



'''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 	lowerCAmelCase_:
  '''simple docstring'''




  def __init__(   self  ,__UpperCAmelCase  ,__UpperCAmelCase=14  ,__UpperCAmelCase=7  ,__UpperCAmelCase=True  ,__UpperCAmelCase=True  ,__UpperCAmelCase=False  ,__UpperCAmelCase=True  ,__UpperCAmelCase=99  ,__UpperCAmelCase=32  ,__UpperCAmelCase=4  ,__UpperCAmelCase=4  ,__UpperCAmelCase=4  ,__UpperCAmelCase=37  ,__UpperCAmelCase="gelu"  ,__UpperCAmelCase=0.1  ,__UpperCAmelCase=0.1  ,__UpperCAmelCase=512  ,__UpperCAmelCase=0.0_2  ,)   ->		Optional[int]:
      lowerCAmelCase__			:  int											= parent
      lowerCAmelCase__			:  int											= batch_size
      lowerCAmelCase__			:  str											= seq_length
      lowerCAmelCase__			:  str											= is_training
      lowerCAmelCase__			:  Any											= use_input_mask
      lowerCAmelCase__			:  Union[str, Any]											= use_token_type_ids
      lowerCAmelCase__			:  List[str]											= use_labels
      lowerCAmelCase__			:  List[str]											= vocab_size
      lowerCAmelCase__			:  Any											= hidden_size
      lowerCAmelCase__			:  int											= rotary_dim
      lowerCAmelCase__			:  Dict											= num_hidden_layers
      lowerCAmelCase__			:  int											= num_attention_heads
      lowerCAmelCase__			:  Union[str, Any]											= intermediate_size
      lowerCAmelCase__			:  int											= hidden_act
      lowerCAmelCase__			:  Optional[int]											= hidden_dropout_prob
      lowerCAmelCase__			:  List[Any]											= attention_probs_dropout_prob
      lowerCAmelCase__			:  Tuple											= max_position_embeddings
      lowerCAmelCase__			:  List[str]											= initializer_range
      lowerCAmelCase__			:  Union[str, Any]											= None
      lowerCAmelCase__			:  List[Any]											= vocab_size - 1
      lowerCAmelCase__			:  str											= vocab_size - 1
      lowerCAmelCase__			:  List[Any]											= vocab_size - 1




  def 							UpperCAmelCase_     (   self				)   ->		Union[str, Any]:
      lowerCAmelCase__			:  Optional[Any]											= ids_tensor([self.batch_size, self.seq_length]  ,self.vocab_size				)

      lowerCAmelCase__			:  Dict											= None
      if self.use_input_mask:
          lowerCAmelCase__			:  List[Any]											= random_attention_mask([self.batch_size, self.seq_length]				)

      lowerCAmelCase__			:  Union[str, 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=__UpperCAmelCase  ,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 							UpperCAmelCase_     (   self				)   ->		str:
      lowerCAmelCase__			:  Optional[Any]											= self.prepare_config_and_inputs()
      lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__			:  Dict											= config_and_inputs
      lowerCAmelCase__			:  List[str]											= {"""input_ids""": input_ids, """attention_mask""": attention_mask}
      return config, inputs_dict




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase				)   ->		Optional[Any]:
      lowerCAmelCase__			:  Dict											= 20
      lowerCAmelCase__			:  Optional[Any]											= model_class_name(__UpperCAmelCase				)

      lowerCAmelCase__			:  List[str]											= model.init_cache(input_ids.shape[0]  ,__UpperCAmelCase				)
      lowerCAmelCase__			:  Dict											= jnp.ones((input_ids.shape[0], max_decoder_length)  ,dtype="""i4"""				)

      lowerCAmelCase__			:  Tuple											= jnp.broadcast_to(
          jnp.arange(input_ids.shape[-1] - 1				)[None, :]  ,(input_ids.shape[0], input_ids.shape[-1] - 1)				)
      lowerCAmelCase__			:  Union[str, Any]											= model(
          input_ids[:, :-1]  ,attention_mask=__UpperCAmelCase  ,past_key_values=__UpperCAmelCase  ,position_ids=__UpperCAmelCase  ,)

      lowerCAmelCase__			:  Dict											= jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,dtype="""i4"""				)
      lowerCAmelCase__			:  List[Any]											= model(
          input_ids[:, -1:]  ,attention_mask=__UpperCAmelCase  ,past_key_values=outputs_cache.past_key_values  ,position_ids=__UpperCAmelCase  ,)

      lowerCAmelCase__			:  Dict											= model(__UpperCAmelCase				)

      lowerCAmelCase__			:  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}"""				)






