


"""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     _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=0.0_2       ,  ):
										__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      snake_case							(	self      ):
										__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      snake_case							(	self      ):
										__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      snake_case							(	self       ,  __a       ,  __a       ,  __a       ,  __a      ):
										__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      snake_case							(	self       ,  __a       ,  __a       ,  __a       ,  __a      ):
										__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    (  lowerCAmelCase__			,lowerCAmelCase__			,unittest.TestCase						):
							'''simple docstring'''







							__UpperCAmelCase		:       int							 =(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
							__UpperCAmelCase		:       Dict							 =(FlaxGPTJForCausalLM,) if is_flax_available() else ()

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     FlaxGPTJModelTester(self      )

							def      snake_case							(	self      ):
										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      snake_case							(	self      ):
										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      snake_case							(	self      ):
										__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      snake_case							(	self      ):
										__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      snake_case							(	self      ):
										__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      snake_case							(	self      ):
										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      )








"""simple docstring"""


import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=lowerCAmelCase__						)
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       str							 =field(default="""image-classification"""			,metadata={"""include_in_asdict_even_if_is_default""": True}						)
							__UpperCAmelCase		:       ClassVar[Features]							 =Features({"""image""": Image()}						)
							__UpperCAmelCase		:       ClassVar[Features]							 =Features({"""labels""": ClassLabel}						)
							__UpperCAmelCase		:       str							 ="image"
							__UpperCAmelCase		:       str							 ="labels"

							def      snake_case							(	self       ,  __a      ):
										if self.label_column not in features:
													raise ValueError(f"Column {self.label_column} is not present in features."      )
										if not isinstance(features[self.label_column]       ,  __a      ):
													raise ValueError(f"Column {self.label_column} is not a ClassLabel."      )
										__lowerCAmelCase							       =     copy.deepcopy(self      )
										__lowerCAmelCase							       =     self.label_schema.copy()
										__lowerCAmelCase							       =     features[self.label_column]
										__lowerCAmelCase							       =     label_schema
										return task_template



							@property
							def      snake_case							(	self      ):
										return {
										    self.image_column: "image",
										    self.label_column: "labels",
										}




