






"""simple docstring"""
import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
      import os

      # The slow tests are often failing with OOM error on GPU
      # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
      # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
      A_			: Any				=			"platform"

      import jax
      import jax.numpy as jnp

      from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer






def        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase=None			,   _lowerCamelCase=None				):



 if attention_mask is None:
  lowerCamelCase__     :			Union[str, Any]     				=						np.where(input_ids != config.pad_token_id			,   1			,   0				)
 return {
     "input_ids": input_ids,
     "attention_mask": attention_mask,
 }



@require_flax
class 		a_      :







      '''simple docstring'''

      def __init__(self,     lowerCamelCase_,     lowerCamelCase_=1_3,     lowerCamelCase_=7,     lowerCamelCase_=True,     lowerCamelCase_=False,     lowerCamelCase_=9_9,     lowerCamelCase_=1_6,     lowerCamelCase_=2,     lowerCamelCase_=4,     lowerCamelCase_=4,     lowerCamelCase_="gelu",     lowerCamelCase_=0.1,     lowerCamelCase_=0.1,     lowerCamelCase_=2_0,     lowerCamelCase_=2,     lowerCamelCase_=1,     lowerCamelCase_=0,     lowerCamelCase_=1_6,     lowerCamelCase_=1_6,     lowerCamelCase_=0.02,     ):

       '''simple docstring'''



       lowerCamelCase__     :			int     				=						parent
       lowerCamelCase__     :			List[Any]     				=						batch_size
       lowerCamelCase__     :			int     				=						seq_length
       lowerCamelCase__     :			List[str]     				=						is_training
       lowerCamelCase__     :			List[Any]     				=						use_labels
       lowerCamelCase__     :			Dict     				=						vocab_size
       lowerCamelCase__     :			Any     				=						hidden_size
       lowerCamelCase__     :			str     				=						num_hidden_layers
       lowerCamelCase__     :			Dict     				=						num_attention_heads
       lowerCamelCase__     :			int     				=						intermediate_size
       lowerCamelCase__     :			Optional[int]     				=						hidden_act
       lowerCamelCase__     :			str     				=						hidden_dropout_prob
       lowerCamelCase__     :			Tuple     				=						attention_probs_dropout_prob
       lowerCamelCase__     :			Optional[Any]     				=						max_position_embeddings
       lowerCamelCase__     :			Any     				=						eos_token_id
       lowerCamelCase__     :			Tuple     				=						pad_token_id
       lowerCamelCase__     :			List[Any]     				=						bos_token_id
       lowerCamelCase__     :			Dict     				=						embed_dim
       lowerCamelCase__     :			Tuple     				=						word_embed_proj_dim
       lowerCamelCase__     :			Union[str, Any]     				=						initializer_range
       lowerCamelCase__     :			str     				=						False




      def 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__     :			Dict     				=						np.clip(ids_tensor([self.batch_size, self.seq_length - 1],     self.vocab_size					),     3,     self.vocab_size					)
       lowerCamelCase__     :			Dict     				=						np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1),     dtype=np.intaa					)),     -1					)

       lowerCamelCase__     :			Any     				=						OPTConfig(
           vocab_size=self.vocab_size,     hidden_size=self.hidden_size,     num_hidden_layers=self.num_hidden_layers,     num_attention_heads=self.num_attention_heads,     ffn_dim=self.intermediate_size,     dropout=self.hidden_dropout_prob,     attention_dropout=self.attention_probs_dropout_prob,     max_position_embeddings=self.max_position_embeddings,     eos_token_id=self.eos_token_id,     bos_token_id=self.bos_token_id,     pad_token_id=self.pad_token_id,     embed_dim=self.embed_dim,     is_encoder_decoder=_lowerCamelCase,     word_embed_proj_dim=self.word_embed_proj_dim,     initializer_range=self.initializer_range,     use_cache=_lowerCamelCase,     )
       lowerCamelCase__     :			int     				=						prepare_opt_inputs_dict(_lowerCamelCase,     _lowerCamelCase					)
       return config, inputs_dict




      def 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__				,     lowerCamelCase__     :			Any     				=						self.prepare_config_and_inputs()
       return config, inputs_dict




      def 			a__							(self,     lowerCamelCase_,     lowerCamelCase_,     lowerCamelCase_					):