  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase				)   ->		int:
      lowerCAmelCase__			:  Dict											= 20
      lowerCAmelCase__			:  Optional[Any]											= model_class_name(__UpperCAmelCase				)

      lowerCAmelCase__			:  int											= jnp.concatenate(
          [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])				)]  ,axis=-1  ,)

      lowerCAmelCase__			:  List[str]											= model.init_cache(input_ids.shape[0]  ,__UpperCAmelCase				)
      lowerCAmelCase__			:  Any											= jnp.broadcast_to(
          jnp.arange(input_ids.shape[-1] - 1				)[None, :]  ,(input_ids.shape[0], input_ids.shape[-1] - 1)				)

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

      lowerCAmelCase__			:  List[str]											= model(__UpperCAmelCase  ,attention_mask=__UpperCAmelCase				)

      lowerCAmelCase__			:  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 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			unittest.TestCase					):
  '''simple docstring'''




  __lowercase							:				Dict           =				(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
  __lowercase							:				Optional[Any]           =				(FlaxGPTJForCausalLM,) if is_flax_available() else ()




  def 							UpperCAmelCase_     (   self				)   ->		List[str]:
      lowerCAmelCase__			:  Optional[Any]											= FlaxGPTJModelTester(self				)




  def 							UpperCAmelCase_     (   self				)   ->		int:
      for model_class_name in self.all_model_classes:
          lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__			:  Dict											= self.model_tester.prepare_config_and_inputs()
          self.model_tester.check_use_cache_forward(__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self				)   ->		str:
      for model_class_name in self.all_model_classes:
          lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__			:  str											= self.model_tester.prepare_config_and_inputs()
          self.model_tester.check_use_cache_forward_with_attn_mask(
              __UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase				)




  @tooslow
  def 							UpperCAmelCase_     (   self				)   ->		Union[str, Any]:
      lowerCAmelCase__			:  Tuple											= GPTaTokenizer.from_pretrained("""gpt2"""  ,pad_token="""<|endoftext|>"""  ,padding_side="""left"""				)
      lowerCAmelCase__			:  Optional[int]											= tokenizer(["""Hello this is a long string""", """Hey"""]  ,return_tensors="""np"""  ,padding=__UpperCAmelCase  ,truncation=__UpperCAmelCase				)

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

      lowerCAmelCase__			:  str											= jax.jit(model.generate				)

      lowerCAmelCase__			:  Optional[Any]											= jit_generate(
          inputs["""input_ids"""]  ,attention_mask=inputs["""attention_mask"""]  ,pad_token_id=tokenizer.pad_token_id				).sequences

      lowerCAmelCase__			:  str											= tokenizer.batch_decode(__UpperCAmelCase  ,skip_special_tokens=__UpperCAmelCase				)

      lowerCAmelCase__			:  Dict											= [
          """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(__UpperCAmelCase  ,__UpperCAmelCase				)




  @is_pt_flax_cross_test
  def 							UpperCAmelCase_     (   self				)   ->		List[str]:
      lowerCAmelCase__				,       lowerCAmelCase__			:  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
              lowerCAmelCase__			:  Dict											= self._prepare_for_class(__UpperCAmelCase  ,__UpperCAmelCase				)
              lowerCAmelCase__			:  Optional[int]											= {k: torch.tensor(v.tolist()				) for k, v in prepared_inputs_dict.items()}

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

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

              lowerCAmelCase__			:  str											= convert_pytorch_state_dict_to_flax(pt_model.state_dict()  ,__UpperCAmelCase				)
              lowerCAmelCase__			:  Optional[int]											= fx_state

              with torch.no_grad():
                  lowerCAmelCase__			:  str											= pt_model(**__UpperCAmelCase				).to_tuple()

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

              with tempfile.TemporaryDirectory() as tmpdirname:
                  pt_model.save_pretrained(__UpperCAmelCase				)
                  lowerCAmelCase__			:  Tuple											= model_class.from_pretrained(__UpperCAmelCase  ,from_pt=__UpperCAmelCase				)

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




  @is_pt_flax_cross_test
  def 							UpperCAmelCase_     (   self				)   ->		Any:
      lowerCAmelCase__				,       lowerCAmelCase__			:  List[Any]											= 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
              lowerCAmelCase__			:  Any											= self._prepare_for_class(__UpperCAmelCase  ,__UpperCAmelCase				)
              lowerCAmelCase__			:  Union[str, Any]											= {k: torch.tensor(v.tolist()				) for k, v in prepared_inputs_dict.items()}

