import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, 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.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
     import torch



class  UpperCamelCase				:

     '''simple docstring'''

     def __init__(					self				,				A_				,				A_=14				,				A_=7				,				A_=True				,				A_=True				,				A_=False				,				A_=True				,				A_=99				,				A_=32				,				A_=4				,				A_=4				,				A_=4				,				A_=37				,				A_="gelu"				,				A_=0.1				,				A_=0.1				,				A_=5_12				,				A_=0.02				,				) ->				Optional[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				parent
           _lowerCamelCase			    =				batch_size
           _lowerCamelCase			    =				seq_length
           _lowerCamelCase			    =				is_training
           _lowerCamelCase			    =				use_input_mask
           _lowerCamelCase			    =				use_token_type_ids
           _lowerCamelCase			    =				use_labels
           _lowerCamelCase			    =				vocab_size
           _lowerCamelCase			    =				hidden_size
           _lowerCamelCase			    =				rotary_dim
           _lowerCamelCase			    =				num_hidden_layers
           _lowerCamelCase			    =				num_attention_heads
           _lowerCamelCase			    =				intermediate_size
           _lowerCamelCase			    =				hidden_act
           _lowerCamelCase			    =				hidden_dropout_prob
           _lowerCamelCase			    =				attention_probs_dropout_prob
           _lowerCamelCase			    =				max_position_embeddings
           _lowerCamelCase			    =				initializer_range
           _lowerCamelCase			    =				None
           _lowerCamelCase			    =				vocab_size - 1
           _lowerCamelCase			    =				vocab_size - 1
           _lowerCamelCase			    =				vocab_size - 1



     def 		UpperCamelCase_						(					self						) ->				Optional[int]:






           """simple docstring"""
           _lowerCamelCase			    =				ids_tensor([self.batch_size, self.seq_length]				,				self.vocab_size						)

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

           _lowerCamelCase			    =				GPTJConfig(
               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=A_				,				bos_token_id=self.bos_token_id				,				eos_token_id=self.eos_token_id				,				pad_token_id=self.pad_token_id				,				rotary_dim=self.rotary_dim				,				)

           return (config, input_ids, input_mask)



     def 		UpperCamelCase_						(					self						) ->				str:






           """simple docstring"""
           _lowerCamelCase			    =				self.prepare_config_and_inputs()
           _lowerCamelCase    ,					_lowerCamelCase    ,					_lowerCamelCase			    =				config_and_inputs
           _lowerCamelCase			    =				{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
           return config, inputs_dict



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_				,				A_						) ->				Union[str, Any]:






           """simple docstring"""
           _lowerCamelCase			    =				20
           _lowerCamelCase			    =				model_class_name(A_						)

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

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

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

           _lowerCamelCase			    =				model(A_						)

           _lowerCamelCase			    =				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				,				A_				,				A_				,				A_				,				A_						) ->				List[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				20
           _lowerCamelCase			    =				model_class_name(A_						)

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

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

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

           _lowerCamelCase			    =				model(A_				,				attention_mask=A_						)

           _lowerCamelCase			    =				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				( __lowercase   ,  __lowercase   ,  unittest.TestCase ):

     '''simple docstring'''

     A_										=  (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
     A_										=  (FlaxGPTJForCausalLM,) if is_flax_available() else ()



     def 		UpperCamelCase_						(					self						) ->				Optional[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				FlaxGPTJModelTester(self						)



     def 		UpperCamelCase_						(					self						) ->				List[str]:






           """simple docstring"""
           for model_class_name in self.all_model_classes:
                 _lowerCamelCase    ,					_lowerCamelCase    ,					_lowerCamelCase			    =				self.model_tester.prepare_config_and_inputs()
                 self.model_tester.check_use_cache_forward(A_				,				A_				,				A_				,				A_						)



     def 		UpperCamelCase_						(					self						) ->				Any:






           """simple docstring"""
           for model_class_name in self.all_model_classes:
                 _lowerCamelCase    ,					_lowerCamelCase    ,					_lowerCamelCase			    =				self.model_tester.prepare_config_and_inputs()
                 self.model_tester.check_use_cache_forward_with_attn_mask(
                     A_				,				A_				,				A_				,				A_						)



     @tooslow
     def 		UpperCamelCase_						(					self						) ->				Optional[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				GPTaTokenizer.from_pretrained('''gpt2'''				,				pad_token='''<|endoftext|>'''				,				padding_side='''left'''						)
           _lowerCamelCase			    =				tokenizer(['''Hello this is a long string''', '''Hey''']				,				return_tensors='''np'''				,				padding=A_				,				truncation=A_						)

           _lowerCamelCase			    =				FlaxGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B'''						)
           _lowerCamelCase			    =				False
           _lowerCamelCase			    =				model.config.eos_token_id

           _lowerCamelCase			    =				jax.jit(model.generate						)

           _lowerCamelCase			    =				jit_generate(
               inputs['''input_ids''']				,				attention_mask=inputs['''attention_mask''']				,				pad_token_id=tokenizer.pad_token_id						).sequences

           _lowerCamelCase			    =				tokenizer.batch_decode(A_				,				skip_special_tokens=A_						)

           _lowerCamelCase			    =				[
               '''Hello this is a long string of text.\n\nI\'m trying to get the text of the''',
               '''Hey, I\'m a little late to the party. I\'m going to''',
           ]

           self.assertListEqual(A_				,				A_						)



