




import tempfile
import unittest

import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, 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
    import numpy as np

    from transformers.modeling_flax_pytorch_utils import (
        convert_pytorch_state_dict_to_flax,
        load_flax_weights_in_pytorch_model,
    )
    from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel


if is_torch_available():
    import torch


@require_flax
class        UpperCAmelCase__				:



     '''simple docstring'''
     def __init__(				self ,    _lowerCAmelCase ,    _lowerCAmelCase=14 ,    _lowerCAmelCase=7 ,    _lowerCAmelCase=True ,    _lowerCAmelCase=True ,    _lowerCAmelCase=True ,    _lowerCAmelCase=99 ,    _lowerCAmelCase=32 ,    _lowerCAmelCase=5 ,    _lowerCAmelCase=4 ,    _lowerCAmelCase=37 ,    _lowerCAmelCase="gelu" ,    _lowerCAmelCase=0.1 ,    _lowerCAmelCase=0.1 ,    _lowerCAmelCase=512 ,    _lowerCAmelCase=0.02 ,    _lowerCAmelCase=None ,    ):
            a			   =parent
            a			   =batch_size
            a			   =seq_length
            a			   =is_training
            a			   =use_input_mask
            a			   =use_labels
            a			   =vocab_size
            a			   =d_model
            a			   =num_hidden_layers
            a			   =num_attention_heads
            a			   =ffn_dim
            a			   =activation_function
            a			   =activation_dropout
            a			   =attention_dropout
            a			   =max_position_embeddings
            a			   =initializer_range
            a			   =None
            a			   =0
            a			   =2
            a			   =1
     def        lowerCAmelCase__  (				self	):
            a			   =np.clip(ids_tensor([self.batch_size, self.seq_length] ,    self.vocab_size	) ,    3 ,    self.vocab_size	)

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

            a			   =XGLMConfig(
                vocab_size=self.vocab_size ,    d_model=self.hidden_size ,    num_layers=self.num_hidden_layers ,    attention_heads=self.num_attention_heads ,    ffn_dim=self.ffn_dim ,    activation_function=self.activation_function ,    activation_dropout=self.activation_dropout ,    attention_dropout=self.attention_dropout ,    max_position_embeddings=self.max_position_embeddings ,    initializer_range=self.initializer_range ,    use_cache=_lowerCAmelCase ,    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        lowerCAmelCase__  (				self	):
            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        lowerCAmelCase__  (				self	):
            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        lowerCAmelCase__  (				self ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase	):
            a			   =20
            a			   =model_class_name(_lowerCAmelCase	)

            a			   =model.init_cache(input_ids.shape[0] ,    _lowerCAmelCase	)
            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=_lowerCAmelCase ,    past_key_values=_lowerCAmelCase ,    position_ids=_lowerCAmelCase ,    )

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

            a			   =model(_lowerCAmelCase	)

            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        lowerCAmelCase__  (				self ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase	):
            a			   =20
            a			   =model_class_name(_lowerCAmelCase	)

            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] ,    _lowerCAmelCase	)
            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=_lowerCAmelCase ,    past_key_values=_lowerCAmelCase ,    position_ids=_lowerCAmelCase ,    )
            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=_lowerCAmelCase ,    position_ids=_lowerCAmelCase ,    )

            a			   =model(_lowerCAmelCase ,    attention_mask=_lowerCAmelCase	)
            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_sentencepiece
@require_flax
class        UpperCAmelCase__				(							UpperCAmelCase__      ,		UpperCAmelCase__      ,		unittest.TestCase				):



