
'''simple docstring'''





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__	:






 def __init__(      self	:List[str]						,					_A	:int						,					_A	:Any=14						,					_A	:Union[str, Any]=7						,					_A	:Optional[int]=True						,					_A	:List[str]=True						,					_A	:int=True						,					_A	:Any=99						,					_A	:Optional[int]=32						,					_A	:List[str]=5						,					_A	:Optional[Any]=4						,					_A	:Union[str, Any]=37						,					_A	:List[Any]="gelu"						,					_A	:int=0.1						,					_A	:Union[str, Any]=0.1						,					_A	:str=512						,					_A	:Optional[Any]=0.02						,					_A	:Tuple=None						,					)					->      Union[str, Any]:







       '''simple docstring'''



       __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 						lowercase_    (      self	:Union[str, Any]      )					->      Any:







       '''simple docstring'''



       __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=_A						,					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 						lowercase_    (      self	:Optional[Any]      )					->      str:







       '''simple docstring'''



       __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 						lowercase_    (      self	:str      )					->      int:







       '''simple docstring'''



       __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 						lowercase_    (      self	:Union[str, Any]						,					_A	:Optional[int]						,					_A	:List[Any]						,					_A	:str						,					_A	:Optional[Any]      )					->      List[Any]:







       '''simple docstring'''



       __A													=      20
       __A													=      model_class_name(_A      )

       __A													=      model.init_cache(input_ids.shape[0]						,					_A      )
       __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=_A						,					past_key_values=_A						,					position_ids=_A						,					)

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

       __A													=      model(_A      )

       __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 						lowercase_    (      self	:int						,					_A	:Union[str, Any]						,					_A	:int						,					_A	:Optional[int]						,					_A	:Tuple      )					->      List[str]:







       '''simple docstring'''



       __A													=      20
       __A													=      model_class_name(_A      )

       __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]						,					_A      )
       __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=_A						,					past_key_values=_A						,					position_ids=_A						,					)
       __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=_A						,					position_ids=_A						,					)

       __A													=      model(_A						,					attention_mask=_A      )
       __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__	(     SCREAMING_SNAKE_CASE   ,  SCREAMING_SNAKE_CASE   ,  unittest.TestCase):
 UpperCAmelCase__     :     Any    					=       (FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
 UpperCAmelCase__     :     Optional[Any]    					=       (FlaxXGLMForCausalLM,) if is_flax_available() else ()






 def 						lowercase_    (      self	:Dict      )					->      Any:







       '''simple docstring'''



       __A													=      FlaxXGLMModelTester(self      )






 def 						lowercase_    (      self	:Dict      )					->      List[str]:







       '''simple docstring'''



       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(_A						,					_A						,					_A						,					_A      )






 def 						lowercase_    (      self	:str      )					->      int:







       '''simple docstring'''



       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(
                 _A						,					_A						,					_A						,					_A      )






 @slow
 def 						lowercase_    (      self	:Tuple      )					->      List[str]:







       '''simple docstring'''



       __A													=      XGLMTokenizer.from_pretrained('XGLM'						,					padding_side='left'      )
       __A													=      tokenizer(['Hello this is a long string', 'Hey']						,					return_tensors='np'						,					padding=_A						,					truncation=_A      )

       __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(_A						,					skip_special_tokens=_A      )

       __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(_A						,					_A      )






 @is_pt_flax_cross_test
 def 						lowercase_    (      self	:int      )					->      Optional[Any]:







       '''simple docstring'''



       __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(_A						,					_A      )
                   __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(_A						,					_A      )

                   __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(_A      ):
                         __A													=      0
                         __A													=      1
                         __A													=      0
                         __A													=      1
                   __A													=      pt_model_class(_A      ).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(_A						,					dtype=jnp.floataa      )
                   __A													=      convert_pytorch_state_dict_to_flax(pt_model.state_dict()						,					_A      )
                   __A													=      fx_state

                   with torch.no_grad():
                         __A													=      pt_model(**_A      ).to_tuple()

