

import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()


class SCREAMING_SNAKE_CASE	(		__snake_case				,    __snake_case				,    __snake_case				,    unittest.TestCase				):






			"""simple docstring"""




			__A    	=	StableUnCLIPPipeline
			__A    	=	TEXT_TO_IMAGE_PARAMS
			__A    	=	TEXT_TO_IMAGE_BATCH_PARAMS
			__A    	=	TEXT_TO_IMAGE_IMAGE_PARAMS
			__A    	=	TEXT_TO_IMAGE_IMAGE_PARAMS

			# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
			__A    	=	False


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			32
				snake_case_   		=			embedder_hidden_size

				# prior components

				torch.manual_seed(0				)
				snake_case_   		=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				)

				torch.manual_seed(0				)
				snake_case_   		=			CLIPTextModelWithProjection(
				    CLIPTextConfig(
				        bos_token_id=0			,					eos_token_id=2			,					hidden_size=__UpperCamelCase			,					projection_dim=__UpperCamelCase			,					intermediate_size=37			,					layer_norm_eps=1E-05			,					num_attention_heads=4			,					num_hidden_layers=5			,					pad_token_id=1			,					vocab_size=10_00			,					)				)

				torch.manual_seed(0				)
				snake_case_   		=			PriorTransformer(
				    num_attention_heads=2			,					attention_head_dim=12			,					embedding_dim=__UpperCamelCase			,					num_layers=1			,					)

				torch.manual_seed(0				)
				snake_case_   		=			DDPMScheduler(
				    variance_type='fixed_small_log'			,					prediction_type='sample'			,					num_train_timesteps=10_00			,					clip_sample=__UpperCamelCase			,					clip_sample_range=5.0			,					beta_schedule='squaredcos_cap_v2'			,					)

				# regular denoising components

				torch.manual_seed(0				)
				snake_case_   		=			StableUnCLIPImageNormalizer(embedding_dim=__UpperCamelCase				)
				snake_case_   		=			DDPMScheduler(beta_schedule='squaredcos_cap_v2'				)

				torch.manual_seed(0				)
				snake_case_   		=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				)

				torch.manual_seed(0				)
				snake_case_   		=			CLIPTextModel(
				    CLIPTextConfig(
				        bos_token_id=0			,					eos_token_id=2			,					hidden_size=__UpperCamelCase			,					projection_dim=32			,					intermediate_size=37			,					layer_norm_eps=1E-05			,					num_attention_heads=4			,					num_hidden_layers=5			,					pad_token_id=1			,					vocab_size=10_00			,					)				)

				torch.manual_seed(0				)
				snake_case_   		=			UNetaDConditionModel(
				    sample_size=32			,					in_channels=4			,					out_channels=4			,					down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D')			,					up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D')			,					block_out_channels=(32, 64)			,					attention_head_dim=(2, 4)			,					class_embed_type='projection'			,					projection_class_embeddings_input_dim=embedder_projection_dim * 2			,					cross_attention_dim=__UpperCamelCase			,					layers_per_block=1			,					upcast_attention=__UpperCamelCase			,					use_linear_projection=__UpperCamelCase			,					)

				torch.manual_seed(0				)
				snake_case_   		=			DDIMScheduler(
				    beta_schedule='scaled_linear'			,					beta_start=0.0_0085			,					beta_end=0.012			,					prediction_type='v_prediction'			,					set_alpha_to_one=__UpperCamelCase			,					steps_offset=1			,					)

				torch.manual_seed(0				)
				snake_case_   		=			AutoencoderKL()

				snake_case_   		=			{
				    # prior components
				    'prior_tokenizer': prior_tokenizer,
				    'prior_text_encoder': prior_text_encoder,
				    'prior': prior,
				    'prior_scheduler': prior_scheduler,
				    # image noising components
				    'image_normalizer': image_normalizer,
				    'image_noising_scheduler': image_noising_scheduler,
				    # regular denoising components
				    'tokenizer': tokenizer,
				    'text_encoder': text_encoder,
				    'unet': unet,
				    'scheduler': scheduler,
				    'vae': vae,
				}

				return components


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase=0				):

