


'''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  _lowercase   :
   def __init__(	self					:			Union[str, Any]					,      SCREAMING_SNAKE_CASE_					:			int					,      SCREAMING_SNAKE_CASE_					:			int=14					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]=7					,      SCREAMING_SNAKE_CASE_					:			List[str]=True					,      SCREAMING_SNAKE_CASE_					:			List[Any]=True					,      SCREAMING_SNAKE_CASE_					:			Any=True					,      SCREAMING_SNAKE_CASE_					:			Any=99					,      SCREAMING_SNAKE_CASE_					:			int=32					,      SCREAMING_SNAKE_CASE_					:			Optional[int]=5					,      SCREAMING_SNAKE_CASE_					:			Union[str, Any]=4					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]=37					,      SCREAMING_SNAKE_CASE_					:			Tuple="gelu"					,      SCREAMING_SNAKE_CASE_					:			Optional[int]=0.1					,      SCREAMING_SNAKE_CASE_					:			Tuple=0.1					,      SCREAMING_SNAKE_CASE_					:			str=512					,      SCREAMING_SNAKE_CASE_					:			List[str]=0.0_2					,      SCREAMING_SNAKE_CASE_					:			Dict=None					,      )   ->       Optional[Any]:
        __snake_case    			=	parent
        __snake_case    			=	batch_size
        __snake_case    			=	seq_length
        __snake_case    			=	is_training
        __snake_case    			=	use_input_mask
        __snake_case    			=	use_labels
        __snake_case    			=	vocab_size
        __snake_case    			=	d_model
        __snake_case    			=	num_hidden_layers
        __snake_case    			=	num_attention_heads
        __snake_case    			=	ffn_dim
        __snake_case    			=	activation_function
        __snake_case    			=	activation_dropout
        __snake_case    			=	attention_dropout
        __snake_case    			=	max_position_embeddings
        __snake_case    			=	initializer_range
        __snake_case    			=	None
        __snake_case    			=	0
        __snake_case    			=	2
        __snake_case    			=	1
   def a     (	self					:			Union[str, Any]			)   ->       List[Any]:
        __snake_case    			=	np.clip(ids_tensor([self.batch_size, self.seq_length]					,      self.vocab_size			)					,      3					,      self.vocab_size			)

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

        __snake_case    			=	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=SCREAMING_SNAKE_CASE_					,      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 a     (	self					:			str			)   ->       Dict:
        __snake_case    			=	self.prepare_config_and_inputs()
        __snake_case			,					__snake_case			,					__snake_case    			=	config_and_inputs
        __snake_case    			=	{'input_ids': input_ids, 'attention_mask': attention_mask}
        return config, inputs_dict
   def a     (	self					:			str			)   ->       Tuple:
        __snake_case			,					__snake_case			,					__snake_case    			=	self.prepare_config_and_inputs()

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

        return (
            config,
            input_ids,
            attention_mask,
            encoder_hidden_states,
            encoder_attention_mask,
        )
   def a     (	self					:			List[Any]					,      SCREAMING_SNAKE_CASE_					:			Optional[int]					,      SCREAMING_SNAKE_CASE_					:			List[str]					,      SCREAMING_SNAKE_CASE_					:			Any					,      SCREAMING_SNAKE_CASE_					:			Any			)   ->       List[str]:
        __snake_case    			=	20
        __snake_case    			=	model_class_name(SCREAMING_SNAKE_CASE_			)

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

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

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

        __snake_case    			=	model(SCREAMING_SNAKE_CASE_			)

        __snake_case    			=	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 a     (	self					:			Tuple					,      SCREAMING_SNAKE_CASE_					:			Optional[int]					,      SCREAMING_SNAKE_CASE_					:			Dict					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]					,      SCREAMING_SNAKE_CASE_					:			List[str]			)   ->       List[Any]:
        __snake_case    			=	20
        __snake_case    			=	model_class_name(SCREAMING_SNAKE_CASE_			)

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

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

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

        __snake_case    			=	model(SCREAMING_SNAKE_CASE_					,      attention_mask=SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	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  _lowercase   (  __lowercase	,				__lowercase	,				unittest.TestCase       ):
   _SCREAMING_SNAKE_CASE     :     Optional[Any]									=  (FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
   _SCREAMING_SNAKE_CASE     :     Any									=  (FlaxXGLMForCausalLM,) if is_flax_available() else ()
   def a     (	self					:			Optional[int]			)   ->       Optional[int]:
        __snake_case    			=	FlaxXGLMModelTester(self			)
   def a     (	self					:			List[Any]			)   ->       str:
        for model_class_name in self.all_model_classes:
             __snake_case			,					__snake_case			,					__snake_case    			=	self.model_tester.prepare_config_and_inputs()
             self.model_tester.check_use_cache_forward(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			Dict			)   ->       Optional[int]:
        for model_class_name in self.all_model_classes:
             __snake_case			,					__snake_case			,					__snake_case    			=	self.model_tester.prepare_config_and_inputs()
             self.model_tester.check_use_cache_forward_with_attn_mask(
                 SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)
   @slow
   def a     (	self					:			int			)   ->       str:
        __snake_case    			=	XGLMTokenizer.from_pretrained('XGLM'					,      padding_side='left'			)
        __snake_case    			=	tokenizer(['Hello this is a long string', 'Hey']					,      return_tensors='np'					,      padding=SCREAMING_SNAKE_CASE_					,      truncation=SCREAMING_SNAKE_CASE_			)