       '''simple docstring'''



       lowerCamelCase__     :			List[str]     				=						2_0
       lowerCamelCase__     :			str     				=						model_class_name(_lowerCamelCase					)

       lowerCamelCase__     :			Optional[int]     				=						inputs_dict['input_ids']
       lowerCamelCase__     :			Any     				=						inputs_dict['attention_mask']

       lowerCamelCase__     :			str     				=						model.init_cache(input_ids.shape[0],     _lowerCamelCase					)
       lowerCamelCase__     :			List[str]     				=						jnp.ones((input_ids.shape[0], max_length),     dtype='i4'					)

       lowerCamelCase__     :			Optional[int]     				=						jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1					)[None, :],     (input_ids.shape[0], input_ids.shape[-1] - 1),     )
       lowerCamelCase__     :			Optional[Any]     				=						model(
           input_ids[:, :-1],     attention_mask=_lowerCamelCase,     past_key_values=_lowerCamelCase,     position_ids=_lowerCamelCase,     )

       lowerCamelCase__     :			List[str]     				=						jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]],     dtype='i4'					)
       lowerCamelCase__     :			Tuple     				=						model(
           input_ids[:, -1:],     attention_mask=_lowerCamelCase,     past_key_values=outputs_cache.past_key_values,     position_ids=_lowerCamelCase,     )

       lowerCamelCase__     :			str     				=						model(_lowerCamelCase					)

       lowerCamelCase__     :			Union[str, Any]     				=						np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
       self.parent.assertTrue(diff < 1e-3,     msg=f'''Max diff is {diff}'''					)





      def 			a__							(self,     lowerCamelCase_,     lowerCamelCase_,     lowerCamelCase_					):

       '''simple docstring'''



       lowerCamelCase__     :			str     				=						2_0
       lowerCamelCase__     :			List[Any]     				=						model_class_name(_lowerCamelCase					)

       lowerCamelCase__				,     lowerCamelCase__     :			str     				=						(
           inputs_dict['input_ids'],
           inputs_dict['attention_mask'],
       )

       lowerCamelCase__     :			str     				=						jnp.concatenate(
           [
               attention_mask,
               jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])					),
           ],     axis=-1,     )

       lowerCamelCase__     :			Tuple     				=						model.init_cache(input_ids.shape[0],     _lowerCamelCase					)
       lowerCamelCase__     :			Any     				=						jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1					)[None, :],     (input_ids.shape[0], input_ids.shape[-1] - 1),     )

       lowerCamelCase__     :			Any     				=						model(
           input_ids[:, :-1],     attention_mask=_lowerCamelCase,     past_key_values=_lowerCamelCase,     position_ids=_lowerCamelCase,     )
       lowerCamelCase__     :			str     				=						jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]],     dtype='i4'					)
       lowerCamelCase__     :			int     				=						model(
           input_ids[:, -1:],     past_key_values=outputs_cache.past_key_values,     attention_mask=_lowerCamelCase,     position_ids=_lowerCamelCase,     )

       lowerCamelCase__     :			List[Any]     				=						model(_lowerCamelCase,     attention_mask=_lowerCamelCase					)

       lowerCamelCase__     :			Optional[int]     				=						np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
       self.parent.assertTrue(diff < 1e-3,     msg=f'''Max diff is {diff}'''					)



@require_flax
class 		a_      (	__lowerCAmelCase  ,						unittest.TestCase  ,						__lowerCAmelCase ):







      '''simple docstring'''

      lowerCamelCase__       :				List[Any] =	(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
      lowerCamelCase__       :				Union[str, Any] =	() if is_flax_available() else ()




      def 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__     :			Dict     				=						FlaxOPTModelTester(self					)




      def 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__				,     lowerCamelCase__     :			Optional[int]     				=						self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
        self.model_tester.check_use_cache_forward(_lowerCamelCase,     _lowerCamelCase,     _lowerCamelCase					)




      def 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__				,     lowerCamelCase__     :			Optional[Any]     				=						self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
        self.model_tester.check_use_cache_forward_with_attn_mask(_lowerCamelCase,     _lowerCamelCase,     _lowerCamelCase					)





