import gc
import unittest

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

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


A_:   List[str]   =      False


class _lowercase       (							unittest.TestCase    ):





							"""simple docstring"""




							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






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



							@property
							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														return 12



							@property
							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														return 12



							@property
							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														return 32



							@property
							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														torch.manual_seed(0	)
														_lowercase					=						VQModel(
														    block_out_channels=[32, 64]  , in_channels=3  , out_channels=3  , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]  , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]  , latent_channels=3  , num_vq_embeddings=self.num_embed  , vq_embed_dim=3  , )
														return model



							@property
							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														_lowercase					=						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""	)
														return tokenizer



							@property
							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														torch.manual_seed(0	)
														_lowercase					=						CLIPTextConfig(
														    bos_token_id=0  , eos_token_id=2  , hidden_size=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=1000  , )
														return CLIPTextModel(UpperCamelCase_	)



							@property
							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														torch.manual_seed(0	)

														_lowercase					=						12
														_lowercase					=						12

														_lowercase					=						{
														    """attention_bias""": True,
														    """cross_attention_dim""": 32,
														    """attention_head_dim""": height * width,
														    """num_attention_heads""": 1,
														    """num_vector_embeds""": self.num_embed,
														    """num_embeds_ada_norm""": self.num_embeds_ada_norm,
														    """norm_num_groups""": 32,
														    """sample_size""": width,
														    """activation_fn""": """geglu-approximate""",
														}

														_lowercase					=						TransformeraDModel(**UpperCamelCase_	)
														return model



							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														_lowercase					=						"""cpu"""

														_lowercase					=						self.dummy_vqvae
														_lowercase					=						self.dummy_text_encoder
														_lowercase					=						self.dummy_tokenizer
														_lowercase					=						self.dummy_transformer
														_lowercase					=						VQDiffusionScheduler(self.num_embed	)
														_lowercase					=						LearnedClassifierFreeSamplingEmbeddings(learnable=UpperCamelCase_	)

														_lowercase					=						VQDiffusionPipeline(
														    vqvae=UpperCamelCase_  , text_encoder=UpperCamelCase_  , tokenizer=UpperCamelCase_  , transformer=UpperCamelCase_  , scheduler=UpperCamelCase_  , learned_classifier_free_sampling_embeddings=UpperCamelCase_  , )
														_lowercase					=						pipe.to(UpperCamelCase_	)
														pipe.set_progress_bar_config(disable=UpperCamelCase_	)

														_lowercase					=						"""teddy bear playing in the pool"""

														_lowercase					=						torch.Generator(device=UpperCamelCase_	).manual_seed(0	)
														_lowercase					=						pipe([prompt]  , generator=UpperCamelCase_  , num_inference_steps=2  , output_type="""np"""	)
														_lowercase					=						output.images

														_lowercase					=						torch.Generator(device=UpperCamelCase_	).manual_seed(0	)
														_lowercase					=						pipe(
														    [prompt]  , generator=UpperCamelCase_  , output_type="""np"""  , return_dict=UpperCamelCase_  , num_inference_steps=2	)[0]

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

														assert image.shape == (1, 24, 24, 3)

														_lowercase					=						np.array([0.6_551, 0.6_168, 0.5_008, 0.5_676, 0.5_659, 0.4_295, 0.6_073, 0.5_599, 0.4_992]	)

														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	):







														'''simple docstring'''






														_lowercase					=						"""cpu"""

														_lowercase					=						self.dummy_vqvae
														_lowercase					=						self.dummy_text_encoder
														_lowercase					=						self.dummy_tokenizer
														_lowercase					=						self.dummy_transformer
														_lowercase					=						VQDiffusionScheduler(self.num_embed	)
														_lowercase					=						LearnedClassifierFreeSamplingEmbeddings(
														    learnable=UpperCamelCase_  , hidden_size=self.text_embedder_hidden_size  , length=tokenizer.model_max_length	)

														_lowercase					=						VQDiffusionPipeline(
														    vqvae=UpperCamelCase_  , text_encoder=UpperCamelCase_  , tokenizer=UpperCamelCase_  , transformer=UpperCamelCase_  , scheduler=UpperCamelCase_  , learned_classifier_free_sampling_embeddings=UpperCamelCase_  , )
														_lowercase					=						pipe.to(UpperCamelCase_	)
														pipe.set_progress_bar_config(disable=UpperCamelCase_	)

														_lowercase					=						"""teddy bear playing in the pool"""

														_lowercase					=						torch.Generator(device=UpperCamelCase_	).manual_seed(0	)
														_lowercase					=						pipe([prompt]  , generator=UpperCamelCase_  , num_inference_steps=2  , output_type="""np"""	)
														_lowercase					=						output.images

														_lowercase					=						torch.Generator(device=UpperCamelCase_	).manual_seed(0	)
														_lowercase					=						pipe(
														    [prompt]  , generator=UpperCamelCase_  , output_type="""np"""  , return_dict=UpperCamelCase_  , num_inference_steps=2	)[0]

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

														assert image.shape == (1, 24, 24, 3)

														_lowercase					=						np.array([0.6_693, 0.6_075, 0.4_959, 0.5_701, 0.5_583, 0.4_333, 0.6_171, 0.5_684, 0.4_988]	)

														assert np.abs(image_slice.flatten() - expected_slice	).max() < 2.0
														assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1e-2






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





							"""simple docstring"""




							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






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



							def      _UpperCAmelCase							(     self	):







														'''simple docstring'''






