import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


UpperCAmelCase_           =				'''bert-base-cased'''
UpperCAmelCase_           =				'''google/pegasus-xsum'''
UpperCAmelCase_           =				[''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
UpperCAmelCase_           =				['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
UpperCAmelCase_           =				'''patrickvonplaten/t5-tiny-random'''
UpperCAmelCase_           =				'''sshleifer/bart-tiny-random'''
UpperCAmelCase_           =				'''sshleifer/tiny-mbart'''
UpperCAmelCase_           =				'''sshleifer/tiny-marian-en-de'''




def    lowerCAmelCase_  (		lowercase:				Path	,       lowercase:				list		)     ->					List[str]:







 '''simple docstring'''
 _UpperCamelCase:  Tuple    						=			'''\n'''.join(lowercase		)
 Path(lowercase		).open('''w'''		).writelines(lowercase		)




def    lowerCAmelCase_  (		lowercase:				List[str]		)     ->					Tuple:







 '''simple docstring'''
 for split in ["train", "val", "test"]:
  _dump_articles(os.path.join(lowercase	,       F"""{split}.source"""		)	,       lowercase		)
  _dump_articles(os.path.join(lowercase	,       F"""{split}.target"""		)	,       lowercase		)
 return tmp_dir





class 			__magic_name__	(		__a     ):





   """simple docstring"""




   @parameterized.expand(
       [
           MBART_TINY,
           MARIAN_TINY,
           T5_TINY,
           BART_TINY,
           PEGASUS_XSUM,
       ]		,	)
   @slow
   def 	lowerCAmelCase    (						self       :	Tuple		,	_lowercase       :	int				):



    """simple docstring"""



    _UpperCamelCase:  Optional[Any]    						=			AutoTokenizer.from_pretrained(_lowercase				)
    _UpperCamelCase:  Optional[Any]    						=			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)
    _UpperCamelCase:  Union[str, Any]    						=			max(len(tokenizer.encode(_lowercase				)				) for a in ARTICLES				)
    _UpperCamelCase:  str    						=			max(len(tokenizer.encode(_lowercase				)				) for a in SUMMARIES				)
    _UpperCamelCase:  Optional[Any]    						=			4
    _UpperCamelCase:  Any    						=			8
    assert max_len_target > max_src_len  # Will be truncated
    assert max_len_source > max_src_len  # Will be truncated
    _UpperCamelCase						,				_UpperCamelCase:  Dict    						=			'''ro_RO''', '''de_DE'''  # ignored for all but mbart, but never causes error.
    _UpperCamelCase:  Optional[int]    						=			SeqaSeqDataset(
        _lowercase		,	data_dir=_lowercase		,	type_path='''train'''		,	max_source_length=_lowercase		,	max_target_length=_lowercase		,	src_lang=_lowercase		,	tgt_lang=_lowercase		,	)
    _UpperCamelCase:  Optional[int]    						=			DataLoader(_lowercase		,	batch_size=2		,	collate_fn=train_dataset.collate_fn				)
    for batch in dataloader:
     assert isinstance(_lowercase		,	_lowercase				)
     assert batch["attention_mask"].shape == batch["input_ids"].shape
     # show that articles were trimmed.
     assert batch["input_ids"].shape[1] == max_src_len
     # show that targets are the same len
     assert batch["labels"].shape[1] == max_tgt_len
     if tok_name != MBART_TINY:
      continue
     # check language codes in correct place
     _UpperCamelCase:  Any    						=			shift_tokens_right(batch['''labels''']		,	tokenizer.pad_token_id				)
     assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
     assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
     assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
     assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

     break  # No need to test every batch



   @parameterized.expand([BART_TINY, BERT_BASE_CASED]				)
   def 	lowerCAmelCase    (						self       :	Any		,	_lowercase       :	int				):



    """simple docstring"""



    _UpperCamelCase:  List[str]    						=			AutoTokenizer.from_pretrained(_lowercase				)
    _UpperCamelCase:  List[str]    						=			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)
    _UpperCamelCase:  List[str]    						=			max(len(tokenizer.encode(_lowercase				)				) for a in ARTICLES				)
    _UpperCamelCase:  str    						=			max(len(tokenizer.encode(_lowercase				)				) for a in SUMMARIES				)
    _UpperCamelCase:  Optional[int]    						=			4
    _UpperCamelCase:  Tuple    						=			LegacySeqaSeqDataset(
        _lowercase		,	data_dir=_lowercase		,	type_path='''train'''		,	max_source_length=20		,	max_target_length=_lowercase		,	)
    _UpperCamelCase:  int    						=			DataLoader(_lowercase		,	batch_size=2		,	collate_fn=train_dataset.collate_fn				)
    for batch in dataloader:
     assert batch["attention_mask"].shape == batch["input_ids"].shape
     # show that articles were trimmed.
     assert batch["input_ids"].shape[1] == max_len_source
     assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
     # show that targets were truncated
     assert batch["labels"].shape[1] == trunc_target  # Truncated
     assert max_len_target > trunc_target  # Truncated
     break  # No need to test every batch



   def 	lowerCAmelCase    (						self       :	List[Any]				):