      @slow
      def 			a__							(self					):

       '''simple docstring'''



       for model_class_name in self.all_model_classes:
        lowerCamelCase__     :			List[Any]     				=						model_class_name.from_pretrained('facebook/opt-125m'					)
        lowerCamelCase__     :			Union[str, Any]     				=						np.ones((1, 1)					) * model.config.eos_token_id
        lowerCamelCase__     :			str     				=						model(_lowerCamelCase					)
        self.assertIsNotNone(_lowerCamelCase					)



@require_sentencepiece
@require_flax
class 		a_      (	unittest.TestCase ):







      '''simple docstring'''

      @slow
      def 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__     :			str     				=						FlaxOPTModel.from_pretrained('facebook/opt-350m'					)
       lowerCamelCase__     :			Union[str, Any]     				=						jnp.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]					)
       lowerCamelCase__     :			Any     				=						model(input_ids=_lowerCamelCase					).last_hidden_state
       lowerCamelCase__     :			Any     				=						(1, 1_1, 5_1_2)
       self.assertEqual(output.shape,     _lowerCamelCase					)
       lowerCamelCase__     :			Tuple     				=						jnp.array(
           [[-0.2_867, -1.9_256, -0.3_062], [-1.2_711, -0.1_337, -0.1_897], [0.4_109, 0.1_187, -1.3_142]]					)
       self.assertTrue(jnp.allclose(output[:, :3, :3],     _lowerCamelCase,     atol=4e-2					)					)



@require_flax
@slow
class 		a_      (	unittest.TestCase ):







      '''simple docstring'''

      def 			a__							(self					):

       '''simple docstring'''



       super().setUp()
       lowerCamelCase__     :			Dict     				=						'facebook/opt-350m'





      def 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__     :			Any     				=						FlaxOPTForCausalLM.from_pretrained(self.path_model					)
       lowerCamelCase__     :			Union[str, Any]     				=						GPTaTokenizer.from_pretrained(self.path_model					)

       lowerCamelCase__     :			Tuple     				=						[
           'Today is a beautiful day and I want to',
           'In the city of',
           'Paris is the capital of France and',
           'Computers and mobile phones have taken',
       ]
       # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
       lowerCamelCase__     :			List[Any]     				=						tokenizer(_lowerCamelCase,     return_tensors='jax',     padding=_lowerCamelCase,     add_special_tokens=_lowerCamelCase					)
       lowerCamelCase__     :			List[str]     				=						model(inputs.input_ids,     attention_mask=inputs.attention_mask					)[0].mean(axis=-1					)
       lowerCamelCase__     :			Optional[Any]     				=						jnp.array(
           [
               [1.3_851, -1_3.8_9_2_3, -1_0.5_2_2_9, -1_0.7_5_3_3, -0.2_309, -1_0.2_3_8_4, -0.5_365, -9.0_947, -5.1_670],
               [-4.7_073, -1_0.6_2_7_6, -3.9_415, -2_1.5_2_4_2, -0.2_822, -0.2_822, -0.2_822, -0.2_822, -0.2_822],
               [0.6_247, -3.4_229, -8.9_179, -1.4_297, -1_4.1_6_5_0, 1.4_146, -9.0_218, -0.2_703, -0.2_703],
               [6.4_783, -1.9_913, -1_0.7_9_2_6, -2.3_336, 1.5_092, -0.9_974, -6.8_213, 1.3_477, 1.3_477],
           ]					)
       self.assertTrue(jnp.allclose(_lowerCamelCase,     _lowerCamelCase,     atol=4e-2					)					)

       lowerCamelCase__     :			Optional[int]     				=						jax.jit(_lowerCamelCase					)
       lowerCamelCase__     :			List[str]     				=						model(inputs.input_ids,     attention_mask=inputs.attention_mask					)[0].mean(axis=-1					)
       self.assertTrue(jnp.allclose(_lowerCamelCase,     _lowerCamelCase,     atol=4e-2					)					)



@require_flax
@slow
class 		a_      (	unittest.TestCase ):







      '''simple docstring'''

