






'''simple docstring'''




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       __lowercase					:

	def __init__(      self   :						str  ,					UpperCAmelCase_   :						str  ,					UpperCAmelCase_   :						str=14  ,					UpperCAmelCase_   :						Tuple=7  ,					UpperCAmelCase_   :						str=True  ,					UpperCAmelCase_   :						str=True  ,					UpperCAmelCase_   :						Optional[int]=False  ,					UpperCAmelCase_   :						Tuple=True  ,					UpperCAmelCase_   :						List[str]=99  ,					UpperCAmelCase_   :						int=32  ,					UpperCAmelCase_   :						str=4  ,					UpperCAmelCase_   :						str=4  ,					UpperCAmelCase_   :						int=4  ,					UpperCAmelCase_   :						List[str]=37  ,					UpperCAmelCase_   :						int="gelu"  ,					UpperCAmelCase_   :						int=0.1  ,					UpperCAmelCase_   :						List[str]=0.1  ,					UpperCAmelCase_   :						Optional[int]=512  ,					UpperCAmelCase_   :						str=0.02  ,					):
			UpperCamelCase__				:					int					=					parent
			UpperCamelCase__				:					List[str]					=					batch_size
			UpperCamelCase__				:					Optional[Any]					=					seq_length
			UpperCamelCase__				:					Tuple					=					is_training
			UpperCamelCase__				:					Optional[Any]					=					use_input_mask
			UpperCamelCase__				:					int					=					use_token_type_ids
			UpperCamelCase__				:					Optional[int]					=					use_labels
			UpperCamelCase__				:					Any					=					vocab_size
			UpperCamelCase__				:					Dict					=					hidden_size
			UpperCamelCase__				:					Any					=					rotary_dim
			UpperCamelCase__				:					Union[str, Any]					=					num_hidden_layers
			UpperCamelCase__				:					Union[str, Any]					=					num_attention_heads
			UpperCamelCase__				:					List[Any]					=					intermediate_size
			UpperCamelCase__				:					List[str]					=					hidden_act
			UpperCamelCase__				:					Tuple					=					hidden_dropout_prob
			UpperCamelCase__				:					Optional[Any]					=					attention_probs_dropout_prob
			UpperCamelCase__				:					List[Any]					=					max_position_embeddings
			UpperCamelCase__				:					str					=					initializer_range
			UpperCamelCase__				:					List[str]					=					None
			UpperCamelCase__				:					str					=					vocab_size - 1
			UpperCamelCase__				:					Optional[Any]					=					vocab_size - 1
			UpperCamelCase__				:					Optional[Any]					=					vocab_size - 1

	def      __UpperCamelCase							(      self   :						List[Any]):
			UpperCamelCase__				:					int					=					ids_tensor([self.batch_size, self.seq_length]  ,					self.vocab_size)

			UpperCamelCase__				:					Dict					=					None
			if self.use_input_mask:
					UpperCamelCase__				:					Optional[Any]					=					random_attention_mask([self.batch_size, self.seq_length])

			UpperCamelCase__				:					Optional[int]					=					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=UpperCAmelCase_  ,					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   :						List[str]):
			UpperCamelCase__				:					int					=					self.prepare_config_and_inputs()
			UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__				:					Optional[int]					=					config_and_inputs
			UpperCamelCase__				:					Optional[int]					=					{'input_ids': input_ids, 'attention_mask': attention_mask}
			return config, inputs_dict

	def      __UpperCamelCase							(      self   :						Dict  ,					UpperCAmelCase_   :						Dict  ,					UpperCAmelCase_   :						List[Any]  ,					UpperCAmelCase_   :						List[Any]  ,					UpperCAmelCase_   :						str):
			UpperCamelCase__				:					Tuple					=					20
			UpperCamelCase__				:					int					=					model_class_name(UpperCAmelCase_)

			UpperCamelCase__				:					int					=					model.init_cache(input_ids.shape[0]  ,					UpperCAmelCase_)
			UpperCamelCase__				:					int					=					jnp.ones((input_ids.shape[0], max_decoder_length)  ,					dtype='i4')

			UpperCamelCase__				:					str					=					jnp.broadcast_to(
			    jnp.arange(input_ids.shape[-1] - 1)[None, :]  ,					(input_ids.shape[0], input_ids.shape[-1] - 1))
			UpperCamelCase__				:					Tuple					=					model(
			    input_ids[:, :-1]  ,					attention_mask=UpperCAmelCase_  ,					past_key_values=UpperCAmelCase_  ,					position_ids=UpperCAmelCase_  ,					)