    """simple docstring"""



    _UpperCamelCase:  int    						=			AutoTokenizer.from_pretrained('''facebook/mbart-large-cc25'''				)

    _UpperCamelCase:  Any    						=			Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)				)
    _UpperCamelCase:  int    						=			tmp_dir.joinpath('''train.source'''				).open().readlines()
    _UpperCamelCase:  int    						=			Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)				)
    pack_data_dir(_lowercase		,	_lowercase		,	128		,	_lowercase				)
    _UpperCamelCase:  Union[str, Any]    						=			{x.name for x in tmp_dir.iterdir()}
    _UpperCamelCase:  Dict    						=			{x.name for x in save_dir.iterdir()}
    _UpperCamelCase:  Tuple    						=			save_dir.joinpath('''train.source'''				).open().readlines()
    # orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
    # desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
    assert len(_lowercase				) < len(_lowercase				)
    assert len(_lowercase				) == 1
    assert len(packed_examples[0]				) == sum(len(_lowercase				) for x in orig_examples				)
    assert orig_paths == new_paths



   @pytest.mark.skipif(not FAIRSEQ_AVAILABLE		,	reason='''This test requires fairseq'''				)
   def 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



    if not FAIRSEQ_AVAILABLE:
     return
    _UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase:  Optional[int]    						=			self._get_dataset(max_len=64				)
    _UpperCamelCase:  Optional[int]    						=			64
    _UpperCamelCase:  Optional[Any]    						=			ds.make_dynamic_sampler(_lowercase		,	required_batch_size_multiple=_lowercase				)
    _UpperCamelCase:  Optional[int]    						=			[len(_lowercase				) for x in batch_sampler]
    assert len(set(_lowercase				)				) > 1  # it's not dynamic batch size if every batch is the same length
    assert sum(_lowercase				) == len(_lowercase				)  # no dropped or added examples
    _UpperCamelCase:  Any    						=			DataLoader(_lowercase		,	batch_sampler=_lowercase		,	collate_fn=ds.collate_fn		,	num_workers=2				)
    _UpperCamelCase:  Union[str, Any]    						=			[]
    _UpperCamelCase:  Optional[int]    						=			[]
    for batch in data_loader:
     _UpperCamelCase:  Union[str, Any]    						=			batch['''input_ids'''].shape
     _UpperCamelCase:  Dict    						=			src_shape[0]
     assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
     _UpperCamelCase:  Any    						=			np.product(batch['''input_ids'''].shape				)
     num_src_per_batch.append(_lowercase				)
     if num_src_tokens > (max_tokens * 1.1):
      failures.append(_lowercase				)
    assert num_src_per_batch[0] == max(_lowercase				)
    if failures:
     raise AssertionError(f"""too many tokens in {len(_lowercase				)} batches"""				)



   def 	lowerCAmelCase    (						self       :	List[Any]				):



    """simple docstring"""



    _UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase:  Optional[Any]    						=			self._get_dataset(max_len=512				)
    _UpperCamelCase:  List[str]    						=			2
    _UpperCamelCase:  List[str]    						=			ds.make_sortish_sampler(_lowercase		,	shuffle=_lowercase				)

    _UpperCamelCase:  Dict    						=			DataLoader(_lowercase		,	batch_size=_lowercase		,	collate_fn=ds.collate_fn		,	num_workers=2				)
    _UpperCamelCase:  Optional[int]    						=			DataLoader(_lowercase		,	batch_size=_lowercase		,	collate_fn=ds.collate_fn		,	num_workers=2		,	sampler=_lowercase				)

    _UpperCamelCase:  Optional[int]    						=			tokenizer.pad_token_id

    def count_pad_tokens(_lowercase       :	Dict		,	_lowercase       :	Tuple="input_ids"				):
     return [batch[k].eq(_lowercase				).sum().item() for batch in data_loader]

    assert sum(count_pad_tokens(_lowercase		,	k='''labels'''				)				) < sum(count_pad_tokens(_lowercase		,	k='''labels'''				)				)
    assert sum(count_pad_tokens(_lowercase				)				) < sum(count_pad_tokens(_lowercase				)				)
    assert len(_lowercase				) == len(_lowercase				)



   def 	lowerCAmelCase    (						self       :	List[str]		,	_lowercase       :	Optional[Any]=1_000		,	_lowercase       :	Any=128				):



