






"""simple docstring"""

import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin




@skip_mps
class  _UpperCAmelCase (    lowerCAmelCase__					,  lowerCAmelCase__					,  unittest.TestCase):
 _lowerCAmelCase     :    Dict	      =	IFPipeline
 _lowerCAmelCase     :    List[str]	      =	TEXT_TO_IMAGE_PARAMS - {"""width""", """height""", """latents"""}
 _lowerCAmelCase     :    Tuple	      =	TEXT_TO_IMAGE_BATCH_PARAMS
 _lowerCAmelCase     :    Optional[int]	      =	PipelineTesterMixin.required_optional_params - {"""latents"""}

 def 		_snake_case      (  self	: Optional[int]       ):
     return self._get_dummy_components()

 def 		_snake_case      (  self	: str						,  lowercase_	: Any						,  lowercase_	: List[str]=0       ):
     if str(lowercase_       ).startswith('''mps'''       ):
         snake_case_				:  Union[str, Any]       =			torch.manual_seed(lowercase_       )
     else:
         snake_case_				:  Any       =			torch.Generator(device=lowercase_       ).manual_seed(lowercase_       )

     snake_case_				:  int       =			{
         '''prompt''': '''A painting of a squirrel eating a burger''',
         '''generator''': generator,
         '''num_inference_steps''': 2,
         '''output_type''': '''numpy''',
     }

     return inputs

 def 		_snake_case      (  self	: Optional[int]       ):
     self._test_save_load_optional_components()

 @unittest.skipIf(torch_device != '''cuda'''						,  reason='''float16 requires CUDA'''       )
 def 		_snake_case      (  self	: Dict       ):
     # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
     super().test_save_load_floataa(expected_max_diff=1E-1       )

 def 		_snake_case      (  self	: int       ):
     self._test_attention_slicing_forward_pass(expected_max_diff=1E-2       )

 def 		_snake_case      (  self	: Union[str, Any]       ):
     self._test_save_load_local()

 def 		_snake_case      (  self	: Optional[Any]       ):
     self._test_inference_batch_single_identical(
         expected_max_diff=1E-2						,  )






 @unittest.skipIf(
     torch_device != '''cuda''' or not is_xformers_available()						,  reason='''XFormers attention is only available with CUDA and `xformers` installed'''						,  )
 def 		_snake_case      (  self	: List[str]       ):
     self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3       )





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

 def 		_snake_case      (  self	: List[str]       ):
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()

 def 		_snake_case      (  self	: int       ):
     # if

     snake_case_				:  str       =			IFPipeline.from_pretrained('''DeepFloyd/IF-I-XL-v1.0'''						,  variant='''fp16'''						,  torch_dtype=torch.floataa       )

     snake_case_				:  Tuple       =			IFSuperResolutionPipeline.from_pretrained(
         '''DeepFloyd/IF-II-L-v1.0'''						,  variant='''fp16'''						,  torch_dtype=torch.floataa						,  text_encoder=lowercase_						,  tokenizer=lowercase_       )

     # pre compute text embeddings and remove T5 to save memory

     pipe_a.text_encoder.to('''cuda'''       )

     snake_case_				:  Any       =			pipe_a.encode_prompt('''anime turtle'''						,  device='''cuda'''       )

     del pipe_a.tokenizer
     del pipe_a.text_encoder
     gc.collect()

     snake_case_				:  Dict       =			None
     snake_case_				:  Optional[int]       =			None

     pipe_a.enable_model_cpu_offload()
     pipe_a.enable_model_cpu_offload()

     pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()       )
     pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()       )

     self._test_if(lowercase_						,  lowercase_						,  lowercase_						,  lowercase_       )

     pipe_a.remove_all_hooks()
     pipe_a.remove_all_hooks()

     # img2img

     snake_case_				:  str       =			IFImgaImgPipeline(**pipe_a.components       )
     snake_case_				:  str       =			IFImgaImgSuperResolutionPipeline(**pipe_a.components       )

     pipe_a.enable_model_cpu_offload()
     pipe_a.enable_model_cpu_offload()

     pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()       )
     pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()       )

     self._test_if_imgaimg(lowercase_						,  lowercase_						,  lowercase_						,  lowercase_       )

     pipe_a.remove_all_hooks()
     pipe_a.remove_all_hooks()

     # inpainting

     snake_case_				:  List[str]       =			IFInpaintingPipeline(**pipe_a.components       )
     snake_case_				:  Optional[Any]       =			IFInpaintingSuperResolutionPipeline(**pipe_a.components       )

     pipe_a.enable_model_cpu_offload()
     pipe_a.enable_model_cpu_offload()

     pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()       )
     pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()       )

     self._test_if_inpainting(lowercase_						,  lowercase_						,  lowercase_						,  lowercase_       )

 def 		_snake_case      (  self	: str						,  lowercase_	: str						,  lowercase_	: Optional[int]						,  lowercase_	: int						,  lowercase_	: int       ):
     # pipeline 1

