




'''simple docstring'''


import warnings

from diffusers import StableDiffusionImgaImgPipeline  # noqa F401


warnings.warn(
    'The `image_to_image.py` script is outdated. Please use directly `from diffusers import'
    ' StableDiffusionImg2ImgPipeline` instead.'
)






'''simple docstring'''


import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

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


if is_flax_available():
 import jax
 import jax.numpy as jnp

 from transformers.modeling_flax_pytorch_utils import (
     convert_pytorch_state_dict_to_flax,
     load_flax_weights_in_pytorch_model,
 )
 from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
 import torch







class      SCREAMING_SNAKE_CASE    :






      def __init__( self   :  Optional[Any]						,						lowercase__   :  Tuple						,						lowercase__   :  Any=14						,						lowercase__   :  int=7						,						lowercase__   :  int=True						,						lowercase__   :  List[str]=True						,						lowercase__   :  Tuple=False						,						lowercase__   :  Optional[Any]=True						,						lowercase__   :  Any=99						,						lowercase__   :  Dict=32						,						lowercase__   :  Tuple=4						,						lowercase__   :  List[str]=4						,						lowercase__   :  Optional[Any]=4						,						lowercase__   :  Union[str, Any]=37						,						lowercase__   :  Union[str, Any]="gelu"						,						lowercase__   :  List[Any]=0.1						,						lowercase__   :  Union[str, Any]=0.1						,						lowercase__   :  List[str]=512						,						lowercase__   :  int=0.02						,						):


           '''simple docstring'''

           a_     :							Optional[int]     						=  parent
           a_     :							str     						=  batch_size
           a_     :							Dict     						=  seq_length
           a_     :							Tuple     						=  is_training
           a_     :							Dict     						=  use_input_mask
           a_     :							str     						=  use_token_type_ids
           a_     :							Tuple     						=  use_labels
           a_     :							List[str]     						=  vocab_size
           a_     :							Union[str, Any]     						=  hidden_size
           a_     :							Optional[Any]     						=  rotary_dim
           a_     :							int     						=  num_hidden_layers
           a_     :							int     						=  num_attention_heads
           a_     :							Any     						=  intermediate_size
           a_     :							Any     						=  hidden_act
           a_     :							Tuple     						=  hidden_dropout_prob
           a_     :							Dict     						=  attention_probs_dropout_prob
           a_     :							str     						=  max_position_embeddings
           a_     :							str     						=  initializer_range
           a_     :							Optional[int]     						=  None
           a_     :							Any     						=  vocab_size - 1
           a_     :							Dict     						=  vocab_size - 1
           a_     :							Tuple     						=  vocab_size - 1






      def 							lowercase_						( self   :  List[str]       ):


           '''simple docstring'''

           a_     :							Any     						=  ids_tensor([self.batch_size, self.seq_length]						,						self.vocab_size       )

           a_     :							str     						=  None
           if self.use_input_mask:
                a_     :							Tuple     						=  random_attention_mask([self.batch_size, self.seq_length]       )

           a_     :							Optional[Any]     						=  GPTJConfig(
               vocab_size=self.vocab_size						,						n_embd=self.hidden_size						,						n_layer=self.num_hidden_layers						,						n_head=self.num_attention_heads						,						n_positions=self.max_position_embeddings						,						use_cache=lowercase__						,						bos_token_id=self.bos_token_id						,						eos_token_id=self.eos_token_id						,						pad_token_id=self.pad_token_id						,						rotary_dim=self.rotary_dim						,						)

           return (config, input_ids, input_mask)






      def 							lowercase_						( self   :  str       ):


           '''simple docstring'''

           a_     :							Optional[Any]     						=  self.prepare_config_and_inputs()
           a_						,     a_						,     a_     :							Dict     						=  config_and_inputs
           a_     :							str     						=  {"""input_ids""": input_ids, """attention_mask""": attention_mask}
           return config, inputs_dict






      def 							lowercase_						( self   :  List[str]						,						lowercase__   :  List[str]						,						lowercase__   :  Tuple						,						lowercase__   :  List[Any]						,						lowercase__   :  Any       ):


           '''simple docstring'''

           a_     :							str     						=  20
           a_     :							Tuple     						=  model_class_name(lowercase__       )

           a_     :							Union[str, Any]     						=  model.init_cache(input_ids.shape[0]						,						lowercase__       )
           a_     :							int     						=  jnp.ones((input_ids.shape[0], max_decoder_length)						,						dtype="""i4"""       )

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

           a_     :							Dict     						=  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]						,						dtype="""i4"""       )
           a_     :							int     						=  model(
               input_ids[:, -1:]						,						attention_mask=lowercase__						,						past_key_values=outputs_cache.past_key_values						,						position_ids=lowercase__						,						)

           a_     :							Dict     						=  model(lowercase__       )

           a_     :							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 							lowercase_						( self   :  Any						,						lowercase__   :  int						,						lowercase__   :  List[Any]						,						lowercase__   :  Dict						,						lowercase__   :  List[str]       ):


