


"""simple docstring"""






import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, 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.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

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=5     ,					__UpperCAmelCase=4     ,					__UpperCAmelCase=37     ,					__UpperCAmelCase="gelu"     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=512     ,					__UpperCAmelCase=0.02     ,					)							->				Tuple:
          _a     						=					parent
          _a     						=					batch_size
          _a     						=					seq_length
          _a     						=					is_training
          _a     						=					use_input_mask
          _a     						=					use_token_type_ids
          _a     						=					use_labels
          _a     						=					vocab_size
          _a     						=					hidden_size
          _a     						=					num_hidden_layers
          _a     						=					num_attention_heads
          _a     						=					intermediate_size
          _a     						=					hidden_act
          _a     						=					hidden_dropout_prob
          _a     						=					attention_probs_dropout_prob
          _a     						=					max_position_embeddings
          _a     						=					initializer_range
          _a     						=					None
          _a     						=					vocab_size - 1
          _a     						=					vocab_size - 1
          _a     						=					vocab_size - 1


     def 				_UpperCAmelCase   (   self				)							->				str:
          _a     						=					ids_tensor([self.batch_size, self.seq_length]     ,					self.vocab_size				)

          _a     						=					None
          if self.use_input_mask:
               _a     						=					random_attention_mask([self.batch_size, self.seq_length]				)

          _a     						=					GPTaConfig(
              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     ,					)

          return (config, input_ids, input_mask)


     def 				_UpperCAmelCase   (   self				)							->				Tuple:
          _a     						=					self.prepare_config_and_inputs()
          _a						,   _a						,   _a     						=					config_and_inputs
          _a     						=					{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
          return config, inputs_dict


     def 				_UpperCAmelCase   (   self				)							->				str:
          _a						,   _a						,   _a     						=					self.prepare_config_and_inputs()

          _a     						=					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
          _a     						=					ids_tensor([self.batch_size, self.seq_length]     ,					vocab_size=2				)

          return (
              config,
              input_ids,
              attention_mask,
              encoder_hidden_states,
              encoder_attention_mask,
          )


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)							->				List[Any]:
          _a     						=					20
          _a     						=					model_class_name(__UpperCAmelCase				)

          _a     						=					model.init_cache(input_ids.shape[0]     ,					__UpperCAmelCase				)
          _a     						=					jnp.ones((input_ids.shape[0], max_decoder_length)     ,					dtype='''i4'''				)

          _a     						=					jnp.broadcast_to(
              jnp.arange(input_ids.shape[-1] - 1				)[None, :]     ,					(input_ids.shape[0], input_ids.shape[-1] - 1)				)
          _a     						=					model(
              input_ids[:, :-1]     ,					attention_mask=__UpperCAmelCase     ,					past_key_values=__UpperCAmelCase     ,					position_ids=__UpperCAmelCase     ,					)

          _a     						=					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]     ,					dtype='''i4'''				)
          _a     						=					model(
              input_ids[:, -1:]     ,					attention_mask=__UpperCAmelCase     ,					past_key_values=outputs_cache.past_key_values     ,					position_ids=__UpperCAmelCase     ,					)

          _a     						=					model(__UpperCAmelCase				)

          _a     						=					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				)							->				List[str]:
          _a     						=					20
          _a     						=					model_class_name(__UpperCAmelCase				)

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

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

          _a     						=					model(
              input_ids[:, :-1]     ,					attention_mask=__UpperCAmelCase     ,					past_key_values=__UpperCAmelCase     ,					position_ids=__UpperCAmelCase     ,					)
          _a     						=					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]     ,					dtype='''i4'''				)
          _a     						=					model(
              input_ids[:, -1:]     ,					past_key_values=outputs_cache.past_key_values     ,					attention_mask=__UpperCAmelCase     ,					position_ids=__UpperCAmelCase     ,					)

          _a     						=					model(__UpperCAmelCase     ,					attention_mask=__UpperCAmelCase				)

          _a     						=					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	(       a__	,       a__	,       unittest.TestCase					):

     '''simple docstring'''
     A_							:						Tuple     =		(FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
     A_							:						Dict     =		(FlaxGPTaLMHeadModel,) if is_flax_available() else ()


     def 				_UpperCAmelCase   (   self				)							->				List[str]:
          _a     						=					FlaxGPTaModelTester(self				)


     def 				_UpperCAmelCase   (   self				)							->				Tuple:
          for model_class_name in self.all_model_classes:
               _a						,   _a						,   _a     						=					self.model_tester.prepare_config_and_inputs()
               self.model_tester.check_use_cache_forward(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				Union[str, Any]:
          for model_class_name in self.all_model_classes:
               _a						,   _a						,   _a     						=					self.model_tester.prepare_config_and_inputs()
               self.model_tester.check_use_cache_forward_with_attn_mask(
                   __UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)


     @slow
     def 				_UpperCAmelCase   (   self				)							->				List[str]:
          _a     						=					GPTaTokenizer.from_pretrained('''gpt2'''     ,					pad_token='''</s>'''     ,					padding_side='''left'''				)
          _a     						=					tokenizer(['''Hello this is a long string''', '''Hey''']     ,					return_tensors='''np'''     ,					padding=__UpperCAmelCase     ,					truncation=__UpperCAmelCase				)

