





import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, 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

	from transformers.modeling_flax_pytorch_utils import (
	    convert_pytorch_state_dict_to_flax,
	    load_flax_weights_in_pytorch_model,
	)
	from transformers.models.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
	import torch

class      A_	:
	def __init__(   self							, _A							, _A=1_4							, _A=7							, _A=True							, _A=True							, _A=False							, _A=True							, _A=9_9							, _A=3_2							, _A=5							, _A=4							, _A=3_7							, _A="gelu"							, _A=0.1							, _A=0.1							, _A=5_1_2							, _A=0.02							, ):






				'''simple docstring'''







				UpperCAmelCase       		=    parent
				UpperCAmelCase       		=    batch_size
				UpperCAmelCase       		=    seq_length
				UpperCAmelCase       		=    is_training
				UpperCAmelCase       		=    use_input_mask
				UpperCAmelCase       		=    use_token_type_ids
				UpperCAmelCase       		=    use_labels
				UpperCAmelCase       		=    vocab_size
				UpperCAmelCase       		=    hidden_size
				UpperCAmelCase       		=    num_hidden_layers
				UpperCAmelCase       		=    num_attention_heads
				UpperCAmelCase       		=    intermediate_size
				UpperCAmelCase       		=    hidden_act
				UpperCAmelCase       		=    hidden_dropout_prob
				UpperCAmelCase       		=    attention_probs_dropout_prob
				UpperCAmelCase       		=    max_position_embeddings
				UpperCAmelCase       		=    initializer_range
				UpperCAmelCase       		=    None
				UpperCAmelCase       		=    vocab_size - 1
				UpperCAmelCase       		=    vocab_size - 1
				UpperCAmelCase       		=    vocab_size - 1
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    ids_tensor([self.batch_size, self.seq_length]							, self.vocab_size		)

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

				UpperCAmelCase       		=    GPTaConfig(
				    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=lowercase__							, 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		):






				'''simple docstring'''







				UpperCAmelCase       		=    self.prepare_config_and_inputs()
				UpperCAmelCase       		=    config_and_inputs
				UpperCAmelCase       		=    {"input_ids": input_ids, "attention_mask": attention_mask}
				return config, inputs_dict
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    self.prepare_config_and_inputs()

				UpperCAmelCase       		=    floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
				UpperCAmelCase       		=    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							, _A							, _A							, _A							, _A		):






				'''simple docstring'''







				UpperCAmelCase       		=    2_0
				UpperCAmelCase       		=    model_class_name(lowercase__		)

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

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

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

				UpperCAmelCase       		=    model(lowercase__		)

				UpperCAmelCase       		=    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							, _A							, _A							, _A							, _A		):






				'''simple docstring'''







				UpperCAmelCase       		=    2_0
				UpperCAmelCase       		=    model_class_name(lowercase__		)

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

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

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

				UpperCAmelCase       		=    model(lowercase__							, attention_mask=lowercase__		)

				UpperCAmelCase       		=    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      A_	(_UpperCAmelCase					,							_UpperCAmelCase					,							unittest.TestCase      ):
	UpperCAmelCase__ =      (FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
	UpperCAmelCase__ =      (FlaxGPTaLMHeadModel,) if is_flax_available() else ()
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    FlaxGPTaModelTester(self		)
	def 					_lowercase    (   self		):






				'''simple docstring'''







				for model_class_name in self.all_model_classes:
							UpperCAmelCase       		=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.check_use_cache_forward(lowercase__							, lowercase__							, lowercase__							, lowercase__		)
	def 					_lowercase    (   self		):






				'''simple docstring'''







				for model_class_name in self.all_model_classes:
							UpperCAmelCase       		=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.check_use_cache_forward_with_attn_mask(
							    lowercase__							, lowercase__							, lowercase__							, lowercase__		)
	@slow
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    GPTaTokenizer.from_pretrained('''gpt2'''							, pad_token='''</s>'''							, padding_side='''left'''		)
				UpperCAmelCase       		=    tokenizer(['''Hello this is a long string''', '''Hey''']							, return_tensors='''np'''							, padding=lowercase__							, truncation=lowercase__		)

				UpperCAmelCase       		=    FlaxGPTaLMHeadModel.from_pretrained('''gpt2'''		)
				UpperCAmelCase       		=    False
				UpperCAmelCase       		=    model.config.eos_token_id

				UpperCAmelCase       		=    jax.jit(model.generate		)

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

				UpperCAmelCase       		=    tokenizer.batch_decode(lowercase__							, skip_special_tokens=lowercase__		)

				UpperCAmelCase       		=    [
				    "Hello this is a long string of words. I'm going to start with the first one.\n",
				    "Hey, I'm not sure if I'm going to be able to do",
				]

				self.assertListEqual(lowercase__							, lowercase__		)
	@is_pt_flax_cross_test
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    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       		=    self._prepare_for_class(lowercase__							, lowercase__		)
										UpperCAmelCase       		=    {k: torch.tensor(v.tolist()		) for k, v in prepared_inputs_dict.items()}