      @property
      def 			a__							(self					):

       '''simple docstring'''



       return [
           "Today is a beautiful day and I want",
           "In the city of",
           "Paris is the capital of France and",
           "Computers and mobile phones have taken",
       ]




      def 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__     :			int     				=						'facebook/opt-125m'

       lowerCamelCase__     :			Any     				=						[
           'Today is a beautiful day and I want to',
           'In the city of New York, the city',
           'Paris is the capital of France and the capital',
           'Computers and mobile phones have taken over the',
       ]

       lowerCamelCase__     :			Union[str, Any]     				=						[]

       lowerCamelCase__     :			Any     				=						FlaxOPTForCausalLM.from_pretrained(_lowerCamelCase					)
       lowerCamelCase__     :			Any     				=						GPTaTokenizer.from_pretrained(_lowerCamelCase					)

       for prompt in self.prompts:
        lowerCamelCase__     :			Tuple     				=						tokenizer(_lowerCamelCase,     return_tensors='jax'					).input_ids

        lowerCamelCase__     :			Union[str, Any]     				=						model.generate(_lowerCamelCase,     max_length=1_0					)
        lowerCamelCase__     :			Dict     				=						generated_ids[0]

        lowerCamelCase__     :			Optional[Any]     				=						tokenizer.batch_decode(_lowerCamelCase,     skip_special_tokens=_lowerCamelCase					)
        predicted_outputs += generated_string

       self.assertListEqual(_lowerCamelCase,     _lowerCamelCase					)




      def 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__     :			int     				=						'facebook/opt-350m'

       lowerCamelCase__     :			str     				=						[
           'Today is a beautiful day and I want to',
           'In the city of San Francisco, the city',
           'Paris is the capital of France and the capital',
           'Computers and mobile phones have taken over the',
       ]

       lowerCamelCase__     :			Any     				=						[]
       lowerCamelCase__     :			str     				=						FlaxOPTForCausalLM.from_pretrained(_lowerCamelCase					)
       lowerCamelCase__     :			List[str]     				=						GPTaTokenizer.from_pretrained(_lowerCamelCase					)

       for prompt in self.prompts:
        lowerCamelCase__     :			Optional[Any]     				=						tokenizer(_lowerCamelCase,     return_tensors='jax'					).input_ids

        lowerCamelCase__     :			Dict     				=						model.generate(_lowerCamelCase,     max_length=1_0					)
        lowerCamelCase__     :			Union[str, Any]     				=						generated_ids[0]

        lowerCamelCase__     :			Any     				=						tokenizer.batch_decode(_lowerCamelCase,     skip_special_tokens=_lowerCamelCase					)
        predicted_outputs += generated_string

       self.assertListEqual(_lowerCamelCase,     _lowerCamelCase					)




      def 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__     :			List[str]     				=						'facebook/opt-125m'
       lowerCamelCase__     :			int     				=						[
           'Today is a beautiful day and I want to thank',
           'In the city of Rome Canaver Canaver Canaver Canaver',
       ]
       lowerCamelCase__     :			str     				=						FlaxOPTForCausalLM.from_pretrained(_lowerCamelCase					)
       lowerCamelCase__     :			Tuple     				=						GPTaTokenizer.from_pretrained(_lowerCamelCase					)
       lowerCamelCase__     :			Any     				=						tokenizer(
           [
               'Today is a beautiful day and I want to',
               'In the city of',
           ],     return_tensors='jax',     padding=_lowerCamelCase,     )

       lowerCamelCase__     :			Optional[Any]     				=						jax.jit(model.generate					)

       lowerCamelCase__     :			List[Any]     				=						jit_generate(inputs['input_ids'],     attention_mask=inputs['attention_mask']					).sequences

       lowerCamelCase__     :			List[Any]     				=						tokenizer.batch_decode(_lowerCamelCase,     skip_special_tokens=_lowerCamelCase					)

       self.assertIsNotNone(_lowerCamelCase,     _lowerCamelCase					)





      def 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__     :			Optional[Any]     				=						'facebook/opt-350m'

       lowerCamelCase__     :			Any     				=						GPTaTokenizer.from_pretrained(_lowerCamelCase					)
       lowerCamelCase__     :			Optional[Any]     				=						FlaxOPTForCausalLM.from_pretrained(_lowerCamelCase					)

       lowerCamelCase__     :			Union[str, Any]     				=						'left'