          _a     						=					FlaxGPTaLMHeadModel.from_pretrained('''gpt2'''				)
          _a     						=					False
          _a     						=					model.config.eos_token_id

          _a     						=					jax.jit(model.generate				)

          _a     						=					jit_generate(inputs['''input_ids''']     ,					attention_mask=inputs['''attention_mask''']				).sequences

          _a     						=					tokenizer.batch_decode(__UpperCAmelCase     ,					skip_special_tokens=__UpperCAmelCase				)

          _a     						=					[
              '''Hello this is a long string of words. I\'m going to start with the first one.\n''',
              '''Hey, I\'m not sure if I\'m going to be able to do''',
          ]

          self.assertListEqual(__UpperCAmelCase     ,					__UpperCAmelCase				)


     @is_pt_flax_cross_test
     def 				_UpperCAmelCase   (   self				)							->				Dict:
          _a						,   _a     						=					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     						=					self._prepare_for_class(__UpperCAmelCase     ,					__UpperCAmelCase				)
                    _a     						=					{k: torch.tensor(v.tolist()				) for k, v in prepared_inputs_dict.items()}

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

                    _a						,   _a     						=					pt_inputs['''input_ids'''].shape
                    _a     						=					np.random.randint(0     ,					seq_length - 1     ,					size=(batch_size,)				)
                    for batch_idx, start_index in enumerate(__UpperCAmelCase				):
                         _a     						=					0
                         _a     						=					1
                         _a     						=					0
                         _a     						=					1
                    _a     						=					pt_model_class(__UpperCAmelCase				).eval()
                    _a     						=					model_class(__UpperCAmelCase     ,					dtype=jnp.floataa				)

                    _a     						=					convert_pytorch_state_dict_to_flax(pt_model.state_dict()     ,					__UpperCAmelCase				)
                    _a     						=					fx_state

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

                    _a     						=					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				)
                         _a     						=					model_class.from_pretrained(__UpperCAmelCase     ,					from_pt=__UpperCAmelCase				)

                    _a     						=					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				)							->				str:
          _a						,   _a     						=					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     						=					self._prepare_for_class(__UpperCAmelCase     ,					__UpperCAmelCase				)
                    _a     						=					{k: torch.tensor(v.tolist()				) for k, v in prepared_inputs_dict.items()}

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

                    _a     						=					pt_model_class(__UpperCAmelCase				).eval()
                    _a     						=					model_class(__UpperCAmelCase     ,					dtype=jnp.floataa				)

                    _a     						=					load_flax_weights_in_pytorch_model(__UpperCAmelCase     ,					fx_model.params				)
                    _a						,   _a     						=					pt_inputs['''input_ids'''].shape
                    _a     						=					np.random.randint(0     ,					seq_length - 1     ,					size=(batch_size,)				)
                    for batch_idx, start_index in enumerate(__UpperCAmelCase				):
                         _a     						=					0
                         _a     						=					1
                         _a     						=					0
                         _a     						=					1

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

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

                    _a     						=					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				)
                         _a     						=					pt_model_class.from_pretrained(__UpperCAmelCase     ,					from_flax=__UpperCAmelCase				)

                    with torch.no_grad():
                         _a     						=					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				)





     @slow
     def 				_UpperCAmelCase   (   self				)							->				str:
          for model_class_name in self.all_model_classes:
               _a     						=					model_class_name.from_pretrained('''gpt2'''     ,					from_pt=__UpperCAmelCase				)
               _a     						=					model(np.ones((1, 1)				)				)
               self.assertIsNotNone(__UpperCAmelCase				)



"""simple docstring"""






import math
import os

import cva
import numpy as np

__snake_case												=							255.0
def    A_ ( _lowerCAmelCase  :       float,  _lowerCAmelCase  :       float							):






     """simple docstring"""

     _a     						=					np.mean((original - contrast) ** 2							)
     if mse == 0:
          return 1_00

     return 20 * math.logaa(PIXEL_MAX / math.sqrt(_lowerCAmelCase							)							)
def    A_ ( ):






     """simple docstring"""

     _a     						=					os.path.dirname(os.path.realpath(_lowerCAmelCase							)							)
     # Loading images (original image and compressed image)
     _a     						=					cva.imread(os.path.join(_lowerCAmelCase,  '''image_data/original_image.png'''							)							)
     _a     						=					cva.imread(os.path.join(_lowerCAmelCase,  '''image_data/compressed_image.png'''							),  1							)

     _a     						=					cva.imread(os.path.join(_lowerCAmelCase,  '''image_data/PSNR-example-base.png'''							)							)
     _a     						=					cva.imread(
         os.path.join(_lowerCAmelCase,  '''image_data/PSNR-example-comp-10.jpg'''							),  1							)

     # Value expected: 29.73dB
     print('''-- First Test --'''							)
     print(f'PSNR value is {peak_signal_to_noise_ratio(_lowerCAmelCase,  _lowerCAmelCase							)} dB'							)

     # # Value expected: 31.53dB (Wikipedia Example)
     print('''\n-- Second Test --'''							)
     print(f'PSNR value is {peak_signal_to_noise_ratio(_lowerCAmelCase,  _lowerCAmelCase							)} dB'							)


if __name__ == "__main__":
   main()