				"""simple docstring"""


				if str(__UpperCamelCase				).startswith('mps'				):
					snake_case_   		=			torch.manual_seed(__UpperCamelCase				)
				else:
					snake_case_   		=			torch.Generator(device=__UpperCamelCase				).manual_seed(__UpperCamelCase				)
				snake_case_   		=			{
				    'prompt': 'A painting of a squirrel eating a burger',
				    'generator': generator,
				    'num_inference_steps': 2,
				    'prior_num_inference_steps': 2,
				    'output_type': 'numpy',
				}
				return inputs


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			torch_device == 'cpu'

				self._test_attention_slicing_forward_pass(test_max_difference=__UpperCamelCase				)







			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			torch_device in ['cpu', 'mps']

				self._test_inference_batch_single_identical(test_max_difference=__UpperCamelCase				)







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






			"""simple docstring"""




			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			load_numpy(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy'				)

				snake_case_   		=			StableUnCLIPPipeline.from_pretrained('fusing/stable-unclip-2-1-l'			,					torch_dtype=torch.floataa				)
				pipe.to(__UpperCamelCase				)
				pipe.set_progress_bar_config(disable=__UpperCamelCase				)
				# stable unclip will oom when integration tests are run on a V100,
				# so turn on memory savings
				pipe.enable_attention_slicing()
				pipe.enable_sequential_cpu_offload()

				snake_case_   		=			torch.Generator(device='cpu'				).manual_seed(0				)
				snake_case_   		=			pipe('anime turle'			,					generator=__UpperCamelCase			,					output_type='np'				)

				snake_case_   		=			output.images[0]

				assert image.shape == (7_68, 7_68, 3)

				assert_mean_pixel_difference(__UpperCamelCase			,					__UpperCamelCase				)







			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				torch.cuda.empty_cache()
				torch.cuda.reset_max_memory_allocated()
				torch.cuda.reset_peak_memory_stats()

				snake_case_   		=			StableUnCLIPPipeline.from_pretrained('fusing/stable-unclip-2-1-l'			,					torch_dtype=torch.floataa				)
				snake_case_   		=			pipe.to(__UpperCamelCase				)
				pipe.set_progress_bar_config(disable=__UpperCamelCase				)
				pipe.enable_attention_slicing()
				pipe.enable_sequential_cpu_offload()

				snake_case_   		=			pipe(
				    'anime turtle'			,					prior_num_inference_steps=2			,					num_inference_steps=2			,					output_type='np'			,					)

				snake_case_   		=			torch.cuda.max_memory_allocated()
				# make sure that less than 7 GB is allocated
				assert mem_bytes < 7 * 10**9



from jiwer import compute_measures

import datasets


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

A									=  '\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe 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.\n\nThis 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.\n\nWord error rate can then be computed as:\n\nWER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n'

A									=  '\nCompute WER score of transcribed segments against references.\n\nArgs:\n    references: List of references for each speech input.\n    predictions: List of transcriptions to score.\n    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n    (float): the word error rate\n\nExamples:\n\n    >>> predictions = ["this is the prediction", "there is an other sample"]\n    >>> references = ["this is the reference", "there is another one"]\n    >>> wer = datasets.load_metric("wer")\n    >>> wer_score = wer.compute(predictions=predictions, references=references)\n    >>> print(wer_score)\n    0.5\n'


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






			"""simple docstring"""




			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				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       __lowerCAmelCase			(     self			,					__UpperCamelCase=None			,					__UpperCamelCase=None			,					__UpperCamelCase=False				):

				"""simple docstring"""


				if concatenate_texts:
					return compute_measures(__UpperCamelCase			,					__UpperCamelCase				)["wer"]
				else:
					snake_case_   		=			0
					snake_case_   		=			0
					for prediction, reference in zip(__UpperCamelCase			,					__UpperCamelCase				):
						snake_case_   		=			compute_measures(__UpperCamelCase			,					__UpperCamelCase				)
						incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
						total += measures["substitutions"] + measures["deletions"] + measures["hits"]
					return incorrect / total
