



"""simple docstring"""

import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
     import os

     # The slow tests are often failing with OOM error on GPU
     # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
     # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
     lowercase__    :  Tuple							    =	'''platform'''

     import jax
     import jax.numpy as jnp

     from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
         FlaxBlenderbotSmallForConditionalGeneration,
         FlaxBlenderbotSmallModel,
         shift_tokens_right,
     )




def __lowercase			(							_a		,			_a		,			_a=None		,			_a=None		,			_a=None		,			_a=None		,			_a=None		,			_a=None		,			):
    if attention_mask is None:
        snake_case_ :     Any				=	np.where(input_ids != config.pad_token_id		,			1		,			0    )
    if decoder_attention_mask is None:
        snake_case_ :     Optional[int]				=	np.where(decoder_input_ids != config.pad_token_id		,			1		,			0    )
    if head_mask is None:
        snake_case_ :     Any				=	np.ones((config.encoder_layers, config.encoder_attention_heads)    )
    if decoder_head_mask is None:
        snake_case_ :     Dict				=	np.ones((config.decoder_layers, config.decoder_attention_heads)    )
    if cross_attn_head_mask is None:
        snake_case_ :     Optional[int]				=	np.ones((config.decoder_layers, config.decoder_attention_heads)    )
    return {
        "input_ids": input_ids,
        "decoder_input_ids": decoder_input_ids,
        "attention_mask": attention_mask,
        "decoder_attention_mask": attention_mask,
    }




class        _UpperCAmelCase       :

 def __init__(     self		:       Optional[int]  ,     lowercase_		:       Optional[int]  ,     lowercase_		:       Optional[int]=13  ,     lowercase_		:       Tuple=7  ,     lowercase_		:       int=True  ,     lowercase_		:       str=False  ,     lowercase_		:       int=99  ,     lowercase_		:       Tuple=16  ,     lowercase_		:       Optional[int]=2  ,     lowercase_		:       str=4  ,     lowercase_		:       Dict=4  ,     lowercase_		:       Optional[int]="gelu"  ,     lowercase_		:       int=0.1  ,     lowercase_		:       List[Any]=0.1  ,     lowercase_		:       int=32  ,     lowercase_		:       Tuple=2  ,     lowercase_		:       Optional[Any]=1  ,     lowercase_		:       Any=0  ,     lowercase_		:       List[str]=0.02  ,     ):
     snake_case_ :     Any				=	parent
     snake_case_ :     Tuple				=	batch_size
     snake_case_ :     Optional[Any]				=	seq_length
     snake_case_ :     Any				=	is_training
     snake_case_ :     Optional[Any]				=	use_labels
     snake_case_ :     Tuple				=	vocab_size
     snake_case_ :     int				=	hidden_size
     snake_case_ :     Any				=	num_hidden_layers
     snake_case_ :     Tuple				=	num_attention_heads
     snake_case_ :     Dict				=	intermediate_size
     snake_case_ :     str				=	hidden_act
     snake_case_ :     Dict				=	hidden_dropout_prob
     snake_case_ :     Union[str, Any]				=	attention_probs_dropout_prob
     snake_case_ :     Tuple				=	max_position_embeddings
     snake_case_ :     List[Any]				=	eos_token_id
     snake_case_ :     List[Any]				=	pad_token_id
     snake_case_ :     Dict				=	bos_token_id
     snake_case_ :     int				=	initializer_range

 def 						_snake_case					(     self		:       Tuple				):
     snake_case_ :     Optional[Any]				=	np.clip(ids_tensor([self.batch_size, self.seq_length - 1]  ,     self.vocab_size				)  ,     3  ,     self.vocab_size				)
     snake_case_ :     List[str]				=	np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)  ,     dtype=np.intaa				))  ,     -1				)

     snake_case_ :     Dict				=	shift_tokens_right(lowercase_  ,     1  ,     2				)

