_case_)



	@skip_mps
	def 		UpperCamelCase				(    self				:     int)->				int:
						__lowerCAmelCase						 =torch_device == """cpu"""
						__lowerCAmelCase						 =True
						__lowerCAmelCase						 =[
						    """decoder_num_inference_steps""",
						    """super_res_num_inference_steps""",
						]

						self._test_inference_batch_single_identical(
						    test_max_difference=snake_case_					,						relax_max_difference=snake_case_					,						additional_params_copy_to_batched_inputs=snake_case_					,						)



	def 		UpperCamelCase				(    self				:     List[Any])->				int:
						__lowerCAmelCase						 =[
						    """decoder_num_inference_steps""",
						    """super_res_num_inference_steps""",
						]

						if torch_device == "mps":
											# TODO: MPS errors with larger batch sizes
											__lowerCAmelCase						 =[2, 3]
											self._test_inference_batch_consistent(
											    batch_sizes=snake_case_					,						additional_params_copy_to_batched_inputs=snake_case_					,						)
						else:
											self._test_inference_batch_consistent(
											    additional_params_copy_to_batched_inputs=snake_case_)



	@skip_mps
	def 		UpperCamelCase				(    self				:     Optional[Any])->				Tuple:
						return super().test_dict_tuple_outputs_equivalent()



	@skip_mps
	def 		UpperCamelCase				(    self				:     Optional[int])->				Tuple:
						return super().test_save_load_local()



	@skip_mps
	def 		UpperCamelCase				(    self				:     Union[str, Any])->				Any:
						return super().test_save_load_optional_components()






@slow
@require_torch_gpu
class     __a			(  unittest.TestCase					):



	def 		UpperCamelCase				(    self				:     Any)->				Optional[Any]:
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()



	def 		UpperCamelCase				(    self				:     Optional[Any])->				List[Any]:
						__lowerCAmelCase						 =load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png""")
						__lowerCAmelCase						 =load_numpy(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/unclip/karlo_v1_alpha_cat_variation_fp16.npy""")

						__lowerCAmelCase						 =UnCLIPImageVariationPipeline.from_pretrained(
						    """kakaobrain/karlo-v1-alpha-image-variations"""					,						torch_dtype=torch.floataa)
						__lowerCAmelCase						 =pipeline.to(snake_case_)
						pipeline.set_progress_bar_config(disable=snake_case_)

						__lowerCAmelCase						 =torch.Generator(device="""cpu""").manual_seed(0)
						__lowerCAmelCase						 =pipeline(
						    snake_case_					,						generator=snake_case_					,						output_type="""np"""					,						)

						__lowerCAmelCase						 =output.images[0]

						assert image.shape == (2_56, 2_56, 3)

						assert_mean_pixel_difference(snake_case_					,						snake_case_					,						15)