        __snake_case    			=	FlaxXGLMForCausalLM.from_pretrained('facebook/xglm-564M'			)
        __snake_case    			=	1
        __snake_case    			=	False

        __snake_case    			=	jax.jit(model.generate			)

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

        __snake_case    			=	tokenizer.batch_decode(SCREAMING_SNAKE_CASE_					,      skip_special_tokens=SCREAMING_SNAKE_CASE_			)

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

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

                  __snake_case			,					__snake_case    			=	pt_inputs['input_ids'].shape
                  __snake_case    			=	np.random.randint(0					,      seq_length - 1					,      size=(batch_size,)			)
                  for batch_idx, start_index in enumerate(SCREAMING_SNAKE_CASE_			):
                       __snake_case    			=	0
                       __snake_case    			=	1
                       __snake_case    			=	0
                       __snake_case    			=	1
                  __snake_case    			=	pt_model_class(SCREAMING_SNAKE_CASE_			).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.
                  __snake_case    			=	False
                  __snake_case    			=	model_class(SCREAMING_SNAKE_CASE_					,      dtype=jnp.floataa			)
                  __snake_case    			=	convert_pytorch_state_dict_to_flax(pt_model.state_dict()					,      SCREAMING_SNAKE_CASE_			)
                  __snake_case    			=	fx_state

                  with torch.no_grad():
                       __snake_case    			=	pt_model(**SCREAMING_SNAKE_CASE_			).to_tuple()

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

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

                  __snake_case    			=	fx_model_loaded(**SCREAMING_SNAKE_CASE_			).to_tuple()
                  self.assertEqual(
                      len(SCREAMING_SNAKE_CASE_			)					,      len(SCREAMING_SNAKE_CASE_			)					,      'Output lengths differ between Flax and PyTorch'			)
                  for fx_output_loaded, pt_output in zip(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			):
                       self.assert_almost_equals(fx_output_loaded[:, -1]					,      pt_output[:, -1].numpy()					,      4e-2			)
   @is_pt_flax_cross_test
   def a     (	self					:			str			)   ->       Tuple:
        __snake_case			,					__snake_case    			=	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
                  __snake_case    			=	self._prepare_for_class(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)
                  __snake_case    			=	{k: torch.tensor(v.tolist()			) for k, v in prepared_inputs_dict.items()}

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

                  __snake_case    			=	pt_model_class(SCREAMING_SNAKE_CASE_			).eval()
                  __snake_case    			=	False
                  __snake_case    			=	model_class(SCREAMING_SNAKE_CASE_					,      dtype=jnp.floataa			)

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

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

                  with torch.no_grad():
                       __snake_case    			=	pt_model(**SCREAMING_SNAKE_CASE_			).to_tuple()

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

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

                  with torch.no_grad():
                       __snake_case    			=	pt_model_loaded(**SCREAMING_SNAKE_CASE_			).to_tuple()

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





   @slow
   def a     (	self					:			Union[str, Any]			)   ->       Union[str, Any]:
        for model_class_name in self.all_model_classes:
             __snake_case    			=	model_class_name.from_pretrained('facebook/xglm-564M'			)
             __snake_case    			=	model(np.ones((1, 1)			)			)
             self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)








'''simple docstring'''




import numpy as np
from PIL import Image





def      _a		(lowercase__ :      np.array	) -> np.array:






     """simple docstring"""
     __snake_case			,					__snake_case			,					__snake_case    			=	rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
     return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b





def      _a		(lowercase__ :      np.array	) -> np.array:






     """simple docstring"""
     return (gray > 1_2_7) & (gray <= 2_5_5)





def      _a		(lowercase__ :      np.array       ,      lowercase__ :      np.array	) -> np.array:






     """simple docstring"""
     __snake_case    			=	np.zeros_like(lowercase__	)
     __snake_case    			=	np.zeros(
         (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)	)

     # Copy image to padded image
     __snake_case    			=	image

     # Iterate over image & apply kernel
     for x in range(image.shape[1]	):
          for y in range(image.shape[0]	):
               __snake_case    			=	(
                   kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
               ).sum()
               __snake_case    			=	int(summation == 5	)
     return output


# kernel to be applied
_a      :      Tuple       				=      np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
       # read original image
       _a      :      Any       				=      np.array(Image.open(R"..\image_data\lena.jpg"))
       # Apply erosion operation to a binary image
       _a      :      List[Any]       				=      erosion(grayabinary(rgbagray(image)), structuring_element)
       # Save the output image
       _a      :      int       				=      Image.fromarray(output).convert("RGB")
       pil_img.save("result_erosion.png")