     _start_torch_memory_measurement()

     snake_case_				:  Union[str, Any]       =			torch.Generator(device='''cpu'''       ).manual_seed(0       )
     snake_case_				:  Tuple       =			pipe_a(
         prompt_embeds=lowercase_						,  negative_prompt_embeds=lowercase_						,  num_inference_steps=2						,  generator=lowercase_						,  output_type='''np'''						,  )

     snake_case_				:  str       =			output.images[0]

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

     snake_case_				:  Union[str, Any]       =			torch.cuda.max_memory_allocated()
     assert mem_bytes < 13 * 10**9

     snake_case_				:  str       =			load_numpy(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy'''       )
     assert_mean_pixel_difference(lowercase_						,  lowercase_       )

     # pipeline 2

     _start_torch_memory_measurement()

     snake_case_				:  Optional[int]       =			torch.Generator(device='''cpu'''       ).manual_seed(0       )

     snake_case_				:  Dict       =			floats_tensor((1, 3, 64, 64)						,  rng=random.Random(0       )       ).to(lowercase_       )

     snake_case_				:  Optional[int]       =			pipe_a(
         prompt_embeds=lowercase_						,  negative_prompt_embeds=lowercase_						,  image=lowercase_						,  generator=lowercase_						,  num_inference_steps=2						,  output_type='''np'''						,  )

     snake_case_				:  Tuple       =			output.images[0]

     assert image.shape == (256, 256, 3)

     snake_case_				:  Optional[int]       =			torch.cuda.max_memory_allocated()
     assert mem_bytes < 4 * 10**9

     snake_case_				:  List[Any]       =			load_numpy(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy'''       )
     assert_mean_pixel_difference(lowercase_						,  lowercase_       )

 def 		_snake_case      (  self	: List[str]						,  lowercase_	: Any						,  lowercase_	: List[str]						,  lowercase_	: Dict						,  lowercase_	: List[Any]       ):
     # pipeline 1

     _start_torch_memory_measurement()

     snake_case_				:  Any       =			floats_tensor((1, 3, 64, 64)						,  rng=random.Random(0       )       ).to(lowercase_       )

     snake_case_				:  List[Any]       =			torch.Generator(device='''cpu'''       ).manual_seed(0       )

     snake_case_				:  List[str]       =			pipe_a(
         prompt_embeds=lowercase_						,  negative_prompt_embeds=lowercase_						,  image=lowercase_						,  num_inference_steps=2						,  generator=lowercase_						,  output_type='''np'''						,  )

     snake_case_				:  List[str]       =			output.images[0]

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

     snake_case_				:  Union[str, Any]       =			torch.cuda.max_memory_allocated()
     assert mem_bytes < 10 * 10**9

     snake_case_				:  Union[str, Any]       =			load_numpy(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy'''       )
     assert_mean_pixel_difference(lowercase_						,  lowercase_       )

     # pipeline 2

     _start_torch_memory_measurement()

     snake_case_				:  List[str]       =			torch.Generator(device='''cpu'''       ).manual_seed(0       )

     snake_case_				:  Any       =			floats_tensor((1, 3, 256, 256)						,  rng=random.Random(0       )       ).to(lowercase_       )
     snake_case_				:  List[Any]       =			floats_tensor((1, 3, 64, 64)						,  rng=random.Random(0       )       ).to(lowercase_       )

     snake_case_				:  Optional[int]       =			pipe_a(
         prompt_embeds=lowercase_						,  negative_prompt_embeds=lowercase_						,  image=lowercase_						,  original_image=lowercase_						,  generator=lowercase_						,  num_inference_steps=2						,  output_type='''np'''						,  )

     snake_case_				:  int       =			output.images[0]

     assert image.shape == (256, 256, 3)

     snake_case_				:  List[str]       =			torch.cuda.max_memory_allocated()
     assert mem_bytes < 4 * 10**9

     snake_case_				:  Any       =			load_numpy(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy'''       )
     assert_mean_pixel_difference(lowercase_						,  lowercase_       )






 def 		_snake_case      (  self	: Any						,  lowercase_	: Dict						,  lowercase_	: Tuple						,  lowercase_	: Dict						,  lowercase_	: Any       ):
     # pipeline 1