     snake_case_ :     str				=	BlenderbotSmallConfig(
         vocab_size=self.vocab_size  ,     d_model=self.hidden_size  ,     encoder_layers=self.num_hidden_layers  ,     decoder_layers=self.num_hidden_layers  ,     encoder_attention_heads=self.num_attention_heads  ,     decoder_attention_heads=self.num_attention_heads  ,     encoder_ffn_dim=self.intermediate_size  ,     decoder_ffn_dim=self.intermediate_size  ,     dropout=self.hidden_dropout_prob  ,     attention_dropout=self.attention_probs_dropout_prob  ,     max_position_embeddings=self.max_position_embeddings  ,     eos_token_id=self.eos_token_id  ,     bos_token_id=self.bos_token_id  ,     pad_token_id=self.pad_token_id  ,     initializer_range=self.initializer_range  ,     use_cache=lowercase_  ,     )
     snake_case_ :     Any				=	prepare_blenderbot_inputs_dict(lowercase_  ,     lowercase_  ,     lowercase_				)
     return config, inputs_dict

 def 						_snake_case					(     self		:       Optional[Any]				):
     snake_case_,	snake_case_ :     List[str]				=	self.prepare_config_and_inputs()
     return config, inputs_dict

 def 						_snake_case					(     self		:       Dict  ,     lowercase_		:       Union[str, Any]  ,     lowercase_		:       List[str]  ,     lowercase_		:       Optional[int]				):
     snake_case_ :     List[Any]				=	20
     snake_case_ :     Any				=	model_class_name(lowercase_				)

     snake_case_ :     Optional[int]				=	model.encode(inputs_dict['''input_ids''']				)

     snake_case_,	snake_case_ :     Dict				=	(
         inputs_dict['''decoder_input_ids'''],
         inputs_dict['''decoder_attention_mask'''],
     )

     snake_case_ :     Dict				=	model.init_cache(decoder_input_ids.shape[0]  ,     lowercase_  ,     lowercase_				)
     snake_case_ :     Any				=	jnp.ones((decoder_input_ids.shape[0], max_decoder_length)  ,     dtype='''i4'''				)

     snake_case_ :     Dict				=	jnp.broadcast_to(
         jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]  ,     (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)  ,     )
     snake_case_ :     Optional[int]				=	model.decode(
         decoder_input_ids[:, :-1]  ,     lowercase_  ,     decoder_attention_mask=lowercase_  ,     past_key_values=lowercase_  ,     decoder_position_ids=lowercase_  ,     )

     snake_case_ :     Any				=	jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]  ,     dtype='''i4'''				)
     snake_case_ :     int				=	model.decode(
         decoder_input_ids[:, -1:]  ,     lowercase_  ,     decoder_attention_mask=lowercase_  ,     past_key_values=outputs_cache.past_key_values  ,     decoder_position_ids=lowercase_  ,     )

     snake_case_ :     List[Any]				=	model.decode(lowercase_  ,     lowercase_				)

     snake_case_ :     List[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}"				)






 def 						_snake_case					(     self		:       str  ,     lowercase_		:       int  ,     lowercase_		:       Any  ,     lowercase_		:       List[str]				):
     snake_case_ :     str				=	20
     snake_case_ :     str				=	model_class_name(lowercase_				)

     snake_case_ :     Tuple				=	model.encode(inputs_dict['''input_ids''']				)

     snake_case_,	snake_case_ :     Union[str, Any]				=	(
         inputs_dict['''decoder_input_ids'''],
         inputs_dict['''decoder_attention_mask'''],
     )

     snake_case_ :     Any				=	jnp.concatenate(
         [
             decoder_attention_mask,
             jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])				),
         ]  ,     axis=-1  ,     )

     snake_case_ :     Any				=	model.init_cache(decoder_input_ids.shape[0]  ,     lowercase_  ,     lowercase_				)
     snake_case_ :     List[Any]				=	jnp.broadcast_to(
         jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]  ,     (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)  ,     )