           '''simple docstring'''

           a_     :							Dict     						=  20
           a_     :							str     						=  model_class_name(lowercase__       )

           a_     :							Union[str, Any]     						=  jnp.concatenate(
               [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])       )]						,						axis=-1						,						)

           a_     :							Optional[int]     						=  model.init_cache(input_ids.shape[0]						,						lowercase__       )
           a_     :							Tuple     						=  jnp.broadcast_to(
               jnp.arange(input_ids.shape[-1] - 1       )[None, :]						,						(input_ids.shape[0], input_ids.shape[-1] - 1)       )

           a_     :							Union[str, Any]     						=  model(
               input_ids[:, :-1]						,						attention_mask=lowercase__						,						past_key_values=lowercase__						,						position_ids=lowercase__						,						)
           a_     :							List[str]     						=  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]						,						dtype="""i4"""       )
           a_     :							List[str]     						=  model(
               input_ids[:, -1:]						,						past_key_values=outputs_cache.past_key_values						,						attention_mask=lowercase__						,						position_ids=lowercase__						,						)

           a_     :							List[str]     						=  model(lowercase__						,						attention_mask=lowercase__       )

           a_     :							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}"       )







@require_flax
class      SCREAMING_SNAKE_CASE    (						snake_case_							,      snake_case_							,      unittest.TestCase				):
      __magic_name__  :       List[str]   							=					(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
      __magic_name__  :       str   							=					(FlaxGPTJForCausalLM,) if is_flax_available() else ()






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


           '''simple docstring'''

           a_     :							str     						=  FlaxGPTJModelTester(self       )






      def 							lowercase_						( self   :  Optional[int]       ):


           '''simple docstring'''

           for model_class_name in self.all_model_classes:
                a_						,     a_						,     a_     :							List[Any]     						=  self.model_tester.prepare_config_and_inputs()
                self.model_tester.check_use_cache_forward(lowercase__						,						lowercase__						,						lowercase__						,						lowercase__       )






      def 							lowercase_						( self   :  List[Any]       ):


           '''simple docstring'''

           for model_class_name in self.all_model_classes:
                a_						,     a_						,     a_     :							List[str]     						=  self.model_tester.prepare_config_and_inputs()
                self.model_tester.check_use_cache_forward_with_attn_mask(
                    lowercase__						,						lowercase__						,						lowercase__						,						lowercase__       )






      @tooslow
      def 							lowercase_						( self   :  Any       ):


           '''simple docstring'''

           a_     :							Any     						=  GPTaTokenizer.from_pretrained("""gpt2"""						,						pad_token="""<|endoftext|>"""						,						padding_side="""left"""       )
           a_     :							Optional[Any]     						=  tokenizer(["""Hello this is a long string""", """Hey"""]						,						return_tensors="""np"""						,						padding=lowercase__						,						truncation=lowercase__       )

           a_     :							Union[str, Any]     						=  FlaxGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""       )
           a_     :							List[str]     						=  False
           a_     :							Optional[int]     						=  model.config.eos_token_id

           a_     :							List[Any]     						=  jax.jit(model.generate       )

           a_     :							Dict     						=  jit_generate(
               inputs["""input_ids"""]						,						attention_mask=inputs["""attention_mask"""]						,						pad_token_id=tokenizer.pad_token_id       ).sequences

           a_     :							Tuple     						=  tokenizer.batch_decode(lowercase__						,						skip_special_tokens=lowercase__       )

           a_     :							List[str]     						=  [
               """Hello this is a long string of text.\n\nI'm trying to get the text of the""",
               """Hey, I'm a little late to the party. I'm going to""",
           ]

           self.assertListEqual(lowercase__						,						lowercase__       )






      @is_pt_flax_cross_test
      def 							lowercase_						( self   :  Dict       ):


           '''simple docstring'''

           a_						,     a_     :							Dict     						=  self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                with self.subTest(model_class.__name__       ):
                     # prepare inputs
                     a_     :							Optional[Any]     						=  self._prepare_for_class(lowercase__						,						lowercase__       )
                     a_     :							int     						=  {k: torch.tensor(v.tolist()       ) for k, v in prepared_inputs_dict.items()}

                     # load corresponding PyTorch class
                     a_     :							Optional[Any]     						=  model_class.__name__[4:]  # Skip the "Flax" at the beginning
                     a_     :							Any     						=  getattr(lowercase__						,						lowercase__       )

                     a_						,     a_     :							Tuple     						=  pt_inputs["""input_ids"""].shape
                     a_     :							List[str]     						=  np.random.randint(0						,						seq_length - 1						,						size=(batch_size,)       )
                     for batch_idx, start_index in enumerate(lowercase__       ):
                          a_     :							List[str]     						=  0
                          a_     :							Optional[Any]     						=  1
                          a_     :							Optional[int]     						=  0
                          a_     :							List[str]     						=  1
                     a_     :							Any     						=  pt_model_class(lowercase__       ).eval()
                     a_     :							Optional[Any]     						=  model_class(lowercase__						,						dtype=jnp.floataa       )