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

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

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






 @is_pt_flax_cross_test
 def 						lowercase_    (      self	:List[str]      )					->      Optional[Any]:







       '''simple docstring'''



       __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(_A						,					_A      )
                   __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(_A						,					_A      )

                   __A													=      pt_model_class(_A      ).eval()
                   __A													=      False
                   __A													=      model_class(_A						,					dtype=jnp.floataa      )

                   __A													=      load_flax_weights_in_pytorch_model(_A						,					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(_A      ):
                         __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(**_A      ).to_tuple()

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

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

                   with torch.no_grad():
                         __A													=      pt_model_loaded(**_A      ).to_tuple()

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






 @slow
 def 						lowercase_    (      self	:Union[str, Any]      )					->      Union[str, Any]:







       '''simple docstring'''



       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(_A      )












from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


__SCREAMING_SNAKE_CASE     : Dict      =logging.get_logger(__name__)


if is_vision_available():
      import PIL






class       A      (		_lowerCAmelCase		):
     """simple docstring"""

     _snake_case:						Any  =["pixel_values"]




     def __init__(     self	: Union[str, Any]						,	A__	: bool = True						,	A__	: Dict[str, int] = None						,	A__	: PILImageResampling = PILImageResampling.BICUBIC						,	A__	: bool = True						,	A__	: Dict[str, int] = None						,	A__	: bool = True						,	A__	: Union[int, float] = 1 / 2_5_5						,	A__	: bool = True						,	A__	: Optional[Union[float, List[float]]] = None						,	A__	: Optional[Union[float, List[float]]] = None						,	A__	: bool = True						,	**A__	: Optional[int]						,	)					->							None:
           super().__init__(**A__      )
           lowerCamelCase__   	=					size if size is not None else {'shortest_edge': 2_2_4}
           lowerCamelCase__   	=					get_size_dict(A__						,	default_to_square=A__      )
           lowerCamelCase__   	=					crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
           lowerCamelCase__   	=					get_size_dict(A__      )

           lowerCamelCase__   	=					do_resize
           lowerCamelCase__   	=					size
           lowerCamelCase__   	=					resample
           lowerCamelCase__   	=					do_center_crop
           lowerCamelCase__   	=					crop_size
           lowerCamelCase__   	=					do_rescale
           lowerCamelCase__   	=					rescale_factor
           lowerCamelCase__   	=					do_normalize
           lowerCamelCase__   	=					image_mean if image_mean is not None else OPENAI_CLIP_MEAN
           lowerCamelCase__   	=					image_std if image_std is not None else OPENAI_CLIP_STD
           lowerCamelCase__   	=					do_convert_rgb




     def 			__snake_case (     self	: Union[str, Any]						,	A__	: np.ndarray						,	A__	: Dict[str, int]						,	A__	: PILImageResampling = PILImageResampling.BICUBIC						,	A__	: Optional[Union[str, ChannelDimension]] = None						,	**A__	: Optional[int]						,	)					->							np.ndarray:
           lowerCamelCase__   	=					get_size_dict(A__						,	default_to_square=A__      )
           lowerCamelCase__   	=					get_resize_output_image_size(
               A__						,	size=(size['height'], size['width'])						,	default_to_square=A__      )
           return resize(A__						,	size=A__						,	resample=A__						,	data_format=A__						,	**A__      )




     def 			__snake_case (     self	: int						,	A__	: np.ndarray						,	A__	: Dict[str, int]						,	A__	: Optional[Union[str, ChannelDimension]] = None						,	**A__	: List[Any]						,	)					->							np.ndarray:
           lowerCamelCase__   	=					get_size_dict(A__      )
           return center_crop(A__						,	size=(size['height'], size['width'])						,	data_format=A__						,	**A__      )




     def 			__snake_case (     self	: Dict						,	A__	: np.ndarray						,	A__	: Union[int, float]						,	A__	: Optional[Union[str, ChannelDimension]] = None						,	**A__	: List[Any]						,	)					->							Tuple:
           return rescale(A__						,	scale=A__						,	data_format=A__						,	**A__      )