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






           """simple docstring"""
           _lowerCamelCase    ,					_lowerCamelCase			    =				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
                       _lowerCamelCase			    =				self._prepare_for_class(A_				,				A_						)
                       _lowerCamelCase			    =				{k: torch.tensor(v.tolist()						) for k, v in prepared_inputs_dict.items()}

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

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

                       _lowerCamelCase			    =				convert_pytorch_state_dict_to_flax(pt_model.state_dict()				,				A_						)
                       _lowerCamelCase			    =				fx_state

                       with torch.no_grad():
                             _lowerCamelCase			    =				pt_model(**A_						).to_tuple()

                       _lowerCamelCase			    =				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_						)
                             _lowerCamelCase			    =				model_class.from_pretrained(A_				,				from_pt=A_						)

                       _lowerCamelCase			    =				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 		UpperCamelCase_						(					self						) ->				List[str]:






           """simple docstring"""
           _lowerCamelCase    ,					_lowerCamelCase			    =				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
                       _lowerCamelCase			    =				self._prepare_for_class(A_				,				A_						)
                       _lowerCamelCase			    =				{k: torch.tensor(v.tolist()						) for k, v in prepared_inputs_dict.items()}

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

                       _lowerCamelCase			    =				pt_model_class(A_						).eval()
                       _lowerCamelCase			    =				model_class(A_				,				dtype=jnp.floataa						)

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

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

                       with torch.no_grad():
                             _lowerCamelCase			    =				pt_model(**A_						).to_tuple()

                       _lowerCamelCase			    =				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_						)
                             _lowerCamelCase			    =				pt_model_class.from_pretrained(A_				,				from_flax=A_						)

                       with torch.no_grad():
                             _lowerCamelCase			    =				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						)





     @tooslow
     def 		UpperCamelCase_						(					self						) ->				List[str]:






           """simple docstring"""
           for model_class_name in self.all_model_classes:
                 _lowerCamelCase			    =				model_class_name.from_pretrained('''EleutherAI/gpt-j-6B'''						)
                 _lowerCamelCase			    =				model(np.ones((1, 1)						)						)
                 self.assertIsNotNone(A_						)
import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
     # Create universe of discourse in Python using linspace ()
     snake_case__				= np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)

     # Create two fuzzy sets by defining any membership function
     # (trapmf(), gbellmf(), gaussmf(), etc).
     snake_case__				= [0, 25, 50]
     snake_case__				= [25, 50, 75]
     snake_case__				= fuzz.membership.trimf(X, abca)
     snake_case__				= fuzz.membership.trimf(X, abca)

     # Compute the different operations using inbuilt functions.
     snake_case__				= np.ones(75)
     snake_case__				= np.zeros((75,))
     # 1. Union = max(µA(x), µB(x))
     snake_case__				= fuzz.fuzzy_or(X, young, X, middle_aged)[1]
     # 2. Intersection = min(µA(x), µB(x))
     snake_case__				= fuzz.fuzzy_and(X, young, X, middle_aged)[1]
     # 3. Complement (A) = (1- min(µA(x))
     snake_case__				= fuzz.fuzzy_not(young)
     # 4. Difference (A/B) = min(µA(x),(1- µB(x)))
     snake_case__				= fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
     # 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
     snake_case__				= young + middle_aged - (young * middle_aged)
     # 6. Algebraic Product = (µA(x) * µB(x))
     snake_case__				= young * middle_aged
     # 7. Bounded Sum = min[1,(µA(x), µB(x))]
     snake_case__				= fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
     # 8. Bounded difference = min[0,(µA(x), µB(x))]
     snake_case__				= fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]

     # max-min composition
     # max-product composition

     # Plot each set A, set B and each operation result using plot() and subplot().
     from matplotlib import pyplot as plt

     plt.figure()

     plt.subplot(4, 3, 1)
     plt.plot(X, young)
     plt.title('Young')
     plt.grid(True)

     plt.subplot(4, 3, 2)
     plt.plot(X, middle_aged)
     plt.title('Middle aged')
     plt.grid(True)

     plt.subplot(4, 3, 3)
     plt.plot(X, union)
     plt.title('union')
     plt.grid(True)

     plt.subplot(4, 3, 4)
     plt.plot(X, intersection)
     plt.title('intersection')
     plt.grid(True)

     plt.subplot(4, 3, 5)
     plt.plot(X, complement_a)
     plt.title('complement_a')
     plt.grid(True)

     plt.subplot(4, 3, 6)
     plt.plot(X, difference)
     plt.title('difference a/b')
     plt.grid(True)

     plt.subplot(4, 3, 7)
     plt.plot(X, alg_sum)
     plt.title('alg_sum')
     plt.grid(True)

     plt.subplot(4, 3, 8)
     plt.plot(X, alg_product)
     plt.title('alg_product')
     plt.grid(True)

     plt.subplot(4, 3, 9)
     plt.plot(X, bdd_sum)
     plt.title('bdd_sum')
     plt.grid(True)

     plt.subplot(4, 3, 10)
     plt.plot(X, bdd_difference)
     plt.title('bdd_difference')
     plt.grid(True)

     plt.subplots_adjust(hspace=0.5)
     plt.show()