    """simple docstring"""



    if os.getenv('''USE_REAL_DATA'''		,	_lowercase				):
     _UpperCamelCase:  int    						=			'''examples/seq2seq/wmt_en_ro'''
     _UpperCamelCase:  List[str]    						=			max_len * 2 * 64
     if not Path(_lowercase				).joinpath('''train.len'''				).exists():
      save_len_file(_lowercase		,	_lowercase				)
    else:
     _UpperCamelCase:  Dict    						=			'''examples/seq2seq/test_data/wmt_en_ro'''
     _UpperCamelCase:  Optional[Any]    						=			max_len * 4
     save_len_file(_lowercase		,	_lowercase				)

    _UpperCamelCase:  List[Any]    						=			AutoTokenizer.from_pretrained(_lowercase				)
    _UpperCamelCase:  Dict    						=			SeqaSeqDataset(
        _lowercase		,	data_dir=_lowercase		,	type_path='''train'''		,	max_source_length=_lowercase		,	max_target_length=_lowercase		,	n_obs=_lowercase		,	)
    return ds, max_tokens, tokenizer



   def 	lowerCAmelCase    (						self       :	List[str]				):



    """simple docstring"""



    _UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase:  Optional[Any]    						=			self._get_dataset()
    _UpperCamelCase:  str    						=			set(DistributedSortishSampler(_lowercase		,	256		,	num_replicas=2		,	rank=0		,	add_extra_examples=_lowercase				)				)
    _UpperCamelCase:  List[str]    						=			set(DistributedSortishSampler(_lowercase		,	256		,	num_replicas=2		,	rank=1		,	add_extra_examples=_lowercase				)				)
    assert idsa.intersection(_lowercase				) == set()



   @parameterized.expand(
       [
           MBART_TINY,
           MARIAN_TINY,
           T5_TINY,
           BART_TINY,
           PEGASUS_XSUM,
       ]		,	)
   def 	lowerCAmelCase    (						self       :	Dict		,	_lowercase       :	Optional[Any]				):



    """simple docstring"""



    _UpperCamelCase:  str    						=			AutoTokenizer.from_pretrained(_lowercase		,	use_fast=_lowercase				)
    if tok_name == MBART_TINY:
     _UpperCamelCase:  int    						=			SeqaSeqDataset(
         _lowercase		,	data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)		,	type_path='''train'''		,	max_source_length=4		,	max_target_length=8		,	src_lang='''EN'''		,	tgt_lang='''FR'''		,	)
     _UpperCamelCase:  int    						=			train_dataset.dataset_kwargs
     assert "src_lang" in kwargs and "tgt_lang" in kwargs
    else:
     _UpperCamelCase:  List[str]    						=			SeqaSeqDataset(
         _lowercase		,	data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)		,	type_path='''train'''		,	max_source_length=4		,	max_target_length=8		,	)
     _UpperCamelCase:  Union[str, Any]    						=			train_dataset.dataset_kwargs
     assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
     assert len(_lowercase				) == 1 if tok_name == BART_TINY else len(_lowercase				) == 0
import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class 			__magic_name__	(		__a   ,						__a   ,						unittest.TestCase     ):





   """simple docstring"""




   lowerCAmelCase   :     List[str]	      = StableDiffusionSAGPipeline
   lowerCAmelCase   :     List[Any]	      = TEXT_TO_IMAGE_PARAMS
   lowerCAmelCase   :     List[str]	      = TEXT_TO_IMAGE_BATCH_PARAMS
   lowerCAmelCase   :     str	      = TEXT_TO_IMAGE_IMAGE_PARAMS
   lowerCAmelCase   :     int	      = TEXT_TO_IMAGE_IMAGE_PARAMS
   lowerCAmelCase   :     int	      = False



   def 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



    torch.manual_seed(0				)
    _UpperCamelCase:  Dict    						=			UNetaDConditionModel(
        block_out_channels=(32, 64)		,	layers_per_block=2		,	sample_size=32		,	in_channels=4		,	out_channels=4		,	down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')		,	up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')		,	cross_attention_dim=32		,	)
    _UpperCamelCase:  List[Any]    						=			DDIMScheduler(
        beta_start=0.00085		,	beta_end=0.012		,	beta_schedule='''scaled_linear'''		,	clip_sample=_lowercase		,	set_alpha_to_one=_lowercase		,	)
    torch.manual_seed(0				)
    _UpperCamelCase:  Optional[int]    						=			AutoencoderKL(
        block_out_channels=[32, 64]		,	in_channels=3		,	out_channels=3		,	down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']		,	up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']		,	latent_channels=4		,	)
    torch.manual_seed(0				)
    _UpperCamelCase:  int    						=			CLIPTextConfig(
        bos_token_id=0		,	eos_token_id=2		,	hidden_size=32		,	intermediate_size=37		,	layer_norm_eps=1E-05		,	num_attention_heads=4		,	num_hidden_layers=5		,	pad_token_id=1		,	vocab_size=1_000		,	)
    _UpperCamelCase:  Any    						=			CLIPTextModel(_lowercase				)
    _UpperCamelCase:  List[str]    						=			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