			UpperCamelCase__				:					List[Any]					=					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,					dtype='i4')
			UpperCamelCase__				:					int					=					model(
			    input_ids[:, -1:]  ,					attention_mask=UpperCAmelCase_  ,					past_key_values=outputs_cache.past_key_values  ,					position_ids=UpperCAmelCase_  ,					)

			UpperCamelCase__				:					Optional[Any]					=					model(UpperCAmelCase_)

			UpperCamelCase__				:					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      __UpperCamelCase							(      self   :						Dict  ,					UpperCAmelCase_   :						Optional[int]  ,					UpperCAmelCase_   :						int  ,					UpperCAmelCase_   :						Optional[int]  ,					UpperCAmelCase_   :						List[Any]):
			UpperCamelCase__				:					int					=					20
			UpperCamelCase__				:					int					=					model_class_name(UpperCAmelCase_)

			UpperCamelCase__				:					Optional[Any]					=					jnp.concatenate(
			    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]))]  ,					axis=-1  ,					)

			UpperCamelCase__				:					Any					=					model.init_cache(input_ids.shape[0]  ,					UpperCAmelCase_)
			UpperCamelCase__				:					str					=					jnp.broadcast_to(
			    jnp.arange(input_ids.shape[-1] - 1)[None, :]  ,					(input_ids.shape[0], input_ids.shape[-1] - 1))

			UpperCamelCase__				:					Optional[int]					=					model(
			    input_ids[:, :-1]  ,					attention_mask=UpperCAmelCase_  ,					past_key_values=UpperCAmelCase_  ,					position_ids=UpperCAmelCase_  ,					)
			UpperCamelCase__				:					Any					=					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,					dtype='i4')
			UpperCamelCase__				:					List[str]					=					model(
			    input_ids[:, -1:]  ,					past_key_values=outputs_cache.past_key_values  ,					attention_mask=UpperCAmelCase_  ,					position_ids=UpperCAmelCase_  ,					)

			UpperCamelCase__				:					str					=					model(UpperCAmelCase_  ,					attention_mask=UpperCAmelCase_)

			UpperCamelCase__				:					Optional[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       __lowercase					(__lowerCamelCase   ,				__lowerCamelCase   ,				unittest.TestCase    ):
	_lowerCamelCase              =  (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
	_lowerCamelCase              =  (FlaxGPTJForCausalLM,) if is_flax_available() else ()

	def      __UpperCamelCase							(      self   :						int):
			UpperCamelCase__				:					List[Any]					=					FlaxGPTJModelTester(self)

	def      __UpperCamelCase							(      self   :						List[Any]):
			for model_class_name in self.all_model_classes:
					UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__				:					str					=					self.model_tester.prepare_config_and_inputs()
					self.model_tester.check_use_cache_forward(UpperCAmelCase_  ,					UpperCAmelCase_  ,					UpperCAmelCase_  ,					UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Tuple):
			for model_class_name in self.all_model_classes:
					UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__				:					Any					=					self.model_tester.prepare_config_and_inputs()
					self.model_tester.check_use_cache_forward_with_attn_mask(
					    UpperCAmelCase_  ,					UpperCAmelCase_  ,					UpperCAmelCase_  ,					UpperCAmelCase_)

	@tooslow
	def      __UpperCamelCase							(      self   :						Optional[Any]):
			UpperCamelCase__				:					Dict					=					GPTaTokenizer.from_pretrained('gpt2'  ,					pad_token='<|endoftext|>'  ,					padding_side='left')
			UpperCamelCase__				:					Optional[int]					=					tokenizer(['Hello this is a long string', 'Hey']  ,					return_tensors='np'  ,					padding=UpperCAmelCase_  ,					truncation=UpperCAmelCase_)

			UpperCamelCase__				:					Optional[Any]					=					FlaxGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B')
			UpperCamelCase__				:					Tuple					=					False
			UpperCamelCase__				:					Any					=					model.config.eos_token_id

			UpperCamelCase__				:					Optional[Any]					=					jax.jit(model.generate)

			UpperCamelCase__				:					Any					=					jit_generate(
			    inputs['input_ids']  ,					attention_mask=inputs['attention_mask']  ,					pad_token_id=tokenizer.pad_token_id).sequences