       # use different length sentences to test batching
       lowerCamelCase__     :			Union[str, Any]     				=						[
           'Hello, my dog is a little',
           'Today, I',
       ]

       lowerCamelCase__     :			List[str]     				=						tokenizer(_lowerCamelCase,     return_tensors='jax',     padding=_lowerCamelCase					)
       lowerCamelCase__     :			Optional[int]     				=						inputs['input_ids']

       lowerCamelCase__     :			Any     				=						model.generate(input_ids=_lowerCamelCase,     attention_mask=inputs['attention_mask'],     trace=_lowerCamelCase					)

       lowerCamelCase__     :			Dict     				=						tokenizer(sentences[0],     return_tensors='jax'					).input_ids
       lowerCamelCase__     :			str     				=						model.generate(input_ids=_lowerCamelCase					)

       lowerCamelCase__     :			Tuple     				=						inputs_non_padded.shape[-1] - inputs['attention_mask'][-1].sum()
       lowerCamelCase__     :			Any     				=						tokenizer(sentences[1],     return_tensors='jax'					).input_ids
       lowerCamelCase__     :			Dict     				=						model.generate(input_ids=_lowerCamelCase,     max_length=model.config.max_length - num_paddings					)

       lowerCamelCase__     :			int     				=						tokenizer.batch_decode(outputs[0],     skip_special_tokens=_lowerCamelCase					)
       lowerCamelCase__     :			str     				=						tokenizer.decode(output_non_padded[0][0],     skip_special_tokens=_lowerCamelCase					)
       lowerCamelCase__     :			Tuple     				=						tokenizer.decode(output_padded[0][0],     skip_special_tokens=_lowerCamelCase					)

       lowerCamelCase__     :			Tuple     				=						[
           'Hello, my dog is a little bit of a dork.\nI\'m a little bit',
           'Today, I was in the middle of a conversation with a friend about the',
       ]
       self.assertListEqual(_lowerCamelCase,     _lowerCamelCase					)
       self.assertListEqual(_lowerCamelCase,     [non_padded_sentence, padded_sentence]					)


"""simple docstring"""


import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


a							='pcuenq/k-upscaler'





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase   ,    *, __lowerCAmelCase   ,    __lowerCAmelCase				)							->					Dict:




						'''simple docstring'''



						lowerCamelCase__					={
						    # norm1
						    F'''{diffusers_resnet_prefix}.norm1.linear.weight''': checkpoint[F'''{resnet_prefix}.main.0.mapper.weight'''],
						    F'''{diffusers_resnet_prefix}.norm1.linear.bias''': checkpoint[F'''{resnet_prefix}.main.0.mapper.bias'''],
						    # conv1
						    F'''{diffusers_resnet_prefix}.conv1.weight''': checkpoint[F'''{resnet_prefix}.main.2.weight'''],
						    F'''{diffusers_resnet_prefix}.conv1.bias''': checkpoint[F'''{resnet_prefix}.main.2.bias'''],
						    # norm2
						    F'''{diffusers_resnet_prefix}.norm2.linear.weight''': checkpoint[F'''{resnet_prefix}.main.4.mapper.weight'''],
						    F'''{diffusers_resnet_prefix}.norm2.linear.bias''': checkpoint[F'''{resnet_prefix}.main.4.mapper.bias'''],
						    # conv2
						    F'''{diffusers_resnet_prefix}.conv2.weight''': checkpoint[F'''{resnet_prefix}.main.6.weight'''],
						    F'''{diffusers_resnet_prefix}.conv2.bias''': checkpoint[F'''{resnet_prefix}.main.6.bias'''],
						}

						if resnet.conv_shortcut is not None:
												rv.update(
												    {
												        F'''{diffusers_resnet_prefix}.conv_shortcut.weight''': checkpoint[F'''{resnet_prefix}.skip.weight'''],
												    }				)

						return rv





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    *, __lowerCAmelCase   ,    __lowerCAmelCase				)							->					int:




						'''simple docstring'''



						lowerCamelCase__					,      lowerCamelCase__					,      lowerCamelCase__					=checkpoint[F'''{attention_prefix}.qkv_proj.weight'''].chunk(3   ,    dim=0				)
						lowerCamelCase__					,      lowerCamelCase__					,      lowerCamelCase__					=checkpoint[F'''{attention_prefix}.qkv_proj.bias'''].chunk(3   ,    dim=0				)
						lowerCamelCase__					={
						    # norm
						    F'''{diffusers_attention_prefix}.norm1.linear.weight''': checkpoint[F'''{attention_prefix}.norm_in.mapper.weight'''],
						    F'''{diffusers_attention_prefix}.norm1.linear.bias''': checkpoint[F'''{attention_prefix}.norm_in.mapper.bias'''],
						    # to_q
						    F'''{diffusers_attention_prefix}.attn1.to_q.weight''': weight_q.squeeze(-1				).squeeze(-1				),
						    F'''{diffusers_attention_prefix}.attn1.to_q.bias''': bias_q,
						    # to_k
						    F'''{diffusers_attention_prefix}.attn1.to_k.weight''': weight_k.squeeze(-1				).squeeze(-1				),
						    F'''{diffusers_attention_prefix}.attn1.to_k.bias''': bias_k,
						    # to_v
						    F'''{diffusers_attention_prefix}.attn1.to_v.weight''': weight_v.squeeze(-1				).squeeze(-1				),
						    F'''{diffusers_attention_prefix}.attn1.to_v.bias''': bias_v,
						    # to_out
						    F'''{diffusers_attention_prefix}.attn1.to_out.0.weight''': checkpoint[F'''{attention_prefix}.out_proj.weight''']
						    .squeeze(-1				)
						    .squeeze(-1				),
						    F'''{diffusers_attention_prefix}.attn1.to_out.0.bias''': checkpoint[F'''{attention_prefix}.out_proj.bias'''],
						}

						return rv





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    *, __lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase				)							->					str:




						'''simple docstring'''



						lowerCamelCase__					,      lowerCamelCase__					=checkpoint[F'''{attention_prefix}.kv_proj.weight'''].chunk(2   ,    dim=0				)
						lowerCamelCase__					,      lowerCamelCase__					=checkpoint[F'''{attention_prefix}.kv_proj.bias'''].chunk(2   ,    dim=0				)

						lowerCamelCase__					={
						    # norm2 (ada groupnorm)
						    F'''{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.weight''': checkpoint[
						        F'''{attention_prefix}.norm_dec.mapper.weight'''
						    ],
						    F'''{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.bias''': checkpoint[
						        F'''{attention_prefix}.norm_dec.mapper.bias'''
						    ],
						    # layernorm on encoder_hidden_state
						    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.weight''': checkpoint[
						        F'''{attention_prefix}.norm_enc.weight'''
						    ],
						    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.bias''': checkpoint[
						        F'''{attention_prefix}.norm_enc.bias'''
						    ],
						    # to_q
						    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.weight''': checkpoint[
						        F'''{attention_prefix}.q_proj.weight'''
						    ]
						    .squeeze(-1				)
						    .squeeze(-1				),
						    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.bias''': checkpoint[
						        F'''{attention_prefix}.q_proj.bias'''
						    ],
						    # to_k
						    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.weight''': weight_k.squeeze(-1				).squeeze(-1				),
						    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.bias''': bias_k,
						    # to_v
						    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.weight''': weight_v.squeeze(-1				).squeeze(-1				),
						    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.bias''': bias_v,
						    # to_out
						    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.weight''': checkpoint[
						        F'''{attention_prefix}.out_proj.weight'''
						    ]
						    .squeeze(-1				)
						    .squeeze(-1				),
						    F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.bias''': checkpoint[
						        F'''{attention_prefix}.out_proj.bias'''
						    ],
						}

						return rv





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase				)							->					Optional[int]:




						'''simple docstring'''



						lowerCamelCase__					="inner_model.u_net.u_blocks" if block_type == "up" else "inner_model.u_net.d_blocks"
						lowerCamelCase__					=F'''{block_prefix}.{block_idx}'''

						lowerCamelCase__					={}

						if not hasattr(__lowerCAmelCase   ,    "attentions"				):
												lowerCamelCase__					=1  # resnet only
						elif not block.attentions[0].add_self_attention:
												lowerCamelCase__					=2  # resnet -> cross-attention
						else:
												lowerCamelCase__					=3  # resnet -> self-attention -> cross-attention)

						for resnet_idx, resnet in enumerate(block.resnets				):
												# diffusers_resnet_prefix = f"{diffusers_up_block_prefix}.resnets.{resnet_idx}"
												lowerCamelCase__					=F'''{block_type}_blocks.{block_idx}.resnets.{resnet_idx}'''
												lowerCamelCase__					=n * resnet_idx if block_type == "up" else n * resnet_idx + 1
												lowerCamelCase__					=F'''{block_prefix}.{idx}''' if block_type == "up" else F'''{block_prefix}.{idx}'''

												diffusers_checkpoint.update(
												    resnet_to_diffusers_checkpoint(
												        __lowerCAmelCase   ,    __lowerCAmelCase   ,    diffusers_resnet_prefix=__lowerCAmelCase   ,    resnet_prefix=__lowerCAmelCase				)				)

						if hasattr(__lowerCAmelCase   ,    "attentions"				):
												for attention_idx, attention in enumerate(block.attentions				):
																		lowerCamelCase__					=F'''{block_type}_blocks.{block_idx}.attentions.{attention_idx}'''
																		lowerCamelCase__					=n * attention_idx + 1 if block_type == "up" else n * attention_idx + 2
																		lowerCamelCase__					=F'''{block_prefix}.{idx}'''
																		lowerCamelCase__					=F'''{block_prefix}.{idx }'''
																		lowerCamelCase__					=1 if not attention.add_self_attention else 2
																		lowerCamelCase__					=(
																		    n * attention_idx + cross_attention_index
																		    if block_type == "up"
																		    else n * attention_idx + cross_attention_index + 1
																		)
																		lowerCamelCase__					=F'''{block_prefix}.{idx }'''

																		diffusers_checkpoint.update(
																		    cross_attn_to_diffusers_checkpoint(
																		        __lowerCAmelCase   ,    diffusers_attention_prefix=__lowerCAmelCase   ,    diffusers_attention_index=2   ,    attention_prefix=__lowerCAmelCase   ,    )				)

																		if attention.add_self_attention is True:
																								diffusers_checkpoint.update(
																								    self_attn_to_diffusers_checkpoint(
																								        __lowerCAmelCase   ,    diffusers_attention_prefix=__lowerCAmelCase   ,    attention_prefix=__lowerCAmelCase   ,    )				)

						return diffusers_checkpoint





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase				)							->					Tuple:




						'''simple docstring'''



						lowerCamelCase__					={}

						# pre-processing
						diffusers_checkpoint.update(
						    {
						        "conv_in.weight": checkpoint["inner_model.proj_in.weight"],
						        "conv_in.bias": checkpoint["inner_model.proj_in.bias"],
						    }				)

						# timestep and class embedding
						diffusers_checkpoint.update(
						    {
						        "time_proj.weight": checkpoint["inner_model.timestep_embed.weight"].squeeze(-1				),
						        "time_embedding.linear_1.weight": checkpoint["inner_model.mapping.0.weight"],
						        "time_embedding.linear_1.bias": checkpoint["inner_model.mapping.0.bias"],
						        "time_embedding.linear_2.weight": checkpoint["inner_model.mapping.2.weight"],
						        "time_embedding.linear_2.bias": checkpoint["inner_model.mapping.2.bias"],
						        "time_embedding.cond_proj.weight": checkpoint["inner_model.mapping_cond.weight"],
						    }				)

						# down_blocks
						for down_block_idx, down_block in enumerate(model.down_blocks				):
												diffusers_checkpoint.update(block_to_diffusers_checkpoint(__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    "down"				)				)

						# up_blocks
						for up_block_idx, up_block in enumerate(model.up_blocks				):
												diffusers_checkpoint.update(block_to_diffusers_checkpoint(__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase   ,    "up"				)				)