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

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

										UpperCAmelCase       		=    convert_pytorch_state_dict_to_flax(pt_model.state_dict()							, lowercase__		)
										UpperCAmelCase       		=    fx_state

										with torch.no_grad():
													UpperCAmelCase       		=    pt_model(**lowercase__		).to_tuple()

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

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

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






				'''simple docstring'''







				UpperCAmelCase       		=    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       		=    self._prepare_for_class(lowercase__							, lowercase__		)
										UpperCAmelCase       		=    {k: torch.tensor(v.tolist()		) for k, v in prepared_inputs_dict.items()}

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

										UpperCAmelCase       		=    pt_model_class(lowercase__		).eval()
										UpperCAmelCase       		=    model_class(lowercase__							, dtype=jnp.floataa		)

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

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

										with torch.no_grad():
													UpperCAmelCase       		=    pt_model(**lowercase__		).to_tuple()

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

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

										with torch.no_grad():
													UpperCAmelCase       		=    pt_model_loaded(**lowercase__		).to_tuple()

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







	@slow
	def 					_lowercase    (   self		):






				'''simple docstring'''







				for model_class_name in self.all_model_classes:
							UpperCAmelCase       		=    model_class_name.from_pretrained('''gpt2'''							, from_pt=lowercase__		)
							UpperCAmelCase       		=    model(np.ones((1, 1)		)		)
							self.assertIsNotNone(lowercase__		)










from typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    np.ndarray:







			'''simple docstring'''

			UpperCAmelCase       		=    np.stack((horizontal_flow, vertical_flow)      ,      2			)

			# Create a grid of all pixel coordinates and subtract the flow to get the
			# target pixels coordinates
			UpperCAmelCase       		=    np.stack(
			    np.meshgrid(np.arange(0      ,      image.shape[1]			)      ,      np.arange(0      ,      image.shape[0]			)			)      ,      2			)
			UpperCAmelCase       		=    np.round(grid - flow			).astype(np.intaa			)

			# Find the locations outside of the original image
			UpperCAmelCase       		=    (grid < 0) | (grid >= np.array([image.shape[1], image.shape[0]]			))
			UpperCAmelCase       		=    0

			UpperCAmelCase       		=    image[grid[:, :, 1], grid[:, :, 0]]

			# Set pixels at invalid locations to 0
			UpperCAmelCase       		=    0

			return warped





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__ = None      ,      )  ->    tuple[np.ndarray, np.ndarray]:







			'''simple docstring'''

			if alpha is None:
						UpperCAmelCase       		=    0.1

			# Initialize flow
			UpperCAmelCase       		=    np.zeros_like(UpperCamelCase__			)
			UpperCAmelCase       		=    np.zeros_like(UpperCamelCase__			)

			# Prepare kernels for the calculation of the derivatives and the average velocity
			UpperCAmelCase       		=    np.array([[-1, 1], [-1, 1]]			) * 0.25
			UpperCAmelCase       		=    np.array([[-1, -1], [1, 1]]			) * 0.25
			UpperCAmelCase       		=    np.array([[1, 1], [1, 1]]			) * 0.25
			UpperCAmelCase       		=    np.array(
			    [[1 / 12, 1 / 6, 1 / 12], [1 / 6, 0, 1 / 6], [1 / 12, 1 / 6, 1 / 12]]			)

			# Iteratively refine the flow
			for _ in range(UpperCamelCase__			):
						UpperCAmelCase       		=    warp(UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)
						UpperCAmelCase       		=    convolve(UpperCamelCase__      ,      UpperCamelCase__			) + convolve(UpperCamelCase__      ,      UpperCamelCase__			)
						UpperCAmelCase       		=    convolve(UpperCamelCase__      ,      UpperCamelCase__			) + convolve(UpperCamelCase__      ,      UpperCamelCase__			)
						UpperCAmelCase       		=    convolve(UpperCamelCase__      ,      UpperCamelCase__			) + convolve(UpperCamelCase__      ,      -kernel_t			)

						UpperCAmelCase       		=    convolve(UpperCamelCase__      ,      UpperCamelCase__			)
						UpperCAmelCase       		=    convolve(UpperCamelCase__      ,      UpperCamelCase__			)

						# This updates the flow as proposed in the paper (Step 12)
						UpperCAmelCase       		=    (
						    derivative_x * avg_horizontal_velocity
						    + derivative_y * avg_vertical_velocity
						    + derivative_t
						)
						UpperCAmelCase       		=    update / (alpha**2 + derivative_x**2 + derivative_y**2)

						UpperCAmelCase       		=    avg_horizontal_velocity - derivative_x * update
						UpperCAmelCase       		=    avg_vertical_velocity - derivative_y * update

			return horizontal_flow, vertical_flow


if __name__ == "__main__":
				import doctest

				doctest.testmod()