			UpperCamelCase__				:					int					=					tokenizer.batch_decode(UpperCAmelCase_  ,					skip_special_tokens=UpperCAmelCase_)

			UpperCamelCase__				:					Any					=					[
			    '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(UpperCAmelCase_  ,					UpperCAmelCase_)

	@is_pt_flax_cross_test
	def      __UpperCamelCase							(      self   :						List[str]):
			UpperCamelCase__,	UpperCamelCase__				:					Union[str, Any]					=					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
							UpperCamelCase__				:					List[str]					=					self._prepare_for_class(UpperCAmelCase_  ,					UpperCAmelCase_)
							UpperCamelCase__				:					Optional[int]					=					{k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

							# load corresponding PyTorch class
							UpperCamelCase__				:					Tuple					=					model_class.__name__[4:]  # Skip the "Flax" at the beginning
							UpperCamelCase__				:					Optional[int]					=					getattr(UpperCAmelCase_  ,					UpperCAmelCase_)

							UpperCamelCase__,	UpperCamelCase__				:					str					=					pt_inputs['input_ids'].shape
							UpperCamelCase__				:					int					=					np.random.randint(0  ,					seq_length - 1  ,					size=(batch_size,))
							for batch_idx, start_index in enumerate(UpperCAmelCase_):
									UpperCamelCase__				:					List[str]					=					0
									UpperCamelCase__				:					Dict					=					1
									UpperCamelCase__				:					Optional[int]					=					0
									UpperCamelCase__				:					Optional[int]					=					1
							UpperCamelCase__				:					Optional[Any]					=					pt_model_class(UpperCAmelCase_).eval()
							UpperCamelCase__				:					str					=					model_class(UpperCAmelCase_  ,					dtype=jnp.floataa)

							UpperCamelCase__				:					Tuple					=					convert_pytorch_state_dict_to_flax(pt_model.state_dict()  ,					UpperCAmelCase_)
							UpperCamelCase__				:					Dict					=					fx_state

							with torch.no_grad():
									UpperCamelCase__				:					str					=					pt_model(**UpperCAmelCase_).to_tuple()

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

							with tempfile.TemporaryDirectory() as tmpdirname:
									pt_model.save_pretrained(UpperCAmelCase_)
									UpperCamelCase__				:					List[Any]					=					model_class.from_pretrained(UpperCAmelCase_  ,					from_pt=UpperCAmelCase_)

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

	@is_pt_flax_cross_test
	def      __UpperCamelCase							(      self   :						int):
			UpperCamelCase__,	UpperCamelCase__				:					Union[str, Any]					=					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
							UpperCamelCase__				:					int					=					self._prepare_for_class(UpperCAmelCase_  ,					UpperCAmelCase_)
							UpperCamelCase__				:					str					=					{k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

							# load corresponding PyTorch class
							UpperCamelCase__				:					Any					=					model_class.__name__[4:]  # Skip the "Flax" at the beginning
							UpperCamelCase__				:					Optional[Any]					=					getattr(UpperCAmelCase_  ,					UpperCAmelCase_)

							UpperCamelCase__				:					List[Any]					=					pt_model_class(UpperCAmelCase_).eval()
							UpperCamelCase__				:					str					=					model_class(UpperCAmelCase_  ,					dtype=jnp.floataa)

							UpperCamelCase__				:					Optional[int]					=					load_flax_weights_in_pytorch_model(UpperCAmelCase_  ,					fx_model.params)
							UpperCamelCase__,	UpperCamelCase__				:					Union[str, Any]					=					pt_inputs['input_ids'].shape
							UpperCamelCase__				:					Tuple					=					np.random.randint(0  ,					seq_length - 1  ,					size=(batch_size,))
							for batch_idx, start_index in enumerate(UpperCAmelCase_):
									UpperCamelCase__				:					List[str]					=					0
									UpperCamelCase__				:					Optional[int]					=					1
									UpperCamelCase__				:					List[str]					=					0
									UpperCamelCase__				:					int					=					1

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

							with torch.no_grad():
									UpperCamelCase__				:					List[Any]					=					pt_model(**UpperCAmelCase_).to_tuple()