						# post-processing
						diffusers_checkpoint.update(
						    {
						        "conv_out.weight": checkpoint["inner_model.proj_out.weight"],
						        "conv_out.bias": checkpoint["inner_model.proj_out.bias"],
						    }				)

						return diffusers_checkpoint





def 			lowerCamelCase_ (					__lowerCAmelCase				)							->					Optional[int]:




						'''simple docstring'''



						lowerCamelCase__					=original_config["input_channels"] + original_config["unet_cond_dim"]
						lowerCamelCase__					=original_config["input_channels"] + (1 if original_config["has_variance"] else 0)

						lowerCamelCase__					=original_config["channels"]

						assert (
						    len(set(original_config["depths"]				)				) == 1
						), "UNet2DConditionModel currently do not support blocks with different number of layers"
						lowerCamelCase__					=original_config["depths"][0]

						lowerCamelCase__					=original_config["mapping_cond_dim"]
						lowerCamelCase__					=original_config["cross_cond_dim"]

						lowerCamelCase__					=[]
						lowerCamelCase__					=[]
						for s, c in zip(original_config["self_attn_depths"]   ,    original_config["cross_attn_depths"]				):
												if s:
																		lowerCamelCase__					="self"
																		lowerCamelCase__					="cross" if c else None
												elif c:
																		lowerCamelCase__					="cross"
																		lowerCamelCase__					=None
												else:
																		lowerCamelCase__					=None
																		lowerCamelCase__					=None
												attna_types.append(__lowerCAmelCase				)
												attna_types.append(__lowerCAmelCase				)

						lowerCamelCase__					=UNetaDConditionModel(
						    in_channels=__lowerCAmelCase   ,    out_channels=__lowerCAmelCase   ,    down_block_types=("KDownBlock2D", "KCrossAttnDownBlock2D", "KCrossAttnDownBlock2D", "KCrossAttnDownBlock2D")   ,    mid_block_type=__lowerCAmelCase   ,    up_block_types=("KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KUpBlock2D")   ,    block_out_channels=__lowerCAmelCase   ,    layers_per_block=__lowerCAmelCase   ,    act_fn="gelu"   ,    norm_num_groups=__lowerCAmelCase   ,    cross_attention_dim=__lowerCAmelCase   ,    attention_head_dim=64   ,    time_cond_proj_dim=__lowerCAmelCase   ,    resnet_time_scale_shift="scale_shift"   ,    time_embedding_type="fourier"   ,    timestep_post_act="gelu"   ,    conv_in_kernel=1   ,    conv_out_kernel=1   ,    )

						return unet





def 			lowerCamelCase_ (					__lowerCAmelCase				)							->					int:




						'''simple docstring'''



						lowerCamelCase__					=torch.device("cuda" if torch.cuda.is_available() else "cpu"				)

						lowerCamelCase__					=huggingface_hub.hf_hub_download(__lowerCAmelCase   ,    "config_laion_text_cond_latent_upscaler_2.json"				)
						lowerCamelCase__					=huggingface_hub.hf_hub_download(
						    __lowerCAmelCase   ,    "laion_text_cond_latent_upscaler_2_1_00470000_slim.pth"				)
						print(F'''loading original model configuration from {orig_config_path}'''				)
						print(F'''loading original model checkpoint from {orig_weights_path}'''				)

						print("converting to diffusers unet"				)
						lowerCamelCase__					=K.config.load_config(open(__lowerCAmelCase				)				)["model"]
						lowerCamelCase__					=unet_model_from_original_config(__lowerCAmelCase				)

						lowerCamelCase__					=torch.load(__lowerCAmelCase   ,    map_location=__lowerCAmelCase				)["model_ema"]
						lowerCamelCase__					=unet_to_diffusers_checkpoint(__lowerCAmelCase   ,    __lowerCAmelCase				)

						model.load_state_dict(__lowerCAmelCase   ,    strict=__lowerCAmelCase				)
						model.save_pretrained(args.dump_path				)
						print(F'''saving converted unet model in {args.dump_path}'''				)


if __name__ == "__main__":
	a							=argparse.ArgumentParser()

	parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
	a							=parser.parse_args()

	main(args)