     _start_torch_memory_measurement()

     snake_case_				:  Optional[Any]       =			floats_tensor((1, 3, 64, 64)						,  rng=random.Random(0       )       ).to(lowercase_       )
     snake_case_				:  List[str]       =			floats_tensor((1, 3, 64, 64)						,  rng=random.Random(1       )       ).to(lowercase_       )

     snake_case_				:  Dict       =			torch.Generator(device='''cpu'''       ).manual_seed(0       )
     snake_case_				:  Any       =			pipe_a(
         prompt_embeds=lowercase_						,  negative_prompt_embeds=lowercase_						,  image=lowercase_						,  mask_image=lowercase_						,  num_inference_steps=2						,  generator=lowercase_						,  output_type='''np'''						,  )

     snake_case_				:  int       =			output.images[0]

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

     snake_case_				:  Optional[int]       =			torch.cuda.max_memory_allocated()
     assert mem_bytes < 10 * 10**9

     snake_case_				:  Optional[Any]       =			load_numpy(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy'''       )
     assert_mean_pixel_difference(lowercase_						,  lowercase_       )

     # pipeline 2

     _start_torch_memory_measurement()

     snake_case_				:  List[Any]       =			torch.Generator(device='''cpu'''       ).manual_seed(0       )

     snake_case_				:  Optional[Any]       =			floats_tensor((1, 3, 64, 64)						,  rng=random.Random(0       )       ).to(lowercase_       )
     snake_case_				:  Any       =			floats_tensor((1, 3, 256, 256)						,  rng=random.Random(0       )       ).to(lowercase_       )
     snake_case_				:  Optional[int]       =			floats_tensor((1, 3, 256, 256)						,  rng=random.Random(1       )       ).to(lowercase_       )

     snake_case_				:  List[Any]       =			pipe_a(
         prompt_embeds=lowercase_						,  negative_prompt_embeds=lowercase_						,  image=lowercase_						,  mask_image=lowercase_						,  original_image=lowercase_						,  generator=lowercase_						,  num_inference_steps=2						,  output_type='''np'''						,  )

     snake_case_				:  Optional[Any]       =			output.images[0]

     assert image.shape == (256, 256, 3)

     snake_case_				:  List[Any]       =			torch.cuda.max_memory_allocated()
     assert mem_bytes < 4 * 10**9

     snake_case_				:  Any       =			load_numpy(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy'''       )
     assert_mean_pixel_difference(lowercase_						,  lowercase_       )





def __lowercase    (							):
    torch.cuda.empty_cache()
    torch.cuda.reset_max_memory_allocated()
    torch.cuda.reset_peak_memory_stats()










"""simple docstring"""

import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM




def __lowercase    (							_a	,    _a	,    _a					):
    snake_case_				:  Tuple       =			AutoConfig.from_pretrained(_a					)
    snake_case_				:  Tuple       =			FlaxAutoModelForSeqaSeqLM.from_config(config=_a					)
    snake_case_				:  Union[str, Any]       =			checkpoints.load_tax_checkpoint(_a					)

    snake_case_				:  Optional[int]       =			'''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

    if config.model_type == "t5":
        snake_case_				:  str       =			'''SelfAttention'''
    if config.model_type == "longt5" and config.encoder_attention_type == "local":
        snake_case_				:  List[str]       =			'''LocalSelfAttention'''
    elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
        snake_case_				:  Dict       =			'''TransientGlobalSelfAttention'''
    else:
        raise ValueError(
            '''Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'''
            ''' attribute with a value from [\'local\', \'transient-global].'''					)

    # Encoder
    for layer_index in range(config.num_layers					):
        snake_case_				:  Any       =			f"layers_{str(_a					)}"

        # Self-Attention
        snake_case_				:  Dict       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
        snake_case_				:  Tuple       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
        snake_case_				:  Tuple       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
        snake_case_				:  Optional[int]       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

        # Global input layer norm
        if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            snake_case_				:  Tuple       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

        # Layer Normalization
        snake_case_				:  List[str]       =			tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

        if split_mlp_wi:
            snake_case_				:  int       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
            snake_case_				:  List[str]       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
        else:
            snake_case_				:  int       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

        snake_case_				:  str       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

        # Layer Normalization
        snake_case_				:  Optional[Any]       =			tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