     snake_case_ :     str				=	model.decode(
         decoder_input_ids[:, :-1]  ,     lowercase_  ,     decoder_attention_mask=lowercase_  ,     past_key_values=lowercase_  ,     decoder_position_ids=lowercase_  ,     )
     snake_case_ :     int				=	jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]  ,     dtype='''i4'''				)
     snake_case_ :     int				=	model.decode(
         decoder_input_ids[:, -1:]  ,     lowercase_  ,     past_key_values=outputs_cache.past_key_values  ,     decoder_attention_mask=lowercase_  ,     decoder_position_ids=lowercase_  ,     )

     snake_case_ :     Union[str, Any]				=	model.decode(lowercase_  ,     lowercase_  ,     decoder_attention_mask=lowercase_				)

     snake_case_ :     Union[str, 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        _UpperCAmelCase       (			unittest.TestCase):
 _lowerCAmelCase   :			int							=		9_9

 def 						_snake_case					(     self		:       Any				):
     snake_case_ :     Any				=	np.array(
         [
             [71, 82, 18, 33, 46, 91, 2],
             [68, 34, 26, 58, 30, 82, 2],
             [5, 97, 17, 39, 94, 40, 2],
             [76, 83, 94, 25, 70, 78, 2],
             [87, 59, 41, 35, 48, 66, 2],
             [55, 13, 16, 58, 5, 2, 1],  # note padding
             [64, 27, 31, 51, 12, 75, 2],
             [52, 64, 86, 17, 83, 39, 2],
             [48, 61, 9, 24, 71, 82, 2],
             [26, 1, 60, 48, 22, 13, 2],
             [21, 5, 62, 28, 14, 76, 2],
             [45, 98, 37, 86, 59, 48, 2],
             [70, 70, 50, 9, 28, 0, 2],
         ]  ,     dtype=np.intaa  ,     )

     snake_case_ :     Optional[int]				=	input_ids.shape[0]
     snake_case_ :     List[str]				=	BlenderbotSmallConfig(
         vocab_size=self.vocab_size  ,     d_model=24  ,     encoder_layers=2  ,     decoder_layers=2  ,     encoder_attention_heads=2  ,     decoder_attention_heads=2  ,     encoder_ffn_dim=32  ,     decoder_ffn_dim=32  ,     max_position_embeddings=48  ,     eos_token_id=2  ,     pad_token_id=1  ,     bos_token_id=0  ,     )
     return config, input_ids, batch_size

 def 						_snake_case					(     self		:       Dict				):
     snake_case_,	snake_case_,	snake_case_ :     Optional[Any]				=	self._get_config_and_data()
     snake_case_ :     List[str]				=	FlaxBlenderbotSmallForConditionalGeneration(lowercase_				)
     snake_case_ :     Dict				=	lm_model(input_ids=lowercase_				)
     snake_case_ :     List[Any]				=	(batch_size, input_ids.shape[1], config.vocab_size)
     self.assertEqual(outputs['''logits'''].shape  ,     lowercase_				)

 def 						_snake_case					(     self		:       int				):
     snake_case_ :     List[str]				=	BlenderbotSmallConfig(
         vocab_size=self.vocab_size  ,     d_model=14  ,     encoder_layers=2  ,     decoder_layers=2  ,     encoder_attention_heads=2  ,     decoder_attention_heads=2  ,     encoder_ffn_dim=8  ,     decoder_ffn_dim=8  ,     max_position_embeddings=48  ,     )
     snake_case_ :     Optional[Any]				=	FlaxBlenderbotSmallForConditionalGeneration(lowercase_				)
     snake_case_ :     List[str]				=	np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]  ,     dtype=np.intaa				)
     snake_case_ :     Any				=	np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]  ,     dtype=np.intaa				)
     snake_case_ :     int				=	lm_model(input_ids=lowercase_  ,     decoder_input_ids=lowercase_				)
     snake_case_ :     int				=	(*summary.shape, config.vocab_size)
     self.assertEqual(outputs['''logits'''].shape  ,     lowercase_				)






