
import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin




class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase							,       unittest.TestCase			):
      _A    :		Tuple     						=  CTRLTokenizer
      _A    :		List[str]     						=  False
      _A    :		List[str]     						=  False
      def A_      (      self			):
          super().setUp()

          # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
          snake_case__    =					["adapt", "re@@", "a@@", "apt", "c@@", "t", "<unk>"]
          snake_case__    =					dict(zip(lowerCamelCase		,						range(len(lowerCamelCase			)			)			)			)
          snake_case__    =					["#version: 0.2", "a p", "ap t</w>", "r e", "a d", "ad apt</w>", ""]
          snake_case__    =					{"unk_token": "<unk>"}

          snake_case__    =					os.path.join(self.tmpdirname		,						VOCAB_FILES_NAMES["vocab_file"]			)
          snake_case__    =					os.path.join(self.tmpdirname		,						VOCAB_FILES_NAMES["merges_file"]			)
          with open(self.vocab_file		,						"w"		,						encoding="utf-8"			) as fp:
              fp.write(json.dumps(lowerCamelCase			) + "\n"			)
          with open(self.merges_file		,						"w"		,						encoding="utf-8"			) as fp:
              fp.write("\n".join(lowerCamelCase			)			)
      def A_      (      self		,						**lowerCamelCase			):
          kwargs.update(self.special_tokens_map			)
          return CTRLTokenizer.from_pretrained(self.tmpdirname		,						**lowerCamelCase			)
      def A_      (      self		,						lowerCamelCase			):
          snake_case__    =					"adapt react readapt apt"
          snake_case__    =					"adapt react readapt apt"
          return input_text, output_text



      def A_      (      self			):
          snake_case__    =					CTRLTokenizer(self.vocab_file		,						self.merges_file		,						**self.special_tokens_map			)
          snake_case__    =					"adapt react readapt apt"
          snake_case__    =					"adapt re@@ a@@ c@@ t re@@ adapt apt".split()
          snake_case__    =					tokenizer.tokenize(lowerCamelCase			)
          self.assertListEqual(lowerCamelCase		,						lowerCamelCase			)

          snake_case__    =					tokens + [tokenizer.unk_token]

          snake_case__    =					[0, 1, 2, 4, 5, 1, 0, 3, 6]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase			)		,						lowerCamelCase			)





import inspect
import unittest
from typing import List

import numpy as np

from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers import (
         TFEfficientFormerForImageClassification,
         TFEfficientFormerForImageClassificationWithTeacher,
         TFEfficientFormerModel,
     )
     from transformers.models.efficientformer.modeling_tf_efficientformer import (
         TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
     )


if is_vision_available():
     from PIL import Image

     from transformers import EfficientFormerImageProcessor




class        _SCREAMING_SNAKE_CASE	:
      def __init__(      self		,						lowerCamelCase		,						lowerCamelCase = 13		,						lowerCamelCase = 64		,						lowerCamelCase = 2		,						lowerCamelCase = 3		,						lowerCamelCase = 3		,						lowerCamelCase = True		,						lowerCamelCase = True		,						lowerCamelCase = 1_28		,						lowerCamelCase=[16, 32, 64, 1_28]		,						lowerCamelCase = 7		,						lowerCamelCase = 4		,						lowerCamelCase = 37		,						lowerCamelCase = "gelu"		,						lowerCamelCase = 0.1		,						lowerCamelCase = 0.1		,						lowerCamelCase = 10		,						lowerCamelCase = 0.0_2		,						lowerCamelCase = 2		,						lowerCamelCase = 1		,						lowerCamelCase = 1_28		,						lowerCamelCase = [2, 2, 2, 2]		,						lowerCamelCase = 2		,						lowerCamelCase = 2		,						):
          snake_case__    =					parent
          snake_case__    =					batch_size
          snake_case__    =					image_size
          snake_case__    =					patch_size
          snake_case__    =					num_channels
          snake_case__    =					is_training
          snake_case__    =					use_labels
          snake_case__    =					hidden_size
          snake_case__    =					num_hidden_layers
          snake_case__    =					num_attention_heads
          snake_case__    =					intermediate_size
          snake_case__    =					hidden_act
          snake_case__    =					hidden_dropout_prob
          snake_case__    =					attention_probs_dropout_prob
          snake_case__    =					type_sequence_label_size
          snake_case__    =					initializer_range
          snake_case__    =					encoder_stride
          snake_case__    =					num_attention_outputs
          snake_case__    =					embed_dim
          snake_case__    =					embed_dim + 1
          snake_case__    =					resolution
          snake_case__    =					depths
          snake_case__    =					hidden_sizes
          snake_case__    =					dim
          snake_case__    =					mlp_expansion_ratio
      def A_      (      self			):
          snake_case__    =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