    _UpperCamelCase:  int    						=			{
        '''unet''': unet,
        '''scheduler''': scheduler,
        '''vae''': vae,
        '''text_encoder''': text_encoder,
        '''tokenizer''': tokenizer,
        '''safety_checker''': None,
        '''feature_extractor''': None,
    }
    return components



   def 	lowerCAmelCase    (						self       :	Dict		,	_lowercase       :	List[Any]		,	_lowercase       :	int=0				):



    """simple docstring"""



    if str(_lowercase				).startswith('''mps'''				):
     _UpperCamelCase:  Tuple    						=			torch.manual_seed(_lowercase				)
    else:
     _UpperCamelCase:  List[str]    						=			torch.Generator(device=_lowercase				).manual_seed(_lowercase				)
    _UpperCamelCase:  Dict    						=			{
        '''prompt''': '''.''',
        '''generator''': generator,
        '''num_inference_steps''': 2,
        '''guidance_scale''': 1.0,
        '''sag_scale''': 1.0,
        '''output_type''': '''numpy''',
    }
    return inputs



   def 	lowerCAmelCase    (						self       :	Dict				):



    """simple docstring"""



    super().test_inference_batch_single_identical(expected_max_diff=3E-3				)




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





   """simple docstring"""




   def 	lowerCAmelCase    (						self       :	Union[str, Any]				):



    """simple docstring"""



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



   def 	lowerCAmelCase    (						self       :	int				):



    """simple docstring"""



    _UpperCamelCase:  Any    						=			StableDiffusionSAGPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''				)
    _UpperCamelCase:  Any    						=			sag_pipe.to(_lowercase				)
    sag_pipe.set_progress_bar_config(disable=_lowercase				)

    _UpperCamelCase:  Tuple    						=			'''.'''
    _UpperCamelCase:  Tuple    						=			torch.manual_seed(0				)
    _UpperCamelCase:  Union[str, Any]    						=			sag_pipe(
        [prompt]		,	generator=_lowercase		,	guidance_scale=7.5		,	sag_scale=1.0		,	num_inference_steps=20		,	output_type='''np'''				)

    _UpperCamelCase:  Optional[Any]    						=			output.images

    _UpperCamelCase:  Tuple    						=			image[0, -3:, -3:, -1]

    assert image.shape == (1, 512, 512, 3)
    _UpperCamelCase:  Any    						=			np.array([0.1568, 0.1738, 0.1695, 0.1693, 0.1507, 0.1705, 0.1547, 0.1751, 0.1949]				)

    assert np.abs(image_slice.flatten() - expected_slice				).max() < 5E-2



   def 	lowerCAmelCase    (						self       :	Dict				):



    """simple docstring"""



    _UpperCamelCase:  Optional[Any]    						=			StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''				)
    _UpperCamelCase:  int    						=			sag_pipe.to(_lowercase				)
    sag_pipe.set_progress_bar_config(disable=_lowercase				)

    _UpperCamelCase:  List[Any]    						=			'''.'''
    _UpperCamelCase:  str    						=			torch.manual_seed(0				)
    _UpperCamelCase:  Dict    						=			sag_pipe(
        [prompt]		,	generator=_lowercase		,	guidance_scale=7.5		,	sag_scale=1.0		,	num_inference_steps=20		,	output_type='''np'''				)

    _UpperCamelCase:  Union[str, Any]    						=			output.images

    _UpperCamelCase:  List[str]    						=			image[0, -3:, -3:, -1]

    assert image.shape == (1, 512, 512, 3)
    _UpperCamelCase:  int    						=			np.array([0.3459, 0.2876, 0.2537, 0.3002, 0.2671, 0.2160, 0.3026, 0.2262, 0.2371]				)

    assert np.abs(image_slice.flatten() - expected_slice				).max() < 5E-2



   def 	lowerCAmelCase    (						self       :	Any				):



    """simple docstring"""



    _UpperCamelCase:  Tuple    						=			StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''				)
    _UpperCamelCase:  int    						=			sag_pipe.to(_lowercase				)
    sag_pipe.set_progress_bar_config(disable=_lowercase				)

    _UpperCamelCase:  Any    						=			'''.'''
    _UpperCamelCase:  List[Any]    						=			torch.manual_seed(0				)
    _UpperCamelCase:  str    						=			sag_pipe(
        [prompt]		,	width=768		,	height=512		,	generator=_lowercase		,	guidance_scale=7.5		,	sag_scale=1.0		,	num_inference_steps=20		,	output_type='''np'''		,	)

    _UpperCamelCase:  str    						=			output.images

    assert image.shape == (1, 512, 768, 3)