        # Assigning
        snake_case_				:  Optional[Any]       =			flax_model.params['''encoder''']['''block'''][str(_a					)]['''layer''']
        snake_case_				:  List[str]       =			tax_attention_key
        snake_case_				:  Optional[Any]       =			tax_attention_out
        snake_case_				:  Any       =			tax_attention_query
        snake_case_				:  str       =			tax_attention_value

        snake_case_				:  Dict       =			tax_attention_layer_norm

        # Global input layer norm
        if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            snake_case_				:  Union[str, Any]       =			tax_global_layer_norm

        if split_mlp_wi:
            snake_case_				:  Any       =			tax_mlp_wi_a
            snake_case_				:  List[Any]       =			tax_mlp_wi_a
        else:
            snake_case_				:  Union[str, Any]       =			tax_mlp_wi

        snake_case_				:  List[Any]       =			tax_mlp_wo
        snake_case_				:  int       =			tax_mlp_layer_norm

        snake_case_				:  Any       =			flax_model_encoder_layer_block

    # Only for layer 0:
    snake_case_				:  Optional[int]       =			tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
    snake_case_				:  Any       =			tax_encoder_rel_embedding

    # Side/global relative position_bias + layer norm
    if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
        snake_case_				:  List[str]       =			tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
        snake_case_				:  Tuple       =			tax_encoder_global_rel_embedding

    # Assigning
    snake_case_				:  Dict       =			tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
    snake_case_				:  Any       =			tax_encoder_norm

    # Decoder
    for layer_index in range(config.num_layers					):
        snake_case_				:  Tuple       =			f"layers_{str(_a					)}"

        # Self-Attention
        snake_case_				:  List[str]       =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
        snake_case_				:  Optional[int]       =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
        snake_case_				:  List[Any]       =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
        snake_case_				:  Optional[int]       =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

        # Layer Normalization
        snake_case_				:  str       =			tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
            '''scale'''
        ]

        # Encoder-Decoder-Attention
        snake_case_				:  Any       =			tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']
        snake_case_				:  Optional[Any]       =			tax_enc_dec_attention_module['''key''']['''kernel''']
        snake_case_				:  str       =			tax_enc_dec_attention_module['''out''']['''kernel''']
        snake_case_				:  Union[str, Any]       =			tax_enc_dec_attention_module['''query''']['''kernel''']
        snake_case_				:  List[str]       =			tax_enc_dec_attention_module['''value''']['''kernel''']

        # Layer Normalization
        snake_case_				:  Optional[int]       =			tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

        # MLP
        if split_mlp_wi:
            snake_case_				:  List[Any]       =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
            snake_case_				:  List[str]       =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
        else:
            snake_case_				:  Dict       =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

        snake_case_				:  List[Any]       =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

        # Layer Normalization
        snake_case_				:  List[Any]       =			tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

        # Assigning
        snake_case_				:  Dict       =			flax_model.params['''decoder''']['''block'''][str(_a					)]['''layer''']
        snake_case_				:  int       =			tax_attention_key
        snake_case_				:  List[Any]       =			tax_attention_out
        snake_case_				:  Any       =			tax_attention_query
        snake_case_				:  Dict       =			tax_attention_value

        snake_case_				:  str       =			tax_pre_attention_layer_norm

        snake_case_				:  Any       =			tax_enc_dec_attention_key
        snake_case_				:  str       =			tax_enc_dec_attention_out
        snake_case_				:  int       =			tax_enc_dec_attention_query
        snake_case_				:  Any       =			tax_enc_dec_attention_value

        snake_case_				:  Optional[Any]       =			tax_cross_layer_norm

        if split_mlp_wi:
            snake_case_				:  Tuple       =			tax_mlp_wi_a
            snake_case_				:  List[Any]       =			tax_mlp_wi_a
        else:
            snake_case_				:  List[Any]       =			tax_mlp_wi

        snake_case_				:  Dict       =			tax_mlp_wo

        snake_case_				:  List[Any]       =			txa_mlp_layer_norm

        snake_case_				:  Optional[int]       =			flax_model_decoder_layer_block

    # Decoder Normalization
    snake_case_				:  str       =			tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
    snake_case_				:  Tuple       =			txa_decoder_norm

    # Only for layer 0:
    snake_case_				:  str       =			tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
    snake_case_				:  Optional[Any]       =			tax_decoder_rel_embedding

    # Token Embeddings
    snake_case_				:  Union[str, Any]       =			tax_model['''target''']['''token_embedder''']['''embedding''']
    snake_case_				:  Optional[int]       =			txa_token_embeddings

    # LM Head (only in v1.1 and LongT5 checkpoints)
    if "logits_dense" in tax_model["target"]["decoder"]:
        snake_case_				:  Union[str, Any]       =			tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

    flax_model.save_pretrained(_a					)
    print('''T5X Model was sucessfully converted!'''					)


if __name__ == "__main__":
     lowercase__					:						Tuple												=			argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path the T5X checkpoint.'''
     )
     parser.add_argument('''--config_name''', default=None, type=str, required=True, help='''Config name of LongT5/T5 model.''')
     parser.add_argument(
         '''--flax_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output FLAX model.'''
     )
     lowercase__					:						Dict												=			parser.parse_args()
     convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)