 def 						_snake_case					(     self		:       Optional[Any]				):
     snake_case_ :     Union[str, Any]				=	np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]  ,     dtype=np.intaa				)
     snake_case_ :     Optional[int]				=	shift_tokens_right(lowercase_  ,     1  ,     2				)
     snake_case_ :     List[str]				=	np.equal(lowercase_  ,     1				).astype(np.floataa				).sum()
     snake_case_ :     str				=	np.equal(lowercase_  ,     1				).astype(np.floataa				).sum()
     self.assertEqual(shifted.shape  ,     input_ids.shape				)
     self.assertEqual(lowercase_  ,     n_pad_before - 1				)
     self.assertTrue(np.equal(shifted[:, 0]  ,     2				).all()				)





@require_flax
class        _UpperCAmelCase       (			lowerCAmelCase__    ,     unittest.TestCase    ,     lowerCAmelCase__):
 _lowerCAmelCase   :			str							=		True
 _lowerCAmelCase   :			List[str]							=		(
     (
         FlaxBlenderbotSmallModel,
         FlaxBlenderbotSmallForConditionalGeneration,
     )
     if is_flax_available()
     else ()
 )
 _lowerCAmelCase   :			List[str]							=		(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()

 def 						_snake_case					(     self		:       Optional[Any]				):
     snake_case_ :     Tuple				=	FlaxBlenderbotSmallModelTester(self				)

 def 						_snake_case					(     self		:       List[str]				):
     snake_case_,	snake_case_ :     List[Any]				=	self.model_tester.prepare_config_and_inputs()
     for model_class in self.all_model_classes:
         self.model_tester.check_use_cache_forward(lowercase_  ,     lowercase_  ,     lowercase_				)

 def 						_snake_case					(     self		:       Tuple				):
     snake_case_,	snake_case_ :     Dict				=	self.model_tester.prepare_config_and_inputs()
     for model_class in self.all_model_classes:
         self.model_tester.check_use_cache_forward_with_attn_mask(lowercase_  ,     lowercase_  ,     lowercase_				)

 def 						_snake_case					(     self		:       str				):
     snake_case_,	snake_case_ :     int				=	self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__				):
             snake_case_ :     Dict				=	self._prepare_for_class(lowercase_  ,     lowercase_				)
             snake_case_ :     List[Any]				=	model_class(lowercase_				)

             @jax.jit
             def encode_jitted(lowercase_		:       List[Any]  ,     lowercase_		:       Optional[int]=None  ,     **lowercase_		:       Dict				):
                 return model.encode(input_ids=lowercase_  ,     attention_mask=lowercase_				)

             with self.subTest('''JIT Enabled'''				):
                 snake_case_ :     List[str]				=	encode_jitted(**lowercase_				).to_tuple()

             with self.subTest('''JIT Disabled'''				):
                 with jax.disable_jit():
                     snake_case_ :     Any				=	encode_jitted(**lowercase_				).to_tuple()

             self.assertEqual(len(lowercase_				)  ,     len(lowercase_				)				)
             for jitted_output, output in zip(lowercase_  ,     lowercase_				):
                 self.assertEqual(jitted_output.shape  ,     output.shape				)

 def 						_snake_case					(     self		:       Optional[int]				):
     snake_case_,	snake_case_ :     List[str]				=	self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__				):
             snake_case_ :     Tuple				=	model_class(lowercase_				)
             snake_case_ :     int				=	model.encode(inputs_dict['''input_ids''']  ,     inputs_dict['''attention_mask''']				)

             snake_case_ :     Optional[int]				=	{
                 '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
                 '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
                 '''encoder_outputs''': encoder_outputs,
             }