          snake_case__    =					None
          if self.use_labels:
              snake_case__    =					ids_tensor([self.batch_size]		,						self.type_sequence_label_size			)

          snake_case__    =					self.get_config()

          return config, pixel_values, labels
      def A_      (      self			):
          return EfficientFormerConfig(
              image_size=self.image_size		,						patch_size=self.patch_size		,						num_channels=self.num_channels		,						hidden_size=self.hidden_size		,						num_hidden_layers=self.num_hidden_layers		,						num_attention_heads=self.num_attention_heads		,						intermediate_size=self.intermediate_size		,						hidden_act=self.hidden_act		,						hidden_dropout_prob=self.hidden_dropout_prob		,						attention_probs_dropout_prob=self.attention_probs_dropout_prob		,						is_decoder=lowerCamelCase		,						initializer_range=self.initializer_range		,						encoder_stride=self.encoder_stride		,						resolution=self.resolution		,						depths=self.depths		,						hidden_sizes=self.hidden_sizes		,						dim=self.dim		,						mlp_expansion_ratio=self.mlp_expansion_ratio		,						)
      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase			):
          snake_case__    =					TFEfficientFormerModel(config=lowerCamelCase			)
          snake_case__    =					model(lowerCamelCase		,						training=lowerCamelCase			)
          self.parent.assertEqual(result.last_hidden_state.shape		,						(self.batch_size, self.seq_length, self.hidden_size)			)
      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase			):
          snake_case__    =					self.type_sequence_label_size
          snake_case__    =					TFEfficientFormerForImageClassification(lowerCamelCase			)
          snake_case__    =					model(lowerCamelCase		,						labels=lowerCamelCase		,						training=lowerCamelCase			)
          self.parent.assertEqual(result.logits.shape		,						(self.batch_size, self.type_sequence_label_size)			)

          # test greyscale images
          snake_case__    =					1
          snake_case__    =					TFEfficientFormerForImageClassification(lowerCamelCase			)

          snake_case__    =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
          snake_case__    =					model(lowerCamelCase		,						labels=lowerCamelCase			)
          self.parent.assertEqual(result.logits.shape		,						(self.batch_size, self.type_sequence_label_size)			)



      def A_      (      self			):
          snake_case__    =					self.prepare_config_and_inputs()
          snake_case__    ,       snake_case__    ,       snake_case__    =					config_and_inputs
          snake_case__    =					{"pixel_values": pixel_values}
          return config, inputs_dict





@require_tf
class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase							,       __UpperCamelCase							,       unittest.TestCase			):
      _A    :		str     						=  (
          (
              TFEfficientFormerModel,
              TFEfficientFormerForImageClassificationWithTeacher,
              TFEfficientFormerForImageClassification,
          )
          if is_tf_available()
          else ()
      )
      _A    :		List[str]     						=  (
          {
              'feature-extraction': TFEfficientFormerModel,
              'image-classification': (
                  TFEfficientFormerForImageClassification,
                  TFEfficientFormerForImageClassificationWithTeacher,
              ),
          }
          if is_tf_available()
          else {}
      )

