






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 			SCREAMING_SNAKE_CASE      :
     '''simple docstring'''


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


     def 					_A							(					self							:						List[str]   ):
       SCREAMING_SNAKE_CASE							:   List[str]						    =     ids_tensor([self.batch_size, self.seq_length]       ,			self.vocab_size   )

       SCREAMING_SNAKE_CASE							:   Tuple						    =     None
       if self.use_input_mask:
         SCREAMING_SNAKE_CASE							:   str						    =     random_attention_mask([self.batch_size, self.seq_length]   )

       SCREAMING_SNAKE_CASE							:   Tuple						    =     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 					_A							(					self							:						str   ):
       SCREAMING_SNAKE_CASE							:   int						    =     self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     config_and_inputs
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     {"input_ids": input_ids, "attention_mask": attention_mask}
       return config, inputs_dict


     def 					_A							(					self							:						Optional[int]       ,			UpperCAmelCase_							:						Optional[int]       ,			UpperCAmelCase_							:						List[Any]       ,			UpperCAmelCase_							:						Dict       ,			UpperCAmelCase_							:						int   ):
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     20
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     model_class_name(UpperCAmelCase_   )

       SCREAMING_SNAKE_CASE							:   Any						    =     model.init_cache(input_ids.shape[0]       ,			UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   Tuple						    =     jnp.ones((input_ids.shape[0], max_decoder_length)       ,			dtype="i4"   )

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

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

       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     model(UpperCAmelCase_   )

       SCREAMING_SNAKE_CASE							:   List[str]						    =     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 					_A							(					self							:						List[Any]       ,			UpperCAmelCase_							:						Any       ,			UpperCAmelCase_							:						Any       ,			UpperCAmelCase_							:						Optional[int]       ,			UpperCAmelCase_							:						Optional[int]   ):
       SCREAMING_SNAKE_CASE							:   Any						    =     20
       SCREAMING_SNAKE_CASE							:   Tuple						    =     model_class_name(UpperCAmelCase_   )

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

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

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

       SCREAMING_SNAKE_CASE							:   List[Any]						    =     model(UpperCAmelCase_       ,			attention_mask=UpperCAmelCase_   )

       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     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 			SCREAMING_SNAKE_CASE      (      lowerCAmelCase   ,		lowerCAmelCase   ,		unittest.TestCase       ):
     '''simple docstring'''


     UpperCamelCase_				:		Optional[Any]								=						(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
     UpperCamelCase_				:		List[Any]								=						(FlaxGPTJForCausalLM,) if is_flax_available() else ()


     def 					_A							(					self							:						Any   ):
       SCREAMING_SNAKE_CASE							:   Dict						    =     FlaxGPTJModelTester(self   )


     def 					_A							(					self							:						Optional[Any]   ):
       for model_class_name in self.all_model_classes:
         SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   Dict						    =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.check_use_cache_forward(UpperCAmelCase_       ,			UpperCAmelCase_       ,			UpperCAmelCase_       ,			UpperCAmelCase_   )


     def 					_A							(					self							:						int   ):
       for model_class_name in self.all_model_classes:
         SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.check_use_cache_forward_with_attn_mask(
             UpperCAmelCase_       ,			UpperCAmelCase_       ,			UpperCAmelCase_       ,			UpperCAmelCase_   )


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

       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     FlaxGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"   )
       SCREAMING_SNAKE_CASE							:   str						    =     False
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     model.config.eos_token_id

       SCREAMING_SNAKE_CASE							:   Any						    =     jax.jit(model.generate   )

       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     jit_generate(
           inputs["input_ids"]       ,			attention_mask=inputs["attention_mask"]       ,			pad_token_id=tokenizer.pad_token_id   ).sequences

       SCREAMING_SNAKE_CASE							:   Dict						    =     tokenizer.batch_decode(UpperCAmelCase_       ,			skip_special_tokens=UpperCAmelCase_   )

       SCREAMING_SNAKE_CASE							:   Optional[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 					_A							(					self							:						int   ):
       SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   Tuple						    =     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
           SCREAMING_SNAKE_CASE							:   Optional[int]						    =     self._prepare_for_class(UpperCAmelCase_       ,			UpperCAmelCase_   )
           SCREAMING_SNAKE_CASE							:   Optional[int]						    =     {k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

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

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

           SCREAMING_SNAKE_CASE							:   List[str]						    =     convert_pytorch_state_dict_to_flax(pt_model.state_dict()       ,			UpperCAmelCase_   )
           SCREAMING_SNAKE_CASE							:   Dict						    =     fx_state

           with torch.no_grad():
             SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     pt_model(**UpperCAmelCase_   ).to_tuple()

           SCREAMING_SNAKE_CASE							:   Tuple						    =     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_   )
             SCREAMING_SNAKE_CASE							:   Dict						    =     model_class.from_pretrained(UpperCAmelCase_       ,			from_pt=UpperCAmelCase_   )

           SCREAMING_SNAKE_CASE							:   str						    =     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 					_A							(					self							:						Dict   ):
       SCREAMING_SNAKE_CASE    ,	SCREAMING_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__   ):
           # prepare inputs
           SCREAMING_SNAKE_CASE							:   Optional[int]						    =     self._prepare_for_class(UpperCAmelCase_       ,			UpperCAmelCase_   )
           SCREAMING_SNAKE_CASE							:   Optional[int]						    =     {k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

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

           SCREAMING_SNAKE_CASE							:   Any						    =     pt_model_class(UpperCAmelCase_   ).eval()
           SCREAMING_SNAKE_CASE							:   str						    =     model_class(UpperCAmelCase_       ,			dtype=jnp.floataa   )

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

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

           with torch.no_grad():
             SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     pt_model(**UpperCAmelCase_   ).to_tuple()

           SCREAMING_SNAKE_CASE							:   Dict						    =     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_   )
             SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     pt_model_class.from_pretrained(UpperCAmelCase_       ,			from_flax=UpperCAmelCase_   )

           with torch.no_grad():
             SCREAMING_SNAKE_CASE							:   int						    =     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 					_A							(					self							:						Optional[Any]   ):
       for model_class_name in self.all_model_classes:
         SCREAMING_SNAKE_CASE							:   Optional[int]						    =     model_class_name.from_pretrained("EleutherAI/gpt-j-6B"   )
         SCREAMING_SNAKE_CASE							:   Dict						    =     model(np.ones((1, 1)   )   )
         self.assertIsNotNone(UpperCAmelCase_   )











"""simple docstring"""







import warnings

from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor


a_							=    logging.get_logger(__name__)







class 					A_(SCREAMING_SNAKE_CASE_      ):







			"""simple docstring"""



			def __init__(	self				,   *A				,   **A							):
						warnings.warn(
						    'The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
						    ' Please use SegformerImageProcessor instead.'				,   A				,   )
						super().__init__(*A				,   **A							)