                     a_     :							Any     						=  convert_pytorch_state_dict_to_flax(pt_model.state_dict()						,						lowercase__       )
                     a_     :							Tuple     						=  fx_state

                     with torch.no_grad():
                          a_     :							Dict     						=  pt_model(**lowercase__       ).to_tuple()

                     a_     :							Any     						=  fx_model(**lowercase__       ).to_tuple()
                     self.assertEqual(len(lowercase__       )						,						len(lowercase__       )						,						"""Output lengths differ between Flax and PyTorch"""       )
                     for fx_output, pt_output in zip(lowercase__						,						lowercase__       ):
                          self.assert_almost_equals(fx_output[:, -1]						,						pt_output[:, -1].numpy()						,						4e-2       )

                     with tempfile.TemporaryDirectory() as tmpdirname:
                          pt_model.save_pretrained(lowercase__       )
                          a_     :							Union[str, Any]     						=  model_class.from_pretrained(lowercase__						,						from_pt=lowercase__       )

                     a_     :							Union[str, Any]     						=  fx_model_loaded(**lowercase__       ).to_tuple()
                     self.assertEqual(
                         len(lowercase__       )						,						len(lowercase__       )						,						"""Output lengths differ between Flax and PyTorch"""       )
                     for fx_output_loaded, pt_output in zip(lowercase__						,						lowercase__       ):
                          self.assert_almost_equals(fx_output_loaded[:, -1]						,						pt_output[:, -1].numpy()						,						4e-2       )






      @is_pt_flax_cross_test
      def 							lowercase_						( self   :  Union[str, Any]       ):


           '''simple docstring'''

           a_						,     a_     :							Optional[int]     						=  self.model_tester.prepare_config_and_inputs_for_common()
           for model_class in self.all_model_classes:
                with self.subTest(model_class.__name__       ):
                     # prepare inputs
                     a_     :							List[Any]     						=  self._prepare_for_class(lowercase__						,						lowercase__       )
                     a_     :							str     						=  {k: torch.tensor(v.tolist()       ) for k, v in prepared_inputs_dict.items()}

                     # load corresponding PyTorch class
                     a_     :							str     						=  model_class.__name__[4:]  # Skip the "Flax" at the beginning
                     a_     :							Optional[Any]     						=  getattr(lowercase__						,						lowercase__       )

                     a_     :							Optional[Any]     						=  pt_model_class(lowercase__       ).eval()
                     a_     :							Any     						=  model_class(lowercase__						,						dtype=jnp.floataa       )

                     a_     :							int     						=  load_flax_weights_in_pytorch_model(lowercase__						,						fx_model.params       )
                     a_						,     a_     :							Any     						=  pt_inputs["""input_ids"""].shape
                     a_     :							Optional[Any]     						=  np.random.randint(0						,						seq_length - 1						,						size=(batch_size,)       )
                     for batch_idx, start_index in enumerate(lowercase__       ):
                          a_     :							Union[str, Any]     						=  0
                          a_     :							List[str]     						=  1
                          a_     :							Any     						=  0
                          a_     :							str     						=  1

                     # make sure weights are tied in PyTorch
                     pt_model.tie_weights()

                     with torch.no_grad():
                          a_     :							List[str]     						=  pt_model(**lowercase__       ).to_tuple()

                     a_     :							List[Any]     						=  fx_model(**lowercase__       ).to_tuple()
                     self.assertEqual(len(lowercase__       )						,						len(lowercase__       )						,						"""Output lengths differ between Flax and PyTorch"""       )
                     for fx_output, pt_output in zip(lowercase__						,						lowercase__       ):
                          self.assert_almost_equals(fx_output[:, -1]						,						pt_output[:, -1].numpy()						,						4e-2       )

                     with tempfile.TemporaryDirectory() as tmpdirname:
                          fx_model.save_pretrained(lowercase__       )
                          a_     :							str     						=  pt_model_class.from_pretrained(lowercase__						,						from_flax=lowercase__       )

                     with torch.no_grad():
                          a_     :							Tuple     						=  pt_model_loaded(**lowercase__       ).to_tuple()

                     self.assertEqual(
                         len(lowercase__       )						,						len(lowercase__       )						,						"""Output lengths differ between Flax and PyTorch"""       )
                     for fx_output, pt_output in zip(lowercase__						,						lowercase__       ):
                          self.assert_almost_equals(fx_output[:, -1]						,						pt_output[:, -1].numpy()						,						4e-2       )






      @tooslow
      def 							lowercase_						( self   :  Optional[int]       ):


           '''simple docstring'''

           for model_class_name in self.all_model_classes:
                a_     :							Union[str, Any]     						=  model_class_name.from_pretrained("""EleutherAI/gpt-j-6B"""       )
                a_     :							str     						=  model(np.ones((1, 1)       )       )
                self.assertIsNotNone(lowercase__       )