     '''simple docstring'''
     _SCREAMING_SNAKE_CASE      :					Tuple     					=    (FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
     _SCREAMING_SNAKE_CASE      :					List[Any]     					=    (FlaxXGLMForCausalLM,) if is_flax_available() else ()
     def        lowerCAmelCase__  (				self	):
            a			   =FlaxXGLMModelTester(self	)
     def        lowerCAmelCase__  (				self	):
            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(_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase	)
     def        lowerCAmelCase__  (				self	):
            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(
                       _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase	)
     @slow
     def        lowerCAmelCase__  (				self	):
            a			   =XGLMTokenizer.from_pretrained("""XGLM""" ,    padding_side="""left"""	)
            a			   =tokenizer(["""Hello this is a long string""", """Hey"""] ,    return_tensors="""np""" ,    padding=_lowerCAmelCase ,    truncation=_lowerCAmelCase	)

            a			   =FlaxXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""	)
            a			   =1
            a			   =False

            a			   =jax.jit(model.generate	)

            a			   =jit_generate(inputs["""input_ids"""] ,    attention_mask=inputs["""attention_mask"""]	).sequences

            a			   =tokenizer.batch_decode(_lowerCAmelCase ,    skip_special_tokens=_lowerCAmelCase	)

            a			   =[
                """Hello this is a long string of questions, but I'm not sure if I'm""",
                """Hey, I'm a newbie to the forum and I'""",
            ]

            self.assertListEqual(_lowerCAmelCase ,    _lowerCAmelCase	)
     @is_pt_flax_cross_test
     def        lowerCAmelCase__  (				self	):
            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(_lowerCAmelCase ,    _lowerCAmelCase	)
                          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(_lowerCAmelCase ,    _lowerCAmelCase	)

                          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(_lowerCAmelCase	):
                                 a			   =0
                                 a			   =1
                                 a			   =0
                                 a			   =1
                          a			   =pt_model_class(_lowerCAmelCase	).eval()
                          # Flax models don't use the `use_cache` option and cache is not returned as a default.
                          # So we disable `use_cache` here for PyTorch model.
                          a			   =False
                          a			   =model_class(_lowerCAmelCase ,    dtype=jnp.floataa	)
                          a			   =convert_pytorch_state_dict_to_flax(pt_model.state_dict() ,    _lowerCAmelCase	)
                          a			   =fx_state

                          with torch.no_grad():
                                 a			   =pt_model(**_lowerCAmelCase	).to_tuple()

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

                          with tempfile.TemporaryDirectory() as tmpdirname:
                                 pt_model.save_pretrained(_lowerCAmelCase	)
                                 a			   =model_class.from_pretrained(_lowerCAmelCase ,    from_pt=_lowerCAmelCase	)

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

                          a			   =pt_model_class(_lowerCAmelCase	).eval()
                          a			   =False
                          a			   =model_class(_lowerCAmelCase ,    dtype=jnp.floataa	)

                          a			   =load_flax_weights_in_pytorch_model(_lowerCAmelCase ,    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(_lowerCAmelCase	):
                                 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(**_lowerCAmelCase	).to_tuple()

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

                          with tempfile.TemporaryDirectory() as tmpdirname:
                                 fx_model.save_pretrained(_lowerCAmelCase	)
                                 a			   =pt_model_class.from_pretrained(_lowerCAmelCase ,    from_flax=_lowerCAmelCase	)

                          with torch.no_grad():
                                 a			   =pt_model_loaded(**_lowerCAmelCase	).to_tuple()

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






     @slow
     def        lowerCAmelCase__  (				self	):
            for model_class_name in self.all_model_classes:
                   a			   =model_class_name.from_pretrained("""facebook/xglm-564M"""	)
                   a			   =model(np.ones((1, 1)	)	)
                   self.assertIsNotNone(_lowerCAmelCase	)






import math
import os

import cva
import numpy as np

_lowerCamelCase							=      255.0





def 							lowerCamelCase    (							UpperCAmelCase_	:      float   ,       UpperCAmelCase_	:      float      )->			float:

       """simple docstring"""




       a			   =np.mean((original - contrast) ** 2      )
       if mse == 0:
              return 100

       return 20 * math.logaa(PIXEL_MAX / math.sqrt(UpperCAmelCase_      )      )





def 							lowerCamelCase    (							)->			None:

       """simple docstring"""




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

       a			   =cva.imread(os.path.join(UpperCAmelCase_   ,       """image_data/PSNR-example-base.png"""      )      )
       a			   =cva.imread(
           os.path.join(UpperCAmelCase_   ,       """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(UpperCAmelCase_   ,       UpperCAmelCase_      )} dB'''      )

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


if __name__ == "__main__":
    main()