      _A    :		Optional[Any]     						=  False

      _A    :		List[Any]     						=  False
      _A    :		Tuple     						=  False
      _A    :		List[Any]     						=  False
      _A    :		Any     						=  False
      def A_      (      self			):
          snake_case__    =					TFEfficientFormerModelTester(self			)
          snake_case__    =					ConfigTester(
              self		,						config_class=lowerCamelCase		,						has_text_modality=lowerCamelCase		,						hidden_size=37			)
      def A_      (      self			):
          self.config_tester.run_common_tests()
      @unittest.skip(reason="EfficientFormer does not use inputs_embeds"			)
      def A_      (      self			):
          pass
      @unittest.skip(reason="EfficientFormer does not support input and output embeddings"			)
      def A_      (      self			):
          pass
      def A_      (      self			):
          snake_case__    ,       snake_case__    =					self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              snake_case__    =					model_class(lowerCamelCase			)
              snake_case__    =					inspect.signature(model.call			)
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              snake_case__    =					[*signature.parameters.keys()]

              snake_case__    =					["pixel_values"]
              self.assertListEqual(arg_names[:1]		,						lowerCamelCase			)
      def A_      (      self			):
          def check_hidden_states_output(lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase			):
              snake_case__    =					model_class(lowerCamelCase			)

              snake_case__    =					model(**self._prepare_for_class(lowerCamelCase		,						lowerCamelCase			)		,						training=lowerCamelCase			)
              snake_case__    =					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

              snake_case__    =					getattr(
                  self.model_tester		,						"expected_num_hidden_layers"		,						self.model_tester.num_hidden_layers + 1			)
              self.assertEqual(len(lowerCamelCase			)		,						lowerCamelCase			)

              if hasattr(self.model_tester		,						"encoder_seq_length"			):
                  snake_case__    =					self.model_tester.encoder_seq_length
                  if hasattr(self.model_tester		,						"chunk_length"			) and self.model_tester.chunk_length > 1:
                      snake_case__    =					seq_length * self.model_tester.chunk_length
              else:
                  snake_case__    =					self.model_tester.seq_length

              self.assertListEqual(
                  list(hidden_states[-1].shape[-2:]			)		,						[seq_length, self.model_tester.hidden_size]		,						)

              if config.is_encoder_decoder:
                  snake_case__    =					outputs.decoder_hidden_states

                  self.asseretIsInstance(lowerCamelCase		,						(list, tuple)			)
                  self.assertEqual(len(lowerCamelCase			)		,						lowerCamelCase			)
                  snake_case__    =					getattr(self.model_tester		,						"seq_length"		,						lowerCamelCase			)
                  snake_case__    =					getattr(self.model_tester		,						"decoder_seq_length"		,						lowerCamelCase			)

                  self.assertListEqual(
                      list(hidden_states[-1].shape[-2:]			)		,						[decoder_seq_length, self.model_tester.hidden_size]		,						)

          snake_case__    ,       snake_case__    =					self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              snake_case__    =					True
              check_hidden_states_output(lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase			)

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              snake_case__    =					True

              check_hidden_states_output(lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase			)
      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase=False			):
          snake_case__    =					super()._prepare_for_class(lowerCamelCase		,						lowerCamelCase		,						return_labels=lowerCamelCase			)

          if return_labels:
              if model_class.__name__ == "TFEfficientFormerForImageClassificationWithTeacher":
                  del inputs_dict["labels"]

          return inputs_dict
      def A_      (      self			):
          snake_case__    =					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowerCamelCase			)
      @unittest.skip(reason="EfficientFormer does not implement masked image modeling yet"			)
      def A_      (      self			):
          snake_case__    =					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_image_modeling(*lowerCamelCase			)
      def A_      (      self			):
          snake_case__    =					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*lowerCamelCase			)
      @slow
      def A_      (      self			):
          for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              snake_case__    =					TFEfficientFormerModel.from_pretrained(lowerCamelCase			)
              self.assertIsNotNone(lowerCamelCase			)
      def A_      (      self			):
          snake_case__    ,       snake_case__    =					self.model_tester.prepare_config_and_inputs_for_common()
          snake_case__    =					True

          snake_case__    =					getattr(self.model_tester		,						"seq_length"		,						lowerCamelCase			)
          snake_case__    =					getattr(self.model_tester		,						"encoder_seq_length"		,						lowerCamelCase			)
          snake_case__    =					getattr(self.model_tester		,						"key_length"		,						lowerCamelCase			)
          snake_case__    =					getattr(self.model_tester		,						"chunk_length"		,						lowerCamelCase			)

          if chunk_length is not None and hasattr(self.model_tester		,						"num_hashes"			):
              snake_case__    =					encoder_seq_length * self.model_tester.num_hashes

          for model_class in self.all_model_classes:
              snake_case__    =					True
              snake_case__    =					False
              snake_case__    =					True
              snake_case__    =					model_class(lowerCamelCase			)

              snake_case__    =					model(**self._prepare_for_class(lowerCamelCase		,						lowerCamelCase			)		,						training=lowerCamelCase			)
              snake_case__    =					outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
              self.assertEqual(len(lowerCamelCase			)		,						self.model_tester.num_attention_outputs			)