             @jax.jit
             def decode_jitted(lowercase_		:       Any  ,     lowercase_		:       Union[str, Any]  ,     lowercase_		:       Union[str, Any]				):
                 return model.decode(
                     decoder_input_ids=lowercase_  ,     decoder_attention_mask=lowercase_  ,     encoder_outputs=lowercase_  ,     )

             with self.subTest('''JIT Enabled'''				):
                 snake_case_ :     List[str]				=	decode_jitted(**lowercase_				).to_tuple()

             with self.subTest('''JIT Disabled'''				):
                 with jax.disable_jit():
                     snake_case_ :     Union[str, Any]				=	decode_jitted(**lowercase_				).to_tuple()

             self.assertEqual(len(lowercase_				)  ,     len(lowercase_				)				)
             for jitted_output, output in zip(lowercase_  ,     lowercase_				):
                 self.assertEqual(jitted_output.shape  ,     output.shape				)






 @slow
 def 						_snake_case					(     self		:       Dict				):
     for model_class_name in self.all_model_classes:
         snake_case_ :     Tuple				=	model_class_name.from_pretrained('''facebook/blenderbot_small-90M'''				)
         # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
         snake_case_ :     Optional[int]				=	np.ones((1, 1)				) * model.config.eos_token_id
         snake_case_ :     Optional[Any]				=	model(lowercase_				)
         self.assertIsNotNone(lowercase_				)










"""simple docstring"""

import copy
import os

import cva
import numpy as np
from matplotlib import pyplot as plt




class        _UpperCAmelCase       :

 def __init__(     self		:       List[Any]				):
     snake_case_ :     List[str]				=	''''''
     snake_case_ :     Tuple				=	''''''
     snake_case_ :     int				=	[]
     snake_case_ :     Optional[int]				=	0
     snake_case_ :     Optional[Any]				=	256
     snake_case_ :     Tuple				=	0
     snake_case_ :     Tuple				=	0
     snake_case_ :     Optional[Any]				=	0
     snake_case_ :     Any				=	0

 def 						_snake_case					(     self		:       Optional[Any]  ,     lowercase_		:       List[Any]				):
     snake_case_ :     List[Any]				=	cva.imread(lowercase_  ,     0				)
     snake_case_ :     Tuple				=	copy.deepcopy(self.img				)
     snake_case_,	snake_case_,	snake_case_ :     List[Any]				=	plt.hist(self.img.ravel()  ,     256  ,     [0, 256]  ,     label='''x'''				)
     snake_case_ :     str				=	np.sum(lowercase_				)
     for i in range(len(lowercase_				)				):
         snake_case_ :     Optional[Any]				=	x[i] / self.k
         self.sk += prk
         snake_case_ :     Any				=	(self.L - 1) * self.sk
         if self.rem != 0:
             snake_case_ :     Dict				=	int(last % last				)
         snake_case_ :     Union[str, Any]				=	int(last + 1 if self.rem >= 0.5 else last				)
         self.last_list.append(lowercase_				)
         snake_case_ :     int				=	int(np.ma.count(self.img				) / self.img[1].size				)
         snake_case_ :     Tuple				=	self.img[1].size
     for i in range(self.number_of_cols				):
         for j in range(self.number_of_rows				):
             snake_case_ :     Union[str, Any]				=	self.img[j][i]
             if num != self.last_list[num]:
                 snake_case_ :     List[str]				=	self.last_list[num]
     cva.imwrite('''output_data/output.jpg'''  ,     self.img				)

 def 						_snake_case					(     self		:       Tuple				):
     plt.hist(self.img.ravel()  ,     256  ,     [0, 256]				)






 def 						_snake_case					(     self		:       int				):
     cva.imshow('''Output-Image'''  ,     self.img				)
     cva.imshow('''Input-Image'''  ,     self.original_image				)
     cva.waitKey(5000				)
     cva.destroyAllWindows()


if __name__ == "__main__":
     lowercase__    :  Any							    =	os.path.join(os.path.basename(__file__), '''image_data/input.jpg''')
     lowercase__    :  Any							    =	ConstantStretch()
     stretcher.stretch(file_path)
     stretcher.plot_histogram()
     stretcher.show_image()