              # load corresponding PyTorch class
              lowerCAmelCase__			:  str											= model_class.__name__[4:]  # Skip the "Flax" at the beginning
              lowerCAmelCase__			:  int											= getattr(__UpperCAmelCase  ,__UpperCAmelCase				)

              lowerCAmelCase__			:  Optional[Any]											= pt_model_class(__UpperCAmelCase				).eval()
              lowerCAmelCase__			:  str											= model_class(__UpperCAmelCase  ,dtype=jnp.floataa				)

              lowerCAmelCase__			:  Optional[int]											= load_flax_weights_in_pytorch_model(__UpperCAmelCase  ,fx_model.params				)
              lowerCAmelCase__				,       lowerCAmelCase__			:  Optional[Any]											= pt_inputs["""input_ids"""].shape
              lowerCAmelCase__			:  Dict											= np.random.randint(0  ,seq_length - 1  ,size=(batch_size,)				)
              for batch_idx, start_index in enumerate(__UpperCAmelCase				):
                  lowerCAmelCase__			:  int											= 0
                  lowerCAmelCase__			:  Optional[int]											= 1
                  lowerCAmelCase__			:  Tuple											= 0
                  lowerCAmelCase__			:  Optional[Any]											= 1

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

              with torch.no_grad():
                  lowerCAmelCase__			:  List[str]											= pt_model(**__UpperCAmelCase				).to_tuple()

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

              with tempfile.TemporaryDirectory() as tmpdirname:
                  fx_model.save_pretrained(__UpperCAmelCase				)
                  lowerCAmelCase__			:  Any											= pt_model_class.from_pretrained(__UpperCAmelCase  ,from_flax=__UpperCAmelCase				)

              with torch.no_grad():
                  lowerCAmelCase__			:  str											= pt_model_loaded(**__UpperCAmelCase				).to_tuple()

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






  @tooslow
  def 							UpperCAmelCase_     (   self				)   ->		int:
      for model_class_name in self.all_model_classes:
          lowerCAmelCase__			:  Optional[int]											= model_class_name.from_pretrained("""EleutherAI/gpt-j-6B"""				)
          lowerCAmelCase__			:  Dict											= model(np.ones((1, 1)				)				)
          self.assertIsNotNone(__UpperCAmelCase				)







'''simple docstring'''





import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin






@skip_mps
class 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			unittest.TestCase					):
  '''simple docstring'''




  __lowercase							:				List[str]           =				IFInpaintingSuperResolutionPipeline
  __lowercase							:				int           =				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
  __lowercase							:				List[str]           =				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'''original_image'''}					)
  __lowercase							:				Optional[Any]           =				PipelineTesterMixin.required_optional_params - {'''latents'''}




  def 							UpperCAmelCase_     (   self				)   ->		Any:
      return self._get_superresolution_dummy_components()




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase=0				)   ->		List[Any]:
      if str(__UpperCAmelCase				).startswith("""mps"""				):
          lowerCAmelCase__			:  Any											= torch.manual_seed(__UpperCAmelCase				)
      else:
          lowerCAmelCase__			:  Dict											= torch.Generator(device=__UpperCAmelCase				).manual_seed(__UpperCAmelCase				)

      lowerCAmelCase__			:  Tuple											= floats_tensor((1, 3, 16, 16)  ,rng=random.Random(__UpperCAmelCase				)				).to(__UpperCAmelCase				)
      lowerCAmelCase__			:  Union[str, Any]											= floats_tensor((1, 3, 32, 32)  ,rng=random.Random(__UpperCAmelCase				)				).to(__UpperCAmelCase				)
      lowerCAmelCase__			:  Any											= floats_tensor((1, 3, 32, 32)  ,rng=random.Random(__UpperCAmelCase				)				).to(__UpperCAmelCase				)

      lowerCAmelCase__			:  Dict											= {
          """prompt""": """A painting of a squirrel eating a burger""",
          """image""": image,
          """original_image""": original_image,
          """mask_image""": mask_image,
          """generator""": generator,
          """num_inference_steps""": 2,
          """output_type""": """numpy""",
      }

      return inputs




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




  def 							UpperCAmelCase_     (   self				)   ->		Optional[int]:
      self._test_save_load_optional_components()




  @unittest.skipIf(torch_device != """cuda"""  ,reason="""float16 requires CUDA"""				)
  def 							UpperCAmelCase_     (   self				)   ->		List[Any]:
      # 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 							UpperCAmelCase_     (   self				)   ->		str:
      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2				)




  def 							UpperCAmelCase_     (   self				)   ->		List[Any]:
      self._test_save_load_local()






  def 							UpperCAmelCase_     (   self				)   ->		int:
      self._test_inference_batch_single_identical(
          expected_max_diff=1E-2  ,)