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

							with tempfile.TemporaryDirectory() as tmpdirname:
									fx_model.save_pretrained(UpperCAmelCase_)
									UpperCamelCase__				:					int					=					pt_model_class.from_pretrained(UpperCAmelCase_  ,					from_flax=UpperCAmelCase_)

							with torch.no_grad():
									UpperCamelCase__				:					Dict					=					pt_model_loaded(**UpperCAmelCase_).to_tuple()

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





	@tooslow
	def      __UpperCamelCase							(      self   :						int):
			for model_class_name in self.all_model_classes:
					UpperCamelCase__				:					Optional[int]					=					model_class_name.from_pretrained('EleutherAI/gpt-j-6B')
					UpperCamelCase__				:					Optional[Any]					=					model(np.ones((1, 1)))
					self.assertIsNotNone(UpperCAmelCase_)












'''simple docstring'''




import numpy as np
from PIL import Image







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)							->     np.ndarray:
		UpperCamelCase__				:					List[Any]					=					np.array(lowerCamelCase_)
		if arr.shape[0] != arr.shape[1]:
				raise ValueError('The input array is not a square matrix')
		UpperCamelCase__				:					Tuple					=					0
		UpperCamelCase__				:					int					=					0
		UpperCamelCase__				:					Optional[int]					=					0
		UpperCamelCase__				:					str					=					0

		# compute the shape of the output matrix
		UpperCamelCase__				:					int					=					(arr.shape[0] - size) // stride + 1
		# initialize the output matrix with zeros of shape maxpool_shape
		UpperCamelCase__				:					Dict					=					np.zeros((maxpool_shape, maxpool_shape))

		while i < arr.shape[0]:
				if i + size > arr.shape[0]:
						# if the end of the matrix is reached, break
						break
				while j < arr.shape[1]:
						# if the end of the matrix is reached, break
						if j + size > arr.shape[1]:
								break
						# compute the maximum of the pooling matrix
						UpperCamelCase__				:					Dict					=					np.max(arr[i : i + size, j : j + size])
						# shift the pooling matrix by stride of column pixels
						j += stride
						mat_j += 1

				# shift the pooling matrix by stride of row pixels
				i += stride
				mat_i += 1

				# reset the column index to 0
				UpperCamelCase__				:					List[Any]					=					0
				UpperCamelCase__				:					Optional[int]					=					0

		return updated_arr







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)							->     np.ndarray:
		UpperCamelCase__				:					Tuple					=					np.array(lowerCamelCase_)
		if arr.shape[0] != arr.shape[1]:
				raise ValueError('The input array is not a square matrix')
		UpperCamelCase__				:					Optional[int]					=					0
		UpperCamelCase__				:					int					=					0
		UpperCamelCase__				:					List[str]					=					0
		UpperCamelCase__				:					List[Any]					=					0

		# compute the shape of the output matrix
		UpperCamelCase__				:					str					=					(arr.shape[0] - size) // stride + 1
		# initialize the output matrix with zeros of shape avgpool_shape
		UpperCamelCase__				:					Union[str, Any]					=					np.zeros((avgpool_shape, avgpool_shape))

		while i < arr.shape[0]:
				# if the end of the matrix is reached, break
				if i + size > arr.shape[0]:
						break
				while j < arr.shape[1]:
						# if the end of the matrix is reached, break
						if j + size > arr.shape[1]:
								break
						# compute the average of the pooling matrix
						UpperCamelCase__				:					List[Any]					=					int(np.average(arr[i : i + size, j : j + size]))
						# shift the pooling matrix by stride of column pixels
						j += stride
						mat_j += 1

				# shift the pooling matrix by stride of row pixels
				i += stride
				mat_i += 1
				# reset the column index to 0
				UpperCamelCase__				:					Union[str, Any]					=					0
				UpperCamelCase__				:					Optional[Any]					=					0

		return updated_arr


# Main Function
if __name__ == "__main__":
		from doctest import testmod

		testmod(name='avgpooling', verbose=True)

		# Loading the image
		lowerCAmelCase__          =						Image.open('path_to_image')

		# Converting the image to numpy array and maxpooling, displaying the result
		# Ensure that the image is a square matrix

		Image.fromarray(maxpooling(np.array(image), size=3, stride=2)).show()

		# Converting the image to numpy array and averagepooling, displaying the result
		# Ensure that the image is a square matrix

		Image.fromarray(avgpooling(np.array(image), size=3, stride=2)).show()