              # check that output_attentions also work using config
              del inputs_dict["output_attentions"]
              snake_case__    =					True
              snake_case__    =					model_class(lowerCamelCase			)
              snake_case__    =					model(**self._prepare_for_class(lowerCamelCase		,						lowerCamelCase			)		,						training=lowerCamelCase			)

              snake_case__    =					outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
              self.assertEqual(len(lowerCamelCase			)		,						self.model_tester.num_attention_outputs			)

              if chunk_length is not None:
                  self.assertListEqual(
                      list(attentions[0].shape[-4:]			)		,						[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]		,						)
              else:
                  self.assertListEqual(
                      list(attentions[0].shape[-3:]			)		,						[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]		,						)



      def A_      (      self			):
          # We use a simplified version of this test for EfficientFormer because it requires training=False
          # and Keras refuses to let us force that during functional construction
          snake_case__    ,       snake_case__    =					self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              # Prepare our model
              snake_case__    =					model_class(lowerCamelCase			)
              # These are maximally general inputs for the model, with multiple None dimensions
              # Hopefully this will catch any conditionals that fail for flexible shapes
              snake_case__    =					{
                  key: tf.keras.Input(shape=val.shape[1:]		,						dtype=val.dtype		,						name=lowerCamelCase			)
                  for key, val in model.input_signature.items()
                  if key in model.dummy_inputs
              }
              snake_case__    =					model(lowerCamelCase			)
              self.assertTrue(outputs_dict is not None			)





def 						SCREAMING_SNAKE_CASE__     (   ):
    snake_case__    =					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
    return image





@require_tf
@require_vision
class        _SCREAMING_SNAKE_CASE	(   unittest.TestCase			):
      @cached_property
      def A_      (      self			):
          return (
              EfficientFormerImageProcessor.from_pretrained("snap-research/efficientformer-l1-300"			)
              if is_vision_available()
              else None
          )
      @slow
      def A_      (      self			):
          snake_case__    =					TFEfficientFormerForImageClassification.from_pretrained("snap-research/efficientformer-l1-300"			)
          snake_case__    =					self.default_image_processor
          snake_case__    =					prepare_img()
          snake_case__    =					image_processor(images=lowerCamelCase		,						return_tensors="tf"			)
          # forward pass
          snake_case__    =					model(**lowerCamelCase		,						training=lowerCamelCase			)
          # verify the logits
          snake_case__    =					tf.TensorShape((1, 10_00)			)
          self.assertEqual(outputs.logits.shape		,						lowerCamelCase			)
          snake_case__    =					tf.constant([-0.0_5_5_5, 0.4_8_2_5, -0.0_8_5_2]			)
          self.assertTrue(np.allclose(outputs.logits[0, :3]		,						lowerCamelCase		,						atol=1e-4			)			)



      @slow
      def A_      (      self			):
          snake_case__    =					TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
              "snap-research/efficientformer-l1-300"			)
          snake_case__    =					self.default_image_processor
          snake_case__    =					prepare_img()
          snake_case__    =					image_processor(images=lowerCamelCase		,						return_tensors="tf"			)
          # forward pass
          snake_case__    =					model(**lowerCamelCase		,						training=lowerCamelCase			)
          # verify the logits
          snake_case__    =					tf.TensorShape((1, 10_00)			)
          self.assertEqual(outputs.logits.shape		,						lowerCamelCase			)
          snake_case__    =					tf.constant([-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9]			)
          self.assertTrue(np.allclose(outputs.logits[0, :3]		,						lowerCamelCase		,						atol=1e-4			)			)