														_lowercase					=						load_numpy(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
														    """/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy"""	)

														_lowercase					=						VQDiffusionPipeline.from_pretrained("""microsoft/vq-diffusion-ithq"""	)
														_lowercase					=						pipeline.to(UpperCamelCase_	)
														pipeline.set_progress_bar_config(disable=UpperCamelCase_	)

														# requires GPU generator for gumbel softmax
														# don't use GPU generator in tests though
														_lowercase					=						torch.Generator(device=UpperCamelCase_	).manual_seed(0	)
														_lowercase					=						pipeline(
														    """teddy bear playing in the pool"""  , num_images_per_prompt=1  , generator=UpperCamelCase_  , output_type="""np"""  , )

														_lowercase					=						output.images[0]

														assert image.shape == (256, 256, 3)
														assert np.abs(expected_image - image	).max() < 2.0









'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a__					:			str          =     logging.get_logger(__name__)

a__					:			Any          =     {
    '''xlm-mlm-en-2048''': '''https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json''',
    '''xlm-mlm-ende-1024''': '''https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json''',
    '''xlm-mlm-enfr-1024''': '''https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json''',
    '''xlm-mlm-enro-1024''': '''https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json''',
    '''xlm-mlm-tlm-xnli15-1024''': '''https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json''',
    '''xlm-mlm-xnli15-1024''': '''https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json''',
    '''xlm-clm-enfr-1024''': '''https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json''',
    '''xlm-clm-ende-1024''': '''https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json''',
    '''xlm-mlm-17-1280''': '''https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json''',
    '''xlm-mlm-100-1280''': '''https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json''',
}






class 				__snake_case     (					__magic_name__							):
	__lowerCAmelCase       						=  '''xlm'''
	__lowerCAmelCase       						=  {
	    '''hidden_size''': '''emb_dim''',
	    '''num_attention_heads''': '''n_heads''',
	    '''num_hidden_layers''': '''n_layers''',
	    '''n_words''': '''vocab_size''',  # For backward compatibility
	}





	def __init__(		self      ,       UpperCamelCase_=3_0145      ,       UpperCamelCase_=2048      ,       UpperCamelCase_=12      ,       UpperCamelCase_=16      ,       UpperCamelCase_=0.1      ,       UpperCamelCase_=0.1      ,       UpperCamelCase_=True      ,       UpperCamelCase_=False      ,       UpperCamelCase_=False      ,       UpperCamelCase_=False      ,       UpperCamelCase_=1      ,       UpperCamelCase_=True      ,       UpperCamelCase_=512      ,       UpperCamelCase_=2048**-0.5      ,       UpperCamelCase_=1E-1_2      ,       UpperCamelCase_=0.0_2      ,       UpperCamelCase_=0      ,       UpperCamelCase_=1      ,       UpperCamelCase_=2      ,       UpperCamelCase_=3      ,       UpperCamelCase_=5      ,       UpperCamelCase_=True      ,       UpperCamelCase_="first"      ,       UpperCamelCase_=True      ,       UpperCamelCase_=None      ,       UpperCamelCase_=True      ,       UpperCamelCase_=0.1      ,       UpperCamelCase_=5      ,       UpperCamelCase_=5      ,       UpperCamelCase_=0      ,       UpperCamelCase_=0      ,       UpperCamelCase_=2      ,       UpperCamelCase_=0      ,       **UpperCamelCase_      ,       )      ->		List[str]:
		snake_case__	       =   vocab_size
		snake_case__	       =   emb_dim
		snake_case__	       =   n_layers
		snake_case__	       =   n_heads
		snake_case__	       =   dropout
		snake_case__	       =   attention_dropout
		snake_case__	       =   gelu_activation
		snake_case__	       =   sinusoidal_embeddings
		snake_case__	       =   causal
		snake_case__	       =   asm
		snake_case__	       =   n_langs
		snake_case__	       =   use_lang_emb
		snake_case__	       =   layer_norm_eps
		snake_case__	       =   bos_index
		snake_case__	       =   eos_index
		snake_case__	       =   pad_index
		snake_case__	       =   unk_index
		snake_case__	       =   mask_index
		snake_case__	       =   is_encoder
		snake_case__	       =   max_position_embeddings
		snake_case__	       =   embed_init_std
		snake_case__	       =   init_std
		snake_case__	       =   summary_type
		snake_case__	       =   summary_use_proj
		snake_case__	       =   summary_activation
		snake_case__	       =   summary_proj_to_labels
		snake_case__	       =   summary_first_dropout
		snake_case__	       =   start_n_top
		snake_case__	       =   end_n_top
		snake_case__	       =   mask_token_id
		snake_case__	       =   lang_id

		if "n_words" in kwargs:
			snake_case__	       =   kwargs['n_words']

		super().__init__(pad_token_id=UpperCamelCase_      ,       bos_token_id=UpperCamelCase_      ,       **UpperCamelCase_		)






class 				__snake_case     (					__magic_name__							):





	@property
	def   _snake_case   (		self		)      ->		Mapping[str, Mapping[int, str]]:
		if self.task == "multiple-choice":
			snake_case__	       =   {0: 'batch', 1: 'choice', 2: 'sequence'}
		else:
			snake_case__	       =   {0: 'batch', 1: 'sequence'}
		return OrderedDict(
		    [
		        ('input_ids', dynamic_axis),
		        ('attention_mask', dynamic_axis),
		        ('token_type_ids', dynamic_axis),
		    ]		)