     def 			__snake_case (     self	: int						,	A__	: np.ndarray						,	A__	: Union[float, List[float]]						,	A__	: Union[float, List[float]]						,	A__	: Optional[Union[str, ChannelDimension]] = None						,	**A__	: Tuple						,	)					->							np.ndarray:
           return normalize(A__						,	mean=A__						,	std=A__						,	data_format=A__						,	**A__      )




     def 			__snake_case (     self	: Tuple						,	A__	: ImageInput						,	A__	: bool = None						,	A__	: Dict[str, int] = None						,	A__	: PILImageResampling = None						,	A__	: bool = None						,	A__	: int = None						,	A__	: bool = None						,	A__	: float = None						,	A__	: bool = None						,	A__	: Optional[Union[float, List[float]]] = None						,	A__	: Optional[Union[float, List[float]]] = None						,	A__	: bool = None						,	A__	: Optional[Union[str, TensorType]] = None						,	A__	: Optional[ChannelDimension] = ChannelDimension.FIRST						,	**A__	: List[str]						,	)					->							PIL.Image.Image:
           lowerCamelCase__   	=					do_resize if do_resize is not None else self.do_resize
           lowerCamelCase__   	=					size if size is not None else self.size
           lowerCamelCase__   	=					get_size_dict(A__						,	default_to_square=A__      )
           lowerCamelCase__   	=					resample if resample is not None else self.resample
           lowerCamelCase__   	=					do_center_crop if do_center_crop is not None else self.do_center_crop
           lowerCamelCase__   	=					crop_size if crop_size is not None else self.crop_size
           lowerCamelCase__   	=					get_size_dict(A__      )
           lowerCamelCase__   	=					do_rescale if do_rescale is not None else self.do_rescale
           lowerCamelCase__   	=					rescale_factor if rescale_factor is not None else self.rescale_factor
           lowerCamelCase__   	=					do_normalize if do_normalize is not None else self.do_normalize
           lowerCamelCase__   	=					image_mean if image_mean is not None else self.image_mean
           lowerCamelCase__   	=					image_std if image_std is not None else self.image_std
           lowerCamelCase__   	=					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

           lowerCamelCase__   	=					make_list_of_images(A__      )

           if not valid_images(A__      ):
                 raise ValueError(
                     'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                     'torch.Tensor, tf.Tensor or jax.ndarray.'      )

           if do_resize and size is None:
                 raise ValueError('Size must be specified if do_resize is True.'      )

           if do_center_crop and crop_size is None:
                 raise ValueError('Crop size must be specified if do_center_crop is True.'      )

           if do_rescale and rescale_factor is None:
                 raise ValueError('Rescale factor must be specified if do_rescale is True.'      )

           if do_normalize and (image_mean is None or image_std is None):
                 raise ValueError('Image mean and std must be specified if do_normalize is True.'      )

           # PIL RGBA images are converted to RGB
           if do_convert_rgb:
                 lowerCamelCase__   	=					[convert_to_rgb(A__      ) for image in images]

           # All transformations expect numpy arrays.
           lowerCamelCase__   	=					[to_numpy_array(A__      ) for image in images]

           if do_resize:
                 lowerCamelCase__   	=					[self.resize(image=A__						,	size=A__						,	resample=A__      ) for image in images]

           if do_center_crop:
                 lowerCamelCase__   	=					[self.center_crop(image=A__						,	size=A__      ) for image in images]

           if do_rescale:
                 lowerCamelCase__   	=					[self.rescale(image=A__						,	scale=A__      ) for image in images]

           if do_normalize:
                 lowerCamelCase__   	=					[self.normalize(image=A__						,	mean=A__						,	std=A__      ) for image in images]

           lowerCamelCase__   	=					[to_channel_dimension_format(A__						,	A__      ) for image in images]

           lowerCamelCase__   	=					{'pixel_values': images}
           return BatchFeature(data=A__						,	tensor_type=A__      )